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 1. INTRODUCTION

Information search behavior has always been in the spotlight in consumer behaviour literature because of its importance in the decision making process. Numerous articles in consumer behavior, economics and marketing literature have been focused on consumer information search since 1950s. Therefore, consumers’ information search behavior has been studied for many years in both pre-purchase and ongoing search context (e.g. Punj and Staelin 1983; Beatty and Smith 1987; Srinivasan and Ratchford 1991). In addition to the conceptual studies, considerable number of studies have proposed and tested theoretical models for understanding consumers’ information search behavior.

In recent years, due to technological advances, researches dealing with the usage of online information sources or online information search behavior have begun to receive more attention. There is no doubt that the Internet affects, and will continue to affect the search activity of consumers. Several researches indicate that the primary goal of consumers to visit web sites is to search for product information (e.g. Ratchford et al., 2001; Bhatnagar and Ghose, 2004). It is clear that with the increasing popularity of the Internet, more consumers have started to use the Internet for information search than before. Moreover, Parker and Plank (2000) imply that more traditional forms of media may be replaced by the Internet. In a national information communication technology usage survey conducted by Turkey Statistical Institute (TURKSTAT) in 2005, it is indicated that 43.3% of consumers use the Internet for information search about goods and services. However, the percentage of the Internet usage in Turkey is 13.93% (TURKSTAT). While the rate of female users is 4.33%, that of male users is 9.60%.

1.1 Aim of the Dissertation

Information search is an important stage of the purchase decision process for consumers to determine alternative brands in the market and to choose the best one among the alternatives at hand. Due to this fact, the information search activities of

 

consumers should be explored and understood in detail by the companies in order to develop effective strategies to influence the search activities of consumers. With the help of the technological advances, online information sources offer new alternatives to get information. For this reason, consumer information behavior could occur not only in an online environment or offline environment but also within the combination of both environments. The evidence found by Klein and Ford (2003) points that consumers replace offline information search behavior by online information search behavior. Contrary to this, the extent of usage of offline information sources was found to be positively correlated to the usage of online information sources in some studies in the literature (Bei et al., 2004; Rha, 2002). That is, search behavior across different information sources is cumulative rather than compensated (Bei et al., 2004); therefore, online information sources cannot be accepted as substitute for offline information sources. The notions of “traditional information search” and “offline information search” are used interchangeably throughout this study.

There are a few studies dealing with preference of one type of information source over the others (Mitra et al., 1999; Mourali et al., 2005). However the studies related to the preference of online information sources over offline sources is too limited in the literature. In addition, the antecedents and consequences of this type of preference are also neglected by the researchers in the related field. Accordingly, in this thesis, it is aimed to find out underlying motives and determinants of consumers’ usage of online information sources relative to offline information sources.

This thesis is comprised of two related studies dealing with consumers’ usage of online information sources compared to offline information sources. In Study 1, it is aimed to elicit underlying motives of consumers’ using online information sources as compared to offline (traditional) information sources. The findings of study 1 ensure major inputs for the conceptual model development. In study 2, the purpose is to test the proposed model for two different product types, cultural activities and cellular phones. While the average usage of cellular phone in Europe is two years; 20% of cellular phone users in Turkey want to purchase a new one within the range of six months or at most one year. Besides, average number of cellular phone used in each home is two (http://www.turkticaret.net/business_center). On the other hand, the number and the variety of cultural activities in Istanbul have been increasing.

 

Istanbul has been elected as the European Capital of Culture for 2010 (http://www.istanbul2010.org). These are important reasons to select cellular phone and cultural activities as focal products for the purposes of this study.

1.2 Methodology and the Data Analysis Methods Used

Two researches were conducted to accomplish the objectives of this study. Laddering technique, as an in-depth, one-to-one interviewing technique which is grounded on means-end theory is used in Study 1 to elicit underlying motives of consumers’ use of online information sources relative to offline information sources.

There are four methods that marketers can follow to learn about the buying decision process for their products (Kotler and Keller, 2006). These methods are introspective method, retrospective method, prospective method, and prescriptive method. In introspective method, marketers can think about how they would act when buying a product. However, in retrospective method, recent purchasers are asked to explain and recall the activities leading to their purchase. Prospective method is applied by asking consumers, who plan to buy their products, to think aloud about the buying process. Finally, marketers can use prescriptive method by asking consumers to describe the ideal way for buying the product. Besides, each method reveals a picture of each stage in the buying process. In the Study 2, retrospective method has been used to explore consumers’ information search behavior in the buying process for a cellular phone and a cultural activity. Structural Equation Modeling was applied in Study 2 to test the proposed model.

Structural Equation Modeling is considered to be a powerful technique for testing causal relationships between constructs of a conceptual model (Diamantopoulos, 1994; Fornell and Larcker, 1981; Steenkamp and Baumgartner, 2000). A two-step approach was taken for the model testing (Anderson and Gerbing, 1988). First the unidimensionality of the constructs was validated on the basis of the measurement models by the application of confirmatory factor analysis (CFA). Following that step, the models were tested by applying Structural Equation Modeling which simultaneously estimates the relationships among constructs (Anderson and Gerbing, 1988; Diamantopoulos, 1994; Steenkamp and Baumgartner, 2000).

SPSS 15.0 (statistical package for social sciences) was used during the descriptive analysis, outlier examination, missing data analysis, exploratory factor analysis,

 

reliability analysis and the normal distribution assessment (Tabachnick and Fidell, 2007). Confirmatory factor analysis and structural model testing were performed using the Lisrel 8.7 (LInear Structural RELationships) (Hair et al., 2006; Jöreskog and Sörbom, 1993; 1996).

1.3 Contribution of the Study

This dissertation provides several important theoretical and managerial contributions to the literature dealing with consumer information search behavior. First, this thesis includes two researches which are the first attempts to investigate the determinants of net preference of online information sources over offline information sources or vice versa. The findings of the first study offer some significant evidences that time availability and shopping orientations are more likely to affect the difference between the use of online information sources and offline information sources.

Another contribution is that, as suggested in the related literature, the impact of shopping orientations on the utilization of different types of information sources is investigated. In this study, economic-conscious shopping orientation, recreational shopping orientation, and quality conscious shopping orientation were taken into consideration to test their impact on the difference between the use of online information sources and the use of offline information sources.

Moreover, the impact of both objective and subjective product knowledge on the difference between the use of online information sources and offline information sources are investigated. This study also confirmed the negative relationship between product knowledge and perceived risk which is the most cited relationship in the marketing literature.

Besides, a causal link between utilization of various information sources and search outcomes was empirically tested in relation to consumer dissonance theory.

Another theoretical contribution is that the impact of optimum stimulation level on the difference between the use of online information sources and the use of offline information sources was examined.

Moreover, this study presents some important managerial contributions. The major contribution is that not only e-companies should give more importance to the offline information sources such as advertisements, brochures to reach their target markets,

 

but also, bricks and mortar companies should have presence on the Internet. Besides, companies which their target segment is composed of higher educated, young consumers and consumers with high OSL could use online information sources to reach their target segment. Older consumers are more likely to use offline information sources providing relatively more understandable information. Finally, the findings of this study suggest that company websites should be designed in a way which enables consumers to easily search product information and compare the alternatives with respect to several product attributes. Accordingly, consumers can take the right decision on their purchases and also take value for their money.

1.4 Organization of the Dissertation

This thesis is composed of nine chapters and organized as follows. The first chapter provides the purpose and contributions of this study as well as the methodologies used in both Study 1 and Study 2.

Chapter 2 gives main aspects of consumer information search behavior beyond its important role in decision making process, including types of information search, types of information sources and dimensions of information search behavior. Furthermore, as a technological advance, the role of the Internet in consumers’ information search behavior is discussed.

Chapter 3 provides a review of the literature associated with information search behavior including both traditional (offline) search behavior and online information search behavior.

In Chapter 4, the purpose of the Study 1 and the laddering technique used in Study 1 are presented. The chapter continues by discussing the findings of the Study 1.

Chapter 5 presents conceptual model of Study 2 indicating causal links among constructs and sets out the hypotheses for empirical testing.

Chapter 6 describes the methodological framework followed in Study 2 including pre-test of the questionnaire, sampling design and questionnaire design.

Chapter 7 presents the analysis of the data collected in Study 2 and the findings of the main survey conducted in Study 2. It consists of the initial data analysis,

 

respondent characteristics, and the procedures for construct validation and model testing for two different product types.

Chapter 8 discusses the results of two studies in relation to the literature review. Finally, the contributions of the two studies are discussed in Chapter 9, along with their limitations, future research directions, and managerial implications.

Figure 1 presents the research process followed in this dissertation.

Figure 1.1: Research Process

 

2. CONSUMER DECISION MAKING PROCESS AND INFORMATION SEARCH

Information search behavior is an important part of the decision process for the consumers considering the purchase of a product or a service category. Marketing scholars have developed a “stages model” of the buying decision process and Figure

2.1 shows the most common and simple form of the buying decision process models (Peter and Olson, 2005; Kotler and Keller, 2006). This model implies that consumers pass through five stages in buying a product and each stage includes different subsequent decisions. Actually, consumers may not go through all stages; instead, they may skip or reverse some stages of the buying decision process. Figure 2.1 is valid in the situation when a consumer faces a highly involving new purchase.

Perceived difference between ideal and actual state of affairs

Seek relevant information about potential solutions to the problem from external environment, or activate knowldege from memory

Evaluate competing alternatives in terms of salient beliefs about relevant consequences and combine this knowledge to make a choice

Buy the chosen alternatives

Use the chosen alternative and evaluate it again in light of this performance

Figure 2.1: A Generic Model of Buying Decision Process Source: Peter and Olson, 2005, p. 169 and Kotler and Keller, 2006, p. 191

 

The first stage of buying decision process is recognition of a problem or a need. At the next level, an aroused consumer is inclined to search for information; s/he gets into the information search activity and uses different types of information sources to get product information. Which major information sources are used and what the relative influence of each information sources is on the subsequent purchase decision are the main concerns for the marketers. There are a variety of factors influencing each stage of the buying process. The amount and influence of these information sources depend on the product category and the buyer’s characteristics (Kotler, 2006). Through searching product information, consumers learn about the alternatives, competing brands and their qualities.

Each stage of the buying decision process includes several important sub-stages. All of the main and sub stages of this process have to be understood and responded effectively by the companies in order to convince the customers to move through the process and reach at the last stage, hence to have a competitive advantage. Undoubtedly, after the recognition of a need, the most important task of the companies is to make the consumers aware of the companies’ products/brands and informed about the objective and subjective attributes of their brands in order to facilitate the movement of consumers to the following stages. During the information search stage, the consumers form an awareness set, then consideration set and finally a choice set which includes the most attractive and appropriate brands that can be evaluated to make a final choice. Thus, it is possible to emphasize that the “information search” stage is the most challenging and the most important stage in the process. It is a prerequisite for the consumers to move to the following stages and choose the most appropriate and attractive brand, hence to be satisfied.

2.1 Ongoing Information Search and Pre-purchase Information Search

Information search activity is classified into two categories, namely, ongoing search and pre-purchase search. The framework for consumer information search presented in Figure 2.2 summarizes the determinants, motives, and outcomes of pre-purchase and ongoing search. It is clear that there is a difference between ongoing search and pre-purchase search. Kelly (1968) has defined pre-purchase search as:

“Information seeking and processing activities which one engages in to facilitate decision making regarding some goal object in the marketplace” (p. 273)

 

On the other hand, Bloch et al. (1986) have conceptualized ongoing search as “search activities that are independent of specific purchase needs or decisions”. The main distinction among two constructs is that whether the information search activities are followed by purchasing a new product or not. While ongoing search behavior occurs on a relatively regular basis and does not depend on purchase needs, pre-purchase search happens only if a new product purchase is considered. Although, both ongoing search behavior and pre-purchase behavior have different purposes, the search activities in which consumers are involved would appear similar to an outside observer (Furse et al., 1984). Both Schmidt and Spreng (1996) and Bloch et al. (1986) acknowledge that ongoing and pre-purchase information searches are conceptually distinct. The exploratory study of Bloch et al. (1986) argued that ongoing search is more related to recreational or hedonic motives than informational motives. They pointed out that product involvement is strongly related to ongoing search. Specifically, pre-purchase information search includes activities of information gathering relevant to a previously recognized consumption problem. When a buying problem is recognized, then information search activity follows to help solving this problem at hand.

An extended framework on consumer information search that distinguishes between pre-purchase information search and ongoing information search was presented by Bloch et al. (1986). The framework illustrated in Figure 2.2 presents the determinants, motives, and outcomes of pre-purchase and ongoing information search. Although these two concepts –pre-purchase information search and ongoing search- are conceptually different from each other, in practice, it is difficult to differentiate them (Teo and Yeong, 2003; Schmidt and Spreng (1996); Bloch et al. 1986). The reason behind this, it is difficult to specify precisely when a need recognition has been revealed and when the decision making process started (Teo and Yeong, 2003). Ongoing search means search activity arising on a relatively regular basis. For instance, if a consumer is interested in a specific product category such as automobiles, he may subscribe to magazines or websites related to these products. This type of information search activity is independent from a specific purchase need. Contrary to this situation, pre-purchase information search -taking place only if a new product purchase is at hand- may include activities such as visiting dealers, taking a test drive or talking to friends etc. In other words, pre-

 

purchase information search activity seem to be an integral part of the consumer buying decision process which starts with a specific purchase need, discussed above. In this respect, pre-purchase search and ongoing search can be distinguished by their purpose.

The primary motive for pre-purchase search is to increase the quality of the purchase outcome (Punj and Staelin, 1983), in other words, to make better purchase decision (Bloch et al., 1986). However, according to the framework developed by Bloch et al. (1986); consumers involve in ongoing search through two motives. The first is to develop inventory of product knowledge in the sense that consumers could benefit from product knowledge in the future on the purpose of personal use or dissemination to others. The second motive is to experience pleasure or recreation, because some consumers get pleasure from searching information about products (Punj and Staelin, 1983). In addition, ongoing information search can result in an impulse buying by increasing the probability of purchasing a product. Bloch et al. (1986) have analyzed ongoing search in the light of informational and recreational motives and found a significant positive relationship between enduring product involvement and ongoing search for clothing and personal computer.

Table 2.1: A Framework for Consumer Information Search

Pre-purchase Search Ongoing Search

DETERMINANTS -Involvement in the purchase

- Market environment

- Situational factors - Involvement with the product

- Market environment

- Situational factors

MOTIVES To make better purchase decisions Build a bank of information for future

use Experience fun and pleasure

OUTCOMES - Increased product and market knowledge

- Better purchase decisions

- Increased satisfaction with the purchase outcome - Increased product and market knowledge leading to:

- Future buying efficiencies

- Personal influence

- Increased impulse buying

- Increased satisfaction from search, and other outcomes.

Source: Bloch, Sherrell and Ridgway, 1986

The distinction between ongoing information search and pre-purchase information search is also valid within the online context (Chiang et al., 2005). Similar to traditional environment, browsing behavior on the Internet represents ongoing information search behavior. On the other hand, pre-purchase information search in the form of goal directed behavior is also observed in the online environment. Goal-

 

directed behavior necessitates consumers to select information sources or type keywords in search engines, fitting to their goals. Hoffman and Novak (1996) specify this type of behavior as purposeful and selective information search.

Chiang et al. (2005) define pre-purchase information search within online environment as the following; “Information search is the effort by a consumer to acquire information in a web-based market space that is directed by a specific purchase under consideration.” Thus, it is possible to state that, the classification of pre-purchase information search and ongoing information search is discussed as valid in the online environment in the literature, however, the number of the studies which examine both ongoing and pre purchase search in online environment is too limited and the existing ones generally discuss the topic conceptually, not empirically. This study focuses on consumers’ pre-purchase information search within not only traditional environment but also online environment in the case of purchasing a cellular phone and a cultural activity.

2.2 Internal Information Search and External Information Search

Information search behavior in the literature can be also classified as internal and external information search (Moore and Lehman, 1980). Consumers involve in both internal and external search for product information (DeSarbo and Choi, 1999; Moore and Lehman, 1980). External information search effort is defined as the degree of attention, perception and effort directed toward obtaining environmental data or information related to the specific purchase under consideration (Beatty and Smith, 1987). The amount of internal search and external search varies in different types of consumer decision making covering habitual or routinized decision making, limited decision making, and extensive decision making. In a habitual decision making, consumers’ choice behavior is habitual or routine so that purchase decision is made depending on a learned decision plan stored in a memory; consequently, consumers do not want to engage in external information search. Internal search can be considered as the main information source mostly for habitual decision-making (Chiang et al. 2005). While limited decision making involves internal and limited external information search, extensive decision making involves not only an extensive internal search but also an extensive external information search. Moreover, in limited decision making process, at the evaluation stage, consumers

 

evaluate a few alternatives on a few attributes using simple decision rules. Contrary to this, consumers consider many alternatives and evaluate them on many attributes using complex selection rules in extended decision making. Accordingly, external information search is so important that consumers get involved in more external information search in the case of complex buying decision making.

Consumers store product knowledge that is accumulated as a result of previous search, product usage, or passively getting information during their daily activities (Punj and Staelin, 1983). Thus, internal information search occurs in the mind of consumer and it involves consumer’s screening his memory. On the other hand, external information search requires search effort for information search activities such as consulting with salesperson, friends/family, and expert consumers, reading printed materials, direct inspection, searching on the Internet and so on. Bettman (1979) suggests that external search follows internal search if there is not adequate information in memory to make a decision. Consumers who plan to purchase a new product, consequently, have engaged in consumer buying decision process often employ both types of search in a sequential and iterative manner. In contrast to the lack of empirical study on internal information search, there have been numerous studies on the determinants of external information search.

Although information search has been dichotomized as internal information search (retrieving information from memory) and external information search (obtaining information from a variety of information sources), this study focuses solely on external information search within online and offline context. This study covers consumers’ external search behavior when making purchases for a cellular phone and a cultural activity.

2.3 The Dimensions of Information Search Behavior

Information search behavior is grounded on economic foundations, in other words, the economics of information theory. According to this theory, consumer information search behavior is guided by a trade-off between the perceived costs of additional search and the expected benefits of that search (Stigler, 1961). Previous studies have offered some differences in the definition and measurement of ‘information search behavior’. Depending on review of past studies, Klein (1998) summarized the components of consumer information search behavior. Figure 2.3 presents the

 

information search process modeled by Klein (1998). This model depends on the economic foundations and defines the components of search behavior.

Figure 2.2: Information Search Process

Source: Klein, 1998

Past studies investigated consumer information search behavior by a variety of dimensions including the number of information sources from which information is obtained, the amount and types of information sought, the time dimension over which information is sought, the number of brands for which information was sought and deliberation occurred, and the manner in which information was sought. The measurement of consumer information search behavior is mainly related to which dimensions of consumer information search behavior are considered. Studies on consumer information search behavior focus on mainly the amount of search, however, other dimensions of information search behavior such as sequence, quality, breadth, depth and duration have been less investigated (Klein and Ford, 2003). Klein and Ford (2003) have been considered two components of information search behavior including amount of time spent on each information source and the number of different information sources used by individuals.

Kiel and Layton (1981) investigated consumer information search behavior regarding to purchasing a car under three dimensions including ‘a source of information

 

dimension’, ‘a brand dimension’ and ‘a time dimension’. The first dimension indicates the number of information sources from which information was sought. However, while ‘brand dimension’ shows the number of brands about which the consumer sought information, ‘time dimension’ of information search process means the time interval during which information search occurs. Kiel and Layton (1981) have also stated that this three dimensional definition of consumer information search behavior does not cover totally all possible dimensions of information search behavior.

As noted in the study of Klein (1998), information technology, mainly the Internet, influences multiple dimensions of consumer information search behavior, including amount of total information search, the number and types of information sources considered, and the distribution and weighting of information obtained through these sources.

2.4 The Classification of Information Sources

As it is stated before, information search behavior has been analyzed based on the different dimensions in the literature. The type and number of information sources used by the consumers are two different and related dimensions of the information search behavior. Consumers can obtain product information from a variety of different information sources. Information sources are usually categorized into commercial, neutral and personal sources (Engel, Blackwell and Miniard 1986). Commercial information sources include visits to retail stores and media, neutral information sources include books or magazine articles and consumer reports; finally personal information sources include seeking advice or opinion of friends, family or neighbors (Kiel and Layton 1981). On the other hand, classification schemes for information sources vary by researchers (e.g. Schmidt and Spreng 1996); for example, Beatty and Smith (1987) suggest media as a separate information source. The majority of previous studies examine the type and the number of sources of information from which consumers obtain information. It has been stated that major information sources for the Turkish consumers are personal sources such as own experience, friends and relatives (Borak, 1985).

A variety of different classifications of external information sources have been introduced in the related literature. Andreasen (1968) classified information sources

 

into four categories. These are impersonal advocate (print media and broadcast advertising), impersonal independent (popular articles and broadcast programming), personal advocate (sales person), and personal independent (friends and relatives). However, Cox (1967) grouped information sources into three categories consisting of consumer dominated, marketer dominated, and neutral sources. According to this classification, marketer dominated sources include information sources controlled by the marketer such as advertising, packaging etc. Consumer dominated sources refer to interpersonal informational channels and the marketer has little control over them. Neutral information sources comprise of information sources which are controlled neither by the marketer nor by the consumer such as consumer reports and newspapers. Beatty and Smith (1987) identified four information sources including media, retailer, interpersonal, and neutral information sources. Bruner (1988) grouped information sources based on two important dimensions: whether or not the source involves interaction with another person, and the extent of control of the source by a marketer. According to this classification, information sources have been grouped into personal-marketer dominated sources (salesperson); personal-non- marketer dominated sources (friends, observation, personal taste, search trips); non- personal-marketer dominated sources (TV advertising, magazine advertising, catalogues, in-store displays); non-personal-non-marketer dominated sources (magazine articles, newspaper articles, TV programs).

Another classification of information sources was proposed by Olshavsky and Wymer (1995). This classification included marketer controlled (e.g. personal selling, advertising, good information on the package, good brochures), reseller information (e.g. catalogs, consultants), third-party independent (e.g. consumer report), interpersonal sources (e.g. friends, acquaintances), and direct inspection of the good by the consumer (e.g. observation, inferencing). Additionally, Schmidt and Spreng (1996) segmented information sources according to who controls the content including marketer, reseller, third party, or interpersonal.

Kotler and Keller (2006) offer a comprehensive classification of information sources. Based on this classification, information sources can be grouped into four groups: personal information sources including family, friends, neighbors, and acquaintances; commercial information sources covering advertising, web sites, sales persons, dealers, packaging, and displays; public information sources consisting of

 

mass media, and consumer-rating organizations; and finally experiential information sources including handling, examining, and using the product.

Lee and Hogarth (2000) examined the interdependency of information search activities, for example, whether obtaining information from one particular information source is related to usage of another information source. They found that there is a statistically significant interdependency among different types of information sources. This finding implies that if a consumer get information from one source, s/he is likely to search for another information source. For this reason, companies have to give importance to different information sources in order to disseminate the required and expected information about their products to the consumers. In addition, alternative information sources have to be investigated and evaluated by the companies to offer variety of information sources to the consumers. Undoubtedly, the Internet has been considered as one of the recent innovations that directly influences the whole marketing activities of the companies. Although it is mostly accepted as an important advertising and promotion medium, the main function of the Internet is to be a medium of communication in general and to provide product information in particular.

2.4.1 The Advent of the Internet as a New Source of Product Information

The Internet has emerged to be an important source of information in recent years. The Internet gives consumers unprecedented access to detailed information about products and services. Many studies have recommended that the Internet can reduce search cost by decreasing the cost of accessing information and that the Internet has been considered to be a useful emerging information source (Bakos, 1997; Klein, 1998; Eighmey and McCord, 1998). An exploratory study related to perceptions of the Internet users revealed that consumers perceived the Internet to be a valuable information source (Hammond et al., 1998). Besides, many studies argue that the Internet has become a powerful tool for consumer information search (Shim et al., 2001).

The main characteristic of the Internet as an information source is that it is “a mass medium and interpersonal communication medium” (Eighmey and McCord, 1998). By using the Internet as a new source of information, consumers not only passively obtain the information provided, but also actively exchange information or their

 

experiences with each other. The Internet ensures consumers to communicate in a more interactive way (Bei et al., 2004). Due to the interactive nature of the Internet as a new information source, it is difficult to categorize the Internet as either interpersonal or mass media channels (Reardon and Rogers, 1998). In addition to this, online information sources differ from offline information sources in that “the Internet is not a unidimensional source of information, it is a composite of media” (Klein and Ford, 2003).

In the conceptual study of Senecal and Nantel (2001), they suggested that categorization of online information sources depends on multiple dimensions such as the provider of the information (consumers, experts, producers, etc.) and the sponsor of the website (commercial websites, commercially linked third party websites, non- commercially linked third party websites). Besides, Bickart and Schindler (2001) grouped online information sources into consumer generated information source such as online forums and marketer-generated information sources such as corporate websites. In an earlier study, Parker and Plank (2000) found that online information sources were relatively unimportant compared to offline sources such as printed media, TV, family and radio, but the reason of this was stated that this could be due to the low level of diffusion of computer networks in 1997. Ratchford et al. (2001) reported that 38% of their sample used the Internet as an information source for purchasing an automobile.

Some studies emphasize that the importance and contribution of the Internet as an information source varies depending the type and nature of the products. For instance, Klein (1998) has suggested that the Internet can be a valuable source of information for search goods in the sense that information acquisition is made easier and less costly. However, for experience goods, the benefits of the Internet as a new source of information is suggested to be its ability to convey one consumer’s experience to other consumers easily and inexpensively, so that consumers can gain “indirect usage experience or vicarious learning” (Klein, 1998). Klein and Ford (2003) investigated the effect of the Internet as a new information source on the consumers’ information search behavior in the case of purchasing an automobile. Their results point evidence that online sources of information are substituted for offline sources of information. Several studies have addressed the relationship between product types and searching product information on the Internet (Klein,

 

1998; Senecal and Nantel, 2001). Consistent with studies of offline (traditional) information sources, these studies state that impersonal information sources would be more useful for search goods and personal online information sources would be more useful for experience goods. Studies of producers’ websites as online information sources recommend that the Internet can be useful for products for which differentiation can be achieved through the provision of detailed information about functional attributes (Shaffer and Zettelmeyer, 1999; Ratchford et al., 2001).

Bakos (1997) has suggested that the Internet can reduce search costs by lowering the costs of accessing information and the Internet has emerged to be a useful new information source. Surveys have indicated that consumers’ primary reason to use the Internet is to search for product information (Bhatnagar and Ghose 2004). The main advantages of using the Internet for information search are: low cost (Porter 2001), easier access to price and product information (Porter 2001), quick access to product information (Klein and Ford 2003), access to more personal information by means of online consumer networks (Klein 1998; Bei et al. 2004) and ability of consumers to compare and contrast the options freely from time and place (Chiang et al. 2005). Another factor suggested to be specifically associated with the usefulness of the Internet as an information source is that the Internet removes geographical boundaries and provides access to product information anywhere in the world. Rathford et al. (2001) suggested that the Internet will be favored in “thin, geographically dispersed markets where information from other sources is unavailable or expensive”.

The Internet facilitates information search compared to offline information sources. Instead of going to the many retail stores or making phone calls to gather product information prior to purchase, visiting multiple web sites requires minimal effort (Teo and Yeong, 2003). Hence, traditional information search activities are more likely to be effortful and time consuming. Online information sources such as search engines provide consumers to browse through comprehensive list of retailers arranged by products or services, or they can easily search for a vendor by name or product information by writing keywords related to the product which they intend to purchase.

Klein and Ford (2003) have suggested that an additional categorization of information sources -both online and offline- should be added to traditional

 

classification. Therefore, the aim of this study is to discover the major motives of consumers when they use online information sources in their pre-purchase information search activity and to understand the hierarchical relations between underlying motives, and values perceived by the cons In this study, it is aimed to analyze whether or not underlying motives of online information search behavior and benefits provided by searching for product information on the Internet differentiate in the case of considering different product types (search vs. experience products) or not. Cellular phone and cultural activity were selected as representative products for this study. One criterion should be satisfied for product selection, that is, both online information sources and offline information sources had to be available and used by consumers for the selected products.

 

3. LITERATURE REVIEW ON INFORMATION SEARCH BEHAVIOR

In this chapter, firstly, the economics of information theory which is the most cited underlying theory of consumer information search behavior is discussed. Later, following two sections present the review of literature associated with offline information search behavior and online information search behavior, respectively.

3.1 The Economics of Information Theory

The literature associated with consumer information search has been traditionally grounded on the economics of information theory, proposed by Stigler (1961). According to this theory, a consumer keeps on searching for product information until perceived cost of information search exceed perceived benefits of information search behavior. In other words, consumer will stop searching for more information when marginal benefits of search equal to marginal costs of search. This cost-benefit framework has been often used in the studies on investigating the extent or amount of consumer information search behavior. Perceptions of search benefits and costs are major determinants of a consumer’s information search behavior. The economics of information theory hypothesizes that other things are held constant, expectation of increased benefits lead to more external information search effort, consequently, expectation of increased costs lead to less external information search effort. Thus, the influence of any variable on external information search behavior can be evaluated in the sense that whether or not this variable causes a decrease in search costs or an increase in search benefits. In addition to monetary costs, search costs include perceived time, travel, and access to media.

A positive relationship between any variable and external information search behavior is explained by one of the following three ways (Guo, 2001); (1) increasing benefits of search, (2) decreasing search cost, and (3) increasing benefit and decreasing cost of information search. Similarly, any variable negatively affects external information search behavior through three ways including increasing search

 

cost, decreasing benefits of information search, or decreasing benefit and increasing cost of information search.

The benefit-cost framework derived from the economics of information theory has been considered in numerous models developed within not only traditional context (e.g. Punj and Staelin, 1983) but also online context (e.g. Kulviwat et al., 2004). For instance, the working paper of Duffy and Wright (1993) analyzed the information search model for automobile purchasing on the cost-benefit framework. A significant relationship between perceived benefit and information search was confirmed. It was found that the amount of search had a positive effect on cost savings and consumer satisfaction. In addition, the amount of search had a direct positive effect on perceived benefits, in turn; perceived benefits of search had a positive effect on consumer satisfaction. With respect to online information search, the choice and use of the Internet as an information source will mainly depend on the perceived benefits of the information provided on the Internet (Lee and Lee, 2004).

3.2 Past Research on Traditional Information Search Behavior

Beatty and Smith (1987), Punj and Staelin (1983) and Srinivasan and Ratchford (1991) defined external information search as the amount of attention, perception, and effort directed toward obtaining environmental data or information relating to the specific purchase under consideration. Information search entails active, motivated, and conscious effort whereby this activity can be physically observed and measured (Heaney and Goldsmith, 1999). Understanding consumer information search behavior is “crucial for designing effective marketing communication campaigns” (Schmidt and Spreng, 1996) in the sense that marketing managers influence consumers’ decisions on a new purchase by providing information about their products or services.

Studies on consumer information search behavior have long historical past within both physical products and services context. Whereas most of past studies are related to the determinants of total search effort rather than the use of individual sources (e.g., Beatty and Smith 1987; Moorthy et al, 1997; Punj and Staelin, 1983), there is also a significant literature on the allocation of effort among information sources. Another stream of research in consumer information search behavior has focused on categorizing consumers according to different search strategies. In addition, the

 

majority of those studies have been focused on certain products. Most of the researches to date have investigated information search behavior for durable, high- cost items such as appliances, automobiles, etc.

The empirical studies of Katona and Mueller (1955) and Newman and Staelin (1972) are the first notable studies on the determinants of total search effort. These studies mainly focused on how consumers obtain pre-purchase information for durable goods. In these studies, pre-purchase external information search was described in terms of type of information gathered, sources used, and duration of search. Newman and Staelin (1972) found that both satisfaction with old products and buying experience was found to be negatively related to information search behavior for car, appliances including television set, refrigerator, washing machine, and air conditioner.

Beatty and Smith (1987) investigated the relationship between external search effort and a number of antecedent variables across five related consumer electronic products (e.g. television, computer, and video cassette recorder). While purchase involvement, attitudes toward shopping and time availability were found to be positively related to external search effort, product class knowledge was found to be negatively related to external search effort. On the other hand, the relationship between ego involvement and external information search was not significant.

In the related literature, there are also remarkable studies that have empirically tested causal relationships between determinants of information search and total information search effort (e.g. Anderson, 1979; Punj and Staelin, 1983; Srinivasan and Ratchford, 1991). For example, Anderson et al. (1979) proposed and tested a model of external information search in the case of automobile purchase. The variables which they considered in their model were attitude toward business, product satisfaction, and experience and they found that the amount of information search was positively related to satisfaction, negatively related to business attitude, and positively related to experience.

The study of Punj and Staelin (1983) is the first attempt to build and estimate a comprehensive model of information search behavior. Later, Srinivasan and Ratchford (1991) extended and improved the study of Punj and Staelin (1983) specifying the role of experience and knowledge in the information search process more precisely and revising measures of some constructs. In Ratchford and

 

Srinivasan (1991), search was defined as the effort aimed at acquiring information from the external environment. Srinivasan and Ratchford (1991) have taken Punj and Staelin’s (1983) model as the starting point of their study. The authors revised the model developed by Punj and Staelin (1983) through clarifying and redefining variables used in their model. However, there are little differences among these two studies. Unlike Punj and Staelin (1983), they did not consider the relationship between search and its outcomes. Srinivasan and Ratchford (1991) considered only value of time as a measure of search costs. The benefits of search refer to realized benefits in the Punj and Staelin (1983) model. That is to say, Punj and Staelin (1983) specified benefits of search as the outcome of search, Srinivasan and Ratchford (1991) considered perceived benefits as the trigger of search behavior. While, the Punj and Staelin (1983) model did not include perceived risk, Srinivasan and Ratchford (1991) model did not consider the outcomes of search. In their model, perceived risk, evoked set, benefits of search and external search effort were taken into consideration as dependent variables.

Schmidt and Spreng (1996) developed a generic model of consumer external information search behavior, but this model was not empirically tested. However, although this model was not empirically tested, it has been the most comprehensive conceptual model which contains 22 constructs representing the combination of both the economics and psychological/motivational approaches.

Prior research mostly focused on discovering consumers’ information search strategies or patterns (e.g. Furse et al., 1984; Kiel and Layton, 1981; Claxton et al., 1974). The main idea underlying these studies is that consumers follow distinct information search patterns when they decide to purchase anything. For example, Claxton et al. (1974) focused on how external information search is accomplished for furniture and appliances and classified purchasers into three distinctive groups in terms of their pre-purchase search patterns. However, Claxton et al. (1974) considered five different aspects of pre-purchase information search activities including type and range of alternatives considered, information sources used, features considered, stores visited, and time spent for the purchasing of furniture and appliances. As a result of the cluster analysis of the 287 furniture buyers, three groups were labeled as thorough (store intense), through (balanced), and non- thorough clusters. Members of the thorough (store intense) cluster make an average

 

of 20 store visits and use more information sources and spend a longer time on the purchase decision. However, non-thorough buyers consult only about one information source, visit two stores and spend much less time on the furniture purchases. Differences in information search patterns were analyzed with respect to individual, situational, and product characteristics. They proposed that product knowledge would influence the nature of information gathered, the nature of the sources used, and the amount of information obtained. They have found that number of information sources used, total visits to store, and deliberation time were useful distinctive measures to derive clusters. Consumers who mainly visited stores to gather pre-purchase information had high income level and education level; also, they considered product differences to be substantial. Besides, they were the most concerned about financial matters. The findings of this study indicated that situational factors such as immediacy of need and financial constraints were respectively negatively and positively related to duration of information search.

Similarly, Furse et al. (1984) clustered purchasers of automobile regarding to external information search patterns. Furse et al. (1984) derived six clusters including a low search group, a purchase-pal-assisted search group, a high-search group, a retailer shopper group, and a moderate search group. The consumers in low-search cluster were older and had highest income, greatest prior purchase experience, and more satisfied with previous purchases. On the other hand, high-search cluster consisted of best educated and least satisfied with previous purchase consumers. This cluster had the highest number of total hours devoted to search activities. Furse et al. (1984) explained this situation in the sense that high-search cluster might compensate for lack of prior positive experience by devoting a large number of hours to search.

Additionally, in the study of Hauser et al (1993), the authors have developed and estimated a model of the sequence of choices of sources in the context of a laboratory simulation of the information search process.

Although the majority of the studies in the literature have been related to the physical goods, researchers have proposed and empirically tested relatively few models dealing with information search behavior for services (Heaney and Goldsmith, 1999). Freiden and Goldsmith (1988, 1989) investigated information search behavior of new residents for professional services and found that consumers used more information sources when their perceptions of differences in the quality of

 

professional services were substantially different. Besides, these consumers used personal information more than impersonal information sources and also there was a positive relation between moving distance of new residents and the number of information sources used.

Heaney and Goldsmith (1999) tested a comprehensive model of external information search for banking service purchasing and considered four exogenous (independent) variables including perceived benefit, perceived risk, perceived cost, and perceived knowledge. Their sample size was 661. The extent of external information search, as a dependent variable, was measured by combination of three types of questions. The first question including three items was related to general information search behavior such as the level of agreement to the statement “when I was looking for a bank, I could not be bored to look for any information”. The second question was asked to indicate the number of times and the extent to which the respondent used each type of information source for six different information sources covering friends/relatives/co-workers, referral from other banks, called/telephone bank, consumer reports/articles/buying guides, advertisements/promotions/yellow pages, and happened to notice bank. At the end of the analysis, they have found that perceived benefits of search and perceived knowledge of product are significantly related to the extent of external information search in a positive direction; however perceived risk and perceived cost of search are significantly related to the extent of external information search in a negative direction.

On the basis of discussion above, there is an extensive literature related to traditional information search behavior. In the following section, previous studies on online information search will be discussed.

3.3 Past Research on Online Search Behavior

Although there is an extensive literature dealing with consumer information search behavior within traditional context, the topic of online information search behavior is a relatively new research area (Kulviwat et al., 2004), and it has just begun to receive more attention (Guo, 2001). As noted by Ratchford et al. (2003), academic studies dealing with the impact of the Internet on the use of other information sources are few.

 

The study of Lynch and Ariely (2000) demonstrated that while lower search costs for price information will increase price sensitivity within the online environment, lower search costs for quality information will decrease price sensitivity.

The study of Ratchford et al. (2001) seems to be the first empirical study to explore who use the Internet, what type of information is sought, and how the Internet affects the usages of other information sources in the case of an automobile purchase. The authors proposed a model of consumer choice of the Internet as an information source and focused on the determinants of individual differences in the Internet usage across consumers. In their logit choice model, they aimed to determine how much time will be spent with each information source, depending on consumer time cost, importance of attributes and price, prior information, skill at using each information source, and income.

Another model of online information search is proposed by Lukosius et al. (2001) on the basis of the revised model which was originally developed by Schmidt and Spreng (1996) for traditional external search behavior. They included new constructs (i.e. internet search experience, information overload management, privacy risk, national culture and telepresence) considering the differences between offline environment and online environment in relation to perceived ability to search, perceived benefits and cost of online search and motivation to search online. Unlike offline environment, consumers have control over the order of information flow in online environment in a way that is appropriate to their desires to seek information (Lukosius et al., 2001).

On the other hand, Klein and Ford (2003) have investigated how consumers use the Internet as an information source, besides, their study has focused on three main topics: whether the Internet reduces time spent for information search and increases the number of sources used by consumers, and whether the online information search substitutes for offline information search. The authors collected survey data using the Internet on search for automobiles from 369 respondents who were either recent buyers or current shoppers. A key finding of their study is that online use appears to have three dimensions as the following; (1) manufacturer/dealer sources (2) buying services and other third parties, and (3) bulletin board/chat sources. Each dimension has offline counterparts listed as; (1) dealer visits/advertisements, (2) consumer reports and other third-party sources of print information, and (3) interpersonal

 

contact with friends/relatives. Their study also implies evidence indicating that the Internet offers information that is provided by all three offline information sources and thus substitutes for all three types of offline information sources.

Ratchford et al. (2003) extended their study published in 2001, by developing a model of the choice of information sources and total search for information in a cost- benefit framework. They aimed to explore the determinants of the use of the Internet as an information source and the impact of the Internet on the usages of offline information sources and information search in general, in the case of automobile purchases. Ratchford et al. (2003) measured dependent variable by self-reports of time spent searching in hours for each type of information source. Shares of use of each source are the sum of time spent with items associated with that source divided by total time. Their independent variables are experience, satisfaction with previous car and dealer, demographics (education, age), time costs (measured by the respondent’s hourly wage), and price paid.

Ratchford et al. (2003) compare the data collected in 1989 with the data collected in 1999 from automobile buyers. The results show that those who use the Internet to search for automobiles are younger and more educated and search more in general. Average shares of other information sources declined correspondingly between 1989 and 1999. However, when they excluded the Internet in 1999, average share of other offline information sources did not change significantly. The Internet affects the use of other information sources in search for automobiles in approximately same proportion. Due to the highest share of dealer/manufacturer sources, the Internet leads to substantial reductions in the time with dealer/manufacturer sources. Total search for automobiles across all sources increases with gains to search, but decreasing with cost of search, and with prior information and age.

There is one study which investigates the impact of intention of using online information sources on the online purchase intention. It was found that attitude toward to Internet shopping, ease of Internet shopping, access to Internet and online purchase experience were positively related to intention of using the Internet for information search for books, computer software and videos (Shim et al., 2001).

Johnson et al. (2004) examined information search within online environment across e-commerce websites in the case of the purchases of books, compact discs and air travel services. In this study, clickstream data from 13 book sites, 16 music sites and

 

22 travel sites was used to track information search activities within online contexts. More active shoppers seem to visit more websites than less active shoppers in any given month. Furthermore, experience with online search and the number of web sites visited were negatively related. It is also found that pre-purchase information search behavior in online environments was actually quite limited, although consumers were “just a mouse click away” from other stores. This study suggests that the Internet does not result in enormous amounts of search. Kulviwat et al. (2004) have proposed a conceptual framework for exploring the determinants of online information search. Also, Chiang et al. (2005) discussed online information search behavior and also proposed a conceptual framework of online information search behavior. Their conceptual model depends on human and computer interaction so that this model consists of system variables including interruption and information load, and personal variables including domain expertise and system expertise. While domain expertise is defined as “the ability to identify, evaluate and exploit marketplace opportunities, and consists of mental representation which guide consumer search behavior” (Chiang et al., 2005, p.11), system expertise is defined as “skills needed to use computers as well as navigate the Internet” (Chiang et al., 2005, p.12). Moreover, it is proposed that system variables have a negative influence on online information search contrary to; personal variables have a positive impact on online information search. However, the model was not empirically tested.

The recent study of Mourali et al. (2005) investigated antecedents of consumer relative preference for interpersonal information search over other information sources including the Internet. The proposed antecedents include personality traits such as individuals’ susceptibility to interpersonal influence, their need for cognition and their self-confidence, as well as individual differences in product knowledge and perceived risk associated with the scenario-based purchase of a laptop computer. The authors found that preference of interpersonal information sources was positively related to informational susceptibility, to interpersonal influence, and self- confidence, however negatively related to need for cognition and subjective product knowledge. There was a non significant negative relationship between perceived risk and preference of interpersonal information sources.

 

3.4 Antecedents of Information Search Behavior

In the literature dealing with information search behavior, a variety of factors influence the amount of search activities which a consumer will engage in. In other words, the amount of a consumer’s search activity is a function of numerous factors. Newman (1977), Bettman (1979), Moore and Lehman (1980) and Beatty and Smith (1987) have suggested categories of variables that are related to amount of external information search. Newman (1977) is the first author to classify the variables dealing with external information search. Newman (1977) has provided a comprehensive review of the extent and determinants of information search activity. Soon later, Bettman (1979) and Moore and Lehman (1980) categorized determinants of external information search. Moore and Lehman (1980) have developed a classification of information search determinants based on earlier classifications by Newman (1977) and Bettman (1979). The classification of Moore and Lehman (1980) presents the combination of two types of classification offered by respectively Newman (1977) and Bettman (1979). Moore and Lehmann (1980) summarized and grouped the determinants of the extent of information search into six groups. Later, Beatty and Smith (1987) added costs of information search to those six categories. Beatty and Smith (1987) also extended the scheme of Moore and Lehman (1980) including relevant empirical findings and types of relationship between the antecedents and external search. These seven distinct categories of variables influencing information search are as follows: market environment such as number of alternatives, situational variables including time pressure, potential payoff such as perceived risk, knowledge/experience such as usage rate of the product, individual differences such as approach to search, conflict and resolution, and costs of search. These variables are given in details in Table 3.1 below. Furthermore, most of the studies have focused on total information search as the dependent variable and examined the antecedents of information search behavior.

 

Table 3.1: Antecedents of External Information Search within Traditional Context

Market Environment

Number of alternatives, complexity of alternatives, marketing mix of alternatives, stability of alternatives on the market (new alternatives), information availability, store distribution (distance), city size of residence, perceived variance in retail operations Individual Differences

Ability, training, approach to problem (compulsiveness, open-mindedness, pre- planning, innovativeness), approach to search (enjoyment of shopping, sources of information, etc.), involvement, demographics (age, income, education, marital status, household size, social class, occupation), personality/life-style    variables    (self-

confidence, etc.)

Situational Variables

Time pressure, social pressure (family, peer, boss), financial pressure, organizational procedures, physical and mental condition, ease

of access to information sources, store loyalty/preference, special buying opportunities Conflict and Conflict-Resolution Strategies

Potential Payoff/Product Importance

Price, social visibility, perceived risk, differences among alternatives, number of crucial attributes, status of decision-making activity (in the family, organization, society), product   class   importance,   length   of

commitment necessary Cost of Search

Knowledge and Experience

Stored knowledge, usage rate of product, previous information and experience, previous choices  (number  and  identity),  prior

satisfaction, brand loyalty/preference

Source: Adapted from Moore and Lehman, 1980; Beatty and Smith, 1987

Table A.1 (see in appendix) combining the Beatty and Smith’s (1987) and Guo’s (2001) schemes summarizes past research on offline information search behavior. This table presents the list of selected studies on traditional information search behavior. As it can be seen in Table A.1, there are four different possibilities for relationship between antecedent and information search behavior, including positive (+), negative (-), inverted-U, or no relationship (0). Past researches to date have not yet considered any variables regarding to conflict and conflict-resolution strategies to investigate consumer information search behavior.

A scheme, similar to the studies of Beatty and Smith (1987) and Guo (2001), has been constructed for summarizing the list of selected past researches dealing with the online information search behavior. Table 3.2 illustrates the summary of these selected researches.

 

Table 3.2: Antecedents of External Information Search within Online Context

Constructs Bivariate

Relationship with Search Product Category Study

I. Market Environment

II. Situational Variables

III. Potential Pay Off/Product Importance

Product importance + Athletic shoes, mobile phone as search products

Travel package, dinner at a new restaurant as experience products Bei, 2004 Chen, Widdows,

Perceived risk + Athletic shoes, mobile phone as search products

Travel package, dinner at a

new restaurant as experience products Bei, 2004 Chen, Widdows,

Perceived Risk + Not tested Hodkinson

2003 and Kiel,

Importance of attributes (functional

and expressive) + Automobile Ratchford, Debabrata and Lee, 2001

Importance

attributes of No significant

relationship Automobile Klein and Ford, 2003

Importance of price - Automobile Ratchford, Debabrata and

Lee, 2001

IV. Knowledge and Experience

Product experience purchase - Athletic shoes, mobile phone as search products

Travel package, dinner at a new restaurant as experience products Bei, 2004 Chen, Widdows,

Prior knowledge + Athletic shoes, mobile phone as search products

Travel package, dinner at a new restaurant as experience

products Bei, 2004 Chen, Widdows,

Prior information - Automobile Ratchford, Debabrata and

Lee, 2001

Product

expertise objective + Automobile Klein and Ford, 2003

Subjective expertise İnverted U Automobile Klein and Ford, 2003

V. Individual Differences

Market mavenism + Athletic shoes, mobile phone as search products

Travel package, dinner at a new restaurant as experience products Bei, Chen, Widdows, 2004

Skill at using each

source + Automobile Ratchford, Debabrata and

Lee, 2001

Income + Automobile Ratchford, Debabrata and

Lee, 2001

- Automobile Klein and Ford, 2003

Age - Automobile Ratchford, Debabrata and

Lee, 2001

Not tested Hodkinson

2003 and Kiel,

- Automobile Klein and Ford, 2003

 

Table 3.2: Antecedents of External Information Search within Online Context (Cont.)

Constructs Bivariate

Relationship with Search Product Category Study

V. Individual Differences

Education + Not tested Hodkinson and Kiel, 2003

+ Automobile Klein and Ford, 2003

Gender Not tested Hodkinson and Kiel,

2003

Female tended to

search more Automobile Klein and Ford, 2003

Enduring involvement + Not tested Hodkinson and Kiel, 2003

Optimum Stimulation

Level + Not tested Hodkinson and Kiel,

2003

Learning Style + Not tested Hodkinson and Kiel,

2003

VI. Perceived Attributes of Information Sources

Perceived usefulness + Athletic shoes, mobile phone as search products

Travel package, dinner at a new restaurant as experience products Bei, Chen, Widdows, 2004

Perceived ease of use, usage of offline information sources + Athletic shoes, mobile phone as search products

Travel package, dinner  at  a  new

restaurant as experience products Bei, Chen, Widdows, 2004

Web use and

experience + Not tested Hodkinson and Kiel,

2003

Internet experience + Automobile Klein and Ford, 2003

Perceptions and

attitudes + Automobile Klein and Ford, 2003

Besides, Moore and Lehman (1980) used this categorization as framework for their experimental study. This study conducted in 1978 with 120 students and staff members who participated in a longitudinal information acquisition and purchase experiment. In their experimental study, they investigated the relationships among individual differences (intelligence, demographics and food purchasing styles), knowledge and experience, potential payoff/product importance (perceived risk and importance of attribute), and situational factors (time pressure and financial pressure) and external information search for health-food bakery purchases. Moore and Lehman (1980) measured external information search in terms of total number of

 

information acquisitions, number of brands searched, and number of attributes searched (price, estimated calories etc.). Besides, three variables were used as a proxy measure for perceived risk; including weakly bread consumption, buying bread for personal consumption and finding adequate variety and quality of bread. In the six-week experimental study, each week, participants were asked to choose one loaf of bread among five types of bread. Prior to each choice, they could obtain product information through an information display board, and also their act of information acquisition was observed. They found that the category of knowledge and experience was the most highly related to external information search in negative direction. In this category, the number of previous choices was found to have a much larger impact on external search behavior than any other variable. Being rushed for time was found to be negatively related to external information search. For the variables in the category of individual differences, the following findings have been revealed: intelligence had a negative influence on external information search, participants who like gourmet cooking and search for new foods were found to search more information about bread, participants who usually plan their purchases before going to the store were found to engage in less external information search. Moreover, only marital status among demographic variables were significantly related to external information search indicating that married people gather less information than singles.

As a conclusion, there are many variables investigated in the literature in terms of their impact on external information search within traditional environment (see in Table A.1). However, online information search has begun to receive more attention in recent years. Compared to the literature dealing with traditional information search, the number of studies on online information search is relatively too few (see in Table 3.2). In the following chapters, the literature associated with information search within not only traditional environment but also online environment is discussed in relation to the conceptual framework developed in this study.

 

4. QUALITATIVE STUDY (STUDY 1): UNDERLYING MOTIVES OF ONLINE INFORMATION SEARCH BEHAVIOR OF CONSUMERS

In this chapter, the method and sample used in study 1 are presented. The objectives of this study are twofold: first, to find out and explain underlying motives of online information search activities by taking into consideration the product type, and second, specifically, to determine other factors -which have not been analyzed in the related literature- influencing to gather product information from online information sources. In addition, in this section, the role of Laddering technique will be discussed in explaining and exploring a process based activity, namely, online search behavior. The present study focuses on consumers’ pre-purchase stage, external information search behavior when choosing a cellular phone and deciding on which cultural activity to go. This qualitative study presents a first look at what motivates consumers using online information sources instead of offline information sources to obtain product-related information.

In this qualitative research stage of the main study, it is aimed to answer the following questions within the context of purchases of a cellular phone and a cultural activity context:

• What are the underlying motives for utilization of online information sources?

• In what situations consumers tend to use online information sources instead of offline information sources?

• What are the determinants of preference of online information sources over offline information sources?

Laddering technique is used to accomplish these objectives so that it would be beneficial to explain the means-end theory and the laddering technique at first in this chapter.

 

4.1 Means-End Theory and Laddering Technique

A means-end chain is a simple knowledge structure that links product attributes to the consequences produced by these attributes (Gutman, 1982). The key idea underlying means-end theory is that the perceived consequences of a product, and how these relate to the individual, are more important than the product’s actual attributes or characteristics. That is, consumers decide to buy a particular product based on their perceptions of what the product can do for them. Likewise, consumers decide to use a particular information source based on their perceptions of what they can benefit from that source. Together with the data-collection method called laddering; means-end approach has been applied to a broad range of products and services.

Laddering is an in-depth, one-to-one interviewing technique and the main focus of this method is to elicit how consumers translate the attributes of products or services into meaningful associations with respect to self. Laddering was first introduced by Hinkle (1965), particularly interested in people’s goals and values. In the marketing literature, this method is widely used to investigate consumers’ goals and values (e.g. Reynolds and Gutman 1988; Zanoli and Naspetti 2002). Literature on means-end chain theory and the laddering technique grounded on this theory can be grouped into conceptual studies, studies related to methodological issues, and studies related to applications in marketing and management sciences (Huber et al., 2004).

While, first applications of laddering method were concerned with products and services, in recent years laddering method based on means-end theory has moved to a broader context. For instance, Bagozzi et al. (1994) analyzed means-end chains of consumers regarding their attitudes towards recycling. Pieters et al. (1995) note that consumer behavior is often described as purposeful and goal-oriented. The study of Pieters et al. (1995) presents a conceptualization of goal-directed consumer behavior in terms of a hierarchical structure of abstract goals connecting to one another through means-end relationships. Their exploratory study has aimed to elicit the higher-level goals underlying consumers’ weights lose behaviors. In addition, Huffman and Houston (1993) investigated the effects of different processing goals on information acquisition through means-end chain.

 

A means-end chain usually includes four levels of abstraction starting from concrete level, shown in below Figure 4.1. The means-end chain theory suggests that consumers structure associations in memory linking identifiable attributes of a product, a service or an activity to consequences experienced in usage or consumption situations. Sequentially, these salient consequences are linked to desired end states (values). The aim of the researcher is to understand the personal values which represent the underlying consumer motivation for the topic researched.

Figure 4.1: The Levels of Means-End Chain

Source: Peter and Olson, 2005, p.81

Laddering uses progressive and iterative questions that allow interviewer to understand how the attributes of a product, the consequences of using the product, and the personal values it satisfies are hierarchically linked together. In other words, the object of a laddering interview is to disclose how product attributes, usage consequences, and personal values are linked in a person’s mind.

After the introduction of the laddering methodology into the consumer research area, a considerable number of researchers have used the method in order to analyze different dimensions of consumer behavior (e.g. Overby et al. 2004; Wansink 2003; Zanoli and Naspetti 2002).

In this qualitative research method, there are no specific sets of questions to ask, since each question relies on the previous answer as given by the interviewee. Each ladder is established by a sequence of variations of the typical laddering question “Why is that important to you that the product or service has x attribute/benefit?” In the laddering interview, additional probing questions need to be posed to reveal linkages among attributes, consequences, and values. For instance, the following questions may be asked: “What would happen if the product or service does not have x attribute?”, “Could you explain further?”, “Could you give me an example of what you mean?”, “Could you explain the benefits of this?” Reynolds and Gutman (1988) discuss laddering process and give more beneficial tactics for overcoming problems that may be occurred during a laddering interview. Each attribute, consequence, and value, and relationships among them are found by questioning the interviewee based

 

on previous responses. Besides, an interview form is constructed to ask additional information related to the research topic.

Therefore, characteristics or attributes of products or services are not inherently important to the consumer. They are important only to the extent that they are perceived to be a means to achieve an emotional outcome that is significant to a consumer which is linked to a higher level consequence or benefit. The traditional laddering technique begins with an elicitation of product attributes. At the beginning of the laddering interview, it is important to expose distinctions made by the individual respondent concerning perceived, meaningful differences among product attributes, brands or different activities (Reynolds and Gutman, 1988). Afterwards, the laddering interview continues on each distinction made by the respondents. For online information search context, the laddering technique has been adapted and starts by asking respondents to provide what the advantages of online information sources have given to consumers over offline information sources.

The next step is to develop a set of codes that reflect everything that has been mentioned by the interviewee. In this step, at first all responses are classified into three basic attribute-consequence-value levels. Then, Hierarchical Value Maps (HVMs) are constructed to display respondents’ ladders in the aggregate picture. HVM is a graphical representation of perceptual orientations of the respondents and represents the cognitive structure of the sample interviewed. HVM is created by means of an implication matrix displaying the number of times each element leads to each other element. In this matrix, both direct and indirect relations are represented. In a single usage or consumption of one product or doing one activity, a consumer may perceive only one linkage between a specific attribute, consequence, and value, while another consumer may perceive multiple linkages between a specific attribute, consequence, and value.

4.2 Sampling Procedure and Data Collection Method

Since the purpose of the Study 1 is to understand the underlying motives and goals of the use of online information sources, the sample consists of people who are experienced users of the Internet. The aim of this study is to explore and understand the hierarchical linkages between attributes of online information sources compared to offline information sources and personal values which have been used in the main

 

body of the research, namely descriptive study (study 2), thus the sample of users of online information sources need not to be representative. Accordingly, the laddering interviews were carried out on a group of 30 consumers; all of them using both online and offline information sources to get product information. However, the study of Reynolds and Gutman (1988) has served as a guide for conducting the laddering method.

In this study, while online information sources include all manufacturer websites, dealer websites, and online consumer forums, etc.; media, sales person and friends/family/neighbors etc. are considered as offline information sources. Two scenarios were formed to carry out laddering interviews for each group of product. Respondents were asked to think about these two scenarios in which they were faced with the decision to purchase a cellular phone and a ticket for a cultural activity. They were then asked questions about how they would handle information search. Each laddering interview started by these two scenarios given below. Each depth- laddering interview took about 75-90 minutes.

Scenario 1

“It is assumed that you have decided to purchase a new cellular phone; what do you do firstly?”

Scenario 2

“It is assumed that you and your friends have agreed to meet for participating in any cultural activity (concert, theater, cinema etc.) and organizing this activity has been given to you. What would you do first?”

Participants were asked to mention information sources they considered while choosing a cellular phone or a cultural activity. After that, subjects were questioned about the reasons why they select or do not select online information sources over offline information sources. Then, respondents were asked to explain advantages/disadvantages of online information sources compared to offline sources. Each of those advantages/disadvantages served as the starting point for the laddering question: “Why is this important to you?”. This question was followed by more probing questions such as “Why is the obtaining detailed information important to you?”.

 

Content analysis and coding of the data were performed according to the relevant literature (Reynolds and Gutman, 1988). The LADDERMAP software by Gengler and Reynolds (1993) was used to derive the implication matrixes and the relevant HVMs.

4.3 Findings of the Study 1

The profile of the sample in which all respondents are Internet users is summarized in Table 4.1.

Table 4.1: Profile of the Sample

Age Frequency

20-25 years old 11

26-30 years old 7

31-35 years old 8

36-45 years old 4 Education Frequency

High School 3

University 14

Postgraduate 13

Internet Usage (hours) Mean

Daily 3

Weekly 20

4.3.1 Results of the Data Analysis

To have a better understanding of linkages in consumers’ mind between attributes, consequences and values, hierarchical value maps were constructed for each product type. After the responses of the laddering interviews content analyzed, attributes, consequences (benefits) and values were identified for each product category. The face validity of the code frame developed was assessed by a psychologist who is the expert in laddering method. In total, the 30 respondents mentioned 74 ladders for cellular phone and 84 ladders for cultural activities. At the end of the analysis of these results by means of LADDERMAP, the results have been presented in two separate hierarchical value maps shown in figure 4.2 and figure 4.3. The concepts in the boxes refer to the most frequently cited benefits/attributes or values.

To construct a hierarchical value map, it is necessary to select a cutoff value for means-end chain linkages. A cutoff value is determined based on linkage occurring for one or more times. Reynolds and Gutman (1988) suggest two approaches to choose a cutoff level. First, they recommend to try multiple cutoff levels and choosing the one leading to the most informative and interpretable solution. Pieters et al. (1995) stated that this rule is similar to the one used in multidimensional scaling.

 

The second approach to determine the cutoff level is to decide on the proportions of total connections illustrated in the hierarchical value map. Reynolds and Gutman (1988) note that “a cutoff of 4 relations with 50 respondents and 125 ladders will account for as many as two-thirds of all relations among elements”. In the present study, these two approaches are considered and the cutoff point is chosen as 4 providing informative and interpretable solution, and also representing 60 % (for cellular phone) and 61 % (for cultural activities) of all direct relations in attributes- consequences-values chains.

In figure 4.2 and figure 4.3, attributes represent tangible or intangible characteristics of online information sources. Consequences, on the other hand, are the outcomes that occur when online information sources are used for seeking product information. Values indicate consumers’ goals achieved in terms of using online information sources for each product type. Each ladder links attributes of online information sources to the functional or psychosocial consequences (benefits), in turn, to more abstract personal values and goals. The concepts in circles mean that these benefits/attributes or values are mentioned frequently. Each attribute is linked by one or more benefits and ultimately with a value at the top. Figure 4.2 shows five attributes, six consequences (benefits) and five values with respect to online information search for a cellular phone. However, figure 4.3 illustrates five attributes, seven consequences (benefits) and four values as regard to online information search for a cultural activity. Values represent more abstract, but the hidden reasons of consumers using of online information sources. The thickness of connecting lines implies that the higher the relative width of line used to connect concepts, the greater the frequency of association between concepts.

 

Figure 4.2: Hierarchical Value Map for Online Information Search (Cellular Phone)

 

Figure 4.3: Hierarchical Value Map for Online Information Search (Cultural Activities)

For both cellular phone and cultural activities, online information sources are perceived as those supplying product information rapidly and a means of accessing to consumer comments and detailed product information. They also provide easy access and enable consumers to see different alternatives in the same product category at one time. As it can be seen in Figure 4.2 and Figure 4.3, the commonly cited characteristics of online information sources for both product types were listed as:

 

• To be quick

• Easy access

• Access to detailed information

• To see different alternatives at one time

• Access to consumer comments

The findings of laddering analysis reveal that online information search depends on cost-benefit framework used to explain consumer’s information search behavior. Both HVMs indicate that cellular phones and cultural activities have similar maps with respect to a number of attributes, consequences, and values. For example, many informants stated that “taking right decision on the product purchase” were so important that they can manage their money (financial responsibility). “Financial responsibility” linked from one consequence of “taking right decision”, and one attribute of “access to consumer comments” through online information sources.

“Not to lose time” was the most implied consequences (benefits) linked to the “quick” and “easy accessible” attributes of online information sources. Respondents mainly preferred online information sources to avoid losing time. It can be said that “time availability” may be a discriminating factor to determine the choice of information sources. “To take right decision” was the second noticeable consequence derived from using online information sources. According to the interpretations of results, this consequence was important to get best value for money. Therefore, two types of shopping orientations including “quality shopping orientation”, and “price shopping orientation” seemed to have a role in determining the utilization of online information sources. Furthermore, one of the main characteristics of online information sources is that they enable consumers to do comparison shopping (Dickson, 2000). In consistency with the literature, one of the consequences drawn from online information search for both product types is the “making comparison among different alternatives”.

The values, attached to online information search for cellular phone, uncovered by laddering analysis are “financial security”, “financial responsibility”, “feeling no guilty”, “peace of mind” and “personal achievement”. Besides, corresponding to cultural activities, the value of “sensual pleasure” emerged in the most ladders with respectively “peace of mind”, “no regrets” and “financial responsibility” in

 

decreasing order of frequency. The common values among both products are “financial responsibility” and “peace of mind”.

Cellular phone compared to cultural activities is such an expensive and complex product that the underlying motives of consumer information search on the Internet are more related to financial conditions (financial responsibility and financial security). Besides, other fundamental motive of online information search behavior corresponding to cellular phone is to avoid consumer dissonance.

In contrast to search products, the quality of experience products cannot be easily evaluated. However, searching for information in terms of experience products is relatively costly and time consuming. Consequently, for cultural activities, the main benefit of using online information sources for consumers is to save time.

Due to the fact that cultural activities are intangible products, using online information sources enables consumers to access to detailed information and consumer comments, thus they become well informed. Afterwards, they can easily make cultural activities tangible in their mind and easily evaluate the quality of any cultural activity. In turn, they can make right decision concerning cultural activity selection.

As revealed in the following one of the excerpts, the notion of product involvement might be a determining factor in the utilization of online information sources. Specifically, consumers who are less involved with a product are more likely to use online information sources rather than offline information sources, because obtaining product information through online information sources necessitates less effort and saves time. One of the respondents explains clearly the relationship between the degree of involvement of the consumers and the use of on-line information sources with his words:

“Since a cellular phone is not interest to me, I do not want to make too much effort on information search related to purchasing a new cellular phone. I would rather spend time for searching information on other products, such as books, cultural activities etc.”

Given the discussion of the results of the qualitative study, two variables seem to be striking factors that would have influence on the preference of online information sources over offline information sources. These variables are “time availability” and “shopping orientations”. Depending on this result, time availability and shopping

 

orientations were included in the proposed model. In addition, based on the findings related to the attributes derived from the Laddering technique, some items are added to the construct of “perceived benefits of online information sources” such as “it provides detailed information about cellular phones (cultural activities)” and “it provides quicker access to the product information about cellular phones (cultural activities)”

 

5. CONCEPTUAL FRAMEWORK

The proposed conceptual model illustrated in Figure 5.1 seeks to determine the variables that might influence the extent of usage of online information sources over offline information sources. The central construct of the proposed model is the gap between the use of online information sources and that of offline information sources, calculated by subtracting sum of online information source usage and offline information source usage. From now, throughout this study, this central construct “the gap between the use of online information sources and that of offline information sources” is cited as the gap.

While, it is not practical to include every variable affecting information search into this study, variables that might influence the extent of usage of online information sources over that of offline information sources considered in the proposed model. The variables included in the study are listed as product involvement, general perceived risk, optimum stimulation level, perceived benefits of online information sources, perceived benefits of offline information sources, objective and subjective product knowledge, attribute importance, time availability, recreational shopping orientation, economic-conscious shopping orientation and quality-conscious shopping orientation. Additionally, the conceptual model suggests link between the difference online and offline information search behavior and search outcomes.

In this thesis, antecedent variables are categorized as in the studies of Moore and Lehman (1980), Beatty and Smith (1987), and Guo (2001). In this conceptual framework, it is proposed that age and education indirectly influence the differential use of information sources through perceived benefits of using online information sources and perceived benefits of using offline information sources. The other individual differences, potential payoff/attribute importance, product knowledge, and situational variable are considered directly related to the differential use of information sources. Finally, it is hypothesized that the differential use of information sources has direct impact on outcome of information search including purchase satisfaction.

 

 

Figure 5.1: The Conceptual Framework

In the following sections, this conceptual framework will be discussed in details.

5.1 The Antecedent of the Difference between Online and Offline Information Search

The antecedents are grouped into four sub-categories including individual differences, potential payoff/attribute importance, product knowledge/experience, and situational factors. In the following sections, each will be taken into consideration separately.

5.1.1 Individual Differences

In this section, individual differences including optimum stimulation level, product involvement, shopping orientations, perceived benefits of using online and offline

 

information sources and demographic characteristics will be discussed with respect to their impacts on the difference between usage of online and offline information sources.

5.1.1.1 Optimum Stimulation Level

Optimum stimulation level (OSL) is a personality trait that characterizes an individual in terms of his general response to environmental stimuli. Hebb (1955) and Leuba (1955) introduced this concept nearly at the same time in the psychology literature (Raju, 1980) and later it has been considered in consumer behavior literature. In their opinion, every person prefers a certain level of stimulation that is named as “optimum stimulation”. An environmental stimulation could be determined by any other properties such as novelty, ambiguity, complexity etc. Individuals take an action to achieve the optimum level of stimulation. These attempts aiming to modify stimulation from the environment can be called “exploratory behavior” (Raju, 1980). OSL is one of the underlying constructs of exploratory behavior. OSL has been associated with exploratory purchase behavior and brand switching behavior (Steenkamp and Baumgartner, 1992; 1995; 1996).

Raju and Venkatesan (1980) recommended that OSL is meaningful to be used in studying the information-search behavior of consumers. Psychologists have found that people with higher OSLs engage in exploratory behaviors to a greater extent than people with lower OSLs (Steenkamp and Baumgartner, 1992). In general, individuals with high OSLs will be more tended to explore new stimuli and situations due to a higher need for environmental stimulation. On the other hand, individuals with low OSLs are likely to feel more comfortable with familiar situations and stimuli, and avoid from new or unusual ones (Raju, 1980). The findings of Raju (1980) indicated that individuals with higher OSLs tend to seek change or variety. Besides, when they encounter with new or unusual stimuli or situations like new brands or retail environments, they prefer to face them rather than to go away from them.

A person with high OSL is described by Kish and Donnenwerth (1969) as “one who has a stronger than average need to seek and approach situations, activities, and ideas which are novel, changing, complex, surprising, and more intense” (Raju, 1980). In the consumer  behavior  literature,  OSL is positively correlated with several

 

exploratory and risk-taking behaviors such as adopting new products, switching brands, and seeking information out of curiosity. Exploratory consumer behavior tendencies have been grouped as curiosity-motivated behaviors, variety seeking, and risk taking and innovative behavior (Raju, 1980; Steenkamp and Baumgartner, 1992). Raju (1980) examined the correlations between OSL and variety of exploratory consumer behavior tendencies (repetitive behavior proneness, innovativeness, risk taking, exploration through shopping, interpersonal communication, brand switching, and information seeking behavior) and found a significant but low positive correlation between OSL and information seeking behavior. In addition to this finding, he suggested a profile for a person with a high OSL as follows:

“One who is not afraid of taking risks or trying new or unusual products/services, is eager to find out about new products/services and takes the initiative in trying them, seeks variety or change in repetitive purchases, and likes introducing new products and brands to others” (p.271).

It is proposed that consumers with high OSLs and consumers with low OSLs may be different in that they might use different evaluative criteria (Raju, 1980). Besides, Raju (1980) stated that those with high OSLs and those with low OSLs could search information at almost equal degree but for very different reasons.

The previous studies examining the relationship of OSL with information seeking have somewhat contradictory/uncertain findings. Joachimsthaler and Lastovicka (1984) found that OSL could not be considered as a mediating variable between personality traits and consumer exploratory behaviors including information seeking behavior. In addition to this, it was stated that OSL with other personality traits directly affects information seeking behavior in a positive direction. Steenkamp and Baumgartner (1992) indicate that OSL has an effect on ongoing information search behavior rather than pre-purchase information search behavior, in other words, OSL is related to information search behavior when information acquisition is motivated by curiosity. However, they stated that the level of OSL has different effect on each type of information search behavior. In their study, it is hypothesized that while individuals with higher OSLs will not search for more information than individuals with lower OSLs when information acquisition is motivated by further end, individuals with higher OSLs are likely to search for more information than

 

individuals with lower OSLs when information acquisition is motivated by curiosity. In addition, Hoffman and Novak (1996) found that a high OSL led to greater exploratory behavior on the Internet. This finding confirms that the Internet is a consumer-driven information source.

Within the pre-purchase context, Steenkamp and Baumgartner (1992) found that there was no relationship between information search and OSL; on the other hand, OSL was found weakly related to ongoing information search behavior.

Hodkinson and Kiel (2003) stated that OSL has not been directly related to a detailed analysis of information search behavior so far. However, they posit that OSL affects information search behaviors of consumers on the Internet. In this study, as proposed by Hodkinson and Kiel (2003), it is expected that individuals with lower OSLs will feel less comfortable with new information and situations, eventually they tend to search less than individuals with higher OSLs. Due to the fact that the Internet is a consumer-driven information source, it is proposed that consumers with low OSLs incline to use online information sources less than offline information sources. On the other hand, people having high OSLs are enthusiastic to find out all information related to products and services and also feel more comfortable with new situations; hence, individuals with higher OSLs will gather product information through using both online information sources and offline information sources.

Based on above discussions, it is proposed that there is a negative relationship between optimum stimulation level and the difference between online and offline information search. That is, an increase in optimum stimulation level results in decrease in the difference score between online information source usages and offline information sources usage, because an individual with high OSL is going to obtain product information from both types of information sources. In other words, consumers with high OSL tend to prefer using online information sources in addition to offline information sources for product information search.

H1: OSL is negatively related to the difference between the use of online and offline information sources

Different types of self-report measures and conceptualization have been used to measure OSL in both Psychology and Consumer behavior literature such as Sensation Seeking scale (Zuckerman et al., 1964; Zuckerman, 1979; Mittelstaedt et

 

al., 1976), the Change Seeker Index (Garlington and Shimota, 1964), the Stimulus Variation Seeking scale (Penney and Reinenhr, 1966), the Similes Pereference Inventory (Pearson and Maddi, 1966), the Novelty Experiencing Scale (NES) (Pearson, 1970; Venkatraman and Price, 1990) and the Arousal Seeking Tendency scale (Mehrabian and Russell, 1974; Goodwin, 1980; Raju, 1980; Joachimsthaler and Lastovicka, 1984; Wahlers and Etzel, 1990). The Arousal Seeking Tendency measures an individual’s preferred arousal level. The Change Seeker Index evaluates “need for variation in one’s stimulus input in order to maintain optimal functioning” (Garlington and Shimota, 1964). The Sensation Seeking scale measures a person’s “need for varied, novel, and complex sensations and experiences and the willingness to take physical and social risks for the sake of such experiences” (Zuckerman, 1979). The Novelty Experiencing Scale (NES), on the other hand, evaluates an individual’s “tendency to approach versus a tendency to avoid novel experiences” (Pearson, 1970). Although different scales have been used so far, they all aimed to cover the concept of OSL. Raju (1980) used the Arousal Seeking Tendency scale containing 40 items scored on nine-point likert scales to examine the relationships between OSL, selected personality traits, demographic variables, and variety exploratory consumer behaviors including repetitive behavior proneness, innovativeness, risk taking, exploration through shopping, interpersonal communication, brand switching, and information seeking behavior.

In this study, the Exploratory Information-Seeking (EIS) scale developed by Steenkamp and Baumgartner (1996) is used to measure OSL construct. Additionally, Hodkinson and Kiel (2003) has suggested EIS scale be used to operationalise this variable.

5.1.1.2 Shopping Orientations

Shopping orientation is one of the most cited concepts in consumer behavior literature in that this concept has been widely used to classify consumers based on their shopping habits and styles. Shopping orientations refer shoppers’ styles that place particular emphasis on certain activities (Moschis, 1976). Consumers have different goals and motivations when shopping for products. Thus, past studies have explored a variety type of shopping orientations or typologies. Consumers can have more than one type of shopping orientation simultaneously (Reynolds and Darden, 1971; Moschis, 1976). In studies having done so far, shopping orientation has been

 

investigated with respect to different variables such as demographics (Sproles and Sproles, 1990), religiosity (Mokhlis, 2006), preferences for information sources (Moschis, 1976), external information search (e.g., Beatty and Smith, 1987; Klein, 1998; Schmidt and Spreng, 1996; Shim et al., 2001), retailer preferences (Shim and Drake, 1990), non-store patronage intentions (Korgaonkar, 1984), and store attribute importance (Lumpkin and Hawes, 1985). The basic idea of shopping orientations is that consumers possessing different shopping orientations have different consumer characteristics and behaviors such as different needs and preferences for information sources (Moschis, 1976).

Stone (1954) is the first researcher who pioneered the research on shopping orientation and he mentions that consumers shop not only for just economic reasons but also for other noneconomic reasons. In his seminal article, Stone (1954) defined four types of shoppers: (1) economic, (2) personalizing, (3) ethical, and (4) apathetic. The purpose of economic shopper is to buy the good and they also give more importance to price, quality, variety and efficiency in their shopping. The personalizing shoppers, on the other hand, incline to “individualize” the shopping trip and they seek closer personal relationship with sales people. Personalizing shoppers choose to shop at stores that are known, usually local stores. The ethical shoppers feel that they have to patronize specific, local stores, rather than chain stores. Supporting local stores rather than chain stores even if it means higher prices and less variety of goods is important for him. The apathetic shoppers wish to minimize their buying efforts and are less interested in shopping, they shop only because of necessity; also the store type is not important. After the Stone’s (1954) work, Stephenson and Willet (1969) defined the typology of shopping orientation on the degree of patronage concentration across retailers; consisting of the store loyal shopper (high concentration-low search), the compulsive and recreational shopper (high concentration-high search), the convenience shopper (low concentration-low search) and the price oriented shopper (low concentration-high search). Moschis (1976) examined shopping orientations of cosmetic buyers using life-style variables including Activities, Interest and Opinions and the author also extended the list of shopping orientations by developing additional lifestyles-based categories such as the psychosocializing shopper, problem solving shopper and name-conscious shopper along with store loyal shopper and brand loyal shopper. Besides, he analyzed each

 

shopper’s communication behavior in terms of the extent to which the consumer had each orientation. The findings of Moschis’s (1976) study supported that shoppers with different shopping orientations have different needs and preferences for communication sources. For instance, advertisements were found to be used as information sources mainly by the brand loyal and the name-conscious shopper, and also it was found that problem solving shoppers relied on salespeople more than any other type of shopper. Williams, Painter and Nicholas (1978) grouped grocery shoppers into apathetic, convenience, price shoppers and they found that there were significant differences across groups in demographics and media usage. However, Bellenger et al. (1977) and Bellenger and Korgaonkar (1980) recommended that according to shoppers’ information seeking tendencies, shoppers could be also classified as the recreational versus the convenience shoppers. Korgaonkar (1984) analyzed the role of shopping orientations embracing price orientation, time and convenience orientation, and brand conscious orientation in the patronage intentions for non-store retailing encompassing mail order selling, telephone selling, catalog showrooms and door-to-door selling. The findings of this study indicated that non- store retailing methods would be mainly attractive to convenience and price oriented consumers rather than brand oriented consumers.

Since the seminal work of Stone (1954), numerous researchers have applied the shopping orientation concept in the study of segmenting markets within different contexts such as online shopping (e.g. Gehrt et al., 2007; Girard et al., 2003; Donthu and Garcia, 1999) and catalog shopping (e.g. Gehrt and Shim, 1998). Lumpkin (1985) examined shopping orientations as segmentation criteria for elderly consumers. He profiled the shopping orientation groups of elderly consumers in terms of patronage behavior, usage of information sources, product attributes and demographics. With respect to usage of information sources, Lumpkin (1985) investigated whether or not the importance of mass media, salespersons, and other interpersonal information sources vary by shopping orientation sub-segments of the elderly. 373 respondents who are aged over 65 were segmented based on their shopping orientations. He found three segments labeled as “active apparel shoppers”, “economic shoppers”, and “apathetic shoppers”. Active shoppers are active and not limited economically. Besides, they like shopping and can be considered as opinion leaders. Economic shoppers are the most concerned with financial circumstances.

 

The results of this study show that these three shopping orientation segments are different in their use of newspapers, friends and salespersons as information sources. Specifically, “apathetic shoppers” use all types of information sources to lesser extent, compared to other two groups. On the other hand, “active shoppers” tend to obtain product information from their friends to a greater degree than salespersons.

Gehrt et al (2007) analyzed shopping orientations in the context of the Internet and classified Japanese Internet shoppers depending on the shopping orientation criterion. In their study, the authors considered seven types of shopping orientations including recreation, novelty, impulse purchase, quality, brand, price, and convenience shopping orientations. Based on these seven shopping orientations, they divided the consumers into four clusters, namely shopping enjoyment, brand browser, price browser, and dislikes shopping. Shopping enjoyment segment includes Internet shoppers who enjoy shopping and are not price sensitive. This segment has a greater number of males and is mainly aged between 30-39 years old. 61% of this segment shop on the Internet at least 1-2 times per year. The Brand Browser segment consists of consumers giving more important to brand and convenience in their online shopping. This segment has a higher percentage of older consumers and high level of household income. Consumers in this segment are also higher educated. Additionally, the Price Browser segment covers consumers driven mainly three shopping orientations including price, convenience and recreation, respectively. This segment has higher percentages of women, younger consumers (50% of them are less than 29 years old), and lower-income consumers. Consumers in this segment have lower educational level. Although, consumers in price browser segment have lower Internet usage experience, 68% of them purchase products on the Internet at least 1-2 times per year. The last segment of Japanese Internet users is the Dislikes Shopping segment. Consumers in this segment are not driven by any of the shopping orientations. They are slightly older, well-educated and they have higher incomes than average.

There are also a few studies related to what type of shopping orientations having a role in consumers’ online purchasing behavior. The study of Girard et al. (2003) revealed that convenience shopping orientations and recreational shopping orientations, rather than price-consciousness, variety seeking and impulsiveness, are significantly related to consumer’s preference of online purchasing. Also, it was

 

found that product type has significant effect on the relationship between shopping orientation and purchase preference for online shopping. Additionally, convenience and recreational shopping orientations were positively related to preference for online shopping for experience and credence product types. Donthu and Garcia (1999) analyzed the differences between the Internet shoppers and non-shoppers related to socioeconomic characteristics and shopping orientations. According to the findings of study, the Internet shoppers are older and have higher income level than non-shoppers. Besides, Donthu and Garcia (1999) found out that Internet shoppers are likely to be more convenience oriented, more impulse, more innovativeness and more variety seeking oriented. The authors also revealed that the Internet shoppers have less brand consciousness and less price consciousness. The common finding of the studies cited is that convenience is an important issue for Internet shoppers (Meuter et al., 2000; Szymanski and Hise, 2000).

Brown et al. (2003) showed that product type, prior purchase and gender, rather than consumers’ shopping orientations, are more likely to influence intention of online purchasing. They clustered online shoppers in terms of their shopping orientations and the findings of this study indicated that recreational shopping oriented and price- oriented consumers formed the two largest clusters among online shoppers. However, it is also stated that this finding is contrary to the belief that consumers who purchase products online are primarily convenience-oriented shoppers.

Furthermore, few researchers discussed that attitudes toward shopping and beliefs about shopping should have a positive influence on external search behavior (Beatty and Smith, 1987; Klein, 1998; Schmidt and Spreng, 1996; Shim et al., 2001). Shim et al. (2001) found a positive significant relationship between attitude towards to shopping and intention to use the Internet for getting product information. It is proposed that consumers who give importance to the economic aspect of shopping will be more likely to use the Internet for an information source.

As noted in the study of Li et al (1999), Internet users prefer online shopping over in- store shopping due to its convenience and time savings. However, online shopping provides little opportunity to experience products by seeing, feeling, smelling or touching them. Consumers who are mainly prefer experiencing products before buying them can be defined as experiential shopping oriented shoppers (Li et al., 1999). Li et al (1999) found that frequent online shoppers are more convenience

 

seekers and are less experiential shopping orientated shoppers than less frequent online shoppers. However, they found no difference between online shoppers and non-online shoppers in terms of economic and recreational shopping orientation.

More recently, Lee and Lee (2005) suggested that shopping attitude is an important variable affecting consumers’ motivation to engage in online information search. The Internet has capacity of providing a large amount of product information with low cost and effort, thus increasing the convenience and economic value of using online sources for product information search. The Internet, however, gives opportunity to see and compare all alternatives in the same product category, thus increasing the value of online information sources for consumers with high variety seekers. In this study, it is aimed to explore the relationship between shopping orientations and preference of online information sources over offline information sources. The shopping orientations are operationalised by a range of attitude, interest, and opinion statements related to the topic of shopping. Besides, in this study, general shopping orientations were measured, hence shopping orientation is considered independent from shopping context such as catalog shopping and online shopping.

Thus, past studies have explored a variety type of shopping orientations or typologies. The main idea underlying the following three hypotheses is that people show each type of shopping orientation to some extent when they purchase something. The selection of these three types of shopping orientations has been based on the following theoretical and empirical rationale: Individuals with high quality conscious in their shopping try to find quality products, for this reason, they search product information in terms of using all kind of information sources. Based on this view, it is assumed that quality shopping orientation is negatively correlated with the difference between online and offline information search. Thus, as the quality shopping orientation increases, the difference between online and offline info search decreases, because both categories of information sources are used almost equally by the consumers.

H2a: Quality shopping orientation and the difference between the use of online and offline information sources are negatively related.

 

Economic shoppers are mainly interested in buying products at the lowest price or getting the best value for the money they spend. Given the results of exploratory study on underlying motives of using online information sources, it is proposed that there is a positive relationship between economic shopping orientation and the difference between online and offline information search. In other words, individuals having more economic orientations in their shopping will use more online information sources than offline information sources. That is, difference score between usage of online information sources and that of offline information sources will increase in favor of online information sources.

H2b: Economic shopping orientation and difference between the use of online and offline information sources are positively related in the favor of online information sources.

Consumers having high recreational shopping orientation find pleasure in shopping even if they do not purchase anything. The recreational shopper was first exposed by Stephenson and Willet (1969). Individuals having high recreational shopping orientations perceive shopping as a cultural activity, consequently, consumers with high recreational oriented in their shopping prefer using offline information sources over online information sources. It is expected that there is a positive relationship between recreational shopping orientations and the difference between online and offline information search. In other words, as the level of recreational shopping orientation of consumers increases the difference between the online and offline information search increases in favor of offline information sources.

H2c: Recreational shopping orientation and difference between the use of online and offline information sources are positively related in the favor of offline information sources.

5.1.1.3 Product Involvement

The term of consumer involvement was first introduced by Krugman (1965). Zaichkowsky (1985) defined involvement as “the personal relevance of an object based on inherent needs, values and interests”. Besides, the meaning of involvement equals to importance, interest, attachment and/or motivation manifested toward an object (Laroche et al., 2003). Bloch (1986) indicated that product involvement refers to the feelings of interest, enthusiasm, and excitement that which consumers have

 

about specific product categories. Therefore, product involvement is an outstanding concept in the explanation of the variation of consumer decision making process (Laroche et al., 2003). Consumer behavior theories notes that consumers engage in more information search activities when involvement is high and less search when involvement is low (Engel et al., 1993; Hawkins et al., 1986; Howard and Sheth, 1969). Besides, product involvement have been shown, both empirically and conceptually, to be related to external information search (Schmidt and Spreng, 1996; Zaickowsky, 1985; Bloch et al., 1986). For example, Jacoby et al. (1978) found a positive relationship between product class importance and amount of external search in their experimental study.

Two different dimensions of product involvement have been determined, including the importance aspect and the hedonic aspect (Chaudhuri, 2000; Laurent and Kapferer, 1985). For instance, Chaudhuri (2000) proposed and analyzed four different types of causal links among product involvement, perceived risk and information search. These four models have taken into consideration perceived risk as an antecedent of information search along with importance and hedonic dimension of product involvement, as a consequence of importance and hedonic dimension of product involvement, as an antecedent of importance and hedonic dimension of product involvement, and as a moderator of relationship between importance and hedonic dimension of product involvement and information search, respectively. The author found that perceived risk mediates the effect of the importance dimension of product involvement on information search but not of the hedonic dimension. The effect of hedonic dimension on information search was found to be direct. That is, specific to the importance dimension of product involvement, products that are important to a consumer are perceived to be risky, whereby, the consumer engages in more information search activity.

Flynn and Goldsmith (1993) found that consumers having higher scores on consumer involvement with both fashionable clothing and travel services are more likely to search information from different types of information sources (newspapers, magazines, TV etc.). For six different services, product involvement was positively correlated with information search measured by source usage and search effort (McColl-Kennedy and Fetter, 1999).

 

Product involvement influences the utilization of different type of online information sources. Bei et al. (2004) found that product involvement was significantly and positively related to the use of online information from retailers and manufacturers.

Bei et al. (2004) proposed that the Internet would be a complementary information source for highly involved consumers to obtain all information about the product in the sense that highly involved consumers are interested in all kinds of information regarding a product, whereas low involved consumers would prefer the Internet over offline information sources to facilitate decision making about the product.

In this study, it is expected that the higher the level of product involvement the smaller the difference between the use of online information sources and offline information sources.

H3: Product involvement is negatively related to the difference between the use of online information sources and offline information sources.

5.1.1.4 Perceived Benefits of Using Information Sources

According to economics of information theory, if a consumer believes that greater benefits will acquire from information search, he will be more inclined to information search activities due to the fact that the perceived benefits will compensate the perceived cost. Within the online context, Teo and Yeong (2003) found a significant relationship between perceived benefits of search on the Internet and the total amount of external search effort on the Internet. This result is also consistent with the findings of Srinivasan and Ratchford (1991)’s study dealing with the traditional information search behavior.

It is expected that when consumers perceive information acquired through the Internet more reliable, credible and trustworthy, they start to use online information sources along with offline information sources. In accordance with this view, the following hypothesis is proposed.

H4: Perception about benefits of offline information sources is negatively related to the difference between the use of online information sources and offline information sources.

 

H5: Perception about benefits of online information sources is negatively related to the difference between the use of online information sources and offline information sources.

In addition to the perceived benefits of the online information sources derived from the literature, several additional characteristics and functions (related to online information sources) have been found important as a result of the qualitative research, and included into this study.

5.1.1.5 Demographic Characteristics

External information search behavior, both online information search and offline information search, has often been found to be correlated with several demographic variables including age, education, gender, income, marital status, and occupation. A number of studies have found income and education to be positively related to external search for durables (Claxton et al., 1974). On the contrary, an inverse relationship between income and the amount of information search have been found in the online information search behavior (e.g. Klein and Ford, 2003) and in the offline information search behavior (e.g. Kiel and Layton, 1981).

Newman and Staelin (1972) ascertained that young and unmarried people have the highest information seeking score as well as they have limited product and buying experience. Besides, these consumers spend more time for shopping. Moore and Lehman (1980) found that marital status was the only demographic variable related to search in the sense that married people searched less information than singles did. Kiel and Layton (1981) found no relationship between information search behavior and gender and occupation.

In this study, specific demographic variables to be included in the proposed model are age and education. The literature does not provide obvious evidence about the relationship between education and information search. In past studies related to consumer information search behavior, a positive linear relation has been found between education and information search for automobiles as well as other durable goods (Claxton et al., 1974; Kiel and Layton, 1981; Ratchford et al., 2001; Ratchford et al., 2003). This finding can be evidenced that individuals with higher education are better able to comprehend and integrate new information, whereby they would be involved in more information search activities. Contrary to, Klein and Ford (2003)

 

have found no significant relationship between education and total information search time. Punj and Staelin (1983) argue that education will indirectly increase search by increasing prior memory structure. In the study of Punj and Staelin (1983), education level is considered as a measure of the consumer’s general learning skills. It is assumed that higher levels of education increase person’s ability to identify, locate and assimilate relevant information. Accordingly, the authors proposed that higher levels of education lead to increased total search activity. However, their results associated with education and prior memory structure were statistically insignificant. Similarly, Ratchford and Srinivasan (1993) do not find a significant relationship between education and information search for automobiles. Ratchford et al. (2003) found a negative relation between education and search efficiency which means that education is associated with increased search. Education may lead consumers to consider more attributes and make finer distinctions, thereby increasing the perceived gains to search. Their empirical evidence on information search for automobiles indicates that the Internet leads to reduced search.

A negative relationship between age and the amount of information search have been found in past studies within the context of both online information search and offline information search (e.g. Klein and Ford, 2003; Kiel and Layton, 1981).

McColl-Kennedy and Fetter (1999) investigated relationship between demographic characteristics and external information search measured as two-dimensional construct embracing source and effort. There was no relationship between education and external information search; women appear to search more than men and also it was found that the level of external information search was negatively correlated with income.

In recent years, the relationships between demographics and online information search behavior have been investigated. Previous studies, on the other hand, indicate that some of the demographic factors directly influence use of online sources. It was found that age, gender, education, and income are distinguishing variables related to using online information sources for product information search. For instance, Ratchford et al (2001) and Ratchford et al (2003) showed that younger, more educated and consumers with higher-income tend to use the Internet for product information search. Weber and Roehl (1999) found that individuals seeking information on the Internet are higher educated and they have higher occupational

 

and income levels. Lee and Ward (1999) found a few demographic variables to have significant effect on online information search. Men not only spend less time searching but also give up searching more quickly than do women. Education appears to make people less successful at information search. People with higher incomes may be more successful searchers, even though they spend less time searching and give up their searches sooner. Their results suggested that as individuals gain more experience using the Internet, they are more likely to search for alternative information sources on the Internet.

Ratchford et al. (2001) noted that age, education, and occupation are the demographic characteristics which are mainly related to ability of Internet usage; hence, it affects usage of online information sources relative to other sources. Bei et al. (2004) have found that younger consumers tend to use more online information sources, excluding retailers’ web sites. However, older consumers relied more on the sources of retailers’ or manufacturers’ websites.

Since demographic factors have also impact on the use of online information sources over offline information sources through perceived benefits of using information sources, demographic variables consisting age and education are included in the proposed model. Given the discussion above, the following hypotheses are proposed;

H6a: There is a positive relationship between age and perceived benefits of using offline information sources.

H6b: There is a negative relationship between age and perceived benefits of using online information sources.

H7a: There is a negative relationship between education and perceived benefits of using offline information sources.

H7b: The higher the level of education, the higher the perceived benefits of using online information sources.

5.1.2 Potential Payoff and Attribute Importance

The impact of perceived risk and importance of product attributes on the difference between online and offline information search will be explained in the following sections, based on the literature review.

 

5.1.2.1 Perceived Risk

Perceived risk as one of the pervasive factors influencing consumer behavior has been included and discussed in a large number of empirical studies. Raymond A. Bauer (1967) introduced the term of “perceived risk” to consumer behavior research as follows:

“Consumer behavior involves risk in the sense that any action of a consumer will produce consequences which he cannot anticipate with anything approximating certainty, and some of which are likely to be unpleasant (p., 24)”.

Bauer (1967) used this construct in explaining such phenomena as information seeking, brand loyalty and reliance on others in purchase decisions. The construct of perceived risk covers the uncertainty involved in a purchase decision and the consequences of taking an unfavorable action (Betman, 1973). Stone and Gronhaug (1993) defined risk as the subjective expectations of loss. It has been found that consumers tend to develop risk-handling strategies (e.g. purchasing the same brand or seeking additional information with respect to the purchase decision problem) so that they can cope with the unexpected results of buying. Many studies in consumer behavior literature emphasize that the underlying reason of information search activity of the consumers is to reduce the perceived risk in the buying situation, hence consumers engage in information search activity as a risk reducing strategy. In consumer behavior literature, it is suggested that consumers engage in information search activity as a risk reducing strategy to reduce perceived risk (Cox, 1967; Dowling and Staelin, 1994). Hugstad et al. (1987) found that consumer information search behaviors varied by purchasing situations having different levels of perceived risk. According to their findings, consumers used more information sources in high- risk purchasing situations than in mid-level or low-level purchasing situations. Empirical studies have found a positive relationship between overall perceived risk and the amount of information search (Germunden, 1985; Dowling and Staelin, 1994; Moorthy et al., 1997). Furthermore, perceived risk was also investigated in relation to the importance of various types of information sources. For example, Arndt (1967) found that “the high-risk perceivers tended to make more effort to seek word-of-mouth information”. Perry and Hamm (1969) examined the relationships between personal information sources including word-of-mouth and observation and perceived risk combining social and economic risk in purchase decisions on 25

 

different products and services (e.g. automobile, golf clubs, haircut, tv) among students through canonical analysis. It was found that the higher the risk involved in a particular purchase decision, the greater the importance of personal influence.

In addition, many studies have also found that perceived risk is a valuable factor to examine and explain consumers’ behavior in the case of purchasing of services. As Mitchell and Greatorex (1993) listed, the most frequently considered services in these studies are life insurance, hairdressing, legal services, banks and dry-cleaning.

Perceived risk is considered as a multidimensional construct in the related literature. Perceived risk has been mainly measured related to the dimensions of risk and uncertainty and negative consequences associated with them. In the extent of marketing literature, perceived risk has been operationalized by considering these different dimensions. For instance, Jacoby and Kaplan (1972) distinguished between five risk dimensions including financial, performance, psychological, physical and social risks in the overall risk construct. The findings of their study indicated that the five risk dimensions accounted for 61.5 per cent of the total variance in the overall risk measure. Roselius (1971) has also suggested that time is an important risk dimension. Stone and Gronhaug (1993), on the other hand, showed that these six dimensions of risk accounted for almost 90 per cent of overall risk. Their findings indicated that other five types of risk were mediated through psychological risk. In addition, they suggested that the importance of each risk dimension alters according to the purchase situation which a consumer faced.

The relative importance of each risk dimension could vary depending on different purchase decisions, as some risk aspects will be more prevalent in some purchase situations than in others. For personal computer, Stone and Gronhaug (1993) have found that the contributions of six risk dimensions to overall risk varied considerably. However, they also reported that financial and psychological risk were the predominant risk dimensions in the case of purchase of personal computer situation due to the fact that personal computer is complex and expensive product and it is difficult to judge. The perceived risk also varies across individuals and products (Stone and Gronhaug, 1993). For example, technological products such as a cellular phone or a laptop may be perceived to be highly risky products since they are complex and higher-priced products. In the study of Germunden (1985) who examined the relationship between perceived risk and information search through

 

meta-analysis of 100 papers, he found that 51 of them reported contradictory results indicating that there was no increase in information search effort with increases in perceived risk. Swartz and Stephens (1984) analyzed the relationship between risk type and information search and they found no relationship between perceived social risk and amount of information search for financial institutions, hairdressers and doctors. However, a weak relationship was found between perceived performance risk and amount of information search for hairdressers, while there was no relationship between performance risk and amount of information search for financial institutions and doctors. Thus, Swartz and Stephens’ (1984) study indicates that the impact of different dimensions of perceived risk on the amount of information search also varies across different service categories.

Mitra et al. (1999) examined the relationships among perceived risk, information search activity, and behavioral intentions with respect to service categories including search-based, experience-based and credence based services. The result of this study indicated that perceived risk was increasing along a continuum from search to experience to credence service purchases. They found that consumers were got into greater information search activities when purchasing high risk credence services to reduce the amount of risk. However, an important finding was that consumers not only use high level of personal information sources, but they also utilize a high level of impersonal information sources when buying credence services compared to experience and search services. Since the quality of credence services can not be evaluated by customers in advance, they tend to use a wide variety of information sources including personal and impersonal sources to obtain more product information in order to reduce the amount of risk inherent in such purchases.

A number of researchers have compared services with goods in terms of perceived risk. The study of Iacobucci (1992) empirically confirms the idea that consumers perceive services more risky than goods. Given the relationship between perceived risk and information search behavior, Murray (1991) found that consumers’ search activities differ between goods and services in that consumers are more inclined to information search activities for services than for goods. Heaney and Goldsmith (1999) found an insignificant negative relationship between perceived risk of purchasing bank services and information search. The explanation of this kind of relationship was that consumers would likely to see few differences among the

 

competing banks, hence they feel that they could not lower risk by searching for more information (Heaney and Goldsmith, 1999). Undoubtedly, those findings are directly related to some of the distinguishing characteristics of the services such as intangibility, variability which make the consumers have difficulty to assess the quality of the services, hence increase the perceived risk in the purchase situation.

Product knowledge and product involvement are the most commonly considered variables with respect to their impact on perceived risk (Dowling and Staelin, 1994). Earlier studies indicate that perceived risk is a mediator of the effect of prior knowledge on information search (Srinivasan and Ratchford 1991; Dowling and Staelin 1994). That is, prior product knowledge indirectly influences information search behavior through perceived risk. For instance, a consumer with low level of prior product knowledge has greater perceived risk in relation to the purchase decision under consideration, which in turn, he will get involved in more information search effort. Within the online environment, the recent study has showed that perceived risk was found to be positively related to the use of online information obtained from other consumers and retailers and manufacturers (Bei et al., 2004).

Given the discussion about the effect of perceived risk on the utilization of online and offline information sources, logically, it is expected that higher perceived risk associated with purchase of products or services is more likely to result in heightened usage of both online and offline information sources on the part of the purchaser.

H8: The higher the perceived risk the smaller the difference between the use of online and offline information sources.

5.1.2.2 Attribute Importance

The general search/experience/credence attribute framework has been applied to understand the depth of information search by attributed dealing with consumer information search behavior. Nelson (1970), and Darby and Karni (1973) are the first authors to discuss the framework of attribute qualities. This framework consists of three categories of attributes including search, experience, and credence qualities. Search attributes represent the qualities of a product that can be accurately and efficiently evaluated prior to purchase, while experience attribute qualities include the qualities can be accurately and efficiently evaluated only after the product has been purchased or/and used for a short time. However, credence attribute qualities

 

cover the qualities that cannot be accurately and efficiently evaluated even after the product is used, this maybe because of higher cost of acquiring sufficient and accurate information about the product. In several studies, this general framework of search/experience/credence has been discussed to understand the depth of information search behavior by attribute (Ford, Smith and Swasy, 1988; Nelson, 1970). Some studies suggest that attribute importance should be positively associated with increased external information search (e.g. Newman, 1977; Moore and Lehman, 1980; Rathford, 1982). In other words, as the perceived importance of the product attributes increases, the extent of information search increases in order to find out and buy the most appropriate product with the attributes required.

The recent work by Ratchford et al. (2003) categorized attributes as functional or expressive and also stated that consumers select the most cost-effective sources for each type of product attribute. While functional attributes relate to the product’s physical aspects, expressive attributes depend on consumer’s self expression about his experiences gained through product usage. However, Klein and Ford (2003) have grouped attributes into three categories according to the effective and efficient way of obtaining credible information: remote search, in-person search and experience. According to their categorization, remote search attributes include those attributes for which information can reliably be obtained without direct inspection (e.g. model reputation, warranty, price, and gas mileage). Those attributes for which direct inspection is the most reliable way of confirming information are included in the category of in-person search attributes such as power, driving and interior styling. Finally the last category of attributes is the experience attributes including attributes that can only be verified post purchase through ongoing usage of the product, such as safety, reliability and service of the automobile. Klein and Ford (2003) proposed that the greater the importance of remote search attributes to the consumer, the greater the proportion of time devoted to online information search relative to his total information search. However, it is found that there is no significant relationship between the importance of different types of product attributes and the proportion of information search conducted on the Internet (Klein and Ford, 2003).

Bhatnagar and Ghose (2004) analyzed the relationship between the two dimensions (duration and frequency) of information search behavior on the Internet and the type of information sought by consumers. Besides, they found that the types of

 

information that consumers seek influence the way of consumer search. Different types of information have varying effects on the duration and frequency of information search. In the study of Bhatnagar and Ghose (2004), the authors considered the following seven types of information: price comparisons, location of stores, availability of products and services, information from vendors, comments of other consumers, reviews and recommendations from experts, and personalized information based on customer profiles.

On the Internet, some of the most commonly sought information are price information at different stores (Ratchford et al., 2001), location of stores (Ghose and Dou, 1998), the availability of products and services and opinions of other consumers and reviews and recommendations from experts (Hoffman and Novak, 1996). Furthermore, Bhatnagar and Ghose (2004) showed that consumers spend the maximum time searching for information from vendors, followed by information about availability of products/services and about price comparisons. Besides, they spend comparatively less time for searching information about store location.

Klein (1998) indicated that some experience attributes may become search attributes due to advances in multimedia technology. However, while online information sources are the most valuable source for information about product attributes that are gathered outside by consumers (such as safety rating, model reputation and warranty information), they are not the most valuable information source about product attributes best gathered by inspection, such as how the vehicle drives and its interior and exterior styling (Klein and Ford, 2003).

Maute and Forrester (1991) investigated relationship among some antecedents of information search (attribute importance and inter-brand differences), product attributes, and external information search that is one component of the consumer decision making process. The dependent variable information search was operationalized as the amount of time and effort of respondents devoted to searching for external information prior to opening an account at a bank. In the study of Maute and Forrester (1991), there is empirical evidence on the presence of search, experience and credence qualities among the attributes of a complex service. Not only these attribute qualities are differentially related to information search, but also search, experience and credence qualities moderate the relationship between search antecedents and external information search. Specifically, they found that experience

 

qualities were strongly and positively related to information search while search and credence qualities were negatively associated with information search. The reason behind this could be that banking service consumers perceived information search costs to exceed information search benefits of these attribute qualities (Maute and Forrester, 1991). Besides, the influence of experience qualities on information search was found to be greater than either search or credence qualities.

In this study, several attributes dealing with a cellular phone and a cultural activity were considered including design, color, and physical attributes for cellular phones, as well as the name of the actors and/or actress, their backgrounds, and characteristics of the related to cultural activity for cultural activities.

Although, the Internet facilitates to obtain knowledge about some types of product attributes such as price and physical attributes, in this study, it is expected that the level of importance of attribute influences positively the difference between online and offline information search in the favor of offline information sources. Consumers are more likely to prefer using offline information sources including friends, salespeople, and advertisements to gather information on the product attributes which are important to them. Consequently, the following hypothesis is developed.

H9: There is a positive relationship between attribute importance and. the difference between the use of online and offline information sources in the favor of offline information sources.

5.1.3 Product Knowledge and Experience

Objective and subjective product knowledge are considered in this section. Besides, the conceptual distinction among these two constructs and their role in the consumer information search behavior will be discussed, respectively.

5.1.3.1 Product Knowledge: Objective Knowledge and Subjective Knowledge

Product knowledge is defined as “knowledge related to brand or product class stored in memory at the time that search commences” (Srinivasan and Ratchford, 1991). Consumer product knowledge is a crucial construct to understand the process of consumer information search. Previous studies have indicated that prior product knowledge is the main factor in determining the amount of external search activities.

 

Prior product knowledge facilitates the acquisition of new information and it increases the efficiency of search activity. A number of studies have investigated the impact of the prior knowledge on external information search (Punj and Staelin, 1983; Brucks, 1985) and choice processes (Bettman and Park, 1980).

In consumer behavior literature, the construct of “product knowledge” has been conceptualized in terms of familiarity with product and product experience. For example, Alba and Hutchinson (1987) stated that product knowledge consists of familiarity with product (which is based on purchase or product usage) and product expertise (which represents the ability to perform product-related tasks). Besides, Marks and Olson (1981) defined product knowledge as product related information stored in memory, such as information about brands, products, attributes, evaluation, decision heuristics and usage situations. Consumers get well knowledgeable about products through information search and usage of information as well as through experience.

In the related literature, the relationship between product knowledge and the amount of external search is not clear. The empirical findings regarding the relationship between prior product knowledge and information search behavior have often yielded conflicting results. A review of empirical studies regarding the relationship between prior product knowledge and information search have presented inconsistent findings and so far four possible relationships including negative, positive, inverted- U, and no relationships between product knowledge and information search have been found (Srinivasan and Agrawal, 1988). Some researchers have found a positive relationship between product knowledge and external information search (Brucks, 1985; Srinivasan and Ratchford, 1991; Schmidt and Spreng, 1996), on the contrary, some studies have indicated that product knowledge has negative effects on external information search (Beatty and Smith, 1987; Moore and Lehman, 1980). However, Bettman and Park (1980) have stated that there is an inverted U relationship between product knowledge and information search.

In several studies, experience has been used as a proxy for product knowledge. Katona and Mueller (1955) and Newman and Staelin (1971:1972) found that there was a negative relationship between product experience and external information search effort for durables. Claxton et al. (1974), on the other hand, found no significant relationship between experience and external information search.

 

Moorthy et al. (1997) found that there is an inverted-U shaped relationship between a consumer’s purchase experience (measured similar to the study of Punj and Staelin (1983)) and amount of search. Moore and Lehman (1980) measured experience in terms of three different questions such as “have you bought anything from the bakery in the last month?”. Bettman and Park (1980) investigated the effects of prior knowledge and product experience on consumer decision making process through protocol analysis. They found that consumers with moderate knowledge and experience did more processing of available information than consumers with high or low knowledge and experience did.

A number of studies have found a negative relationship between product experience and external information search (Moore and Lehman, 1980). The logical explanation behind that experienced consumers have high prior product knowledge, so the need of experienced consumers for acquiring product information from external information sources decreases. Some of the studies in the literature classifies the product knowledge in two categories, namely objective and subjective knowledge, and examine the impact of each on information search behavior of consumers. For instance, Urbany et al. (1989) found that consumers with high subjective knowledge concerning which brand to buy engaged in less information search due to low choice uncertainty. In addition, consumers with high product experience know which attributes are the most useful for evaluating different alternatives; they can also quickly make a decision on the best product (Brucks, 1985). These two studies indicate that high level of subjective knowledge and product experience have similar effects on the duration of information search .Contrary to this, there are studies found a positive relationship between product experience and amount of search. Punj and Staelin (1983) notes that prior product knowledge motivates consumers for obtaining product-related information by making easier to process new information. Due to the cost-benefit theory of information search, prior product knowledge reduce the cognitive cost of using product information and increase the benefit of acquiring the new information, thus it leads to greater search with increased product knowledge.

Betman and Park (1980) found that an inverted-U shaped relationship between prior product knowledge and information search. The inverted-U shape describes a positive relationship between prior product knowledge and information search at low-to-moderate levels of product knowledge and a negative relationship at

 

moderate-to-high levels. However, Punj and Staelin (1983) test the validity of the inverted-U shaped relationship between product knowledge and information search empirically and found only a negative linear relationship between information search and “usable prior knowledge”.

On the other hand, Claxton et al. (1974) considered five different aspects of pre- purchase information search activities including type and range of alternatives considered, information sources used, features considered, stores visited, and time spent for the purchasing of furniture and appliances. Claxton et al. (1974) found no relationship between product knowledge and information search.

Brucks (1985) proposes three explanations for relationship between product knowledge and total amount of information search. First, highly knowledgeable consumers substitute internal information search for external information search, as a result, the amount of external information search decreases. Second, consumers with high level of prior product knowledge search more efficiently. Srinivasan and Ratchford (1991) stated that experienced consumers tend to search more efficiently than inexperienced consumers, hence confirmed the explanation done by Brucks in the same context. Finally, the contention of “knowledge facilitates information search” explains the relationship between product knowledge and total amount of information search. That is, consumers with high product knowledge can easily determine which information sources are consulted, what types of information are collected in relation to the purchase under consideration.

Past studies have often considered product knowledge as a single construct (Schmidt and Spreng, 1996). Contrary to this approach, several studies acknowledge that there is a conceptual distinction between subjective knowledge (self-assessed) and objective knowledge (Brucks, 1985; Park et al., 1994; Schmidt and Spreng, 1996), thus, they suggested to measure the construct by multi item scale. Objective knowledge is defined as accurate information about the product class stored in long term memory, while subjective knowledge or self-assessed knowledge is defined as an individual’s perception of how much he knows about a product class (Brucks, 1985; Park et al., 1994).The rationale behind this distinction is that there might be a difference between “what individuals perceive they know” and “what is actually stored in memory” (Brucks, 1985; Srinivasan and Agrawal, 1988). In a few studies in the literature, product knowledge has been dichotomized into subjective knowledge

 

and objective knowledge. Some studies empirically confirm that there is a discrepancy between objective knowledge and subjective knowledge (e.g. Brucks, 1985; Park et al., 1994).

The first study on comparison of the effect of subjective knowledge with objective knowledge has been conducted by Rudell (1979). Rudell (1979) concluded that objective knowledge facilitates deliberation and usage of new information, on the other hand, subjective knowledge increases the reliance on previously stored information. However, both objective and subjective knowledge was found not to be significantly related to amount of information acquired.

Brucks (1985) is the first author to compare the effects of objective and subjective knowledge on information search behavior by her laboratory study. Brucks (1985) discussed the conceptual difference between subjective knowledge and objective knowledge. Besides, the author noted that subjective knowledge makes the consumer feel more confident, and it helps eliminate alternatives, on the other hand, objective knowledge increases one’s ability to process attributes. Specifically, it was found that while objective knowledge is only related to the number of attributes examined in a positive direction, subjective knowledge is significantly related to the tendency to request dealer opinions rather than attribute information in negative direction. It means that the higher the subjective knowledge, the lower the utilization of sales person recommendation. Due to the fact that subjective knowledge is closely related to confidence in one’s decision making abilities, consumers with high subjective knowledge are likely to be more efficient searchers (Brucks, 1985).

Selnes and Grönhaugh (1986) investigated that whether subjective knowledge and objective knowledge were related or unrelated concepts of product knowledge. Their findings indicated that these two aspects of product knowledge were related but not substitutable constructs. Selnes and Grönhaugh (1986) also recommended that it would be beneficial to analyze effects of these two types of knowledge on the process of consumer buying behavior such as information search intensity and direction, consumer’s ability of selecting alternatives in a differentiated market.

The other study by Park et al. (1994) have found that product related experiences including amount of search, amount of product usage and product ownership are more related to subjective knowledge, while stored product class information is relatively more important antecedent of objective knowledge. However, it was also

 

found that product class information stored in memory (objective knowledge) has a mediating role in the relationship between product experience and subjective knowledge (self-assessed knowledge).

Srinivasan and Agrawal (1988) found a positive significant relationship between product knowledge (subjective knowledge and objective knowledge) and information search in the context of purchasing a car.

Klein and Ford (2003) have found that while there is a positive relation between objective expertise and total information search time, the inverted U relation is found between subjective expertise and total information search time. The more recent study of Mattila and Wirtz (2002) reported that there is a conceptual distinction between objective and subjective knowledge in the usage of various information sources for physician services. Subjective knowledge was found to be positively linked to internal memory search, while there was a negative relationship between subjective knowledge and tendency to search information from other consumers (WOM). However, objective knowledge was found to be positively related to the utilization of impersonal information sources (e.g. books and newspaper articles). They showed that subjective knowledge had a stronger influence on the usage of personal information sources (e.g. memory and word of mouth communications), while objective knowledge had a more important role in affecting the usage of impersonal information sources such as books, newspapers and advertisements. Overall, the results of their study confirm the idea of conceptual distinction between objective and subjective knowledge in the utilization of various information sources for services.

Given the two-dimensional product knowledge construct, Park et al. (1994) recommended that multiple knowledge constructs should be taken into consideration within a research context. As a future research direction, Mattila and Wirtz (2002) point out that investigating the relative impact of both subjective knowledge and objective knowledge on information search on the Internet would be more beneficial. This topic has not yet been examined so far. Although some of the studies in the literature as mentioned above examine the impact of different types of the product knowledge, namely subjective and objective knowledge, on the external search behavior of consumers in the off-line environment, the empirical studies in online environment has been neglected.

 

According to the recommendations and discussions implied in related literature, in this study, objective and subjective knowledge are taken into consideration as conceptually different constructs and each type of product knowledge is measured separately. Three different types of measure have been used for product knowledge (Brucks, 1985) in the literature. One of them measures an individual’s perception of how much he/she knows. The second type of measures represents the amount, type, or organization of what an individual actually stored in memory. The last group measures the amount of purchasing or usage experience with the product.

Selnes and Grönhaughn (1986) used objective knowledge measures covering number of attributes, number of salient brands, level of discrimination of attributes, and knowledge of terminology about a personal computer. Brucks (1985) measured objective knowledge by the summated scale of responses to a variety of questions about such as terminology relevant to the product (sewing machine), and available attributes.

Selnes and Grönhaughn (1986) used the following subjective knowledge measures: level of subjective knowledge, confidence (the thought of respondents about how their closest friend would evaluate their familiarity with personal computers), advice- giving (whether the respondents were asked for advice about personal computers), self-evaluation of brand knowledge (how well the respondent be successful in evaluation of various alternatives). In the study of Brucks (1985), subjective knowledge was measured by the summed score of two items indicating self rating of product class knowledge and product class familiarity. Selnes and Gronhaug (1986) stated that higher subjective knowledge leads to a more confident person so that he will less rely on interpersonal sources and other types of information sources.

On the basis of related literature, both subjective product knowledge and objective product knowledge have a negative impact on the difference between online and offline information search; however, the underlying reasons of these impacts are expected dissimilar. Difference between online and offline information search may be directly and indirectly affected (through perceived risk) by consumers’ subjective knowledge and objective knowledge. As a result, the following hypotheses are developed.

 

H10a: Subjective product knowledge is negatively related to perceived risk

H10b: The higher the level of subjective product knowledge, the smaller the difference between use of offline and online information sources.

H11a: There is a negative relationship between objective product knowledge and perceived risk.

H11b: Objective product knowledge is negatively related to the difference between use of offline and online information sources.

5.1.3.2 Situational Variable: Time Availability

The situational variable considered in the proposed model is the time availability. Time pressure, opposite of time availability, reflects the consumer’s perception of time availability. The view that constraints on one’s time result in less information search has been confirmed by several studies (e.g. Beatty and Smith, 1987). Time availability has consistently found to be related to external information search, although the operationalization of this construct has been varied across studies. For example, it is defined as urgency in Katona and Mueller (1955); as immediacy of need in Claxton et al. (1974); and as the perceived amount of time available when purchase of a product is considered in Beatty and Smith (1987). In the study of Beatty and Smith (1987), time availability was found to be not related to interpersonal search, on the other hand, gathering product information from neutral sources was found to be positively related to total information search. That is, consumers may perceive using neutral information sources as time consuming (Beatty and Smith, 1987). Time pressure was measured every week by a single item of “I feel especially rushed for time this week” (Moore and Lehman, 1980). Besides, it was found that there is a negative relationship between being rushed for time and total search (Moore and Lehman, 1980).

Time availability is also a fundamental variable to determine whether consumers tend to prefer online information sources over offline information sources or not. Klein and Ford (2003) found that there is a strong significant correlation between total hours spent searching product information and the number of sources used (r= 0.48), hence, individuals having more time are more likely to search product information through usage of greater number of information sources.

 

Additionally, in the qualitative study conducted at the beginning of the research, participants cited “quick access to information” as one of the most commonly cited attributes of online information sources, thus this provides consumers to save time. On the basis of the related literature, if a consumer has more time, they would like to search product information through using both online information sources and offline information sources. Hence, the following hypothesis is proposed;

H12: Time availability is negatively related to the difference between use of online and offline information sources.

5.2 Consequences (Outcomes) of Information Search Behavior

Search activities direct to a variety of outcomes. Search outcomes mentioned in the marketing literature include increased product and market knowledge, better purchase decisions, increased satisfaction with the purchase outcome, and increased impulse buying (Bloch et al., 1986). In addition, Bettman (1979) states that benefits of search would involve outcomes such as getting the product with the lowest price, getting the product with the best style/appearance or the highest quality, increased satisfaction with the product. Lee and Hogarth (2000) suggested that “there needs to be a link between search activities and outcomes, …. this should be studied”.

Similar to the study of Punj and Staelin (1983), in this study, search outcomes are considered as realized benefits of information search. The outcomes of pre-purchase information search considered in this study are “purchase satisfaction” and “certainty of making better purchase decisions”. Punj and Staelin (1983) measured outcomes of search (satisfaction) by both “certainty of getting good buy” and “overall satisfaction with the purchase decision”. They hypothesized a positive relationship between satisfaction and amount of search, but the impact of amount of search on satisfaction was not found to be statistically significant.

If a consumer gathers product information through using both online information sources and offline information sources, he would get more positive search outcomes. Therefore, in this study, the following hypothesis is expected;

H13: The difference between use of online information sources and offline sources is negatively related to the satisfaction with search outcomes.

 

This hypothesis is consistent with dissonance theory, and this hypothesis was proposed and tested firstly by Cardozo (1965). As noted by Cardozo (1965), if a consumer expends physical and mental effort to purchase a product, it is likely that the outcome of this process has become more important to the consumer. Accordingly, when a customer spends substantial effort, the prediction from dissonance theory might be aroused, because the consequences of that situation, -in this study, outcomes of information search activity- are important to him. Besides, the consumer will rate the product higher than the customer who expended little effort. Also, the study of Anderson et al. (1979) confirmed that information search effort is positively related to product purchase satisfaction in the case of an automobile purchasing. In other words, when a customer expends a greater effort for seeking product information at the pre-purchase stage in decision making process, the customer gets more satisfied with that product.

5.3 The Proposed Model

Consequently, evidence from past research and insights from qualitative study are combined in a conceptual model that defines causal directional relationships between optimum stimulation level, product involvement, demographic characteristics, perceived risk, attribute importance, shopping orientations, product knowledge, time availability and differential use of online information sources and offline information sources as well as causal directional links between differential use of information sources and outcomes of information search behavior. The conceptual framework indicating hypothesized relationships among constructs is illustrated in figure 5.2.

 

 

Figure 5.2: Proposed Model

 

6. RESEARCH METHODOLOGY AND DESIGN

This chapter presents the scales used to measure the constructs, the pretest of the questionnaire form, the sampling and the data collection method, and the methods used to test the proposed model and hypotheses.

6.1 Measures

All measures have been adapted from existing literature. Perceived risk, product involvement, subjective knowledge, objective knowledge, perceived benefits of using online and offline information sources, purchase satisfaction, purchase certainty, and information search behavior were measured separately for each product category, cellular phone and cultural activities. Furthermore, optimum stimulation level, shopping orientations, and time availability were measured in the last section of the questionnaire, regardless of product types under consideration.

Perceived risk - Including each kind of risk separately into the proposed model would make the model more complex. Consequently, a significant and pertinent scale of overall perceived risk, originally developed by Stone and Gronhaug (1993), was employed in this study. Overall perceived risk was measured using Stone and Gronhaug (1993)’s 3-item self report likert type 5 point ranging from (1) strongly disagree to (5) strongly agree. The scale included following these items: “Overall, I thought of buying a cellular phone causes me to be concerned with experiencing some kind of loss if I went ahead with the purchase”, “All things considered, I thought I would be making a mistake I bought a cellular phone” and “When all is said and done, I really feel that the purchase of a cellular phone poses problems for me that I just do not need”.

Product involvement - The involvement scale developed by Zaickhkowsky is used in this study. This is the most largely used was developed by Zaichkowsky (1985). Since its first introduction to the related literature, Personal Involvement Inventory (PII) has become the most widely used product involvement measure in marketing research. This scale originally consists of 20 items. Flynn and Goldsmith (1993)

 

conducted two studies for fashionable clothing and travel services by using the revised 10-item (PII) to illustrate the use of this involvement scale in identifying involved consumers. Laroche et al (2003) have also employed a revised version of this involvement scale under the consideration of different product types. Zaichkowsky’s revised PII scale measures personal involvement with a specific product category using 10, 7-point, bipolar adjectives. In addition, this revised product involvement scale is a short, easily administered measurement instrument. Flynn and Goldsmith (1993) demonstrated the external validity of the PII. Accordingly, Zaichkowsky’s (1985) revised PII scale was used to measure product involvement for cellular phone and cultural activities using 9, 5-point, bipolar adjectives. Subjects were asked to indicate level of involvement with cellular phone and cultural activities on nine five point semantic differential items selected from Zaickowsky’s (1985) involvement scale. The items had the following end-poles: “unimportant-important”, “worthless-valuable”, “means nothing to me-means a lot me”, “undesirable-desirable”, “uninterested-interested”, and “of no concern-of concern to me”, “not beneficial-beneficial”, “not needed-needed”, and “unexciting- exciting”.

Optimum stimulation level (OSL) - OSL was adapted from Steenkamp and Baumgarther (1996)’s “Exploratory Information Search” (EIS) scale. EIS indicates a tendency to obtain cognitive stimulation through the acquisition of consumption- relevant knowledge out of curiosity OSL was measured by 11-item self report likert type 5 point (Strongly disagree……..Strongly agree). Exploratory information seeking dimension of exploratory buying behavior was used to measure optimum stimulation level. EIS is not a unidimensional construct by itself. The unidimesionality of OSL was obtained by eliminating some items through the discussion with marketing academicians. Statements relating to the information search activities that necessitate great cognitive efforts and being done consciously, remained to measure OSL. These statements are; “I generally read even my junk mail just to know what is about”, “I like to browse through mail order catalogs even when I don’t plan to buy anything”, “I often read advertisements just out of curiosity”, “I am inclined to read e-advertisements and get informed”. The last item was added to include the effect of online information sources on OSL construct.

 

Subjective knowledge - Subjective knowledge or self-assessed knowledge is defined as an individual’s perception of how much he knows about a product class (Brucks, 1985; Park et al., 1994). In this study, subjective knowledge for each product was measured by the scale adapted from the study of Park et al. (1994). The construct of subjective product knowledge was measured by three items. It was asked subjects to rate how much they felt knowledgeable about cellular phone/cultural activities in general, compared to their friends and acquaintances, and compared to experts in the products selected for this study, on five-point differential scale.

Objective Knowledge - Objective knowledge is defined as accurate information about the product class stored in long term memory (Brucks, 1985). In the related literature, a set of questions about the specific product or services is developed by reviewing books, journals and discussing with experts to measure level of objective knowledge. The measure of objective knowledge usually depends on a kind of quiz score. Objective knowledge is measured by asking some questions consisting of several true and false statements about specific product’s functions and attributes and asked which one is relevant for the selected product or service. The number of correct answers on the developed scale is used to form an objective index. This approach to developing an objective knowledge scale is consistent with past studies (Mattila and Wirtz, 2002; Maheswaran et al., 1990; Park et al., 1994; Sujan, 1985). For instance, Sujan (1985) has designed a 12-item questionnaire to assess knowledge of personal computers. The number of correct answers on the 12-item personal computer knowledge questionnaire determined the level of objective knowledge.

In this study, consistent with past studies, a set of questions about both cellular phone and cultural activities are developed by reviewing brochures, searching on the Internet, and applying content analysis to opinions of other consumers about both products presented on the Internet to measure the objective knowledge. Objective knowledge was measured three types of questions. In the first type question, it was asked to list all the things considered by the respondent when choosing a new cellular phone, similarly, for cultural activities, it was asked to write the name of the cultural activities that the respondent has purchased and gone to, in last one year. As a second type question, respondents were asked to tick the features of cellular phones that are common to almost all modern cellular phones. In a similar sense, it was asked respondents to tick the following activities which have been carried out in

 

2006. Finally, the third question was composed of three true-false statements for both product groups. The overall score for each respondent was determined by adding these three types of questions.

Shopping orientation - In this study, similar to some studies (e.g., Gehrt and Shim, 1998) shopping orientations were assessed in general instead of focusing one type of product or shopping place (store, home-shopping). Accordingly, shopping orientation covering recreational shopping orientation, economic shopping orientation and quality shopping orientation was measured by 17-item self report likert type 5 point ranging from 1: strongly disagree to 5: strongly agree. These items were adopted from several studies including by Girard et al. (2003), Westbrook and Black (1985), Mokhlis (2006). Moreover, these statements captured five shopping orientations including Economic-conscious (4 items); Convenience (4 items); Recreational (3 items); Variety Seeking (3 items); Quality-Conscious (3 items). This scale included items such as “Shopping is fun”, “Buying makes me happy”, “I notice price differences”, “I prefer to purchase items on sale”, “I look for bargain prices”.

Time availability- A major component of search costs is the buyer’s perceived value of time per unit of search effort (Srinivasan and Ratchford, 1991). This variable is measured by three items reflecting time availability; this scale is adapted from the study of Srinivasan and Ratchford (1991). This scale included the following three items: “I seem to be busier than most people I know”, “Usually there is so much to do that I wish I had more time”, and “I usually find myself pressed for time”.

Attribute importance- For each product -cultural activities and cellular phones-, the list of product attributes was derived from the conducted qualitative research by asking respondents to list product attributes which they searched information about. Respondents were given the list of 10 attributes for each product and then asked to rate the importance of each attribute on 1-10 scale in making a decision on product purchase.

Benefits of using online information sources and offline information sources- the 11 items were developed on the basis of (1) findings of the qualitative study conducted in Study 1 (2) literature review on previous studies on information search (Rha, 2002; Bei et al., 2004). This scale was composed of 11 statements such as “Offline (Online) information sources provide reliable product information on cellular phones”, “Offline (online) information sources make easy to compare different

 

alternatives of cellular phones (cultural activities)” and “It is easy to get product information about different models for cellular phones through offline (online) information sources” etc.

Dependent Variable: Difference between online and offline information search- Beatty and Smith (1987) defined “external information search” as the degree of attention, perception, and effort directed toward obtaining information related to the specific purchase under consideration. It is difficult to measure external information search behavior. The difficulty with measuring consumer’s information search behavior has been long acknowledged (Newman and Lockeman, 1975; Newman and Staelin, 1972; Beatty and Smith, 1987; Lee and Hogarth, 2000).

Different measures (single measures or aggregate measures) have been developed to measure consumer information search behavior. These measures are summarized in Table 6.1. This table presents the measures of information search behavior used in some previous studies. Some studies have used single item measuring one aspect of search behavior, while other studies have developed aggregate measures of search. Researchers have used the number of stores visited (e.g. Katona and Mueller, 1955), the number of shopping trips made prior to purchase (e.g. Kiel and Layton, 1981), the number of pre-purchase visits to the store of purchase, time spent at a shopping center (e.g. Punj and Staelin, 1983), the number of brands examined (e.g. Srinivasan and Ratchford, 1991), the extent to which buyers sought information on product characteristics, cost and service (e.g. Claxton et al., 1974).

Katona and Miller (1955) developed an index of the extent to which buyers of four major household appliances sought information from reading materials, engaging in discussions with family or friends, examining products owned by others, and visiting retailers. Newman and Staelin (1972) developed indices for out-of-store search (types of information sought and types of sources used) and in-store search (the number of store visited) separately and pooled them into an overall index. Claxton et al. (1974) considered five different aspects of pre-purchase information search activities including type and range of alternatives considered, information sources used, features considered, stores visited, and time spent for the purchasing of furniture and appliances.

Beatty and Smith (1987) measured external information search effort in terms of eight questions representing four major dimensions of information search (media

 

search, retailer search, interpersonal search and neutral sources). Total search index calculated as a linear combination of these four indices.

Moore and Lehman (1980) characterized external information search in terms of total number of information acquisitions, number of brands searched, and number of attributes searched (price, estimated calories etc.). Moorthy et al. (1997) criticize the usage of a weighted index of the self-reported usage of various information sources. They notified that reported usage of various sources also reflect the value of different sources, so it is recommended to use an unweighted index of total information obtained from various sources as a measure of amount of information search. Besides, they got similar results through using both weighted and unweighted search measures. Total amount of search was measured by a summated scale of the extent usage of each information sources, measured on seven-point scales ranging from “hardly anything” to “quite a bit”.

Punj and Staelin (1983) measured external information search by a linear composite of five different measures. These measures comprised of the time spent by the main shopper and other members of the household in different search activities, number of visits to dealers and the total number of search activities. Srinivasan and Agrawal (1988) operationalised amount of search by using three measures. These were the followings; total time spent in various search activities such as talking with friends and relatives and showroom visits; the number of dealers visited and the number of models test-driven. Respondents asked to estimate time spent in each activity.

McColl-Kennedy and Fetter (1999) defined external information search as a two- dimensional construct. According to their study on review of literature about consumer external information search, there are two different aspects of consumer information search activities; these are the information sources used by consumers and effort of search that consumers are involved in. Their study empirically confirmed that source and effort were two distinct aspects of information search behavior in a service setting. Specifically, consumers’ external search effort commonly covers number of stores visited, number of brands examined and time spent in the overall shopping experience (McColl-Kennedy and Fetter, 1999). External information search has also been measured by assessing for which aspects of a product searched by consumers such as price, physical attributes, etc. (Brucks, 1985; Newman and Staelin, 1971).

 

Lee and Hogarth (2000) measured information search behavior through number of credit cards considered, number of information sources consulted, number of terms used to compare alternatives (e.g. Interest rate, annual fee), and perceived extent of search (how much comparison shopping they did when they last applied for a credit card).

Consisted with these measures of traditional information search behavior, in the studies of online information search behavior, similar measures such as number of web sites visited, number of pages read in the specific web site, time devoted to online information search have been defined to measure information search behavior on the Internet.

Table 6.1: Measures of Information Search

Single Measures Authors

Number of Stores visited Katona and Mueller, 1955; Newman and Staelin, 1972; Claxton et al. 1974; Newman and Lockeman, 1975; Newman, 1977; Westbrook and Fornell, 1979; Kiel and Layton 1981; Duncan and Olshavsky, 1982; Punj and Staelin, 1983; Carlson and Gieske, 1983; Urbany, 1986;

Urbany et al., 1989; Srinivasan and Ratchford, 1991

Time Spent in Store/Deliberation Time Newman and Staelin, 1971; Claxton et al. 1974; Newman and Lockeman, 1975; Kiel and Layton 1981; Punj and Staelin, 1983; Midgley, 1983; Ozanne et al., 1992;

Urbany et al. 1989; Ratchford et al., 2003

Number of Trips Made Kiel and Layton 1981

Number of Phone Calls Made Kiel and Layton 1981

Number of Alternative Models

Considered Duncan and Olshavsky, 1982; Srinivasan and Ratchford,

1991

Number of Brands Considered Kiel and Layton 1981; Srinivasan and Ratchford, 1991

Terms Considered: Binary measures or whether or not considered specific terms: brands, price, alternative use of

money, style, quality, size Claxton et al. 1974

Information Source Consulted: Binary measures or whether or not considered specific sources of information: friends, salesperson, stores, advertisements, and

other sources Claxton et al. 1974; Freiden and Goldsmith, 1989

Number of times specific Information Source Consulted: Frequency of specific information source consulted: friends, opinion leaders, advertisements, magazines & newspapers, professionals,

media, and third party. Westbrook and Fornell, 1979; Kiel and Layton 1981; Beatty and Smith, 1987; Urbany et al., 1989; Bei et al., 2004

Aggregate Measures Authors

Number of Information Sources Used Claxton et al. 1974; Duncan and Olshavsky, 1982; Punj

and Staelin, 1983; Freiden and Goldsmith, 1989

Number of Terms Considered Claxton et al. 1974; Ozanne et al., 1992

Extent of Search: Self-Reported

Tendency Goldman and Johansson, 1978; Srinivasan and Ratchford,

1991

 

Table 6.1: Measures of Information Search (Cont.)

Aggregate Measures Authors

Number of

Activities Taken Information Search Punj and Staelin, 1983; Srinivasan and Ratchford, 1991

Amount of Search Efforts Devoted: Newman and Staelin, 1972; Claxton et al. 1974; Newman

Composite measure with assigned and Lockeman, 1975; Duncan and Olshavsky, 1982;

weights on each information search Midgley, 1983; Beatty and Smith, 1987; Ozanne et al.,

activities 1992

Source: Lee and Hogarth (2000)

It is suggested that multiple measures of information search should be used to capture all aspects of information search. However, in this study, it is aimed to analyze the effects of several variables on use of online information sources compared to use of offline sources.

This study is based on an assumption which states that online and offline information search activities are not substitutes, they are complementary. Besides, both of them can be used by the consumers at different degrees. However the extent usage of one type of information sources compared to the other might be different from one consumer to the other. For that reason a comparative approach to measure the search activity can be more meaningful, more realistic and gives more important insights to the researchers. In accordance with past research (Murray, 1991; Mitra et al., 1999), dependent variable was measured as the net difference in the sum of online information sources over that of offline information source, entitled as “gap or differences between online and offline information search”. The absolute values of difference scores are used to test the proposed model.

 

Table 6.2: The Constructs, Sources and the Number of Items

Variables Source # of items

Optimum Stimulation Level (OSL) Steenkamp and Baumgartner (1996) 11 items

The use of online and offline information

sources Mattila ve Wirtz (2002), Bei et al. (2004) 6 items

Product involvement Zaichkowsky

(2003) (1985); Laroche et al.. 9 items

Search Outcomes Punj and Staelin (1983) 2 items

Subjective Knowledge Laroche et al. (2003) 3 items

Perceived Risk Stone and Gronhaug (1993) 3 items

Perceived benefits of using online

information sources Rha (2002), and developed by researcher 11 items

Perceived benefits of using offline

information sources Rha (2002), and developed by researcher 11 items

Objective Knowledge Adapted from Mattila ve Wirtz (2002),

Brucks (1985), Sujan (1985). 3 items

Time Availability Srinivasan and Ratchford (1991) 3 items

Shopping orientation Adapted  from  Girard  et  al  (2003);

Mokhlis (2006); Westbrook and Black, 1985 17 items

6.2 Pre-Test of the Survey Instrument

The survey instrument is composed of four main sections. In the first section, filter questions were asked to reach the target respondents. The following second and third section included the questions associated with information search behavior for the purchasing of a cellular phone and a cultural activity, respectively. In the last section, questions dealing with personal characteristics including optimum stimulation level, shopping orientations and demographics were asked. Prior to survey administration, a pilot study was conducted by using a student sample of 130, 83 usable questionnaires were returned. Feedback was obtained regarding the wording of the questions asked in the questionnaire, and also asked to evaluate layout of the survey. Furthermore, each product was asked in a different order in each interview to avoid order effects. Based on the result of pretest of the survey, some minor modifications were made.

The profile of the respondents is summarized in Table 6.3. Table 6.4 presents the descriptive statistics and reliabilities of the survey constructs as well as the characteristics of the pretest sample.

 

Table 6.3: The Profile of Pretest Sample of Study 2

Demographics Percentage (%) Internet Usage Mean (hours)

Gender Daily 3.95

Female 51.8 Weekly 25.25

Male 48.2

Age

19-21 years old 36.1

22-24 years old 59.0

25 years and over 4.8

Education

High school 68.7

University and Post 31.3

graduate

As it can be seen in Table 6.4, with one exception including economic-conscious shopping orientation, the reliabilities of all constructs are above the value of 0.70. Based on the result of pretest of the survey, some minor modifications were made. Specifically, some items measuring shopping orientations were modified to be understood clearly by the respondents.

 

Table 6.4: Descriptive Statistics and Reliabilities of Constructs for Pretest (N=83)

Cellular Phones

Construct

Mean

Std. Dev. Reliability

Cronbach’s Alpha

Difference between offline and online 2.84 2.84 NA*

Perceived benefits of using offline information

sources 3.07 0.73 0.89

Perceived benefits of using online information

sources 4.19 0.67 0.91

Product involvement 3.94 0.69 0.85

Subjective knowledge 2.88 0.82 0.87

Objective knowledge 9.49 3.61 NA

Search outcomes 3.92 1.05 0.70

General perceived risk 2.53 0.94 0.74

Cultural Activities

Constructs

Mean

Std. Dev. Reliability Cronbach’s

Alpha

Difference between offline and online 2.66 2.68 NA

Perceived benefits of using offline information

sources 3.22 0.66 0.87

Perceived benefits of using online information

sources 4.18 0.66 0.90

Product involvement 3.91 0.83 0.92

Search outcomes 3.99 1.08 0.60

General perceived risk 2.33 0.90 0.85

Objective knowledge 4.86 1.77 NA

Subjective knowledge 3.19 0.91 0.90

Personal Characteristics

Constructs

Mean

Std. Dev. Reliability Cronbach’s

Alpha

Time availability 3.78 0.81 0.79

Optimum stimulation level 3.06 0.58 0.76

Recreational shopping orientation 3.27 1.02 0.78

Quality-conscious shopping orientation 3.66 0.79 0.76

Economic-conscious shopping orientation 3.92 0.56 0.54

Education (years) 15.99 2.30 NA

Note: NA means “not applicable”

6.3 Sampling Design of the Main Survey

In scope of the sampling and design process of the study, the population is defined as “the people who has purchased any ticket for cultural activities in past 3 months and bought a new cellular phone in last one year and living in İstanbul”. Besides, the population consists of males and females using both online information sources and offline (traditional) information sources to get product information. Thus, the

 

Internet usage is the prerequisite of the consumers should be selected as sample members.

A variety of data collection methodologies have been conducted in various studies on information search behavior, such as surveys (e.g. Punj and Staelin, 1983; Srinivasan and Ratchfor; 1991; Bei and Widdows, 2004), field experiment (e.g. Moore and Lehman, 1980); laboratory experiment through questionnaire (e.g. Brucks, 1985), interview (Newman and Staelin, 1971), and protocol analysis (Betmann and Park, 1980), longitudinal study through survey (Moorthy et al., 1997). In addition to these methodologies, within the online context, with the help of technological developments, different methodologies can be used to examine online search behavior such as through the data obtained from page to page clickstream to examine consumers’ information search behavior across web sites (e.g. Moe, 2003; Johnson et al., 2004), online-based survey (e.g. Bei et al., 2004; Teo and Yeong, 2003; Klein and Ford, 2003; Shim et al., 2001). The method which is the most frequently used to collect data is to conduct a survey.

In this study, a professional fieldwork company was charged to collect the data. The fieldworkers were given a briefing by the researcher on the instructions to be followed during the execution of the questionnaires. They conducted the questionnaires face-to-face with respondents. This procedure allowed the researcher to eliminate the risk of having a missing data due to external events. It also guaranteed that the questionnaire was completed by the respondent who was targeted (Wilson, 1997). Moreover, a small gift was given to each respondent to motivate for participating into this study.

The survey was conducted in Anatolian and European parts of Istanbul. According to the year 2000 census figures, this city is the most populated city in Turkey with approximately 10 million people, which is 1/7th of the whole Turkish population (DIE, 2003). Its population has increased 12.4 times between 1923 and 2000, whereas the overall population in Turkey has increased only five times for the same period (DIE, 2003). These figures indicate that Istanbul can be considered as embracing the Turkish people from different backgrounds, thereby showing a high potential of representing the overall population. Besides, since Istanbul was selected as the capital city of culture of 2010, the number of cultural activities organized in Istanbul has been increased.

 

The sampling procedure used in this study is the cluster sampling method. This sampling approach was chosen firstly because it is the most used sampling method in large-scale surveys which involve face-to-face questionnaire collection. Secondly, it is more economical compared to other sampling techniques (Churchill and Iacobucci, 2005). This study was conducted in ten districts in Istanbul and these ten districts were systematically chosen from the list of districts by Istanbul Metropolis Municipality. Blocks were systematically chosen and then fieldworkers contacted every flat in each block. The male and female consumers who were at least 18 years- old and who had at least successfully completed secondary school education were considered eligible for the study. The fieldwork was started in March and completed in April 2007.

6.3.1 Sample Size Determination and Data Collection

where,   – population proportion

z – standard normal variable associated with the confidence level

H – Level of precision (Churchill and Iacobucci, 2005) According to the results of the national information communication technology usage survey conducted by Turkey Statistical Institute (TURKSTAT) in 2005, the proportion (  of consumers using the Internet for information search about products and services is %43.3. Therefore, the sample size at 0.05 precision levels (D) is about 378 determined by the equation. When the population proportion is assumed 0.50, the sample size is calculated 384 where the value of z at the 95% confidence level was 1.96. The conceptual model proposed in this study will be tested through structural equation modeling (SEM). Although, there is no general rule for the sample size for SEM, as a rule of thumb, it is recommended that the sample size should be more than 25 times the number of parameters to be estimated, the minimum being a subject parameter-ratio of 10:1 (Nachtigal et al., 2003). Kline (1998) notes the lower bound of total sample size should be at least 200. By considering all of these principles, 685 face-to-face surveys were carried out with consumers; with %95 confidence level, ± %3.74 error margin and 0,5 of distribution rate in this study.

 

6.4 Structural Equation Modeling and Assessment of Model Fit

Hair et al. (2006) state that “[Structural Equation Modelling (SEM)] provides the appropriate and most efficient estimation technique for a series of separate multiple regression equations estimated simultaneously” (p. 22). SEM includes both the principles of factor analysis and multiple regression in one procedure. It has two main components including the measurement model, and the structural model.

After the model specified and then tested, the matter coming to mind is whether the model is good or not. Because a single index reflects only a particular aspect of model fit, a favorable value of that index does not by itself indicate good fit. That is why model fit is assessed based on the values of multiple indices. Goodness-of-fit measures are classified into three groups: absolute measures, incremental measures, and parsimony fit measures. In general, absolute measures and incremental measures are frequently reported measures in studies.

6.4.1 Absolute Fit Indices

Absolute fit indices are “a direct measure of how well the model specified by the researcher reproduces the observed data” (Hair et al., 2006). One component of a “good” model is the fit between the sample covariance (correlation) matrix and the estimated population (fitted) covariance (correlation) matrix, which is assessed by non significant χ2. The most fundamental absolute fit index is the chi-square statistic. Chi-square is a measure of overall fit of the model to the data at hand. The chi-square statistic is an indicative of a test of perfect fit in which the null hypothesis is that the model fits the population data perfectly (Diamontopulos and Siguaw, 2000). Chi- square, on the other hand, is sensitive to departures from multivariate normality and sample size. For N>100, chi square is almost always significant since the magnitude is affected by the sample size, since chi-square statistic is computed by mathematical function of sample size. Besides, chi-square statistic is less meaningful to assess fit of models when sample size or the number of observed variables is large. For this reason, chi-square statistic is not used as the sole indicator of model fits. Thus, alternative goodness-of-fit measures attempt to correct for bias against large samples and complex models. Complex models, consisting of more indicator variables require large samples.

 

Numerous goodness-of-fit measures have been proposed and studied in the literature. These fit indicators will be discussed in detail in the following sections. On the other hand, there is a rule of thumb to assess model fit. That is, if the ratio of the χ2 to the degrees of freedom is less than 2, this may point out a good-fitting model (Tabachnick and Fidell, 2007).

The root mean square error of approximation (RMSEA; Browne and Cudeck, 1993) approximates the lack of fit in a model compared to a perfect (saturated) model. RMSEA developed to correct for propensity of chi-square statistic to reject models with a large samples or a large number of indicator variables. While the values of

0.06 or less indicate a good-fitting model (Hu and Bentler, 1999), values larger than

0.10 are indicative of poor-fitting models (Browne and Cudeck, 1993). Furthermore, Browne and Cudeck (1993) suggest that values up to 0.08 represent reasonable errors of approximation in the population. Values less than 0.05 indicate good fit, between

0.05 and under 0.08 indicate reasonable fit, between 0.08 and 0.10 imply mediocre fit and values larger than 0.10 serve as indicators of poor fit. RMSEA is sometimes called badness-of-fit index in the sense that higher values indicate poorer fit.

Tanaka and Huba (1985) note that goodness-of-fit (GFI) index can be considered as analogous to R2 in multiple regression. That is, GFI indicates the relevant amount of variances and covariances accounted for by the model. AGFI represents the adjusted form of GFI for the number of parameters estimated in the model. Both indicators measure how much better the model fits as compared to no model at all. However, GFI and AGFI do not depend on sample size explicitly. The values of the GFI and AGFI vary across range of between o and 1 and values larger than 0.90 usually taken as indicator of acceptable fits. As generally recommended, GFI is the most reliable measure of absolute fit in most circumstances. The goodness-of-fit index (GFI) is an indicator of “the relative amount of variances and covariances jointly accounted by the model”. The adjusted goodness-of-fit index (AGFI) is simply the GFI adjusted for the degrees of freedom in the model. Both indices take values between 0 and 1. Besides, the closer to unity, the better the model fit.

There are two indices based on residuals, the root mean square residual (RMR) and the standardized root mean square residual (SRMR). These indices are calculated by the average differences between the sample variances and covariances and the estimated population variances and covariances. Good-fitting models have small

 

value of RMR and SRMR. The SRMR has a range of 0 to 1; Hu and Bentler (1999) suggest values of 0.08 or less are desired. Hu and Bentler (1999) recommend reporting two types of fit indices; SRMR and then a comparative fit index.

6.4.2 Incremental Fit Indices

Incremental fit indices assess how well the specified model fits as compared to a baseline model, usually the independence model. These indices are sometimes referred to as comparative fit measures (Hair et al., 2006).

The Bentler-Bonett (1980) normed fit index (NFI) evaluates the estimated model by comparing the χ2 value of the model to the χ2 value of the independence model. Value greater than .90 of NFI indicates of a good-fitting model. However, an adjustment to the NFI incorporating the degrees of freedom in the model yields the non-normed fit index (NNFI).

The comparative fit index (CFI) was derived from NFI index to take account of model complexity in a fit measure (Bentler, 1990). CFI values greater than .95 are often indicative of good-fitting models (Hu and Bentler, 1999).

Consequently, the fit of the proposed model is assessed in light of these indexes explained in above section.

 

7. ANALYSIS AND FINDINGS

This chapter presents the results of data analysis and hypothesis testing. This chapter begins with a profile of the sample used in this study. Then, the results of the exploratory (EFA) and confirmatory factor analyses (CFA) are presented. Following the EFA and CFA results, the results of the structural model in relation to the hypothesis testing for both product types are presented. Finally, a detailed discussion is provided for each hypotheses testing.

7.1 Sample Characteristics

The data for the main survey was collected in Istanbul in Turkey. The characteristics of the respondents such as age, gender, marital status, education and income level and employment status were asked in the questionnaire. 685 face-to-face surveys were carried out with consumers in this study. Table 7.1 summarizes the profile of the respondents. The mean of the respondents’ age is 28 years old, 65% of the respondents are male, and 86% of them have graduated at least from a high school. Besides, the means of the daily Internet usage and weekly Internet usage are, respectively, 2.5 and 17 hours. 78% of respondents use the Internet 3 hours or less a day, while weekly Internet usage of 64.3% of the respondents is ranged from 6 hours to 19 hours a week.

However, the percentage of the Internet usage in Turkey is 13.93% (TURKSTAT). While the rate of female users is 4.33%, that of male users is 9.60%. That is, 69% of internet users are male and 31% of them are female. As seen in Table 7.1, 65% of the respondents are male, while 35% of them are female. Based on these figures, it can be said that this sample is representative of the main population of internet users.

 

Table 7.1: Characteristics of the Sample of Study 2

Frequency Percentage (%)

Education Level Primary School 38 5

Secondary School 63 9

High School 429 63

Undergraduate and Graduate 155 23

Gender Male 444 65

Female 241 35

Age 18-24 years old 309 45

25-34 years old 218 32

35-44 years old 96 14

45-54 years old 49 7

55-74 years old 13 2

Marital Status Single 188 27

Married 497 73

Social Class A Social Class 26 4

B Social Class 123 18

C1 Social Class 318 46

C2 Social Class 218 32

Daily Internet usage 1 hour and less 270 39.4

2-3 hours 265 38.7

4-5 hours 88 12.8

6-7 hours 35 5.1

8 hours and above 27 3.9

Weekly Internet usage 5 hours and less 34 5.0

6-9 hours 229 33.4

10-19 hours 212 30.9

20-29 hours 101 14.7

30-39 hours 53 7.7

40-49 hours 25 3.6

50 hours and above 31 4.5

Total 685 100

Widow and divorced individuals were added to the single category.

General tendency in the respondents’ usage of information sources was examined by paired sample t-test for two products. Paired sample t-test helps us to understand the difference score between usage of online information sources and that of offline information sources increases in which type of information source’s favour. The results are given in the Table 7.2. As it can be seen in this table, for both product types, the usage of offline information sources of respondents is significantly higher than that of online information sources of respondents. Besides, the mean value of difference between online and offline information search is -2.70 and -2.10 for

 

cellular phone and cultural activity, respectively. That is, respondents use more offline information sources than online information sources to obtain product information in two cases where they purchase a cellular phone and a ticket for any cultural activity. Accordingly, an increase in the DONOFF points an increase in the favour of offline information sources.

Table 7.2: The Results of Paired Sample t Test

Information Source Type Cellular Phone Cultural Activities

Mean t-value p Mean t-value p

Online information sources 9.41 -11.32 0.00 9.78 -8.13 0.00

Offline information sources 11.11 10.88

Descriptive analysis, outlier examination, exploratory factor analysis, reliability analysis and the tests for the assumptions of multivariate analysis were done using SPSS 15.0 (statistical package for social sciences) (Tabachnick and Fidell, 2007). Lisrel 8.7 (LInear Structural RELationships) was used to perform both the confirmatory factor analysis and the structural model testing (Hair et al., 2006; Jöreskog and Sörbom, 1993; 1996).

7.2 Initial Data Analysis

As suggested by Hair et al. (2006), (1) graphical and descriptive analysis, (2) analysis of outliers, and (3) test of normality were conducted to check whether or not the data satisfy assumptions of structural equation modeling.

Mahalanobis D2 measure assess whether each observation is a multivariate outlier or not across a set of variables. This method measures distance of each observation from the mean center of all observations, in multidimensional space. Higher D2 values indicate observations farther away from the general distribution of observations in the multidimensional space. The D2 measure divided by the number of variables included in the model (D2/df) is approximately distributed as a t-value. According to the general rule of thumb for multivariate outlier detection, observations with D2/df value exceeding 2.5 in small samples and 3 or 4 in large

 

samples can be considered as possible outliers (Hair et al., 2006). Hence, there is no multivariate outliers detected in the data.

Two conditions are necessary to apply SEM: One is that an adequate sample size is required; the other is that the data usually have to be normally disturbed. Three indices are typically used to evaluate the normality of distribution: univariate skewness, univariate kurtosis, and multivariate kurtosis. Unfortunately, there is no clear consensus on an acceptable value of non-normality (Finney and Distefano, 2006). Studies investigating the impact of univariate normality on the results based on the estimation method of Maximum Likelihood (ML) propose that problems may occur when univariate skewness and univariate kurtosis approach values of 2 and 7, respectively (e.g. Chou and Bentler, 1995; Curran et al., 1996). Besides, Finney and Distefano (2006) note that there is no generally accepted cutoff value of multivariate kurtosis indicating non-normality. EQS, one kind of SEM software program, offers a guideline which suggests that data associated with a value of Mardia’s normalized multivariate kurtosis greater than 3 could produce inaccurate results when used with ML estimation.

The statistics of skewness and kurtosis and graphical assessment of the data pointed out that the majority of variables showed departure from normality. Although this outcome meant violation of underlying assumptions of structural equation modeling,

Bagozzi and Yi (1988) assert that

“… in managerial and social science research it is unlikely that the statistical assumptions will ever be met in a strict sense” (p. 81).

Additionally, Bentler and Chou (1987) state that in structural equation modeling

“… normal theory maximum likelihood (ML) estimators are almost always acceptable even when data are non-normally distributed” (p.89).

Although application of ML instead of asymptotic distribution free estimation methods may yield an untrustworthy 2 (Chi-square) statistic and standard errors, if the model fit indices show reliable results it may be concluded that this problem has been overcome (Bentler and Chou, 1987). Therefore, none of the data transformation approaches to achieve normality were applied at this stage.

Although the method of Weighted Least Squares (WLS) method has main advantage of requiring only minimal assumptions about the distributions of observed variables,

 

it has some disadvantages (Bollen, 1989). WLS does not require multivariate normality and offers an asymptotically distribution-free (ADF) approach. Besides, it might need very large samples. Boomsma (2000) states that if the number of variables 15 or greater, the sample should consist of several thousand cases, which often not available in psychological research (Muthén & Kaplan, 1985; 1992). Simulation studies by Yung and Bentler (1994) propose a minimum sample size of 2000 to obtain satisfactory results (Nachtigal et al., 2003).

Engel et al (2003) recommend that ML estimation method may also be applied if the distribution of observed variables does not deviate from the normal distribution by a considerable amount. Besides, Chou and Bentler (1995) do not suggest using WLS as an estimation method for practical applications when models are complex and when the sample size is small.

Multicollinearity in the data was evaluated by SPSS depending on the criteria for multicollinearity suggested by Tabachnick and Fidell (2007). According to these criteria, no evident was found for multicollinearity. Table 7.3 illustrates the reliabilitiy measures for the constructs taken into consideration in this study.

 

Table 7.3: Reliability Measures for the Constructs

Cellular Phones

Construct Mean Std. Dev. Reliability

Cronbach’s Alpha

Usage of Online information sources 3.14 1.01 0.65

Usage of Offline information sources 3.70 0.90 0.55

Perceived benefits of using offline

information sources 3.53 0.69 0.88

Perceived benefits of using online

information sources 4.07 0.61 0.86

Product involvement 4.20 0.59 0.85

Subjective knowledge 3.54 0.88 0.81

Objective knowledge 12.71 4.42 NA*

Search outcomes 4.13 0.79 0.76

Overall risk 2.46 0.96 0.75

Cultural Activities

Constructs Mean Std. Dev. Reliability

Cronbach’s Alpha

Usage of Online information sources 3.26 0.99 0.65

Usage of Offline information sources 3.69 0.84 0.42

Perceived benefits of using offline

information sources 3.49 0.70 0.88

Perceived benefits of using online

information sources 4.12 0.61 0.87

Product involvement 4.10 .64 0.88

Search outcomes 4.17 0.68 0.62

Overall risk 2.27 0.96 0.80

Objective knowledge 4.70 2.56 NA

Subjective knowledge 3.47 0.81 0.78

Personal Characteristics

Constructs Mean Std. Dev. Reliability

Cronbach’s Alpha

Time availability 2.64 1.05 0.85

Optimum stimulation level 3.09 0.49 0.73

Recreational shopping orientation 3.68 0.88 0.74

Quality-conscious shopping

orientation 3.49 0.87 0.72

Economic-conscious shopping

orientation 3.87 0.72 0.76

Age (years) 28.27 9.62 NA

Education (years) 11.68 2.65 NA

Note: NA indicates “not applicable” meaning that the construct was measured by single item.

7.3 Scale Refinement, Scale Validation and Modelling

Even though confirmatory factor analysis should be applied in order to examine construct validity more strictly rather than using traditional methods (Gerbing and

 

Anderson, 1988; Steenkamp and Trijp, 1991), it is still important to carry out exploratory factor analysis and a Cronbach alpha test in the early stages of a scale validation in order to simplify the scales (Aaker, 1997; Babin et al., 2000). In the next section, the results from exploratory factor analysis and reliability assessment by Cronbach alpha measures are explained. This is followed by the confirmatory stage of scale validation. Lastly, the model testing is explained.

7.3.1 Exploratory Factor Analysis and Reliability Assessment

Exploratory factor analysis (EFA) is a useful technique in the early stages of scale refinement and validation (Aaker, 1997; Babin et al., 2000) since it allows the researcher to have a preliminary understanding of the relationships between the indicators and their relevant constructs. It becomes especially useful when there is very little known in theory about the constructs under investigation (Gerbing and Anderson, 1988). Since most of the items were generated from anecdotal articles and some were adapted from empirical studies, it was necessary to apply EFA. EFA was applied to all constructs for cellular phone and cultural activities separately.

On the basis of both initial data analysis and results of EFA, some items were eliminated. The number of eliminated items and the constructs associated with them were presented in Table 7.4.

Table 7.4: The Number of Eliminated Items and the Related Constructs

Constructs # Initial Items #Items eliminated

Optimum Stimulation Level 11 items 7 items

Shopping Orientation 17 items measuring five different shopping

orientations 7 items measuring two types of shopping orientations

(convenience and variety seeking)

Product Involvement 9 items 3 items

Table 7.5 shows that fourteen major factors were extracted based on the principal component method. That is to say that the eigen values of fourteen underlying factors were bigger than one (Hair et al., 2006). The sample was adequate for the factor analysis in that the Kaiser-Mayer-Olkin measure of sampling adequacy (MSA) was 0.855, which is considered as marvelous (Kaiser, 1974), and the Bartlett Test of Sphericity (BTS) suggested that the bivariate correlations among the scales’ items

 

were significantly different from zero (BTS= 16610.39, p=0.000). These fourteen factors captured an acceptable level of 63.78 % of the variance.

The generally agreed on lower limit for Cronbach’s alpha is 0.70 to assess the consistency of the entire scale (Hair et al., 2006; Nunnally, 1978). Table 7.5 shows that all Cronbach alphas for the twelve factors were above 0.70. Hence, it was confirmed that each factor can be regarded as a reliable construct.

 

Table 7.5: Exploratory Factor Analysis results for Cellular Phone

Factors and Related Items Factor

Loadings

 

Cronbach Alpha

 

Factor 1

Online information sources provide detailed information about cellular phones.

Online information sources are helpful to gather product information about cellular phones.

Online information sources provide enough product information about cellular phones.

It is easy to get product information about different models for cellular phones through online information sources.

Online information sources are easy accessible for product information about cellular phones.

Online information sources do not cause me to get tired of getting product information about cellular phones

Online information sources are helpful to make a right decision about purchase of cellular phones.

Online information sources make easy to compare different alternatives of cellular phones.

Online information sources provide reliable product information on cellular phones

Factor 2

It is easy to get product information about different models for cellular phones through offline information sources

Offline information sources are helpful to make a right decision about purchase of cellular phones

Offline information sources provide detailed information about cellular phones. Offline information sources are helpful to gather product information about cellular phones

Offline information sources provide enough product information about cellular phones

Offline information sources make easy to compare different alternatives of cellular phones.

Offline information sources are easy accessible for product information about cellular phones.

Offline information sources provide reliable product information on cellular phones

Offline information sources do not cause me to get tired of getting product information about cellular phones

Factor 3 Valuable Desirable Concern to me Interested

Means a lot to me Important

 

.801

.786

.782

.767

.747

.737

.721

.704

.672

.815

.789

.772

.771

.752

.718

.716

.705

.644

.766

.754

.754

.746

.729

.620

 

0.91

0.90

0.84

 

 

 

Table 7.5: Exploratory Factor Analysis results for Cellular Phone (Cont.)

 

Factors and Related Items Factor

Loadings

 

Cronbach Alpha

 

 

Consumer comments on the Internet (e-groups, shopping websites etc.)

Factor 12

Family and/or friends, acquaintances Newspaper/magazines/TV/radio advertisements Store visits or salespeople

 

.652

.709

.685

.656

 

0.55

 

 

 

Table 7.5: Exploratory Factor Analysis results for Cellular Phone (Cont.)

 

Factors and Related Items Factor

Loadings

 

Cronbach Alpha

 

Factor 12

Family and/or friends, acquaintances Newspaper/magazines/TV/radio advertisements Store visits or salespeople

Factor 13

Shopping is great for my mood. Buying makes me happy.

Shopping is a kind of social activity for me

Factor 14

 

.709

.685

.656

.647

.602

.540

.844

 

0.55

0.74

 

Satisfaction with the purchase decision on cellular phone Certainty of getting a good purchase of cellular phone

Kaiser-Mayer-Olkin measure of sampling adequacy = 0.855 Bartlett Test of Sphericity (X2)= 16610.39, p=0.000

 

.778 0.76

Total variance explained = 63.78%

 

 

Note 1: All items were retained. There were no items with communalities less than 0.50 and with Factor loadings less than 0.50. None of the items were loaded to more than one factor either (Hair et al., 2006).

Note 2: Principal component analysis and orthogonal varimax rotation were used.

On the basis of EFA result for the cellular phone, the factors were described and named as the following:

Factor 1 - perceived benefits of using online information sources (BENON): This factor includes the items that were designed to measure people’s perceptions about benefits of using online information sources

Factor 2 - perceived benefits of using offline information sources (BENOFF): This factor consist of the items that are related to perceived benefits of using offline information sources

Factor 3 - product involvement (INV): This third factor encompassess all of the theoretically defined items measuring people’s product involvement with the cellular phone.

Factor 4 - economic-conscious shopping orientation (ECSHOP): This factor refers to all price-oriented shopping activities defined in theory.

Factor 5 - optimum stimulation level (OSL): Even though seven items were excluded, the remaining four items represent the main aspects of exploratory information seeking behavior involving people to spend effort to get product information.

 

Factor 6 - time availability (TIME): This factor includes all of the theoretically defined items referring perceived amount of time available.

Factor 7 - subjective product knowledge (SUBKN): this factor is composed of the initially defined indicators measuring the level of subjective product knowledge.

Factor 8 - quality consicous shopping orientation (QCSHOP): This factor refers to the actions of an individual concerning quality oriented issues in his shopping activities and covers all of the theoretically defined items.

Factor 9 – importance of attributes (ATTR): Although nine items were initially included to measure different product attributes, the remaining three items illustrate the affective aspects of a cellular phone i.e. aesthetic design, colour, and phsycial dimensions.

Factor 10 - perceived risk (RISK): This factor consists of the theoretically defined indicators measuring general perceived risk that are related to the purchase of a cellular phone.

Factor 11 – usage of online information sources: This factor includes all online information sources which are used to obtain product information on a cellular phone.

Factor 12 - usage of offline information sources: This factor encompasses all offline information sources which consumers gather product information on a cellular phone.

Factor 13 - recreational shopping orieantations (RSHOP): This factor presents all the items that were designed to measure another type of shopping orientation, recreational shopping orientations.

Factor 14 - search outcomes (SOUT): this factor is composed of two items reflecting outcomes of information search behavior.

Similar to the results of EFA for cellular phone, the fourteen factor structure below (Table 7.6) was obtained for cultural activities as a result of EFA. The factors which had eigen values greater than 1.00 were preserved. The MSA measure of sampling adequacy (MSA = 0.862) (Kaiser, 1974) and Bartlett Test of Sphericity (BTS=17280.87, p=0.000) demonstrated that the EFA was applied correctly. The amount of variance explained by these fourteen factors was 64.19%.

 

Cronbach alpha statistics for each factor was ranging from 0.42 to 0.92 (e.g., factor1= 0.92 > 0.70, factor2= 0.91 > 0.70, factor3= 0.87 > 0.70). Although cronbach alpha statistics for Factor 13 and Factor 14 (factor13= 0.66 and factor14= 0.42) are lower than the cut-off point 0.70 (Nunnally, 1978), based on the aim of this study, these two factors were used to calculate difference score. Hence, it can be said that in general the items in each factor were internally consistent (Table 7.6).

Table 7.6: Exploratory Factor Analysis results for Cultural Activities

Factors and Related Items Factor

Loadings Cronbach

Alpha

Factor 1

Online information sources are helpful to gather product information .800

about cultural activities

It is easy to get product information about different alternatives for .777

cultural activities through online information sources.

Online information sources make easy to compare different alternatives of cultural activities. .771

Online information sources provide detailed information about .764

cultural activities.

Online information sources provide enough product information .762 0.92

about cultural activities.

Online information sources provide reliable product information on .751

cultural activities

Online  information  sources  are  easy  accessible  for  product .740

information about cultural activities.

Online information sources do not cause me to get tired of getting .735

product information about cultural activities.

Online information sources are helpful to make a right decision .703

about purchase of cultural activities.

Factor 2

Offline information sources are helpful to make a right decision .785

about purchase of cultural activities.

Offline information sources provide enough product information .784

about cultural activities

Offline information sources are helpful to gather product information .772

about cultural activities.

It is easy to get product information about different alternatives for .770

cultural activities through offline information sources

Offline information sources provide detailed information about .759 0.91

cultural activities.

Offline information sources provide reliable product information on .748

cultural activities

Offline information  sources make easy to  compare different .738

alternatives of cultural activities

Offline  information  sources  are  easy accessible  for  product .732

information about cultural activities

Offline information sources do not cause me to get tired of getting .693

product information about cultural activities.

 

Table 7.6: Exploratory Factor Analysis results for Cultural Activities (Cont.)

Factors and Related Items Factor Cronbach

Loadings Alpha

Factor 3

Valuable .829

Concern to me .780

Means a lot to me .779 0.87

Interested .717

Desirable .713

Important .693

Factor 4

I notice price differences .762

I do comparison shopping .750 0.77

I look for bargain/competitive prices .728

I prefer to purchase items on sale .702

Factor 5

I often read advertisements just out of curiosity .766

I like to browse through mail order catalogs even when I don’t plan to buy anything .751 0.73

I am inclined to read e-advertisements and get informed .699

I generally read even my junk mail just to know what is about .678

Factor 6

Usually there is so much to do that I wish I had more time

.893 0.85

I usually find myself pressed for time. .860

I seem to be busier than most people I know .826

Factor 7

Overall, I thought of buying a ticket for a cultural activity causes me .882

to be concerned with experiencing some kind of loss if I went ahead with the purchase

0.80

All things considered, I thought I would be making a mistake I .861

bought a ticket for a cultural activity

When all is said and done, I really feel that the purchase of a ticket .732

for a cultural activity poses problems for me that I just do not need

Factor 8

It is generally worth it to pay more for quality. .765

The quality of product/service I buy is more important to me than the .744 0.73

prices I have to pay.

I look for quality in a product/service and I am willing to pay extra .720

for it.

Factor 9

In general, my knowledge of cultural activities is: (very weak to very .789

strong)

Compared with experts in the topic of cultural activities, my .779 0.78

knowledge of cultural activities is: (weaker to stronger)

Compared with my friends and acquaintances, my knowledge of .698

cultural activities is: (weaker to stronger)

Factor 10

Information about backgrounds of actors and actresses .816

Information about comments of the cultural activity .760 0.74

Information about which actors and actresses will be participated .724

into the cultural activity

 

Table 7.6: Exploratory Factor Analysis results for Cultural Activities (Cont.)

 

Factors and Related Items Factor

Loadings

 

Cronbach Alpha

 

Factor 11

Shopping is great for my mood. Buying something makes me happy.

Shopping is a kind of social activity for me.

Factor 12

Specialized websites in cultural activities. Organizer firms / theathers’ websites.

Consumer comments on the Internet (organiser firms’ websites, websites such as biletix, e-groups)*

 

.736

.728

.571

.795

.754

.543

 

0.74

0.66

 

Factor 13

Certanity of getting a good purchase of a ticket for the cultural activity (cinema, theater, concert etc.) that you last participated in.

Satisfaction with the purchase decision on the cultural activity (cinema, theather, concert etc.) that you last participated in. .807

.726 0.62

Factor 14

 

Newspaper/magazines/TV/radio advertisements Family and/or friends, acquaintances

Information desk in the place which cultural activity organized*

 

.733

.728

.471

 

0.42

 

Kaiser-Mayer-Olkin measure of sampling adequacy = 0.862 Bartlett Test of Sphericity (X2)= 17280.87, p=0.000

 

Total variance explained = 64.19%

 

 

Note 1*: Although two items had communalities less than 0.50, one of them with less than 0.50 factor loadings, these two items are retained to be used for calculating difference scores between usage of online information sources and usage of offline information sources.

Note 2: Principal component analysis and orthogonal varimax rotation were used.

On the assessment of EFA result for cultural activities, the factors were described and named as the following:

Factor 1- perceived benefits of using online information sources (BENON): This first factor encompasses nine items referring an individual’s perception about benefits of using online information sources.

Factor 2- perceived benefits of using offline information sources (BENOFF): This second factor represents people’s perceived benefits of using offline information sources.

Factor 3- product involvement (INV): This third factor resulted as expected, that is, all items for product involvement with cultural activities loaded on their underlying construct.

 

Factor 4- economic-conscious shopping orientation (ECSHOP): This fourth factor includes all of the theoretically defined items that are related to price-oriented shopping activities.

Factor 5- optimum stimulation level (OSL): Even though four items of eleven items were retained, this factor still represents the main aspects of exploratory information seeking construct.

Factor 6- time availability (TIME): This factor encompasses all of the theoretically defined items regarding as perceived amount of time available.

Factor 7- perceived risk (RISK): This factor is composed of all items defined in the literature, measuring general perceived risk related to the purchase of a cultural activity.

Factor 8- quality consicous shopping orientation (QCSHOP): This factor represents the items that were designed to measure one type of shopping orientation, which is the quality-consicous shopping orientation.

Factor 9- subjective product knowledge (SUBKN): This factor consists of the theoretically defined items measuring the level of subjective product knowledge about cultural activities.

Factor 10- importance of attributes (ATTR): Although nine items were used to measure the attribute importance related to cultural activities, at the end of the analysis, three items reflecting affective attributes of cultural activities were remained.

Factor 11- recreational shopping orieantations (RSHOP): This factor resulted as expected, hence, all items for recreational shopping orientation construct loaded on its underlying dimension.

Factor 12- usage of online information sources: This factor is composed of online information sources which are used by consumers to obtain product information related to cultural activities.

Factor 13- search outcomes (SOUT): This factor represents two items capturing outcomes of information search behavior, which is defined in the related literature.

 

Factor 14- usage of offline information sources: This last factor includes offline information sources which consumers search for product information on cultural activities.

In the consistency with the finding of the study of Klein and Ford (2001), these two exploratory factor analyses have confirmed that there is a new dimension to classify information sources, -online information sources and offline (traditional) information sources-.

The results of two EFA applications denote that these factors can be considered for the confirmatory factor analysis application for both product types.

7.3.2 Confirmatory Factor Analysis and Measurement Models

The test of measurement model entails specifying which observed variables define a construct and ascertaining the extent to which the indicator items actually measure the latent construct proposed in the research model. Lisrel 8.7 was used to analyze the data, where the analyses were conducted on the covariance matrix of the variables. The maximum likelihood (ML) estimation method was used.

To assess model fit, both the measurement and the structural parts of the model need to be considered. The squared multiple correlations (SMCs) for the manifest variables of constructs indicate the extent to which the individual manifest variables are free from measurement error. It means that the closer to 1, the better the manifest variable acts as an indicator of the corresponding latent variables. SMC is similar to the idea of communality from EFA (Hair et al., 2006).

Table 7.7 shows the composite reliability (CR) and average variance extracted (AVE) for all fourteen latent variables in the model for cellular phone. It can be seen that, with one exception, all composite reliabilities exceed the 0.60 threshold and, the average variance extracted falls short of 0.50 thresholds for five of fourteen variables.

 

Table 7.7: Measurement Model for Cellular Phone

 

 

Model Fit

 

χ2 Df RMSEA GFI NFI NNFI CFI AGFI

 

Indicators 2677.80 1502 0.034 0.88 0.92 0.96 0.96 0.86

Variables SMC t- value

Online Information Source

Producers’ website 0.58 17.15

Retailers’ websites 0.34 13.51

Consumer comments on the Internet (e-groups, shopping websites etc.) 0.27 12.07

Offline Information Sources

Newspaper/magazines/TV/radio advertisements 0.29 11.06

Family and/or friends, acquaintances 0.26 10.56

Store visits or salespeople 0.31 11.36

Product Involvement

Important 0.35 15.97

Concern to me 0.51 20.29

Valuable 0.53 20.95

Means a lot to me 0.48 19.45

Interested 0.48 19.32

Desirable 0.54 21.02

Search Outcomes

Certainty of getting a good purchase of cellular phone 0.65 18.52

Satisfaction with the purchase decision on cellular phone 0.57 17.66

Subjective Knowledge

Compared with my friends and acquaintances, my knowledge of

cellular phones is: (weaker to stronger) 0.68 23.21

Compared with experts in the topic of cellular phones, my knowledge

of cellular phones is: (weaker to stronger) 0.52 19.71

In general, my knowledge of cellular phone is: (very weak to very

strong) 0.51 19.48

General Perceived Risk

Overall, I thought of buying a cellular phone causes me to be

concerned with experiencing some kind of loss if I went ahead with the purchase 0.66 20.46

All things considered, I thought I would be making a mistake I bought

a cellular phone. 0.70 21.14

When all is said and done, I really feel that the purchase of a cellular

phone poses problems for me that I just do not need 0.25 12.70

Product Attributes

Aesthetic design 0.68 23.17

Color 0.70 23.60

Physical dimensions (weight, length etc.) 0.38 16.61

Perceived Benefits of Using Online Information Sources

Online information sources provide reliable product information on

cellular phones 0.40 17.72

Online information sources provide enough product information about

cellular phones. 0.54 21.76

Online information sources are helpful to gather product information

about cellular phones. 0.61 23.59

Online  information  sources  make  easy  to  compare  different

alternatives of cellular phones. 0.47 19.68

Online information sources are easy accessible for product information

about cellular phones. 0.52 21.03

Online information sources provide detailed information about cellular

phones. 0.62 23.83

 

Table 7.7: Measurement Model for Cellular Phone (Cont.)

Variables SMC t- value

Perceived Benefits of Using Online Information Sources

It is easy to get product information about different models for cellular

phones through online information sources. 0.60 23.32

Online information sources do not cause me to get tired of getting

product information about cellular phones 0.47 19.62

Online information sources are helpful to make a right decision about

purchase of cellular phones. 0.56 20.40

Perceived Benefits of Using Offline Information Sources

Offline information sources provide reliable product information on

cellular phones 0.40 17.54

Offline information sources provide enough product information about

cellular phones 0.48 19.78

Offline information sources are helpful to gather product information

about cellular phones 0.52 20.98

Offline  information  sources  make  easy  to  compare  different

alternatives of cellular phones. 0.45 19.17

Offline  information  sources  are  easy  accessible  for  product

information about cellular phones. 0.47 19.75

Offline information sources provide detailed information about cellular

phones. 0.56 22.31

It is easy to get product information about different models for cellular

phones through offline information sources. 0.65 24.64

Offline information sources do not cause me to get tired of getting

product information about cellular phones 0.41 17.88

Offline information sources are helpful to make a right decision about

purchase of cellular phones 0.60 23.24

Optimum Stimulation Level

I generally read even my junk mail just to know what is about 0.39 15.72

I like to browse through mail order catalogs even when I don’t plan to

buy anything 0.43 16.58

I am inclined to read e-advertisements and get informed 0.35 14.83

I often read advertisements just out of curiosity 0.49 17.90

Time Availability (reverse scored)

I seem to be busier than most people I know. 0.55 21.41

Usually there is so much to do that I wish I had more time. 0.86 28.65

I usually find myself pressed for time. 0.60 22.41

Recreational Shopping Orientation

Shopping is great for my mood 0.66 22.99

Shopping is a kind of social activity for me. 0.34 15.20

Buying makes me happy 0.56 20.80

Quality-Conscious Shopping Orientation

It is generally worth it to pay more for quality. 0.49 17.83

The quality of product/service I buy is more important to me than the

prices I have to pay. 0.44 16.71

I look for quality in a product/service and is willing to pay extra for it. 0.50 18.03

 

Table 7.7: Measurement Model for Cellular Phone (Cont.)

Variables SMC t- value

Economic-Conscious Shopping Orientation

I prefer to purchase items on sale 0.44 17.45

I do comparison shopping 0.57 20.38

I notice price differences 0.50 18.80

I look for bargain/competitive prices 0.34 15.10

Constructs Internal Consistency

Average Composite Reliability (ρc) Cronbach Variance

Alpha (α) Extracted

(AVE) (ρv)

Product Involvement 0.85 0.84 0.50

Subjective Knowledge 0.80 0.79 0.57

Product Attributes 0.81 0.80 0.58

Benefits of Using Online

Information Sources 0.91 0.91 0.53

Benefits of Using Offline

Information Sources 0.90 0.90 0.51

Search Outcomes 0.76 0.76 0.61

General Perceived Risk 0.77 0.75 0.54

Online information source usage 0.66 NA 0.40

Offline information source usage 0.56 NA 0.30

Time Availability 0.86 0.85 0.67

Quality-Conscious Shopping

Orientation 0.73 0.73 0.47

Economic-Conscious Shopping

Orientation 0.77 0.77 0.46

Recreational Shopping

Orientation 0.76 0.74 0.52

Optimum Stimulation Level 0.74 0.73 0.41

* 2– Chi square; df – degrees of freedom; RMSEA – Root mean square error of approximation; GFI – Goodness-of-fit index; NFI – Normated-fit index; CFI – Comparative-fit index; AGFI – Adjusted

 goodness-of-fit index; SMC – Squared multiple correlation (Variable’s own individual reliability)

The findings of measurement model for cultural activities along with the composite reliability and average variance extracted for all fourteen latent variables are illustrated in Table 7.8. Similar to the findings of measurement model for cellular phones, with one exception, all composite reliabilities exceed the 0.60 threshold and, the average variance extracted falls short of 0.50 thresholds for four of fourteen variables.

 

Table 7.8: Measurement Model for Cultural Activities

Model Fit χ2 Df RMSEA GFI NFI NNFI CFI AGFI

Indicators 2900.19 1502 0.037 0.87 0.92 0.96 0.96 0.85

Variables SMC t- value

Online Information Source

Organizer firms / theathers’ websites. 0.60 19.70

Specialized websites in cultural activities. 0.64 20.31

Consumer comments on the Internet (organiser firms’ websites,

websites such as biletix, e-groups)* 0.15 9.32

Offline Information Sources

Newspaper/magazines/TV/radio advertisements 0.23 9.12

Family and/or friends, acquaintances 0.20 8.53

Information desk in the place which cultural activity organized 0.18 8.11

Product Involvement

İmportant 0.48 19.74

Concern to me 0.60 20.96

Valuable 0.69 25.54

Means a lot to me 0.55 21.67

Interested 0.43 18.39

Desirable 0.43 18.40

Search Outcomes

Certanity of getting a good purchase of a ticket for the cultural activity (cinema, theather, concert etc.) that you last participated

in. 0.33 13.08

Satisfaction with the purchase decision on the cultural activity

(cinema, theather, concert etc.) that you last participated in. 0.60 16.09

Subjective Knowledge

Compared with my friends and acquaintances, my knowledge of

cultural activities is: (weaker to stronger) 0.59 21.26

Compared with experts in the topic of cultural activities, my

knowledge of cultural activities is: (weaker to stronger) 0.49 18.90

In general, my knowledge of cultural activities is: (very weak to

very strong) 0.56 20.63

General Perceived Risk

Overall, I thought of buying a ticket for a cultural activity causes

me to be concerned with experiencing some kind of loss if I went ahead with the purchase 0.82 25.62

All things considered, I thought I would be making a mistake I

bought a ticket for a cultural activity 0.70 23.32

When all is said and done, I really feel that the purchase of a ticket for a cultural activity poses problems for me that I just do

not need 0.30 14.34

Product Attributes

Information about comments of the cultural activity 0.47 17.60

Information about backgrounds of actors and actresses 0.63 20.53

Information about which actors and actresses will be participated

into the cultural activity 0.41 16.24

 

Table 7.8: Measurement Model for Cultural Activities (Cont.)

Variables SMC t- value

Perceived Benefits of Using Online Information Sources

Online information sources provide reliable product information

on cultural activities 0.50 20.71

Online information sources provide enough product information

about cultural activities. 0.53 21.51

Online information  sources are helpful to gather  product

information about cultural activities 0.59 23.29

Online information sources make easy to compare different

alternatives of cultural activities. 0.57 22.69

Online information sources are easy accessible for product

information about cultural activities. 0.52 21.25

Online information sources provide detailed information about

cultural activities. 0.58 22.80

It is easy to get product information about different alternatives

for cultural activities through online information sources. 0.58 22.81

Online information sources do not cause me to get tired of getting

product information about cultural activities. 0.51 20.79

Online information sources are helpful to make a right decision

about purchase of cultural activities. 0.49 20.43

Perceived Benefits of Offline Information Sources

Offline information sources provide reliable product information

on cultural activities 0.48 19.83

Offline information sources provide enough product information

about cultural activities 0.52 21.12

Offline information sources are helpful to gather product

information about cultural activities. 0.54 21.58

Offline information sources make easy to compare different

alternatives of cultural activities 0.48 19.81

Offline information sources are easy accessible for product

information about cultural activities 0.49 20.24

Offline information sources provide detailed information about

cultural activities. 0.52 20.95

It is easy to get product information about different alternatives

for cultural activities through offline information sources 0.56 22.12

Offline information sources do not cause me to get tired of

getting product information about cultural activities. 0.46 19.53

Offline information sources are helpful to make a right decision

about purchase of cultural activities. 0.60 23.45

Optimum Stimulation Level

I generally read even my junk mail just to know what is about 0.39 15.66

I like to browse through mail order catalogs even when I don’t

plan to buy anything 0.41 16.15

I am inclined to read e-advertisements and get informed 0.36 15.01

I often read advertisements just out of curiosity 0.49 17.93

Time Availability (reverse scored)

I seem to be busier than most people I know 0.55 21.45

Usually there is so much to do that I wish I had more time 0.86 28.63

I usually find myself pressed for time. 0.60 22.41

Recreational Shopping Orientation

Shopping is great for my mood. 0.66 22.75

Shopping is a kind of social activity for me. 0.57 20.79

Buying something makes me happy 0.33 15.03

 

Table 7.8: Measurement Model for Cultural Activities (Cont.)

Variables SMC t- value

Quality-Conscious Shopping Orientation

It is generally worth it to pay more for quality. 0.49 17.96

The quality of product/service I buy is more important to me than

the prices I have to pay. 0.43 16.54

I look for quality in a product/service and is willing to pay extra

for it. 0.50 18.11

Economic-Conscious Shopping Orientation

I prefer to purchase items on sale 0.43 17.24

I do comparison shopping 0.56 20.38

I notice price differences 0.50 18.96

I look for bargain/competitive prices 0.35 15.27

Constructs Internal Consistency

Composite

Reliability (ρc) Cronbach (α) Alpha AVE

(ρv)

Product Involvement 0.87 0.87 0.53

Subjective Knowledge 0.78 0.78 0.55

Affective Attributes 0.75 0.74 0.50

Benefits of Using

Information Sources Online 0.92 0.92 0.55

Benefits of Using

Information Sources Offline 0.91 0.91 0.52

Search Outcomes 0.68 0.62 0.52

General Perceived Risk 0.82 0.80 0.61

Online information source usage 0.74 0.66 0.50

Offline information source usage 0.56 0.42 0.30

Time Availability 0.86 0.85 0.67

Quality-Conscious

Orientation Shopping 0.73 0.73 0.47

Economic-Conscious

Orientation Shopping 0.77 0.77 0.46

Recreational Shopping Orientation 0.76 0.74 0.52

Optimum Stimulation Level 0.74 0.73 0.41

* 2– Chi square; df – degrees of freedom; RMSEA – Root mean square error of approximation; GFI – Goodness-of-fit index; NFI – Normated-fit index; CFI – Comparative-fit index; AGFI – Adjusted goodness-of-fit index; SMC – Squared multiple

 correlation (Variable’s own individual reliability)

In this study, for both models (cellular phone and cultural activities) all factor loadings for indicators measuring the same construct are statistically significant (Table 7.7 and Table 7.8). That is, all indicators effectively measure their corresponding construct (Anderson and Gerbing, 1988) and support convergent validity.

The chi-square difference test can be used to assess the discriminant validity (Hatcher, 1994). In this study, discriminant validity of the measures was assessed on the basis of the criteria recommended by Anderson and Gerbing (1988). The models were estimated twice for every possible pair of constructs in the measurement

 

models. In the first model, the phi correlation between the constructs was set to vary (unconstrained model) and in the second one, the phi was constrained to 1.00 (constrained model) (Anderson and Gerbing, 1988). The χ2 difference and the degrees of freedom were computed for both the constrained and the unconstrained models. Discriminant validity is confirmed if the chi-square difference (with 1 d.f.) is significant, meaning that the unconstrained model in which the two constructs are viewed as distinct (but correlated) factors is superior. The results showed that all the models in which the phi was fixed at 1 displayed a worse fit (All χ2 differences > 3.841, df=1 and p= 0.05), the minimum χ2 difference is 13.80 and 140.21 for cultural activities and cellular phones respectively, thereby confirming discriminant validity (Bagozzi and Philips, 1982).

In summary, the assessment of the measurement part of the model for both product types revealed good evidence of validity and reliability for the operationalizations of most of the latent variables. Although a case of low variance extracted for five latent variables was observed, on the whole, the assessment of the measurement part of the model for both product types did not reveal any crucial deficiencies.

7.4 Structural Model Testing

Before analysing the structural relations for both models, the overall fit of these two models to the observed data was examined in order to assess whether these two models were valid. Table 7.9 represents the values for the goodness-of-fit indices. Although the χ2 values were statistically significant (χ2cp= 2656.19, df= 1502; χ2 ca=2980.63, df= 1502) at a 0.000 significance level, thereby indicating poor fit, the other absolute and incremental fit indices pointed that these two models represent adequately relationships proposed among the constructs.

The assessment of overall fit for the model regarding to cellular phone- RMSEA was 0.038, which is below the cut-off value of 0.05 (Garver and Mentzer, 1999; Hair et al., 2006). The GFI and AGFI were 0.88 and 0.86, respectively. There appears to be some difference regarding the recommended threshold value of 0.90 (Hair et al., 1998). However, Doll et al. (1994) and Durande-Moreau and Usunier (1999) suggest that a criterion of 0.80 is considered acceptable. Since the values for GFI and AGFI were within the acceptable range of 0.80 and 0.90, the model fit was considered acceptable. The NNFI and CFI measures demonstrated that the model can be

 

evaluated as a good fit. The figures for the latter indices were 0.96 and 0.96, respectively, which are above the 0.95 criterion value (Diamantopoulos and Siguaw, 2000; Doll et al., 1994; Hair et al., 1998; Hu and Bentler, 1999; Mueller, 1996). Overall, the model fit indices confirmed that the model was valid for the focal product of cellular phone.

The assessment of overall fit for the model relevant to cultural activities- RMSEA was 0.040, which is below the cut-off value of 0.05 (Garver and Mentzer, 1999; Hair et al., 2006). The GFI and AGFI were 0.87 and 0.85, respectively. There appears to be some difference regarding the recommended threshold value of 0.90 (Hair et al., 1998). However, Doll et al. (1994) and Durande-Moreau and Usunier (1999) suggest that a criterion of 0.80 is considered acceptable. Since the values for GFI and AGFI were within the acceptable range of 0.80 and 0.90, the model fit was considered acceptable. The NNFI and CFI measures demonstrated that the model can be evaluated as a good fit. The figures for the latter indices were 0.95 and 0.96, respectively, which are above the 0.95 criterion value (Diamantopoulos and Siguaw, 2000; Doll et al., 1994; Hair et al., 2006; Hu and Bentler, 1999; Mueller, 1996). The NFI was 0.92, value greater than .90 of NFI indicates of a good-fitting model. Overall, the model fit indices confirmed that the model was valid for another chosen product of cultural activity.

Table 7.9: Model Fit Indicators

Model Indicators χ2 Df RMSEA GFI NFI NNFI CFI AGFI

Model for

cellular phone 2656.19 1502 0.038 0.88 0.92 0.96 0.96 0.86

Model for cultural

activities 2980.063 1502 0.040 0.87 0.92 0.95 0.96 0.85

The results of test of structrural model for cellular phone and cultural activities are graphically illustrated in figure 7.1 and figure 7.2, including standardized coefficients, t values and variance explained.

 

 

AGE

-0.089

(-2.198)

mu

 

OSL

Figure 7.1: The Structural Model for Cellular Phone, Standardized Coefficients, t values, and Variance Explained.

Note: Dotted line presents the relationship suggested by modification indices

 

 

Figure 7.2: The Structural Model for Cultural Activities, Standardized Coefficients, t values, and Variance Explained.

Note: Dotted line presents the relationship suggested by modification indices

This thesis aims to predict the direct causal relationships between risk, attribute importance, product involvement, optimum stimulation level, shopping orientations (quality conscious, price-consicous and recreational), perceived benefits of using online information sources, perceived benefits of using offline information sources, product knowledge (subjective product knowledge and objective product knowledge), time avaliability, difference in usage of online information sources and offline information sources and search outcomes.

7.4.1 Results for Cellular Phone

At first, the research hypotheses were tested on the basis of proposed structural model for cellular phone. An examination of the path estimates and t values in Figure

7.1 illustrates that fifteen of the paths had statistically significant coefficients. Ten of the t values were above the critical value of 1.96 at the 0.05 significance level. The t

 

values for the relationships between time availability and DONOFF, quality conscious shopping orientation and DONOFF, recreational shopping orientation and DONOFF as well as education and perceived benefits of using online information sources, age and perceived benefits of using offline information sources were significant at the 0.90 confidence level (tcritical=1.283, p=0.10).

It was found that five of individual differences, i.e. optimum stimulation level (OSL), product involvement (INV), quality conscious shopping orientation (QCSHOP), recreational shopping orientation (RSHOP), and perceived benefits of using online information sources (BENON), had a statistically significant impact on DONOFF. OSL, INV and BENON were significant at the 0.95 confidence level (tOSL= -2.16 < - 1.96; tINV= 1.96 and tBENON= -3.24< -1.96) (H1, H3 and H5 not be rejected), whereas QCSHOP and RSHOP were significant at the 0.90 confidence level (tQCSHOP= -1.38 <

-1.283 and tRSHOP= 1.66 > 1.283) (H2b and H2c not be rejected). The influences of economic conscious shopping orientation (ECSHOP) and perceived benefits of using offline information sources (BENOFF) on DONOFF were not confirmed (tECSHOP= 0.56 < 1.96 and tBENOFF= 0.96< 1.96) (H2a and H4 rejected). Among the variables of individual differences, the INV construct had the highest impact on the DONOFF (γINV=0.32) (Figure 7.1). That is, ceteris paribus, a one unit increase in INV resulted in a 0.32 increase in DONOFF. The four coefficients for the optimum stimulation level (OSL), quality conscious orientation (QCSHOP), recreational orientation (RSHOP) and the perceived benefits of using online information sources (BENON) constructs were -0.25, -0.18, 0.28 and -0.29, respectively.

Among factors grouped in potential payoff/attribute importance, only perceived risk (RISK) was found to have a significant impact on DONOFF at the 0.95 confidence level (tRISK= 2.59 > 1.96; H8 not be rejected). The influence of affective attribute importance on DONOFF was not confirmed (tATTR= 0.13 <1.96; H9 rejected). The coefficient for the perceived risk (RISK) construct was 0.23 meaning that a one unit increase in RISK resulted in a 0.23 increase in DONOFF.

It was proposed that product knowledge related factors i.e subjective product knowledge and objective product knowledge influence the model in two ways. First, these two product knowledge constructs have impact on perceived risk construct (RISK). Second, both subjective and objective product knowledge influences the DONOFF. The first direct relationship between subjective product knowledge and

 

perceived risk was found to be statistically significant (tSUBKN-RISK= -3.43 < -1.96; H10a not be rejected), whereas the direct relationship between objective product knowledge and perceived risk was not confirmed (tOBJKN-RISK= -1.15 > -1.96; H11a rejected). On the part of the influences of SUBKN and OBJKN on DONOFF, the only significant relationship was found between SUBKN and DONOFF at the 0.95 confidence level (tSUBKN= -4.62 < -1.96; H10b not be rejected). However, no direct causal link was found between OBJKN and DONOFF (tOBJKN= -0.77 > -1.96; H11a rejected). The significant coefficient for SUBKN was -0.66 meaning that, ceteris paribus, one unit decrease in SUBKN resulted in a 0.66 increase in DONOFF.

As a situational variable, the direct relationship proposed between time availability construct (TIME) and DONOFF was confirmed. The t value of path between TIME and DONOFF was -1.86 which was statistically lower than the critical t value of -

1.283 at the 0.10 significance level (H12 not be rejected).

It was predicted that demographic characteristics i.e. age and education have impact on both perceived benefit of using online information sources (BENON) and perceived benefit of using offline information sources (BENOFF). On the basis of the results of analysis, it was found that the relationships between age (AGE), education (EDU), perceived benefits of using online information sources (BENON) and perceived benefits of using offline information sources (BENOFF) were confirmed. The relationship between AGE and BENON was significant at 0.95 confidence level (tAGE-BENON= -3.01 < -1.96; H6b not be rejected), whereas the relationship between EDU and BENON was significant at 0.10 significance level (tEDU-BENON= 1.33 > 1.283; H7b not be rejected). The direct causal links between demographic characteristics and perceived benefits of using offline information sources were statistically confirmed. While the t value of path between AGE and BENOFF was 1.89 which was higher than the critical t value of 1.283 at the 0.10 significance level (H6a not be rejected), the coefficient of between EDU and BENOFF was -2.19 that was lower than the critical t value of -1.96 at the 0.95 confidence level (H7a not be rejected).

Based on the assessment of the right side of the proposed model, it was found that DONOFF had a statitically significant impact on search outcomes (SOUT). DONOFF was significant at the 0.95 confidence level (tDONOFF= -4.97 > -1.96; H13 not be rejected). Modification indices calculated by LISREL propose structural

 

relations among latent variables to improve the main model. If the modifications have theoretical base, modifications can be made to improve the main model. The results of the analysis of structural model suggest adding a path from involvement to search outcomes. That is to say the higher the involvement in the product the higher the satisfaction with search outcomes. This proposed relationship between INV and SOUT was found to be statistically significant. The t value of path between INV and SOUT was 4.69 that was higher the critical value of 1.96 at the 0.05 significance level.

7.4.2 Results for Cultural Activities

At second, the proposed structural model was tested for cultural activities. As it can be seen in Figure 7.2, thirteen standardized estimates were significant. While eleven of the t values were significant at the 0.05 significance level, two t values for the relationships between objective knowledge and DONOFF as well as importance of affective attribute and DONOFF were above the critical value of 1.283 at the 0.10 significance level.

Among factors related to individual differences, four direct causal links between optimum stimulation level (OSL), quality conscious shopping orientation (QCSHOP), perceived benefits of using online information sources (BENON) and DONOFF were found to be statistically significant. The influences of OSL, QCSHOP and BENON on DONOFF were significant at the 0.95 level (tOSL= -3.75; tQCSHOP= -2.19; tBENON= -4.47 < -1.96) (H1, H2b, H5 not be rejected). The OSL

construct had the highest impact among individual differences on the DONOFF (γOSL= -0.44). This means that a one unit increase in OSL resulted in a 0.44 decrease in DONOFF. The remaining two coefficients for the QCSHOP and BENON constructs were -0.25 and -0.42, respectively. However, the influences of economic conscious shopping orientation (ECSHOP), recreational shopping orientation (RSHOP), product involvement (INV) and perceived benefits of using offline information sources (BENOFF) on DONOFF were not confirmed. The t values of paths between these four constructs and DONOFF were 0.85, 0.86, 0.62 and -1.25, respectively (H2a, H2c, H3, and H4 rejected).

Among demographic characteristics, age was found to have a significant impact on the perceived benefits of using online information sources (BENON) (tAGE-BENON= -

 

4.04 < -1.96; H6b not be rejected), whereas the direct relationhip between education (EDU) and BENON was not confirmed (tEDU-BENON= 0.93 < 1.96; H7b rejected). However, the influence of EDU on perceived benefits of using offline information sources (BENOFF) was found to be significant at the 0.95 confidence level (tEDU- BENOFF= -2.15 < -1.96; H7a accepted), while AGE had no significant impact on BENOFF (tEDU-BENOFF= 1.06 < 1.96; H6a rejected).

Both perceived risk (RISK) and affective attribute importance (ATTR) has a statistically significant impact on the DONOFF. RISK was significant at the 0.95 confidence level (tRISK= 2.42 > 1.96; H8 not be rejected), whereas ATTR was significant at the 0.90 confidence level (tATTR= 1.336 > 1.283; H9 not be rejected). The impact of RISK was slightly higher than that of ATTR on the DONOFF (γRISK=0.187 > γATTR=0.127).

The role of product knowledge in the proposed model was twofold. First, among the relationship between product knowledge types and perceived risk, only the impact of SUBKN on perceived risk was confirmed at 0.95 confidence level (tSUBKN-RISK=-2.4

>1.96, H10a accepted; tOBJKN-RISK=-0.612 <1.96, H11a rejected). Second, the direct relationships imposed between SUBKN, OBJKN and DONOFF were found to be statistically significant. While the t value of path between SUBKN and DONOFF was -4.55 which was higher than the critical value of 1.96 at the 0.05 significance level, that of between OBJKN and DONOFF was -1.75 which was higher than the critical value of 1.283 at the 0.10 significance level (H10b and H11b not be rejected).

Time availability (TIME), a stituational variable, was found to have a significant impact on the DONOFF at 0.95 confidence level (tTIME= -2.09 > 1.96; H12 not be rejected).

It was found that DONOFF and product involvement (INV) had a statistically significant impact on search outcomes (SOUT). DONOFF and INV were significant at the 0.95 confidence level (tDONOFF= -4.95 < -1.96 and tINV= 1.96) (H12 and H13 not be rejected).

7.4.3 Comparison of the Results for Cellular Phone and Cultural Activities

Results are summarised in Table 7.10 and Table 7.10 also comparatively displays standardized estimates and t-values of the structural model for both cellular phone and cultural activities.

 

Table 7.10: Standardized Estimates of the Structural Model

Path Cellular Phone Cultural Activities

estimate t-value estimate t-value

OSL → DONOFF (H1) -0.252** -2.161 -0.437** -3.753

QCSHOP → DONOFF (H2a) -0.183* -1.387 -0.255** -2.139

ECSHOP → DONOFF (H2b) 0.063 0.564 0.087 0.847

RCSHOP→ DONOFF (H2c) 0.284* 1.662 0.123 0.863

INV→ DONOFF (H3) 0.323** 1.96 0.104 0.618

BENOFF→ DONOFF (H4) 0.075 0.96 -0.090 -1.249

BENON → DONOFF (H5) -0.287** -3.238 -0.415** -4.469

AGE → BENOFF (H6a) 0.077* 1.898 0.043 1.060

AGE → BENON (H6b) -0.122** -3.011 -0.163** -4.045

EDU→ BENOFF (H7a) -0.089** -2.198 -0.087** -2.152

EDU → BENON (H7b) 0.054* 1.335 0.037 0.933

RISK→ DONOFF (H8) 0.234** 2.594 0.187** 2.42

ATTR → DONOFF (H9) 0.013 0.131 0.127* 1.336

SUBKN → RISK (H10a) -0.167** -3.43 -0.110** -2.402

SUBKN → DONOFF (H10b) -0.659** -4.619 -0.643** -4.548

OBJKN → RISK (H11a) -0.050 -1.153 -0.025 -0.612

OBJKN→ DONOFF (H11b) -0.064 -0.777 -0.135* -1.752

TIME → DONOFF (H12) -0.165* -1.86 -0.171** -2.098

DONOFF → SOUT (H13) -0.461** -4.967 -0.554** -4.951

INV→ SOUT 0.368** 4.688 0.187** 1.96

** Significant at p <0.05 significance level

* Significant at p< 0.10 significance level

The findings associated with the structural model can be interpreted in the following:

Cellular phone- perceived risk and product involvement were found to be positively related to difference between the use of online information sources and the use of offline information sources (DONOFF). Consumers with higher perceived risk and with higher product involvement tend to use more offline information sources than online information sources. In other words, they mainly prefer using offline information sources over online information sources to obtain product related information.

The variables having significantly negative impacts on DONOFF are optimum stimulation level, time availability, quality-conscious shopping orientation, perceived benefits of using online information sources, and subjective product knowledge. When consumers have more time (higher time availability), they tend to use not only

 

offline information sources but also online information sources to make a precise decision on the purchase of a cellular phone. This explanation is valid for the consumers with having more quality-conscious shopping orientation.

It has also found that the higher the perceived benefits of using online information sources, the smaller the DONOFF. This means that anticipation of benefits from using online information sources make consumers use online information sources along with offline information sources to get product information in the case of a cellular phone purchase.

Consumers having higher subjective product knowledge dealing with cellular phones begin to use online information sources relative to the offline information sources, hence, the difference between usage of online information sources and that of offline information sources becomes smaller.

Due to the fact that online information sources are consumer-driven information sources, consumers with higher optimum stimulation level are likely to use online information sources in addition to offline information sources, consequently, the gap between usage of online information sources and usage of offline information sources decreases.

Cultural activities- Similar to the finding associated with cellular phone, perceived risk has positive influence on the DONOFF. It is difficult to evaluate the quality of cultural activities, hence, consumers prefer using offline information sources rather than online information sources to gather product information for cultural activities.

Attribute importance for cultural activities was found to be positively related to DONOFF. When consumers give more importance to the attributes of cultural activities such as information on actors and actress, they tend to use more offline information sources than online information sources.

Similarly, optimum stimulation level, time availability, quality-conscious shopping orientation, perceived benefits of using online information sources, and subjective product knowledge about cultural activities have negative effect on DONOFF.

Different from the case of a cellular phone purchase, a significant negative relationship was found between objective product knowledge dealing with cultural activities and DONOFF.

 

In summary, the results of testing the proposed model for both product types – cellular phones and cultural activities have indicated almost similar causal relationships among constructs.

 

8. DISCUSSION

The goal of this thesis was to examine the influences of individual differences, potential payoff and attribute importance, product knowledge, and a situational variable on the extent usage of online information sources over that of offline information as well as it was aimed to explore underlying motives of using online information sources. Consequently, the impact of difference between online information search and offline information search on outcomes of information search including purchase certainty and purchase satisfaction was investigated.

This chapter discusses the results of the data analysis presented in chapter 7 with support from the literature review and the information obtained from the qualitative study (Study 1). First, the results of Study 1 are presented. Second, the findings of hypothesis testing in Study 2 are reviewed and evaluated with respect to theoretical expectation for two different product types, cellular phones and cultural activities.

8.1 Underlying Motives of Using Online Information Sources

The objectives of Study 1 was to explore underlying motives of using online information sources in relative to offline information sources by taking into consideration of two product types -cellular phones and cultural activities-. For both cellular phones and cultural activities, online information sources are perceived as those supplying product information rapidly and a means of accessing to consumer comments and detailed product information. They also provide easy access and enable consumers to see different alternatives in the same product category at one time. This study revealed that the frequently cited characteristics of online information sources for two product types were listed as: quickness, easy access, access to detailed information, to see different alternatives at one time, access to consumer comments.

In consistency with the literature dealing with information search behavior, underlying motives of using online information sources depend on cost-benefit

 

framework which was first introduced by Stigler (1961). On the basis of results of the qualitative study (Study 1), benefits obtained from using online information sources are the followings; online information sources ensure time saving, to get satisfaction in return of money and to take the right decision on the purchase under consideration, and to make comparison among alternatives. Due to these benefits, consumers tend to use online information sources. This finding shows consistency with several studies. For instance, Korgaonkar and Wolin (1999) which found that economic motivation is one of the underlying motivations of Web usage.

“Not to lose time” was the most implied consequences (benefits) linked to the “quick” and “easy accessible” attributes of online information sources. Respondents mainly preferred online information sources to avoid losing time. Besides, “To take right decision” was the second noticeable consequence derived from using online information sources. Accordingly, it should be noted that, as an outcome of the Study 1, time availability and shopping orientations were included as the antecedents of the difference between online information search and offline information search into the proposed model.

8.2 Determinants of Difference between Online Information Search and Offline Information Search

In the following sub-sections, findings dealing with the antecedent variables of the difference between online and offline information search will be discussed.

8.2.1 Individual Differences

The finding related to optimum stimulation level confirmed the theoretical expectations that an individual with high OSL is going to obtain product information about either cellular phone or cultural activity from both types of information sources (H1 supported). According to the description made by Raju (1980), the person with high OSL has the following characteristics:

“One who is not afraid of taking risks or trying new or unusual products/services, is eager to find out about new products/services and takes the initiative in trying them, seeks variety or change in repetitive purchases, and likes introducing new products and brands to others”.(p.271)

 

On the basis of this description, consumers with high OSLs are more likely to be interested in different and new types of information sources, and also, they would get in greater information search activity.

In this study, it is proposed that the higher the level of product involvement the smaller the difference between the use of online information sources and offline information sources. Contrary to our expectation, a significant positive relationship was found between product involvement and the difference between online and offline information search for both product types (H3 supported in an unexpected direction). Offline information sources considered in this study are composed of sales persons for a cellular phone, advertisements, friends and acquaintances, and information desk for a cultural activity. Accordingly, this finding indicates that consumers with high product involvement tend to seek product information by using more offline information sources than online information sources.

As previously noted in the literature, Lee and Lee (2005) suggested that shopping attitude would be an important variable affecting consumers’ motivation to engage in online information search. As a result of quantitative assessment, items which related to the variety seeking shopping orientation and the convenience shopping orientation were excluded; consequently, these two constructs were dropped from the model. The results related to the relationships between the remaining three shopping orientations and difference between online and offline information search are evaluated.

The hypothesis testing established that only quality-conscious shopping orientation has a negative impact on the difference between online and offline information search, for either cellular phones or cultural activities (H2a supported). This is evidenced that consumers with high in quality-conscious shopping orientation use not only offline information sources but also online information sources to purchase high quality products. Individuals with high quality-conscious shopping orientation give more importance to the quality of product, whereby they look for quality in a product or a service. Consequently, they obtain product information from all kind of information sources, especially in a case where they purchase a new product. On the other hand, the statistical evidence from this study demonstrated that while economic-conscious shopping orientation does not influence the difference between online and offline information search for both product types (H2b not supported),

 

recreational shopping orientation has positive impact on the difference between online and offline information search for only cellular phones (H2c partially supported). This result related to the relationship between recreational shopping orientation and difference in the use of information sources indicates that consumers with high in recreational shopping orientations use more offline information sources

i.e. store visits and salespeople due to hedonic benefits gained from these information sources. Thus, it is possible to state that online information search does not provide hedonic benefits to the customers. Those who have recreational shopping orientation might be expect to have enjoyable, socially interactive shopping environment for not only to do shopping but also to examine the products and get information about them. So, this type of consumers will be more satisfied with the examination of products based on the information obtained from the offline sources. The companies which have been given importance to online activities have to take into consideration the preference of the consumers with recreational shopping orientation and have to try to make their website more attractive, testimonial.

On the basis of economics of information theory (e.g., Punj and Staelin, 1983; Kulviwat et al., 2004), it was hypothesized that a consumer’s perceptions about using of online information sources along with offline information sources affect their preference of information sources which are consulted. The main benefits of using the Internet for information search are: low cost (Porter 2001; Wolin and Korgaonkar, 1999), easier access to price and product information (Porter, 2001; Wolin and Korgaonkar, 1999), quick access to product information (Klein and Ford, 2003; Wolin and Korgaonkar, 1999), access to more personal information by means of online consumer networks (Klein, 1998; Bei et al., 2004) and ability consumers to compare and contrast the options freely from time and place (Chiang et al., 2005). As hypothesized in H4, it was found that there is a positive relationship between perceived benefits of using offline information sources and difference between online and offline information search activity. In other words, positive perception about using offline information results in an increase in the difference between online and offline information search behavior in favor of offline information sources (H4 supported). Moreover, as expected, perceived benefit of using online information sources has negative impact on the difference between online and offline information search (H5 supported). That is, when a consumer perceives benefits from using

 

online information sources, they tend to use not only offline information but also online information sources, in turn, the difference becomes smaller. Thus, the companies which prefer to give importance to online activities rather than offline information sources or activities (in order to have competitive advantage against the powerful competitors with dominant positions in the traditional distribution system or to maintain to growth with a limited marketing budget) should try to inform consumers about the benefits of offline information for the consumers.

The results also indicate that demographic characteristics including age and education have impact on the difference between online and offline information search through perceived benefits of using information sources. The hypothesis testing showed that age is negatively related to perceived benefits of using online information sources for two different product types (H6b supported); on the other hand, there is a positive relation between age and perceived benefits of using offline information sources for cellular phones (H6a partially supported). This result is consistent with the studies on online information search behavior (Ratchford et al.; 2001; Ratchford et al., 2003; Bei et al., 2004) which empirically showed that younger consumers are more inclined to use online information sources. Undoubtedly, positive attitude toward to change and technology directly influences adoption of new technologies. Furthermore, as the complexity of new product increases, the adoption of this product decreases. This type of relation is usually observed in older consumers. Consequently, an increase in age causes a decrease in perceived benefits of using online information sources. However, while age increases, the perceived benefits of using offline information sources increases.

The statistical evidence from the hypothesis testing related to demographic characteristics also demonstrated that while education is negatively related to perceived benefits of using offline information sources for two product types (H7b supported), in contrast, education has a positive influence on perceived benefits of using online information sources for only cellular phones (H7a partially supported). These findings complement the past studies (Ratchford et al.; 2001; Ratchford et al., 2003; Weber and Roehl, 1999) which found that consumers gathering product information from online information sources are higher educated. These findings imply that more educated and younger people prefer to use online information sources more heavily than the older and less educated consumers. For these reasons,

 

as the level of education increase, but age decreases the difference in the use of online information sources and offline information sources decreases in the favor of online info sources.

8.2.2 Potential Payoff/Product Attributes

Perceived risk is mainly cited as one of the risk reducing strategies in consumer behavior literature. Few studies attempted to investigate the role of perceived risk in the utilization of various types of information sources (e.g. Arndt, 1967; Perry and Hamm, 1969; Mourali et al., 2005). The statistical evidence from this study implied that perceived risk is positively related to the difference between online and offline information search behavior in favor of offline information sources (H8 supported). Although Mourali et al. (2005) found that there was an insignificant negative relationship between perceived risk and preference of interpersonal information sources, the finding of this study is consistent with the studies (Arndt, 1967; Perry and Hamm, 1969) which assert that the higher the risk involved in a particular purchase decision, the greater the importance of personal influence including word- of-mouth and observation.

The inference drawn from this result is that consumers may prefer using more offline information sources i.e. friends and family and salespeople rather than online information sources in the case of a cellular phone purchase or a cultural activity purchase, consequently, the difference in the use of information sources may increase in favor of offline information sources.

It was also proposed that there was a positive relationship between attribute importance and difference in the use of information sources in favor of offline information sources. However, although the sign of the relationship was found as expected, the relationship was found to be insignificant (H9 not supported).

8.2.3 Product Knowledge

Another goal of this study is to examine the influences of two different product knowledge types including subjective product knowledge and objective product knowledge on the difference between online and offline information search behavior. First, it was expected that product knowledge has indirect impact on the difference between online and offline information search through mediating role of perceived

 

risk. The hypothesis testing related to the relationship between product knowledge and perceived risk indicated that there is a negative relation between product knowledge and perceived risk, however, the only significant relationship was found between subjective product knowledge and perceived risk (H10a supported; H11a not supported).

Second, it was also found that both subjective product knowledge and objective product knowledge has negative direct influences on the difference between online and offline information search, in other words, higher product knowledge lead to smaller difference between use of online information sources and that of offline information sources (H10b supported; H11b partially supported for cultural activities). This result suggests that individuals having high level of product knowledge are likely to search for more product information from not only offline information sources but also online information sources, for this reason, the difference between the use of online information sources and the use of offline information sources will decline. These findings support the arguments by scholars (e.g. Brucks, 1985; Srinivasan and Ratchford, 1991; Schmidt and Spreng, 1996) who confirmed that there is a positive relationship between product knowledge and external information search.

8.2.4 Situational Factor: Time Availability

As evidenced in the previous study (Klein and Ford, 2003) which asserts that there is a strong significant correlation between total hours spent searching product information and the number of sources used (r= 0.48), it was found that there is a negative influence of time availability on the difference between online and offline information search (H12 supported). That is, if a consumer has more time, he is more likely to obtain product information from many different information sources in a situation where he purchases a product or service. This finding is consistent with the view that constraints on one’s time result in less information search (e.g. Beatty and Smith, 1987).

8.3 Determinants of Search Outcomes

Consistent with consumer dissonance theory, it is expected that information search effort is positively related to product purchase satisfaction (Cardozo, 1965; Anderson

 

et al., 1979). The test of hypothesis evidenced that if a consumer obtains product information from both online information sources and offline information sources at the same degree, he would get more positive search outcomes (H13 supported). That is, a decrease in difference between online information search and offline information search leads to an increase in search outcomes including purchase satisfaction and purchase certainty. Similarly, based on consumer dissonance theory, it was found that there is a positive relationship between product involvement and search outcomes (H14 supported). If an individual gives more important to a product, he will rate the degree of satisfaction with this product higher than the actual level.

In the next section, the theoretical and managerial implications of these findings will be presented. The limitations of the research and potential future research directions will also be noted.

 

9. CONCLUSION AND IMPLICATIONS

Studies on information search behavior within offline context have long historical past in the related literature. On the other hand, there are several conceptual articles and few empirical studies associated with understanding information search behavior within the online context. However, there is still a lack of empirical research which aims to explore determinants of preference of one type of information source over the other type of information source, specifically online information sources over offline information sources or vice versa. That is, in what situations consumers tend to use mainly one type of information sources or prefer using both information sources need to be explored by considering both online and offline environment.

In this chapter, first the theoretical contribution of this thesis is discussed. Second, the managerial implications of its findings are described. This is followed by a discussion of the methodological and theoretical limitations of the research. Finally, some future research directions are suggested.

9.1 Theoretical Implications of the Thesis

The major contribution of this thesis is that it is one of the first attempts to investigate the determinants of net preference of online information sources over offline information sources or vice versa. Moreover, this study is also pioneering in studying underlying motives of online information sources to obtain product information as compared to offline information sources. The findings of the qualitative study offer some significant evidences that time availability and shopping orientations are more likely to affect the difference between the use of online information sources and offline information sources. If shopping orientations and time availability are excluded from the main model for each product type, the results show that the main model has a worse fit (χ2 cp=1874.89, χ2 ca=1991.03).

Another contribution is that, as suggested in the related literature, the impact of shopping orientations on the utilization of different types of information sources is

 

investigated. Several studies has discussed the notion of shopping orientation in relation to external information search (e.g., Beatty and Smith, 1987; Klein, 1998; Schmidt and Spreng, 1996; Shim et al., 2001). In this study, economic-conscious shopping orientation, recreational shopping orientation, and quality conscious shopping orientation were taken into consideration to test their impact on the difference between the use of online information sources and the use of offline information sources. Economic-conscious shopping orientation was found to be not significantly related to the difference between the use of online and offline information sources. However, for both cellular phones and cultural activities, a negative relationship was found between the use of online information sources and the use of offline information sources. That is, individuals with high quality conscious in their shopping are more likely to search product information in terms of using all kind of information sources, whereby they can get quality products. Moreover, the two significant coefficients for the quality conscious shopping orientation are -0.18 and -2.55 for cellular phones and cultural activities, respectively. This finding confirms that the evaluation of service qualities is relatively more difficult than that of product qualities. Furthermore, recreational shopping orientation has a significant positive effect on the difference between the use of online information sources and the use of offline information sources in favor of offline information sources. This relationship was empirically confirmed for cellular phones.

Moreover, there are a few studies which aim to examine the role of different types of product knowledge in the external information search within the traditional environment (e.g. Brucks, 1985; Mattila and Wirtz, 2002). However, there is no study to date that investigates the impact of both objective and subjective product knowledge on online information search activity. Besides, Mattila and Wirtz (2002) suggested that it would be beneficial to investigate the relationship between both product knowledge types -subjective knowledge and objective knowledge- and online information search behavior. This study also confirmed the negative relationship between product knowledge and perceived risk which is the most cited relationship in the marketing literature

Since the study of Punj and Staelin (1983), there has been no attempt to analyze the outcomes of information search in relation to consumers’ external information search

 

behavior. Punj and Staelin (1983) measured outcomes of search (satisfaction) by both “certainty of getting good buy” and “overall satisfaction with the purchase decision”. Although, they proposed a positive relationship between satisfaction and amount of search, the relationship between amount of search and satisfaction was not found to be statistically significant.

The earlier study of Anderson et al. (1979) tested the relationship between information search behavior and product satisfaction which was considered as an outcome. Besides, the authors found that a significant positive relationship between information search behavior and product satisfaction. Besides, as recommended by Lee and Hogarth (2000) “there needs to be a link between search activities and outcomes, …. this should be studied”. Specifically, in this study, a causal link between utilization of various information sources and search outcomes was tested. It was found that there is a significant negative relationship between outcomes of search and the difference between the use of online information sources and offline information sources. This result is consistent with consumer dissonance theory and the findings of the study of Cardozo (1965). That is, since a consumer spends more efforts to seek product information about cellular phones or cultural activities through using both online and offline information sources, he will evaluate the product that he purchased more positively.

9.2 Managerial Implications of the Thesis

Understanding how online information sources are evaluated relative to offline information sources and which factors affect the use of online information sources compared to their counterparts in traditional environment will provide guidance to marketing managers in the development of integrated marketing communication plans. Managers have to allocate their limited communication budget among different types of communication channels to reach consumers. Even though online information sources have received more attention, this study indicates that in general Turkish consumers mainly prefer using offline information sources relative to their counterparts in online environments. However, although respondents seem to be using more offline information sources for getting product information about cellular phones or cultural activities, managers should not neglect the importance of online information sources. Bakos (1997) has suggested that the Internet can reduce search

 

costs by lowering the costs of accessing information and the Internet has emerged to be a useful new information source. However, Porter (2001) has mentioned that “the winners will be those that view the Internet as a complement to, not a cannibal of, traditional ways of competing”. Accordingly, e-commerce companies should give more importance to the offline information sources such as advertisements, brochures to reach their target markets. In similar sense, bricks and mortar companies should have presence on the Internet. Specifically, sellers of highly risky products had to make available a variety of information sources combining of online and offline information sources. This is also important whereby, when consumers engage in an extensive information search by using a wide variety of sources, they will get more satisfied with the product which they purchased.

Companies which have a target segment composed of higher educated, young consumers and consumers with high OSLs could use online information sources to reach their target segment. Older consumers are more likely to use offline information sources providing relatively more understandable information.

Additionally, consumers obtain some benefits from using online information sources, these benefits are the followings; online information sources ensure time saving, to get satisfaction in return of money and to take the right decision on the purchase under consideration, and to make comparison among alternatives. Accordingly, companies should be careful about the design of their websites. The findings of this study suggest that websites should be designed in a way which enables consumers to easily search product information and compare the alternatives with respect to several product attributes. Accordingly, consumers can take the right decision on their purchases and also take value for their money.

9.3 Limitations of the Study and Future Research Directions

Despite the findings and implications revealed by this study, it is important to recognize its limitations and future research directions. One limitation of this study is the usage of self-report measures for external information search. Newman and Lockeman (1975) ascertained that there was little or no correlation between observation-based measure of search and a survey-based measure of search. Several studies have attempted to overcome this issue by designing experimental and laboratory studies to measure external information search. However, Beatty and

 

Smith (1987) noted that “self-report measures seem to be the most reasonable measures of external information search activities available for “real” purchase decisions until a superior methodology appears” (p.93).

In this study, information search behavior was measured by covering only one aspect of search behavior (to what extent they obtain information from each information source listed in this study). Although, it is aimed to explore what variables have impact on the difference between the use of online information sources and offline information sources or vice versa. As a future research direction, it would be beneficial to compare online information search behavior with offline information search behavior regarding to different aspects of information search behavior such as number of and types of attribute considered, the number of brands considered, time spent on each information source.

The other limitation of this thesis pertains to the fact that not all possible variables were considered in the proposed model. Future studies could take this study a step further by including other moderating, antecedent, and mediating variables. In addition, some measures could be improved such as measures of optimum stimulation level and shopping orientations, since a number of items were eliminated from the scales measuring OSL (7 items) and shopping orientations (7 items). As a future research direction, it would be better to test the impact of other types of shopping orientations including convenience shopping orientation and variety seeking shopping orientation on the net preference of online information sources over offline information sources. Furthermore, this model should be tested with other types of products in this way external validity could be improved.

Also, it would be beneficial to test this model within different cultural contexts to discover the cultural dissimilarities. There are a few empirical researches considering the relationship between search outcomes -specifically, purchase satisfaction- and information search behavior (e.g. Cardozo, 1965; Anderson et al., 1979). Accordingly, a wider range of search outcomes such as cost savings and increased product and market knowledge should be tested in future research.

 

REFERENCES

Aaker, J. L., (1997) Dimensions of brand personality, Journal of Marketing Research, 34, No. 3, 347-356.

Alba, J. W. and Hutchinson, J. W., 1987. Dimensions of consumer expertise,

Journal of Consumer Research, 13, 411-454.

Anderson, R. D., Engledow, J. L. and Becker, H., 1979. Evaluating the relationships among attitude toward business, product satisfaction, experience, and search effort, Journal of Marketing Research, 16, 394-400.

Anderson, J. and Gerbing, D., 1988. Structural equation modeling in practice: A review and recommended two-step approach, Psychological Bulletin, 103, 411-423.

Arndt, J. 1967. Perceived risk, sociometric integration and word of mouth in the adoption of new good product. in Risk taking and information handling in consumer behavior (pp. 289-323; 188-239). Ed., Cox, D.F., Boston: Harvard University Press.

Babin, B., Boles, J. and Robin, D., 2000. Representing the perceived ethical work climate among marketing employees, Journal of the Academy of Marketing Science, 28, 345-358.

Bagozzi, R., Pratibha, P. and Dabholkar, A., 1994. Consumer recycling goals and their effect on decisions to recycle: a means-end chain analysis, Psychology and Marketing, Jul/Aug: 313-340.

Bagozzi, R. P. and Yi, Y., 1988. On the evaluation of structural equation models,

Academy of Marketing Science, 16, 74-94.

Bakos, J. Y., 1997. Reducing buyer search costs: implications for electronic marketplaces, Management Science, 43, 1676-1692.

Baumgartner, H. and Steenkamp, J. E.M., 1996. Exploratory consumer buying behavior: conceptualization and measurement, International Journal of Research in Marketing, 13, 121-137.

 

Beatty, S. E. and Smith, S. M., 1987. External search effort: an investigation across several product categories, Journal of Consumer Research, 14, 83-94.

Bei, L., Chen, E. Y. I. and Widdows, R., 2004. Consumers’ online information search behavior and the phenomenon of search vs. experience products, Journal of Family and Economic Issues, 25, 449-467.

Bellenger, D. N. and Korgaonkar, N. P., 1980. Profiling the recreational shopper,

Journal of Retailing, 56, 77-92.

Bennet, P. D. and Mandell, R. M., 1969. Prepurchase information seeking behavior of new car purchasers-the learning hypothesis, Journal of Marketing Research, 6, 430-433.

Bentler, P. M., 1990. Comparative Fit Indexes in Structural Models, Psychological Bulletin, 107, 238-246.

Bentler, P. M. and Chou, C. P., 1987. Practical issues in structural modeling,

Sociological Methods and Research, 16, 78-117.

Bettman, J. R. and Park, W., 1980. Effects of prior knowledge and experience and phase of the choice process on consumer decision processes: a protocol analysis, Journal of Consumer Research, 7, 234-248.

Betman, J. R., 1973. Perceived risk and its components: a model and empirical test,

Journal of Marketing Research, 10, 184-190.

Bhatnagar, A. and Ghose, S., 2004. An analysis of frequency and duration of search on the internet, The Journal of Business, 77, 311-330.

Bickart, B. and Schindler, R. M., 2001. Internet forums as influential sources of consumer information, Journal of Interactive Marketing, 15, 31-40.

Bloch, P. H., Sherrell, D. L. and Ridgway, N. M., 1986. Consumer search: an extended framework, Journal of Consumer Research, 13, 119-127.

Bollen, K., 1989 Structural Equations with Latent Variables, John Wiley & Sons, New York, NY.

Borak, E., 1985. A study on consumer problems/protection issues in turkey, in historical perspective in consumer research: national and international perspectives, Proceedings of the ACR International Meeting in Singapore, Eds: Tan, C.T.& Sheth, J.N., 19-23.

Brown, M., Pope, N., and Voges, K., 2003. Buying or browsing? An exploration of shopping orientations and online purchase intention, European Journal of Marketing, 37, 1666-1684.

 

Brucks, M., 1985. The effects of product class knowledge on information search behavior, Journal of Consumer Research, 12, 1-15.

Bruner, G. C., 1988. Problem recognition style and information source importance: Study and replication, Akron Business and Economic Review, 19, 41- 51.

Cardozo, R. N., 1965. An experimental study of customer effort, expectation, and satisfaction, Journal of Marketing Research, 2, 244-249.

Chaudhuri, A., 2000. A macro analysis of the relationship of product involvement and information search: the role of risk, Journal of Marketing Theory and Practice, 8, 1-14.

Chiang, K., Dholakia, R. R. and Westin, S., 2005. E-Search: A Conceptual Framework of Online Consumer Behavior, Web Systems Design and Online Consumer Behavior, Idea Group, 1018-1030.

Churchill, G. A. and Iacobucci, D., 2005. Marketing Research: Methodological Foundations, Thompson, South-Western.

Claxton, J. O., Fry, J. N., and Portis, B., 1974. A taxonomy of prepurchase information gathering patterns, Journal of Consumer Research, 1, 35- 42.

Cox, D. F., 1967. Risk Taking and Information Handling in Consumer Behavior, Cambridge, MA: Harvard University Press.

Darby, M. R. and Karni, E., 1973. Free competition and the optimal amount of fraud, Journal of Law and Economics, 16, 67-86.

Darden, W.R. and Reynolds, F.D., 1971. Shopping orientations and product usage rates, Journal of Marketing Research, 8, 505–508.

Diamantopoulos, A., 1994. Modeling with Lisrel: a guide for the uninitiated,

Journal of the Marketing Management, 10, 105-136.

Diamantopoulos, A. and Siguaw, J., 2000. Introducing LISREL, Sage Publications, London.

Dickson, P. R., 2000. Understanding the trade winds: the global evolution of production, consumption and the Internet, Journal of Consumer Research, 27, 115-121.

DIE (State Institute of Statistics Prime Ministry Republic of Turkey) (2003) 2000 Census of Population Turkey: Social and Economic Characteristics of Population, DIE Printing Division, Ankara.

 

Doll, W., Xia, W. and Torkzadeh, G., 1994. A confirmatory factor analysis of the end-user computing satisfaction instrument, MIS Quarterly, 18, 453- 461.

Donthu, N. and Garcia, A., 1999. The Internet Shopper, Journal of Advertising Research, May-June, 52-58.

Dowling, G. R., and Staelin, R., 1994. A Model of perceived risk and intended risk- handling activity, Journal of Consumer Research, 21, 119-134.

Duffy, M. G. and Wright, G. P., 1993. An empirical study of customer satisfaction and search behavior: an application to the automobile market, Working paper, Purdue University.

Durande-Moreau, A. and Usunier, J. C., 1999. Time styles and the waiting experience: an exploratory study, Journal of Service Research, 2, 173- 186.

Eastlick, M. A. and Feinberg, R. A., 1999. Shopping motives for mail catalog shopping, Journal of Business Research, 45, 281-290

Eighmey, J. and McCord, L., 1998. Adding value in the information age: Uses and gratifications of sites on the World Wide Web, Journal of Business Research, 41, 187-194.

Engel, K. S., Moosbrugger, H. and Mueller, H., 2003. Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures, Methods of Psychological Research Online, 8, 23-74.

Finney, S. J. and Distefano, C., 2006. Non-normal and categorical data in structural equation modeling, in Structural Modeling: A Second Course, pp. 269-311, Eds. Hancook, G.R.& Mueller, R.O., Information Age Publishing Inc.

Flanagin, A. J. and Metzger, M. J., 2000. Perceptions of Internet information credibility, Journalism and Mass Communication Quarterly, 77, 515- 540.

Ford, G. T., Smith, D. B. and Swasy, J. L., 1988. An empirical test of the search, experience, and credence attributes framework, Advances in Consumer Research, 15, 239-243.

Fornell, C. and Larcker, D. F., 198. Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18, 39-50.

 

Freiden, J. B. and Goldsmith, R. E., 1989. Pre-purchase information seeking for professional services, Journal of Services Marketing, 3, 45-55.

Furse, D. H., Punj, G. N. and Stewart, D. W., 1984. A typology of individual search strategies among purchasers of new automobiles, Journal of Consumer Research, 10, 417-431.

Garver, M. and Mentzer, J., (1999). Logistics research methods: employing structural equation to the test for construct validity, Journal of Business Logistics, 20, 33-57.

Gerbing, D. and Anderson, J., 1988. An updated paradigm for scale development incorporating unidimensionality and its assessment, Journal of Marketing Research, 25, 186-192.

Gehrt, K. C., Onzo, N., Fujita, K. and Rajan, M. N., 2007. The emergence of internet shopping in japan: identification of shopping orientation- defined segments, Journal of Marketing Theory and Practice, 15, 167- 177.

Gehrt, K. C. and Shim, S., 1998. A shopping orientation segmentation of french consumers: implications for catalog marketing, Journal of Interactive Marketing, 12, 34-46.

Germunden, H. G., 1985. Perceived risk and information search: a systematic meta- analysis of the empirical evidence, International Journal in Marketing, 2, 79-100.

Ghose, S. and Dou, W., 1998. Interactive functions and their impacts on the appeal of internet presence sites, Journal of Advertising Research, 38, 29-44.

Girard, T., Korgaonkar, P. and Silverblatt, R., 2003. Relationship of types of product, shopping orientations, and demographics with preference for shopping on the internet, Journal of Business and Psychology, 18, 101-120.

Goodwin, S. A., 1980. Impact of stimulus variables on exploratory behavior,

Advances in Consumer Research, 7, 264-296.

Guo, C., 2001. A review on consumer external search: amount and determinants,

Journal of Business and Psychology, 15, 505-519.

Gutman, J., 1982. A means-end chain model based on consumer categorization processes, Journal of Marketing, 46, 60-72.

 

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. and Tatham, R. L., 2006. Multivariate Data Analysis, 6th edition, New Jersey, Pearson Prentice Hall.

Hammond, K., McWilliam, G. and Diaz, A. N., 1998. Fun and work on the web: differences in attitudes between novices and experienced users, Advances in Consumer Research, 25, 372-378.

Hauser, J., Urban, G. and Weinberg, B., 1993. How consumers allocate their time when searching for information, Journal of Marketing Research, 30, 452-466.

Heaney, J. G. and Goldsmith, R. E., 1999. External information search for banking services, International Journal of Bank Marketing, 17, 305-323.

Hodkinson, C. and Kiel, G., 2003. Understanding web information search behavior: an exploratory model, Journal of End User Computing, 15, 27-48.

Hodkinson, C., Kiel, G. and McColl-Kennedy, J. R., 2000. Consumer web search behavior: diagrammatic illustration of way finding on the web, International Journal of Human-Computer Studies, 52, 805-830.

Hoffman, D. L. and Novak, T. P., 1996. Marketing in hypermedia computer- mediated environments: conceptual foundations, Journal of Marketing, 60, 50-68.

Hu, L. and Bentler, P. M., 1999. Cut-off Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives, Structural Equation Modeling, 6, 1-55.

Huber, F., Beckmann, S. C. and Herrmann, A., 2004. Means-end analysis: does the affective state influence information processing styles, Psychology & Marketing; 21, 715-737.

Hugstad, P., Taylor, J. W. and Bruce, G. D., 1987. The effects of social class and perceived risk on consumer information search, The Journal of Services Marketing, 1, 47-52.

Jacoby, J., Chestnut, R. W. and Fisher, W. A., 1978. A behavioral process approach to information acquisition in non-durable purchasing, Journal of Marketing Research, 15, 532-544.

Joachimsthaler, E. A. and Lastovicka, J. L., 1984. Optimal stimulation level- exploratory behavior models, Journal of Consumer Research, 11, 830- 835.

 

Johnson, E. J., Moe, W. W., Fader, P. S., Bellman, S. and Lohse, G. L., 2004. On the depth and dynamics of online search behavior, Management Science, 50 (3), 299-308.

Jöreskog, K. and Sörbom, D., 1993 LISREL 8: Structural Equation Modeling with SIMPLIS Command Language, Scientific Software International Inc., Lincolnwood: IL, USA.

Jöreskog, K. and Sörbom, D., 1996 LISREL 8: User’s Reference Guide, Scientific Software International Inc. Chicago, USA.

Katona, G. and Mueller, E., 1955. A Study of Purchasing Decisions, in Consumer Behavior: The Dynamics of Consumer Reaction, ed. Lincoln H. Clark, New York: New York University Press, 30-87.

Kelly, R. F., 1968. The Search Components of the Consumer Decision Process: A Theoretic Examination, in Marketing and the New Science of Planning, ed. Robert King, Chicago, IL: American Marketing Association, 271-274.

Kiel, G. C. and Layton, R. A., 1981. Dimensions of consumer information seeking behavior, Journal of Marketing Research, 18, 233-139.

Klein, L. R., 1998. Evaluating the potential of interactive media through a new lens: search versus experience goods, Journal of Business Research, 41, 195-203.

Klein, L. R. and Ford, G. T., 2003. Consumer search for information in the digital age: an empirical study of pre-purchase search for automobiles, Journal of Interactive Marketing, 17, 29-49.

Korgaonkar, P. K., 1984. Consumer shopping orientations, non-store retailers, and consumers’ patronage intentions: a multivariate investigation, Journal of the Academy of Marketing Science, 12, 11–22.

Korgaonkar, P. K. and Wolin, L. D., 1999. A multivariate analysis of web usage,

Journal of Advertising Research, 39, 29-34.

Kotler, P. and Keller, K. L., 2006. Marketing Management, Pearson Education, Upper Saddle River, New Jersey.

Krugman, H., 1965. The impact of television advertising: learning without involvement, Public Opinion Quarterly, 29, 349-356.

 

Kulviwat, S., Guo, C. and Engchanil, N., 2004. Determinants of online information search: a critical review and assessment, Internet Research, 14, 245- 253.

Laroche, M., Bergeron, J. and Goutaland, C., 2003. How intangibility affects perceived risk: the moderating role of knowledge and involvement, The Journal of Services Marketing, 17, 122-140.

Laurent, G. and Kapferer, J-N., 1985. Measuring consumer involvement profiles,

Journal of Marketing Research, 22, 41-53.

Lee, J. and Hogarth, J. M., 2000. Relationships among information search activities when shopping for a credit card, The Journal of Consumer Affairs, 34, 330-360.

Lehman, D. R. and Moore, W. L., 1980. Validity of information display boards: an assessment using longitudinal data, Journal of Marketing Research, 17, 450-459.

Li, H., Kuo, C. and Russell, M. G., 1999. The impact of perceived channel utilities, shopping orientations, and demographics on the consumer’s online buying behavior, Journal of Computer-Mediated Communication, 5, 1-23.

Loudon, D. L. and Bella, A. D., 1988. Consumer Behavior: Concepts and Applications, McGraw-Hill.

Lueg, J. E., Moore, R. S. and Warkentin, M., 2003. Patient health information search: an exploratory model of web-based search behavior, Journal of End User Computing, 15, 49-61.

Lukosius, V., Hyman, M. R. and Stratemeyer, A. W., 2001. A model of online information search, American Marketing Association, Conference Proceedings, 12, 353-354.

Lumpkin, J. R., 1985. Shopping orientation segmentation of the elderly consumer,

Journal of the Academy of Marketing Science, 13, 272-289.

Lumpkin, J. R. and Hawes, J. M., 1985. Retailing without store: an examination of catalog shoppers, Journal of Business Research, 13, 139-151.

Lynch, J. G. and Ariely, D., 2000. Wine online: search costs affect competition on price, quality, and distribution, Marketing Science, 19, 83-103.

 

Maheswaran, D. and Sternthal, B., 1990. The effects of knowledge, motivation, and the type of message on ad processing and product judgments, Journal of Consumer Research, 17, 66-73.

Marks, L. J. and Olson, J. C., 1981. Toward a cognitive structure conceptualization of product familiarity, Advances in Consumer Research, 8, 145-150.

Mattila, A. S. and Wirtz, J., 2002. The impact of knowledge types on the consumer search process, International Journal of Service Industry Management, 13, 214-230.

Maute, M. F. and Forrester, W. R. Jr., 1991. The effect of attribute qualities on consumer decision making: a causal model of external information search, Journal of Economic Psychology, 12, 643-666.

McColl-Kennedy, J. R. and Fetter, R. E. Jr., 1999. Dimensions of consumer search behavior in services, The Journal of Services Marketing, 13, 242-265.

Meuter, M. L., Ostrom, A. L., Roundtree, R. I. and Bitner, M. J., 2000. Self- Service Technologies: Understanding Customer Satisfaction with Technology-Based Service Encounters, Journal of Marketing, 64, 50- 64.

Mitchell, V. W. and Greatorex, M., 1993. Risk perception and reduction in the purchase of consumer services, The Service Industries Journal, 13, 179-200.

Mitra, K., Reiss, M. C. and Capella, L. M., 1999. An examination of perceived risk, information search and behavioral intentions in search, experience and credence services, The Journal of Services Marketing, 13, 208-228.

Mittelstaedt, R. A., Grossbart, S. L., Curtis, W. W. and Devere, S. P., 1976. Optimum Stimulation Level and the Adoption Decision Process, Journal of Consumer Research, 3, 84-94.

Moe, W. W., 2003. Buying, searching, or browsing: differentiating between online shoppers using in-store navigational clickstream, Journal of Consumer Psychology, 13, 29-39.

Mokhlis, S., 2006. The effect of religiosity on shopping orientation: an exploratory study in Malaysia, Journal of American Academy of Business, 9, 64- 74.

 

Moore, W. L. and Lehmann, D. R., 1980. Individual differences in search behavior for a nondurable, Journal of Consumer Research, 7, 296-307.

Moorman, C., Diehl, K., Brinberg, D. and Kidwell, B., 2004. Subjective knowledge, search locations, and consumer choice, Journal of Consumer Research, 31, 673-680.

Moorthy, S., Ratchford, B. T. and Talukdar, D., 1997. Consumer information search revisited: theory and empirical analysis, Journal of Consumer Research, March, 23, 263-277.

Moschis, G. P., 1976. Shopping orientations and consumer uses of information,

Journal of Retailing, 52, 61-70, 93.

Mourali, M., Laroche, M. and Pons, F., 2005. Antecedents of consumer relative preference for interpersonal information sources in pre-purchase search, Journal of Consumer Behavior, 4, 307-318.

Mueller, R., 1996. Basic Principles of Structural Equation Modeling, Springer, London.

Murray, K. B., 1991. A test of services marketing theory: consumer information acqusition activities, Journal of Marketing, 55, 10-25.

Muthén , B. O. And Kaplan, D., 1985. A comparison of some methodologies for the factor analysis of non-normal Likert variables, British Journal of Mathematical and Statistical Psychology, 38, 171-189.

Muthén , B. O. And Kaplan, D., 1992. A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of the model, British Journal of Mathematical and Statistical Psychology, 45, 19-30.

Nachtigall, C., Kroehne, U, Funke, F. And Steyer, R., 2003. (Why) should we use SEM? Pros and cons of structural equation modeling, Methods of Psychological Research Online, 8, 1-22.

Nelson, P., 1970. Information and consumer behavior, Journal of Political Economy,

78, 311-329.

Newman, J. W. and Staelin, R., 1971. Multivariate analysis of differences in buyer decision time, Journal of Marketing Research, 8, 192-198.

Newman, J. W. and Staelin, R., 1972. Pre-purchase information seeking for new cars and major household appliances, Journal of Marketing Research, 9, 249-257.

 

Newman, J. W. and Lockeman, B. D., 1975. Measuring pre-purchase information seeking, Journal of Consumer Research, 2, 216-222.

Nunnally, J. C., 1978. Psychometric Theory, McGraw Hill, London.

Olshavsky, R. W. and Wymer, W., 1995. The desire for new information from external sources, Proceedings of the Society for Consumer Psychology, Eds. Mackenzie, S. & Stayman, R. Bloomington, IN: Printmaster, 17-27.

Overby, J. W., Gardial, S. F., Woodruff, R. B., 2004. French versus American consumers’ attachment of value to a product in a common consumption context: a cross-national comparison Academy of Marketing Science, 32, 437-460.

Öörni, A., 2003. Consumer search in electronic markets: an experimental analysis of travel services, European Journal of Information Systems, 12, 30-40.

Park, C. W., Mothersbaugh, D. L. and Feick, L., 1994. Consumer Knowledge Assessment, Journal of Consumer Research, 21, 71-82.

Parker, B. J. and Plank, R. E., 2000. A uses and gratifications perspective on the Internet as a new information source, American Business Review, 18, 43-49.

Perry, M. and Hamm, B. C., 1969. Canonical analysis of relations between socioeconomic risk and personal influence in purchase decisions, Journal of Marketing Research, 6, 351-354.

Peterson, R. A., Barasuebramanian, S. and Bronnenberg, B. J., 1997. Exploring the implications of the internet for consumer marketing, Academy of Marketing Science, 25, 329-346.

Peterson, R. A. and Merino, M. C., 2003. Consumer information search behavior and the Internet, Psychology and Marketing, 20, 99-121.

Porter, M. E., 2001. Strategy and the Internet, Harvard Business Review, March, 63-78.

Punj, G. N. and Staelin, R., 1983. A model of consumer information search behavior for new automobiles, Journal of Consumer Research, 9, 366- 380.

Raju, P. S. and Venkatesan, M., 1980. Exploratory behavior in the consumer context: a state of the art review, Advances in Consumer Research, 7, 258-263.

 

Rathford, B. T., 1982. Cost benefit models for explaining consumer choice and information seeking behavior, Management Science, 28, 197-212.

Ratchford, B. T., Talukdar, D. and Lee, M-S., 2001. A model of consumer choice of the internet as an information source, International Journal of Electronic Commerce, 5, 7-21.

Ratchford, B. T., Lee, M-S. and Talukdar, D., 2003. The impact of the internet on information search for automobiles, Journal of Marketing Research, 40, 193-209.

Raju, P.S., 1980. Optimum stimulation level: its relationship to personality, demographics, and exploratory behavior, Journal of Consumer Research, 7, 272-282.

Rha, J. Y., 2002. Consumers in the internet era: essays on the impact of electronic commerce from a consumer perspective, Ph.D. Dissertation, The Ohio State University.

Reynolds, T. J. and Gutman, J., 1988. Laddering theory, method, analysis, and interpretation, Journal of Advertising Research, 28: 11-31.

Roselius, T., 1971. Consumer rankings of risk reduction methods, Journal of Marketing, 35, 56-61.

Schmidt, J. B. and Spreng, R. A., 1996. A proposed model of external consumer information search, Journal of the Academy of Marketing Science, 24, 246-256.

Selnes, F. and Grönhaugh, K., 1986. Subjective and objective measures of product knowledge contrasted, Advances in Consumer Research, 13, 67-71.

Senecal, S. and Nantel, J., 2001. Online interpersonal influence: a framework, Working Paper, Ecole des Hautes Etudes Commercials, Retrieved from http://e-commerce.mit.edu/cgi-bin/viewpaper, 1-26.

Shim, S. and Drake, M. F., 1990. Consumer intentions to utilize electronic shopping, Journal of Direct Marketing, 4, 22-23.

Shim, S., Eastlick, M. A., Lotz, S. L. and Warrington, P., 2001. An online prepurchase intentions model: the role of intention to search, Journal of Retailing, 77, 397-416.

Sproles, E. K. and Sproles, G. B., 1990. Consumer decision making styles as a function of individual learning styles, Journal of Consumer Affairs, 24, 135-147.

 

Srinivasan, N. and Agrawal, J., 1988. The relationship between prior knowledge and external search, Advances in Consumer Research, 15, 27-31.

Srinivasan, N. and Ratchford, B. T., 1991. An empirical test of a model of external search for automobiles, Journal of Consumer Research, 18, 233-242.

Steenkamp, J-B.E.M. and Baumgartner, H., 1992. The role of optimum stimulation level in exploratory consumer behavior, Journal of Consumer Research, 19, 434-448.

Steenkamp, J-B.E.M., and Baumgartner, H., 1995. Development and cross- cultural validation of a short form of CSI as a measure of optimum stimulation level, International Journal of Research in Marketing, 12, 97-104.

Steenkamp, J-B.E.M. and Baumgartner, H., 2000. On the use of structural equation models for marketing modeling, International Journal of Research in Marketing, 17, 195-202.

Steenkamp, J-B.E.M. and Trijp, H. C. M., 1991. The Use of LISREL in Validating Marketing Constructs, International Journal of Research in Marketing, 8, 283-299.

Stigler, G. J., 1961. The economics of information, The Journal of Political Economy, 69, 213-225.

Stone, G. P., 1954. City and urban identification: observations on the social psychology of social life, American Journal of Sociology, 60, 35-45.

Stone, R. N. and Gronhaug, K., 1993. Perceived risk: further considerations for the marketing discipline, European Journal of Marketing, 27, 39-50.

Sujan, M., 1985. Consumer knowledge: effects on evaluation processes mediating consumer judgments, Journal of Consumer Research, 12, 31-46.

Swartz, T. A. and Stephens, N., 1984. Information search for services: the maturity segment, Advances in Consumer Research, 11, 244-249.

Szymanski, D. M. and Hise, R. T., 2000. E-satisfaction: an initial examination,

Journal of Retailing, 76, 309-322.

Tabachnick, B. G. and Fidell, L. S., 2007. Using Multivariate Statistics, Allyn and Bacon, Boston

Teo, T. S.H. and Yeong, Y. D., 2003. Assessing the consumer decision process in the digital marketplace, The International Journal of Management Science, 31, 349-363.

 

Urbany, J. E., Dickson, P. R. and Wilkie, W. L., 1989. Buyer Uncertainty and Information Search, Journal of Consumer Research, 16, 208-215.

Venkatraman, M. P., and Price, L. L., 1990. Differentiating between cognitive and sensory innovativeness: concepts, measurement, and implications, Journal of Business Research, 20, 293-315.

Wahlers, R. G. and Etzel, M. J., 1990. A structural examination of two optimal stimulation level measurement models, Advances in Consumer Research, 17, 415-425.

Ward, M. R. and Lee, M. J., 2000. Internet shopping, consumer search and product branding, Journal of Product and Brand Management, 9, 6-18.

Weber, K. and Roehl, W. S., 1999. Profiling people searching for and purchasing travel products on the World Wide Web, Journal of Travel Research, 37, 291-298.

Westbrook, R. A. and Black, W. C., 1985. A motivation-based shopper typology,

Journal of Retailing, 61, 78-103.

Williams, R. H., Painter, J. J. and Nicholas, H. R., 1978. A policy-oriented typology of grocery shoppers, Journal of Retailing, 54, 27-42.

Zaichkowsky, J. L., 1985. Measuring the involvement concept, Journal of Consumer Research, 12, 341-352.

Zanoli, R. and Naspetti, S., 2002. Consumer motivations in the purchase of organic food: a means-end approach, British Food Journal, 104, 643-652.

Url-1 <http://www.tuik.gov.tr/ >.

Url-2 <http://www. turkticaret.net/business_center / >.

Url-3 <http://www. istanbul2010.org />.

 

APPENDICES

Appendix A. Past Research on Traditional Information Search Behavior

Table A.1 Past Research on Traditional Information Search Behavior

Constructs Bivariate

Relationship with Search Product Category Study

I. Marketing Environment

Size of evoked set + Cars,

Appliances Newman and Staelin 1972

+ Cars Srinivasan and Ratchford 1991

Number of alternatives + Apparel,

Furniture Cox and Rich 1964

Complexity

alternatives of + Furniture,

Appliances Claxton et al. 1974

Perceived differences product + Durables,

Conveniences Schaninger

1981 and Sciglimpaglia

+ TV Duncan and Olshavsky 1982

Information

Availability + Groceries Russo 1977

Information

Accessibility + Groceries Russo 1977

Store distribution

(geographic distance) - Nonfood items Bucklin 1966

- Apparel Cort and Dominguez 1997

City size of residence + Cars Newman and Staelin 1972

0 Appliances Newman and Staelin 1972

Perceived variance in

retail operations + TV Duncan and Olshavsky 1982

II. Situational Variables

Time Availability + Electronics Beatty and Smith 1987

Time pressure + Bread Moore and Lehmann 1980

Urgency - Furniture,

Appliances Katona and Mueller 1955

Immediate need - Appliances Claxton et al. 1974

Immediate need - Cars, Appliances Newman and Staelin 1971;1972

Social Pressure 0 Appliances Katona and Mueller 1955

Financial pressure 0 Bread Moore and Lehmann 1980

0 Cars, Appliances Newman and Staelin 1972

+ Furniture,

Appliances Claxton et al. 1974

Special

opportunity buying -/+ Appliances Katona and Mueller 1955

Bargaining opportunity - Microcomputer Brucks and Schurr 1990

Store

loyalty/preference - Nonfood items Bucklin 1966

III. Potential Payoff/Product Importance

Price + Furniture,

Appliances Claxton et al. 1974

+ Apparel Dommermuth 1965

+ Apparel Dommermuth and Cundiff 1967

 

+ Appliances Katona and Mueller 1955

+ Nonfood items Bucklin 1966

+ Cars Kiel and Layton 1981

0 Appliances Newman and Staelin 1972

0 Cars Newman and Staelin 1972

+ Small

Appliances Udell 1966

Expectation of

obtaining a better price + Cars Kiel and Layton 1981

Style and importance appearance + Apparel Dommermuth and Cundiff 1967

+ Apparel Cox and Rich 1964

+ Furniture,

Appliances Claxton et al. 1974

+ Furniture Legrand and Udell 1964

+ Cars Newman and Staelin 1972

Perceived risk + Range

products of Perry and Hamm 1969

+ Household items Cunningham 1964; 1966; 1967

+ Bread Moore and Lehmann 1980

+ Apparel,

Furniture Cox and Rich 1964

Perceived dispersion price + Nonfood items Bucklin 1966

+ Furniture,

Appliances Claxton et al. 1974

Attribute importance + Bread

(experiment) Lehmann and Moore 1980

Product

importance class + Nondurables

(experiment) Jacoby et al. 1978

Length of commitment

necessary + Appliances Katona and Mueller 1955

IV Knowledge and Experience

Objective knowledge 0 Food items Rudell 1979

+ Sewing machine Brucks 1985

Subjective knowledge 0 Food items Rudell 1979

+ Cars Srinivasan and Ratchford 1991

0 Sewing machine Brucks 1985

Perceived knowledge + Cars Kiel and Layton 1981

Prior

structure memory 0 Cars Punj and Staelin 1983

Times product class

purchased - Applicances,

Cars Newman and Staelin 1972

Prior usage of

product class 0 Furniture,

Appliances Claxton et al. 1974

Usable

knowledge prior - Cars Punj and Staelin 1983

Prior

knowledge product U shape Women’s blazer Rao and Sieben 1992

Product

knowledge class - Electronics Beatty and Smith 1987

Experience 0 Cars Bennet and Mandell 1969

Inverted U Microwave Bettman and Park 1980

Inverted U Cars Johnson and Russo 1984

- Cars Srinivasan and Ratchford 1991

Past experience - Cars Kiel and Layton 1981

+ Nondurables

(experiment) Jacoby et al. 1978

 

Positive experience - Appliances Katona and Mueller 1955

- Applicances,

Cars Bennet and Mandell 1969

- Cars Srinivasan and Ratchford 1991

Satisfaction (with past results) - Cars Bennet and Mandell 1969

- Applicances,

Cars Newman and Staelin 1971

- Cars Kiel and Layton 1981

- Cars Punj and Staelin 1983

Brand loyalty - Nondurables Jacoby et al. 1978

Product familiarity - Nondurables Russo and Leclerc 1994

Inverted U Cars Johnson and Russo 1984

Discrepancy Inverted U Automobiles Ozanne et al. 1992

V. Individual Differences

Ability to judge + TV Duncan and Olshavsky 1982

Ability - Applicances,

Cars Newman and Staelin 1971

Optimum Stimulation

Level 0 Automobiles Steenkamp

1992 and Baumgarther

Enjoyment of search + Appliances Katona and Mueller 1955

Need for cognition + Groceries Inman et al. 1990

Approach to problem

solving

Dependence on

others 0 Applicances,

Cars Newman and Staelin 1972

Tolerance for

ambiguity + Range

products of Schaninger

1981 and Sciglimpaglia

Need for justifying decision + Bread Moore and Lehmann 1980

+ Organizational

buyers Doney and Armstrong 1996

Positive attitude toward search + Cars Kiel and Layton 1981

+ Cars Punj and Staelin 1983

+ Electronics Beatty and Smith 1987

Perceived search

benefits + TV Duncan and Olshavsky 1982

+ Cars Srinivasan and Ratchford 1991

Involvement + Electronics Beatty and Smith 1987

Education + Small

applicances Udell 1966

+ Range

products of Schaninger

1981 and Sciglimpaglia

+ Food products Pearce 1976

+ Furniture,

Appliances Claxton et al. 1974

+ Cars Kiel and Layton 1981

+ Appliances Katona and Mueller 1955

- Sports shirts Katona and Mueller 1955

+ Applicances,

Cars Newman and Staelin 1971;1972

Income + Furniture,

Appliances Claxton et al. 1974

- Small appliances Udell 1966

Age - Small

applicances Udell 1966

- Cars Kiel and Layton 1981

- Appliances Katona and Mueller 1955

+ Toys Gregan-Paxton and Roedder John

1995

- Cereals Cole and Balasubrahmanian 1993

 

Personality (self-

confidence) - Cars Kiel and Layton 1981

Perceived role

(household role) + Food items Bucklin 1969

Optimizer vs.

satisfier + Nondurables Jacoby et al. 1978

VI. Conflict and conflict resolution

strategies

VII. Cost of search

Psychological cost - Cars Punj and Staelin 1983

- Nonfood items Bucklin 1966

 

Appendix B.1. INTERVIEW FORM (Cellular Phone)

Now, I am going to ask some questions about buying process for a cellular phone. Although some questions seem to be repetitive, please do not think that your answers are not taken into consideration since similar questions being asked in different ways provide detailed answers related to the same topic.

1- Now, Let me think, You have decided to purchase a new cellular phone. At first, What would you do? After that?

2- Would you search for information to decide on which cellular phone you should buy? What types of information sources would you use to get product information? (family/friends, advertisements, sales person, web-sites, consumer reviews on the Internet? Among these information sources, which one do you think is the most effective information source? (If the respondent do not list any online information source, ask question 4)

3- Please share the total score of 100 among information sources which you have used to get product information about cellular phones according to the content, detail, informativeness and effectiveness of each information source? (the higher the informativeness the higher the score)

4- What are the reasons for not using online information sources to get product information about cellular phone? In What situations, you begin to use online information sources to get product information? What are the reasons of your using online information sources?

5- How would you distribute the total score of 100 which you have used while searching for information sources according to your search effort?.

6- IF THE RESPONDENTS DO NOT USE ON-LINE INFORMATION SOURCES, ABSOLUTELY ASK THE FOLLOWING QUESTIONS: Why do

not you use online information sources to obtain product information dealing with celular phones? What are the other reasons? Are there any other reasons? Will you please give more detailed explanation? For each reason mentioned by the respondent, ask the question of “Why is it important to you?”

7- IF THE RESPONDENTS DO NOT USE ON-LINE INFORMATION SOURCES, ABSOLUTELY ASK THE FOLLOWING QUESTIONS: What

reasons for people’s using online information sources could be? Why do people use online information sources to get product information about cellular phone? What will happen If you use online information sources?

8- IF THE RESPONDENTS USE ON-LINE INFORMATION SOURCES, ABSOLUTELY ASK THE FOLLOWING QUESTIONS: Why do you use

online information sourcesto gather product information about cellular phones? What are the other reasons? Are there any other reasons? What would have

 

happened If there were not any online information sources? For each reason mentioned by the respondent, ask the question of “Why is this important to you?” (LADDER)

9- What type of information about cellular phone do you search for? (price, quality, etc.)

 

Apendix B.2. INTERVIEW FORM (Cultural Activities)

Now, I am going to ask some questions about buying process for a cultural activity. Although some questions seem to be repetitive, please do not think that your answers are not taken into consideration since similar questions being asked in different ways provide detailed answers related to the same topic.

1- You agreed to meet your friends for a cultural event two weeks later (could be a concert, an exhibition, theatre, opera, balet, cinema, festivals). You are supposed to arrange this cultural event organization. What would you do first? And how would you proceed?

2- Would you search for information to decide on Which cultural activity you should participate in? What types of information sources would you use to get product information about cultural activities? (family/friends, advertisements, sales person, web-sites, consumer reviews on the Internet? Among these information sources, which is the most effective information source? (If the respondent do not list any online information source, ask question 4)

3- Please share the total score of 100 among information sources which you have used to get product information about cultural activities according to the content, detail, informativeness and effectiveness of each information source? (the higher the informativeness the higher the score).

4- What are the reasons for not using online information sources to get product information about cultural activities? In What situations, you begin to use online information sources to get product information dealing with cultural activities? What are the reasons of your using online information sources?

5- How would you distribute the total score of 100 which you have used while searching for information sources according to your search effort?.

6- IF THE RESPONDENTS DO NOT USE ON-LINE INFORMATION SOURCES, ABSOLUTELY ASK THE FOLLOWING QUESTIONS: Why do

not use online information sources to seek product information about cultural activities? What are the other reasons? Are there any other reasons? Will you please give more detailed explanation? For each reason mentioned by the respondent, ask the question of “Why is it important to you?”

7- IF THE RESPONDENTS DO NOT USE ON-LINE INFORMATION SOURCES, ABSOLUTELY ASK THE FOLLOWING QUESTIONS: What

reasons for people’s using online information sources to obtain product information about cultural activities could be? Why do people use online information sources to get product information about cellular phone? What will happen If you use online information sources?

 

8- IF THE RESPONDENTS USE ON-LINE INFORMATION SOURCES, ABSOLUTELY ASK THE FOLLOWING QUESTIONS: Why do you use

online information sources to seek information about cultural activities? What are the other reasons? Are there any other reasons? What would have happened If there were not any online information sources? For each reason mentioned by the respondent, ask the question of “Why is this important to you?” (LADDER)

9- What type of information about cultural activites do you search for? (price, place, authors, actor/actress etc..)

 

Appendix C.1 QUESTIONNAIRE FORM

Aim of the Research (Please read it to the respondents)

This research is carried out by Gonca Ulubaşoğlu who is currently a PhD student and a research assistant in the Istanbul Technical University. This study focuses on understanding how consumers search information in the case of purchases of both a cellular phone and a ticket for a cultural activity (cinema, theater, concert etc.) at the pre-purchase stage of consumer decision making process. This study also aims to explore what factors affect the extent of usage of online information sources and offline information sources.

We would like to ask your valuable time to complete the questionnaire as a part of this research. Your cooperation is very important, as the findings of this research will be used to provide guideless to other researchers, as well as serve as input to a doctoral dissertation.

All responses will be kept strictly confidential. Thank you in advance for your kind cooperation. Yours Sincerely,

Gonca Ulubaşoğlu

Phone: 0 212 2931300-2068

Email: ulubasogl1@itu.edu.tr

Notes to the fieldworkers:

1. Please be sure that the respondent uses the Internet to obtain product information, and have purchased the focal products within the determined time, 12 months for the cellular phone and 3 months for the cultural activity.

2. Please ask each product in a different order in each interview to avoid order effects.

3. Please read the questions very clearly and slowly enough in order to give sufficient time to the respondents to elaborate on the statements.

4. Please read the labels of the scales clearly in order to indicate how they could express their opinion on the basis of the scale points.

 

Starting Time : / Finishing Time : / Date:   /  /

Section 1:

1- Do you use the Internet to search product information?

□ Yes □No (Finalize the questionnaire)

2- Have you actually purchased a new cellular phone within last 12 months?

□ Yes □No (Finalize the questionnaire)

3- Have you actually purchased a ticket for any cultural activity (cinema, theater, concert etc.) within last 3 months?

□ Yes □No (Finalize the questionnaire)

4- Internet usage:  ……….hours/a day ………hours/ a week

Section 2:

In this section, we are interested in your information search activities for the purchasing of a cellular phone.

Now please think back to the time when you purchased your last cellular phone and refer back to that time for your responses.

5- Please state the degree of obtaining product information about the cellular phone from each of the following information source, when purchasing your cellular phone. (1: Definitely would not seek the particular source,……., 5: Definitely would seek the particular source)

Definitely would not seek the particular source Definitely would seek the particular source

1 2 3 4 5

 1> Newspaper/magazines/TV/radio advertisements 1 2 3 4 5 

2> Producers’ website 1 2 3 4 5

 3> Retailers’ websites 1 2 3 4 5 

4> Family and/or friends, acquaintances 1 2 3 4 5

 5> Store visits or salespeople 1 2 3 4 5 

6> Consumer comments on the Internet (e-groups, shopping

websites etc.) 1 2 3 4 5

 

6- Please circle the most appropriate option for each following adjective by considering the role of a cellular phone in your life.

Cellular Phone;

A Unimportant 1 2 3 4 5 Important to me

B Of no concern 1 2 3 4 5 Of concern to me

C Worthless   1 2 3 4 5 Valuable

D Means nothing to me 1 2 3 4 5 Means a lot to me

F Not beneficial   1 2 3 4 5 Beneficial

G Uninterested 1 2 3 4 5 Interested

H Undesirable   1 2 3 4 5 Desirable

I Not needed 1 2 3 4 5 Needed

J Unexciting   1 2 3 4 5 Exciting

7- When you considered your cellular phone that you last purchased. Please indicate the degree of certainty about getting a good purchase of cellular phone

Very uncertain 1 2 3 4 5 Very certain

8- When you considered your cellular phone that you last purchased. Please indicate the degree of satisfaction with the purchase decision on cellular phone

Very dissatisfied 1 2 3 4 5 Very satisfied

9- Compared with my friends and acquaintances, my knowledge of cellular phones is:

Weaker 1 2 3 4 5 Stronger

10- Compared with experts in the topic of cellular phones, my knowledge of cellular phones is:

Weaker 1 2 3 4 5 Stronger

11- In general, my knowledge of cellular phone is:

Very weak 1 2 3 4 5 Very strong

 

12- The section below is prepared to understand what your feelings are about the purchasing of a cellular phone. Please state your opinion about the following statements by ticking the most appropriate option below for each statement.

Strongly Disagree Disagree Neutral Agree Strongly Agree

1 2 3 4 5

1> Overall, I thought of buying a cellular phone causes me to be

concerned with experiencing some kind of loss if I went ahead with the purchase 1 2 3 4 5

2> All things considered, I thought I would be making a mistake I

bought a cellular phone 1 2 3 4 5

3> When all is said and done, I really feel that the purchase of a

cellular phone poses problems for me that I just do not need 1 2 3 4 5

13- When you seeking product information about cellular phone, how important to obtain information about the each of the following attribute. Please rate the importance of each attribute on 1-10 scale in making a decision on a cellular phone purchase. (1: Extremely unimportant 10: Extremely important)

Point

1> Imaging and Video features

2> Music features

3> Connectivity (Bluetooth wireless technology)

4> Design

5> Color

6> Messaging

7> Call management

8> Physical dimensions (volume, weight, thickness, length)

9> Price

10> Payment terms

11 Ease of use

12 Other (Please write)..................

 

14a- This section below is prepared to understand your opinion about online information sources. On the basis of your experience in searching product information for cellular phone, Please evaluate online information sources according to the following statements by ticking the most appropriate option below for each statement.

Strongly Disagree Disagree Neutral Agree Strongly Agree

1 2 3 4 5

Online information sources provide reliable product

information on cellular phones

1 1 2 3 4 5

2 Online information sources provide enough product

information about cellular phones. 1 2 3 4 5

Online information sources are helpful to gather

product information about cellular phones.

3 1 2 3 4 5

4 Online information sources make easy to compare

different alternatives of cellular phones. 1 2 3 4 5

Online information sources cause to expend more

time while searching product information about cellular phones.

5 1 2 3 4 5

6 Online information sources are easy accessible for

product information about cellular phones. 1 2 3 4 5

Online information  sources  provide information

about cellular phones at low cost

7 1 2 3 4 5

8 Online information sources provide detailed

information about cellular phones 1 2 3 4 5

It is easy to get product information about different

models for cellular phones through online information sources.

9 1 2 3 4 5

10 Online information sources do not cause me to get tired of getting product information about cellular

phones 1 2 3 4 5

11 Online information sources are helpful to make a

right decision about purchase of cellular phones.   1 2 3 4 5

 

14b- This section below is prepared to understand your opinion about offline information sources. On the basis of your experience in searching product information for cellular phone, Please evaluate offline information sources (advertisements, sales person, friends/acquaintances, etc.) according to the following statements by ticking the most appropriate option below for each statement.

Strongly Disagree Disagree Neutral Agree Strongly Agree

1 2 3 4 5

Offline information sources  provide reliable product

information on cellular phones

1 1 2 3 4 5

2 Offline information sources  provide  enough  product

information about cellular phones. 1 2 3 4 5

Offline information sources are helpful to gather product

information about cellular phones.

3 1 2 3 4 5

4 Offline  information  sources  make  easy  to  compare

different alternatives of cellular phones. 1 2 3 4 5

Offline information sources cause to expend more time

while searching product information about cellular phones.

5 1 2 3 4 5

6 Offline  information  sources  are  easy  accessible  for

product information about cellular phones. 1 2 3 4 5

Offline information sources provide information about

cellular phones at low cost

7 1 2 3 4 5

8 Offline information sources provide detailed information

about cellular phones 1 2 3 4 5

It is easy to get product information about different models for cellular phones through offline information

sources.

9 1 2 3 4 5

10 Offline information sources do not cause me to get tired

of getting product information about cellular phones 1 2 3 4 5

11 Offline information sources are helpful to make a right

decision about purchase of cellular phones.   1 2 3 4 5

15- Please list all the things you might consider when choosing a new cellular phone?



 

16- Please circle the following features that are common to almost all modern cellular phones.

Push to talk (PoC) 

Video recorder 

MP3 player 

FM radio 

Infrared 

WAP 

GPRS 

EDGE 

Hands free 

Voice dialing 

Audio recording 

Video call 

Video player 

Mobile video 

17- This section below includes true-false statements dealing with a cellular phone. There is no right or wrong answer. Please circle the most appropriate option for each statement.

• GPRS is a technique providing data transfer through permanent connection between wireless device and a cellular network

1> True 2> False 3> Do not know

• Push to talk over Cellular (PoC) is a service that makes one-on-one and group conversations possible over a cellular network.

1> True 2> False 3> Do not know

• Bluetooth is the technology which provides wireless communication only among cellular phones.

1> True 2> False 3> Do not know

Section 3:

Now in this section, we are interested in your information search activities for the purchasing of a ticket for any cultural activity.

Now please think back to the time when you purchased a ticket for the cultural activity which you last participated in, and refer back to that time for your responses.

 

18- Please state the degree of obtaining product information about the cultural activity (cinema, theater, concert, etc.) from each of the following information source, when purchasing a ticket for the cultural activity which you last participated in. (1: Definitely would not seek the particular source,……., 5: Definitely would seek the particular source)

Definitely would not seek the

particular source Definitely would seek the particular

source

1 2 3 4 5

 1> Newspaper/magazines/TV/radio advertisements 1 2 3 4 5

2> Organizer firms / theaters’ websites 1 2 3 4 5

 3> Specialized websites in cultural activities 1 2 3 4 5

4> Family and/or friends, acquaintances 1 2 3 4 5

 5> Information desk in the place which cultural activity organized 1 2 3 4 5

6> Consumer comments on the Internet (organiser firms’ websites,

websites such as biletix, e-groups) 1 2 3 4 5

19- Please circle the most appropriate option for each following adjective by considering the role of any cultural activity (cinema, theater, concert etc.) in your life.

Cultural Activities (cinema, theater, concert etc.);

A Unimportant 1 2 3 4 5 Important

B Of no concern 1 2 3 4 5 Of concern to me

C Worthless 1 2 3 4 5 Valuable

D Means nothing to me 1 2 3 4 5 Means a lot to me

E Not beneficial 1 2 3 4 5 Beneficial

F Uninterested 1 2 3 4 5 Interested

G Undesirable 1 2 3 4 5 Desirable

H Not needed 1 2 3 4 5 Needed

I Unexciting 1 2 3 4 5 Exciting

20- When you considered the cultural activity that you last participated in. Please indicate the degree of certainty of getting a good purchase of a ticket for the cultural activity (cinema, theater, concert etc.) that you last participated in.

Very uncertain 1    2    3    4    5    Very certain

 

21- When you considered the cultural activity that you last participated in. Please indicate the degree of satisfaction with the purchase decision on the cultural activity (cinema, theater, concert etc.) that you last participated in.

Very dissatisfied 1 2 3 4 5 Very satisfied

22- Compared with my friends and acquaintances, my knowledge of cultural activities is:

Weaker 1 2 3 4 5 Stronger

23- Compared with experts in the topic of cultural activities, my knowledge of cultural activities is:

Weaker 1 2 3 4 5 Stronger

24- In general, my knowledge of cultural activities is:

Very weak 1 2 3 4 5 Very strong

25- The section below is prepared to understand what your feelings are about the purchasing of a ticket for any particular cultural activity. Please state your opinion about the following statements by ticking the most appropriate option below for each statement.

Strongly Disagree Disagree Neutral Agree Strongly Agree

1 2 3 4 5

1> Overall, I thought of buying a ticket for a cultural activity

causes me to be concerned with experiencing some kind of loss if I went ahead with the purchase 1 2 3 4 5

 2> All things considered, I thought I would be making a mistake

I bought a ticket for a cultural activity 1 2 3 4 5

3> When all is said and done, I really feel that the purchase of a 1 2 3 4 5

 

ticket for a cultural activity poses problems for me that I just

do not need

26- When you seeking product information about cultural activity, how important to obtain information about the each of the following attribute. Please rate the importance of each attribute on 1-10 scale in making a decision on the purchasing of a ticket for a cultural activity. (1: Extremely unimportant………..10: Extremely important)

Point

1> Place

2> Time

3> Price of ticket

4> Information about theme or type of play/movie/concert

5> Information about comments of the cultural activity

6> Information about backgrounds of actors and actresses

7> Information about which actors and actresses will be participated into

the cultural activity

8> Information about the map of the place where the activity is organized

9> Information about the physical conditions related to the place where the

activity is organized

10> Other (please write) ..................

27a- This section below is prepared to understand your opinion about online information sources. On the basis of your experience in searching product information for cultural activities, Please evaluate online information sources according to the following statements by ticking the most appropriate option below for each statement.

Strongly Disagree Disagree Neutral Agree Strongly Agree

1 2 3 4 5

Online  information  sources  provide  reliable  product

information on cultural activities.

1 1 2 3 4 5

2 Online  information  sources  provide  enough  product

information about cultural activities. 1 2 3 4 5

Online information sources are helpful to gather product

information about cultural activities

3 1 2 3 4 5

4 Online information sources make easy to compare different

alternatives of cultural activities. 1 2 3 4 5

Online information sources cause to expend more time

while searching product information about cultural

5 1 2 3 4 5

 

activities.

6 Online information sources are easy accessible for product

information about cultural activities. 1 2 3 4 5

Online information sources provide information about

cultural activities at low cost.

7 1 2 3 4 5

8 Online information sources provide detailed information

about cultural activities. 1 2 3 4 5

It is easy to get product information about different alternatives for cultural activities through online

information sources.

9 1 2 3 4 5

10 Online information sources do not cause me to get tired of

getting product information about cultural activities. 1 2 3 4 5

Online information sources are helpful to make a right

decision about purchase of cultural activities.

11 1 2 3 4 5

27b- This section below is prepared to understand your opinion about offline information sources. On the basis of your experience in searching product information for cultural activities, Please evaluate offline information sources according to the following statements by ticking the most appropriate option below for each statement.

Strongly Disagree Disagree Neutral Agree Strongly Agree

1 2 3 4 5

Offline  information sources  provide reliable  product

information on cultural activities.

1 1 2 3 4 5

2 Offline  information  sources  provide  enough  product

information about cultural activities. 1 2 3 4 5

Offline information sources are helpful to gather product

information about cultural activities

3 1 2 3 4 5

4 Offline  information  sources  make  easy  to  compare

different alternatives of cultural activities. 1 2 3 4 5

Offline information sources cause to expend more time while  searching  product information  about  cultural

activities.

5 1 2 3 4 5

6 Offline  information  sources  are  easy  accessible  for

product information about cultural activities. 1 2 3 4 5

Offline information sources provide information about

cultural activities at low cost.

7 1 2 3 4 5

8 Offline information sources provide detailed information

about cultural activities. 1 2 3 4 5

It is easy to get product information about different alternatives for cultural activities through offline

information sources.

9 1 2 3 4 5

10 Offline information sources do not cause me to get tired of

getting product information about cultural activities. 1 2 3 4 5

Offline information sources are helpful to make a right

decision about purchase of cultural activities.

11 1 2 3 4 5

 

28- Please write the name of the cultural activities that you have purchased and gone to, in last one year.



29- There is a list of selected cultural activities below. Please circle the following activities which have been carried out in 2006.

Paul Anka Concert 1

Gloria Gaynor Concert 2

Musical of Broadway’den Günümüze Müzikaller 3

Mucizeler Komedisi Musical 4

Yıldızların Altında Musical 5

Rodin Exhibition 6

Folklorama 7

Ballet of Ağır Roman 8

27 Numara 9

Pink Concert   10

Sting Concert 11

30- This section below includes true-false statements dealing with the cultural activities organized in İstanbul. There is no right or wrong answer. Please circle the most appropriate option for each statement.

• The arts of Leonardo da Vinci were exhibited in İstanbul Modern Art Museum in 2006.

1> True 2> False 3> Do not know

• The arts of Picasso were presented in Sakıp Sabancı Museum.

1> True 2> False 3> Do not know

• Gönül Yarası has been selected as the candidate for nominee of Oscar Award.

1> True 2> False 3> Do not know

 

 

Section 4: This section includes the questions dealing with your personal characteristics. Please circle the most appropriate option for each following statement to describe your personality well.

31- Please thick the response that best indicates the degree to which you agree or disagree with each of the following statement.

Strongly Disagree Disagree Neutral Agree Strongly Agree

1 2 3 4 5

1

Reading mail advertising to find out what’s new is a

waste of time. 1 2 3 4 5

2 I like to go window shopping and find out about the

latest styles 1 2 3 4 5

3 I  get  very bored  listening  to  others  about  their

purchases 1 2 3 4 5

4 I generally read even my junk mail just to know what is

about 1 2 3 4 5

5 I don’t like to shop around just out of curiosity 1 2 3 4 5

6 I like to browse through mail order catalogs even when

I don’t plan to buy anything 1 2 3 4 5

7 I usually throw away mail advertisements without

reading them. 1 2 3 4 5

8 I like to shop around and look at displays 1 2 3 4 5

9 I don't like to talk to my friends about my purchases. 1 2 3 4 5

10 I  am  inclined  to  read  e-advertisements  and  get

informed 1 2 3 4 5

11 I often read advertisements just out of curiosity 1 2 3 4 5

32- Please thick the response that best indicates the degree to which you agree or disagree with each of the following statement.

Strongly Disagree Disagree Neutral Agree Strongly Agree

1 2 3 4 5

1 I seem to be busier than most people I know 1 2 3 4 5 

2 Usually there is so much to do that I wish I had more time 1 2 3 4 5

3 I usually find myself pressed for time. 1 2 3 4 5 

 

33- This section below is prepared to understand your attitudes to shopping in the case of purchasing of a product or service. Please thick the response that best indicates the degree to which you agree or disagree with each of the following statement.

Strongly Disagree Disagree Neutral Agree Strongly Agree

1 2 3 4 5

1 Shopping is great for my mood. 1 2 3 4 5

2 I like new styles 1 2 3 4 5

3 I look for quality in a product and is willing to pay extra for it 1 2 3 4 5

4 I prefer to purchase items on sale 1 2 3 4 5

5 I do comparison shopping 1 2 3 4 5

6 The quality of product or service I buy is more important to me

than the prices I have to pay 1 2 3 4 5

7 Buying something makes me happy 1 2 3 4 5

8 I notice price differences 1 2 3 4 5

9 I like great deal of variety 1 2 3 4 5

10 I shop whenever I want. 1 2 3 4 5

11 I look for bargain/competitive prices 1 2 3 4 5

12 It is generally worth it to pay more for quality. 1 2 3 4 5

13 It is important to find what I want in least time. 1 2 3 4 5

14 Shopping is a kind of social activity for me. 1 2 3 4 5

15 I like to try different things. 1 2 3 4 5

16 I do not like spend time while searching product information. 1 2 3 4 5

17 It is important to save effort while gathering information related

to products or services that I want to purchase. 1

2

3

4

5

34- Please indicate the duration of education in your whole life: ……. (years)

35- Your age ………………..

36- Your gender: □ Female □ Male

37- Your marital status: □Married  □Single □ Divorced\Widow

38- Please state the net income of your family per month (including all benefits, rent income and salaries etc.)

□ Less than 500 YTL □ 501 – 1,000 YTL

□ 1, 001 YTL – 1,500 YTL □ 1,501 YTL – 2,000 YTL

□ 2,001 YTL – 2,500 YTL □ 2,501 YTL – 3,000 YTL

□ 3,001 YTL – 3,500 YTL □ 3,501 YTL – 4,000 YTL

□ 4,001 YTL – 4,500 YTL □ 4,501 YTL – 5,000 YTL

□ More than 5,001 YTL

39- Please state the last degree you have earned.

□ Secondary school and below □ High School □ Undergraduate

□ Masters and above

 

40- Please tick the most appropriate option below that indicates your employment status.

□ I am not employed □ Top executive or manager

□ Student □ Owner of a large or medium size company

□ House wife □ Lawyer, dentist, architect etc.

□ Retired □ Office/Clerical staff

□ Worker □ Civil servant

□ Craftsman □ Other (Please write) .......................

 

Appendix C.2. ANKET FORMU

Araştırmanın Amacı (Lütfen bu kısmı anketi cevaplayanlara okuyunuz.)

Bu araştırma İstanbul Teknik Üniversitesi’nde araştırma görevlisi olan ve halen İşletme Mühendisliği Doktora programında doktora eğitimini sürdüren Gonca Ulubaşoğlu tarafından yürütülmektedir. Çalışma, tüketici satın alma karar sürecinde satın alma öncesi aşamada, cep telefonu satın alma ve herhangi bir kültürel faaliyet (sinema, tiyatro, konser vb.) için bilet satın alma durumunda tüketicilerin bilgi arama davranışını incelemeyi amaçlamaktadır. Ayrıca, İnternet temelli ve geleneksel bilgi kaynaklarının kullanımında etkili faktörlerin neler olduğunun da araştırılması amaçlanmaktadır.

İşbirliğiniz çalışmanın tamamlanması için zorunludur. Anketi cevaplandırmak için değerli zamanınızı ayırmanızı rica ederim. Bütün cevaplarınız gizli tutulacaktır.

Yardımlarınız için teşekkür ederim. Saygılarımla,

Gonca Ulubaşoğlu

Tel.: 0 212 2931300-2068

E-posta: ulubasogl1@itu.edu.tr

Anketörün Dikkate Etmesi Gereken Hususlar:

1. Lütfen cevaplayıcının ürün bilgisi aramak için İnterneti kullandığına, son 12 ay içerisinde cep telefonu satın almış olmasına ve son 3 ay içerisinde herhangi bir kültürel faaliyet için bilet satın almış olmasına dikkat ediniz.

2. Lütfen her görüşmede ürünlerin sırasını değiştirerek ankete başlayınız.

3. Lütfen cevaplayıcıya cümleleri değerlendirmesi için yeterli zamanı verecek şekilde, soruları yavaş ve anlaşılabilir şekilde okuyunuz.

4. Lütfen ölçekli sorularda cevaplayıcının fikrini net bir şekilde açıklayabilmesi için her bir rakamın ne ifade ettiğini anlaşılır bir şekide okuyunuz.

 

Başlama Saati : / Bitiş Saati : / Tarih: /

Bölüm 1:

1- Herhangi bir ürün ile ilgili bilgi aramak amacıyla internetten yararlanıyor musunuz?

Evet 1 DEVAM

Hayır 2 SON VER

2- Son 12 ay (1 yıl) içinde cep telefonu satın aldınız mı?

Evet 1 DEVAM

Hayır 2 SON VER

3- Son 3 ay içinde tiyatro, konser, sinema gibi herhangi bir kültürel faaliyete katıldınız mı?

Evet 1 DEVAM

Hayır 2 SON VER

4- Günde/haftada kaç saat internet kullanıyorsunuz?

………………. saat/hafta  saat/gün

Bölüm 2:

Bu bölümde, cep telefonu satınalma durumunda bilgi arama faaliyetlerinizle ilgili sorular bulunmaktadır.

Şimdi cep telefonunuzu aldığınız zamana geri dönün ve aşağıdaki soruları o zamanı düşünerek cevaplandırınız.

5- Cep telefonu satın alırken birazdan size okuyacağım bilgi kaynaklarından ne derecede bilgi elde ettiğinizi belirtiniz. (1: Kesinlikle Bilgi Almam; 5: Kesinlikle Bilgi Alırım)

Kesinlikle Bilgi

Almam Bilgi Almam Ne Alırım

Ne Almam Bilgi Alırım Kesinlikle

Bilgi Alırım

1 2 3 4 5

1> Gazete/dergi/televizyon/radyo reklamları 1 2 3 4 5 

2> Üretici firmanın web sayfalarındaki cep telefonu 1 2 3 4 5

 

ile ilgili bilgiler

3> Perakendeci  firmaların  web sayfalarındaki cep

telefonu ile ilgili bilgiler 1 2 3 4 5 

4> Aile üyeleri ve/veya arkadaşlar 1 2 3 4 5

5> Mağaza ziyaretleri veya satış elemanları 1 2 3 4 5 

6> İnternette (perakendeci sayfalarında, alışveriş

sitelerinde, e-gruplarda vb.) yer alan diğer tüketicilerin görüşleri. 1 2 3 4 5

6- Cep telefonunu, bir ürün olarak yaşamınızdaki yerini düşünerek, şimdi size okuyacağım özellikler açısından değerlendiriniz. (1 den 5 e kadar puan veriniz.)

Cep Telefonu;

A Önemli değil 1 2 3 4 5 Önemli

B Beni ilgilendirmiyor 1 2 3 4 5 Beni ilgilendiriyor

C Değerli değil 1 2 3 4 5 Değerli

D Bana  hiçbir  şey  ifade

etmiyor 1 2 3 4 5 Bana çok şey ifade ediyor

F Yararlı değil 1 2 3 4 5 Yararlı

G İlgi alanıma girmiyor 1 2 3 4 5 İlgi alanıma giriyor

H Arzu edilmeyen 1 2 3 4 5 Arzu edilen

I İhtiyaç değil 1 2 3 4 5 İhtiyaç

J Sıkıcı 1 2 3 4 5 İlginç

7- Satın aldığınız cep telefonunu düşündüğünüzde iyi bir satın alım yaptığınızdan ne derecede emin olduğunuzu belirtiniz.

Hiç emin değilim 1 2 3 4 5 Çok eminim

8- Satın aldığınız cep telefonunu düşündüğünüzde satın alma kararınızdan ne derecede memnun olduğunuzu belirtiniz.

Hiç memnun kalmadım1 2 3 4 5 Çok memnun kaldım

9- Arkadaşlarınızla kıyasladığınızda cep telefonu hakkındaki bilgi düzeyiniz; Zayıf 1 2 3 4 5 Güçlü

10- Cep telefonu konusunda uzman olan kişilerle kıyasladığınızda cep telefonu hakkındaki bilgi düzeyiniz;

Zayıf 1 2 3 4 5 Güçlü

11- Cep telefonu ile ilgili olarak bilgi kendimi;

Az bilgili buluyorum 1 2 3 4 5 Çok bilgili buluyorum

 

12- Cep telefonu satın alma sırasında hangi cep telefonunu satın alacağınıza karar verirken, aşağıda belirtilen görüş ve düşüncelere ne derecede katılıp katılmadığınızı belirtiniz. Aşağıdaki ifadeler için doğru ya da yanlış cevap yoktur, bizim için önemli olan sizin görüş ve düşüncelerinizdir.

Kesinlikle Katılmıyorum

Katılmıyorum Ne Katılıyorum Ne

Katılmıyorum

Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

1> Cep telefonu satın almaya karar verdiğimde,

bazı açılardan zarara uğrayacağım konusunda endişelendim 1 2 3 4 5

 2> Bütün herşeyi dikkate aldığımda, cep telefonu satın alma konusunda hata yapabileceğimi

düşündüm. 1 2 3 4 5

3> Cep telefonu satın alırken, gerçekten hiç

istemediğim sorunlara yol açacağını düşündüm. 1 2 3 4 5

13- Cep telefonu satın alma sürecinde bilgi ararken aşağıda belirtilen her bir özelliğe ilişkin bilgi edinmenin sizin için ne derecede önemli olduğunu değerlendiriniz. Lütfen her bir özelliğe, 1: Hiç önemli değil 10: Çok önemli olmak üzere, 10 üzerinden bir puan veriniz.

Puan

1> Fotoğraf ve video fonksiyonlarına ilişkin bilgi

2> Müzik dinleme özelliklerine ilişkin bilgi

3> Kablosuz erişim fonksiyonunun varlığına ilişkin bilgi

4> Estetik tasarımına ilişkin bilgi

5> Renk alternatiflerine ilişkin bilgi

6> Mesaj fonksiyonlarına ilişkin bilgi

7> Çağrı fonksiyonlarına ilişkin bilgi

8> Boyut, ağırlık gibi fiziksel özelliklerine ilişkin bilgi

9> Fiyatına ilişkin bilgi

10> Ödeme seçeneklerine ilişkin bilgi

11> Menünün kullanım kolaylığına ilişkin bilgi

12> Diğer (belirtiniz) ..................

 

14a- Cep telefonu ile ilgili olarak bilgi arama sürecinde edindiğiniz deneyime dayanarak online bilgi kaynakları ile ilgili aşağıda belirtilen herbir ifadeye ne derecede katılıp katılmadığınızı belirtiniz.

Kesinlikle Katılmıyorum Katılmıyorum Ne Katılıyorum

Ne Katılmıyorum Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

Online bilgi kaynakları cep telefonu ile ilgili güvenilir

bilgi sağlamaktadır.

1 1 2 3 4 5

2 Online bilgi kaynakları cep telefonu ile ilgili yeterli

düzeyde bilgi sağlamaktadır. 1 2 3 4 5

Online bilgi kaynakları cep telefonu ile ilgili bilgi

ararken yardımcı olmaktadır

3 1 2 3 4 5

4 Online  bilgi kaynakları alternatif cep  telefonlarını

karşılaştırmayı kolaylaştırmaktadır 1 2 3 4 5

Online bilgi kaynakları cep telefonu ile ilgili bilgi

ararken zaman kaybına neden olmaktadır.

5 1 2 3 4 5

6 Online bilgi kaynakları cep telefonu ile ilgili bilgiye

erişimi kolaylaştırmaktadır. 1 2 3 4 5

Online bilgi kaynakları cep telefonu ile ilgili bilgiye

ulaşmak daha az maliyetlidir.

7 1 2 3 4 5

8 Online bilgi kaynakları cep telefonu ile ilgili detaylı

bilgi sağlamaktadır. 1 2 3 4 5

Online bilgi kaynakları ile farklı cep telefon modelleri

hakkında bilgi elde etmek kolaydır.

9 1 2 3 4 5

10 Online bilgi kaynakları yardımıyla cep telefonu ile

ilgili bilgiye yorulmadan ulaşabilirim. 1 2 3 4 5

Online bilgi kaynakları cep telefonu ile ilgili doğru

karar vermemde yardımcı olmaktadır.

11 1 2 3 4 5

14b- Cep telefonu ile ilgili olarak bilgi arama sürecinde edindiğiniz deneyime dayanarak geleneksel bilgi kaynakları (reklam, satış elemanı, yakın çevre vb.) ile ilgili aşağıda yeralan her ifadeye ne derecede katılıp katılmadığınızı belirtiniz.

Kesinlikle Katılmıyorum Katılmıyorum Ne Katılıyorum Ne

Katılmıyorum Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

Geleneksel bilgi kaynakları cep telefonu ile ilgili güvenilir

bilgi sağlamaktadır.

1 1 2 3 4 5

2 Geleneksel bilgi kaynakları cep telefonu ile ilgili yeterli 1 2 3 4 5

 

düzeyde bilgi sağlamaktadır.

Geleneksel bilgi kaynakları cep telefonu ile ilgili bilgi ararken

yardımcı olmaktadır

3 1 2 3 4 5

4 Geleneksel bilgi kaynakları alternatif cep telefonlarını

karşılaştırmayı kolaylaştırmaktadır 1 2 3 4 5

Geleneksel bilgi kaynakları cep telefonu ile ilgili bilgi ararken

zaman kaybına neden olmaktadır.

5 1 2 3 4 5

6 Geleneksel bilgi kaynakları cep telefonu ile ilgili bilgiye

erişimi kolaylaştırmaktadır. 1 2 3 4 5

Geleneksel bilgi kaynakları aracılığıyla cep telefonu ile ilgili

bilgiye ulaşmak daha az maliyetlidir.

7 1 2 3 4 5

8 Geleneksel bilgi kaynakları cep telefonu ile ilgili detaylı bilgi

sağlamaktadır. 1 2 3 4 5

Geleneksel bilgi  kaynakları farklı cep  telefon  modelleri

hakkında bilgi elde etmek kolaydır.

9 1 2 3 4 5

10 Geleneksel bilgi kaynakları sayesinde cep telefonu ile ilgili

bilgiye yorulmadan ulaşabilirim. 1 2 3 4 5

11 Geleneksel bilgi kaynakları cep telefonu ile ilgili doğru karar

vermemde yardımcı olmaktadır. 1 2 3 4 5 

15- Bir cep telefonu satın alırken, satın alacağınız cep telefonunun hangi özelliklere sahip olmasını isterdiniz? Listeleyiniz.



16- Şimdi size okuyacağım cep telefonu ile ilgili fonksiyonların hangileri bütün son model cep telefonlarında ortak olarak bulunmaktadır? İşaretleyiniz.

Bas-Konuş 

Video kamera 

MP3 çalma özelliği 

FM radyo 

Kızılötesi 

WAP 

GPRS 

EDGE 

Hands free 

Sesle arama 

Sesli video kayıt 

Görüntülü konuşma 

Video Oynatma 

Entegre kamera 

 

17- Aşağıda belirtilen ifadelerin hangileri doğrudur, işaretleyiniz.

• GPRS kablosuz cihaz ile network arasında sürekli bağlantı sağlayan bir GSM veri taşıma tekniğidir.

1> Doğru 2> Yanlış 3> Bilmiyorum

• Bas-konuş fonksiyonu cep telefonunda herhangi bir kişi veya grup ile tek yönlü iletişimi sağlamaktadır

1> Doğru 2> Yanlış 3> Bilmiyorum

• Bluetooth sadece cep telefonları arasında kablosuz iletişimi sağlayan bir teknolojidir.

1> Doğru 2> Yanlış 3> Bilmiyorum

Bölüm 3:

Bu bölümde, kültürel faaliyet için bilet satınalma durumunda bilgi arama faaliyetlerinizle ilgili sorular bulunmaktadır.

Şimdi kültürel faaliyet için bilet satın aldığınız zamana geri dönün ve aşağıdaki soruları o zamanı düşünerek cevaplandırınız.

18- Kültürel faaliyetleri (sinema, tiyatro, konser vb.) satın alırken aşağıda belirtilen bilgi kaynaklarından ne derecede bilgi elde ettiğinizi belirtiniz. (1: Kesinlikle Bilgi Almam; 5: Kesinlikle Bilgi Alırım)

Kesinlikle Katılmıyorum Katılmıyorum Ne Katılıyorum Ne

Katılmıyorum Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

1> Gazete/dergi/televizyon/radyo reklamları 1 2 3 4 5

2> Faaliyeti düzenleyen kurumun / sinemanın web

sayfalarındaki faaliyetle ilgili bilgiler 1 2 3 4 5

3> Kültürel faaliyetlerle ilgili web sayfalarındaki faaliyetle

ilgili bilgiler 1 2 3 4 5

4> Aile üyeleri ve/veya arkadaşlar 1 2 3 4 5

5> Faaliyetin yapılacağı yerdeki bilet-danışma gişeleri 1 2 3 4 5

6> İnternette (faaliyetle ilgili firmalarin web sayfaları,

biletix gibi siteler, e-gruplarda vb.) yer alan izleyici görüşler 1 2 3 4 5

 

19- Sinema, tiyatro, konser vb. kültürel faaliyetlerden birinin veya tümününün yaşamınızdaki yerini düşünerek, aşağıda belirtilen nitelikler açısından değerlendiriniz. (1 den 5 e kadar puan veriniz.)

Kültürel faaliyetler (sinema, tiyatro, konser vb.);

A Önemli değil 1 2 3 4 5 Önemli

B Beni ilgilendirmiyor 1 2 3 4 5 Beni ilgilendiriyor

C Değerli değil 1 2 3 4 5 Değerli

D Bana hiçbir şey ifade

etmiyor 1 2 3 4 5 Bana çok şey ifade ediyor

F Yararlı değil 1 2 3 4 5 Yararlı

G İlgi alanıma girmiyor 1 2 3 4 5 İlgi alanıma giriyor

H Arzu edilmeyen 1 2 3 4 5 Arzu edilen

I İhtiyaç değil 1 2 3 4 5 İhtiyaç

J Sıkıcı 1 2 3 4 5 İlginç

20- En son gittiğiniz kültürel faaliyeti (sinema, tiyatro, konser vb.) düşündüğünüzde iyi bir satın alım yaptığınızdan ne derecede emin olduğunuzu belirtiniz.

Hiç emin değilim 1 2 3 4 5 Çok eminim

21- En  son  gittiğiniz kültürel faaliyetleri (sinema, tiyatro, konser  vb.)

düşündüğünüzde satın alma kararınızdan ne derecede memnun olduğunuzu belirtiniz.

Hiç memnun değilim 1 2 3 4 5 Çok memnunum

22- Arkadaşlarınızla kıyasladığınızda kültürel faaliyetler (sinema, tiyatro, konser vb.) hakkında bilgi düzeyiniz;

Zayıf 1 2 3 4 5 Güçlü

23- Kültürel faaliyetler (sinema, tiyatro, konser vb.) konusunda uzman olan kişilerle kıyasladığınızda kültürel faaliyetler (sinema, tiyatro, konser vb.) hakkındaki bilgi düzeyiniz;

Zayıf 1    2    3    4    5    Güçlü

24- Kendimi kültürel faaliyetlerle (sinema, tiyatro, konser vb.) ilgili olarak; Az bilgili 1    2    3    4    5    Çok bilgili

 

25- Kültürel faaliyetlerle (sinema, tiyatro, konser vb.) ilgili satın alma kararı sırasında hangi kültürel faaliyete katılacağınıza karar verirken, aşağıda belirtilen görüş ve düşüncelere ne derecede katılıp katılmadığınızı belirtiniz. Aşağıdaki ifadeler için doğru ya da yanlış cevap yoktur, bizim için önemli olan sizin görüş ve düşüncelerinizdir.

Kesinlikle Katılmıyorum Katılmıyorum Ne Katılıyorum Ne

Katılmıyorum Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

1> Gideceğim/katılacağım kültürel faaliyet için bilet satın almaya karar verdiğimde, bazı açılardan zarara

uğrayacağım konusunda endişe duydum. 1 2 3 4 5

2> Bütün herşeyi dikkate aldığımda,

gideceğim/katılacağım kültürel faaliyet için bilet satın alma konusunda hata yapabileceğimi düşündüm. 1 2 3 4 5

3> Gideceğim/katılacağım kültürel faaliyet için bilet satın alırken, gerçekten hiç istemediğim sorunlara yol

açacağını düşündüm. 1 2 3 4 5

26- Kültürel faaliyetlerle (sinema, tiyatro, konser vb.) ilgili satınalma sürecinde bilgi ararken aşağıda belirtilen herbir özelliğe ilişkin bilgi edinmenin sizin için ne derece önemli olduğunu değerlendiriniz. Lütfen her bir özelliğe, 1: Hiç önemli değil 10: Çok önemli olmak üzere, 10 üzerinden bir puan veriniz)

Puan

1> Faaliyetin yapıldığı mekanın yeri

2> Faaliyetin zamanı

3> Bilet fiyatı

4> Oyunun/filmin/konserin konusu, türü hakkında bilgi

5> Faaliyetle ilgili yorumlar

6> Oyuncuların/sanatçıların özgeçmişine yönelik bilgiler

7> Hangi oyuncu ve sanatçıların sahne aldığına ilişkin bilgi

8> Mekana nasıl gidileceğini gösteren kroki bilgisi

9> Faaliyetin yapıldığı yerin fiziksel şartlarına ilişkin bilgi

10> Diğer (belirtiniz) ..................

 

27a- Kültürel faaliyetlerle ile ilgili olarak bilgi arama sürecinde edindiğiniz deneyime dayanarak online bilgi kaynakları ile ilgili aşağıda belirtilen her ifadeye ne derece katıldığınızı belirtiniz.

Kesinlikle Katılmıyorum Katılmıyorum Ne Katılıyorum Ne

Katılmıyorum Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

 1 Online bilgi kaynakları kültürel faaliyetlerle ilgili güvenilir

bilgi sağlamaktadır. 1 2 3 4 5 

2 Online bilgi kaynakları kültürel faaliyetlerle ilgili yeterli

düzeyde bilgi sağlamaktadır. 1 2 3 4 5

 3 Online bilgi kaynakları kültürel faaliyetlerle ilgili bilgi

ararken yardımcı olmaktadır. 1 2 3 4 5 

4 Online bilgi kaynakları alternatif kültürel faaliyetleri

karşılaştırmayı kolaylaştırmaktadır. 1 2 3 4 5

 5 Online bilgi kaynakları kültürel faaliyetlerle ilgili bilgi

ararken zaman kaybına neden olmaktadır. 1 2 3 4 5 

6 Online bilgi kaynakları kültürel faaliyetlerle ilgili bilgiye

erişimi kolaylaştırmaktadır. 1 2 3 4 5

 7 Online bilgi kaynakları ile kültürel faaliyetlerle ilgili bilgiye

ulaşmak daha az maliyetlidir. 1 2 3 4 5 

8 Online bilgi kaynakları kültürel faaliyetlerle ilgili detaylı

bilgi sağlamaktadır. 1 2 3 4 5

 9 Online bilgi kaynakları ile kültürel faaliyetlerle ilgili olarak

farklı seçenekler hakkında bilgi elde etmek kolaydır. 1 2 3 4 5 

10 Online bilgi kaynakları sayesinde kültürel faaliyetlerle ilgili

bilgiye yorulmadan ulaşabilirim. 1 2 3 4 5

11 Online bilgi kaynakları kültürel faaliyetlerle ilgili doğru

karar vermemde yardımcı olmaktadır. 1 2 3 4 5 

27b- Kültürel faaliyetlerle ile ilgili olarak bilgi arama sürecinde edindiğiniz deneyime dayanarak geleneksel bilgi kaynakları (reklam, satış elemanı, yakın çevre vb.) ile ilgili aşağıda belirtilen her ifadeye ne derece katıldığınızı belirtiniz.

Kesinlikle Katılmıyorum Katılmıyorum Ne Katılıyorum

Ne Katılmıyorum Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

 1 Geleneksel bilgi kaynakları kültürel faaliyetlerle ilgili

güvenilir bilgi sağlamaktadır. 1 2 3 4 5 

2 Geleneksel bilgi kaynakları kültürel faaliyetlerle ilgili

yeterli düzeyde bilgi sağlamaktadır. 1 2 3 4 5

 

Geleneksel bilgi kaynakları kültürel faaliyetlerle ilgili

bilgi ararken yardımcı olmaktadır.

3 1 2 3 4 5

4 Geleneksel bilgi kaynakları alternatif kültürel faaliyetleri

karşılaştırmayı kolaylaştırmaktadır. 1 2 3 4 5

Geleneksel bilgi kaynakları kültürel faaliyetlerle ilgili

bilgi ararken zaman kaybına neden olmaktadır.

5 1 2 3 4 5

6 Geleneksel bilgi kaynakları kültürel faaliyetlerle ilgili

bilgiye erişimi kolaylaştırmaktadır. 1 2 3 4 5

Geleneksel bilgi kaynakları aracılığıyla kültürel

faaliyetlerle ilgili bilgiye ulaşmak daha az maliyetlidir.

7 1 2 3 4 5

8 Geleneksel bilgi kaynakları kültürel faaliyetlerle ilgili

detaylı bilgi sağlamaktadır. 1 2 3 4 5

Geleneksel bilgi kaynakları aracılığıyla kültürel faaliyetlerle ilgili olarak farklı seçenekler hakkında bilgi

elde etmek kolaydır.

9 1 2 3 4 5

10 Geleneksel bilgi kaynakları sayesinde kültürel

faaliyetlerle ilgili bilgiye yorulmadan ulaşabilirim. 1 2 3 4 5

Geleneksel bilgi kaynakları kültürel faaliyetlerle ilgili

doğru karar vermemde yardımcı olmaktadır.

11 1 2 3 4 5

28- Herhangi bir kültürel faaliyete gitmek için karar verirken ne tür bilgiler ararsınız belirtiniz.



29- Aşağıda yer alan kültürel faaliyetlerin hangileri 2006 yılı içerisinde gerçekleştirilmiştir. İşaretleyiniz.

Paul Anka Konseri 

Gloria Gaynor Konseri 

Broadway’den Günümüze Müzikaller 

Mucizeler Komedisi Muzikali 

Yıldızların Altında Müzikali 

Rodin Sergisi 

Folklorama 

Ağır Roman Balesi 

27 Numara 

Pink Concert 

Sting Concert 

 

30- Aşağıda belirtilen ifadelerin hangileri doğrudur, işaretleyiniz.

• Leonardo da Vinci’nin eserleri 2006 yılı içerisinde İstanbul Modern Sanat Müzesi’nde sergilenmiştir.

1> Doğru 2> Yanlış 3> Bilmiyorum

• Picasso’nun eserleri Sakıp Sabancı Müzesi’nde sergilenmiştir. 1> Doğru 2> Yanlış 3> Bilmiyorum

• Gönül Yarası filmi geçtiğimiz yılın Oscar aday adayı olarak seçilmiştir. 1> Doğru 2> Yanlış 3> Bilmiyorum

Bölüm 4: Bu bölümde kişisel özelliklerle ilgili sorular yer almaktadır. Lütfen sizin kişiliğinizi tanımlayan her bir ifade için en uygun olanı işaretleyiniz.

31- Aşağıda belirtilen ifadelere ne derecede katılıp katılmadığınızı belirtiniz.

Kesinlikle Katılmıyorum Katılmıyorum Ne Katılıyorum Ne

Katılmıyorum Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

1

Postayla gelen veya gazete/dergi ile dağıtılan

reklam/broşür materyallerini okumak vakit kaybıdır. 1 2 3 4 5

2 En son yenilikleri öğrenmek için vitrinlere bakmayı

severim. 1 2 3 4 5

3 Diğer insanların yaptıkları alışverişlerle ilgili

konuşmalarını dinlemekten çok sıkılırım. 1 2 3 4 5

4 Ne  hakkında  olduğunu  ögrenmek  için  genellikle

katalog, broşür bile okurum. 1 2 3 4 5

5 Sadece meraktan dolayı alışveriş yapmayı sevmem. 1 2 3 4 5

6 Herhangi bir şey almayı planlamadığımda bile posta ile gelen sipariş kataloglarını incelemeyi

severim. 1 2 3 4 5

7 Genellikle posta ile gelen reklamları okumadan

atarım. 1 2 3 4 5

8 Vitrinlere bakıp keyifle dolaşarak alışveriş

yapmaktan hoşlanırım. 1 2 3 4 5

9 Arkadaşlarımla satın aldıklarım hakkında

konuşmayı sevmem. 1 2 3 4 5

10 E-posta ile gelen reklamları okumaya ve

bilgilenmeye çoğunlukla istekliyimdir. 1 2 3 4 5

11 Sadece meraktan dolayı sık sık gazete/dergi ile

gelen reklam broşürlerini okurum. 1 2 3 4 5

 

32- Aşağıda belirtilen ifadelere ne derecede katılıp katılmadığınızı belirtiniz.

Kesinlikle Katılmıyorum Katılmıyorum Ne Katılıyorum Ne

Katılmıyorum Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

1

Tanıdığım çoğu insandan daha meşgul bir

insanım. 1 2 3 4 5

2 Genellikle yapacak çok işim oluyor. Keşke daha

fazla zamanım olsaydı. 1 2 3 4 5

3 Kendimi genellikle zaman konusunda baskı

altında hissediyorum. 1 2 3 4 5

33- Herhangi bir ürün veya hizmet satın alma sürecini düşündüğünüzde, aşağıda belirtilen ifadelere ne derecede katılıp katılmadığınızı belirtiniz.

Kesinlikle Katılmıyorum Katılmıyorum Ne Katılıyorum

Ne Katılmıyorum Katılıyorum Kesinlikle Katılıyorum

1 2 3 4 5

1 Alışveriş yapmak benim için eğlencelidir. 1 2 3 4 5

2 Yeni tarzları severim. 1 2 3 4 5

3 Kaliteli ürün ararım ve bunun için fazladan para

ödemeye istekliyimdir. 1 2 3 4 5

4 İndirimde olan ürünleri satın almayı tercih ederim. 1 2 3 4 5

5 Satın alacağım ürün veya hizmetin fiyatlarını

karşılaştırırım. 1 2 3 4 5

6 Satın aldığım ürün veya hizmetin kalitesi onlar için

ödemek zorunda olduğum fiyattan daha önemlidir. 1 2 3 4 5

7 Bir şeyler satınalmak beni mutlu eder. 1 2 3 4 5

8 Fiyat farklılıklarına dikkat ederim. 1 2 3 4 5

9 Çok çeşitlilikten hoşlanırım. 1 2 3 4 5

10 İstediğim an alışveriş yapabilmeliyim. 1 2 3 4 5

11 Ucuz fiyatlı ürünleri ararım. 1 2 3 4 5

12 Kalite için daha fazla para ödemeye değer olduğunu

düşünüyorum. 1 2 3 4 5

13 İstediğim ürün veya hizmeti en kısa zamanda bulmak

benim için önemlidir. 1 2 3 4 5

14 Alışveriş  yapmanın  sosyal  bir  faaliyet  olduğunu

düşünüyorum. 1 2 3 4 5

15 Farklı şeyleri denemekten hoşlanırım. 1 2 3 4 5

16 Ürünlerle ilgili bilgi toplamak için zaman harcamaktan

hoşlanmam. 1 2 3 4 5

17 Aradığım ürün veya hizmete çok az çaba harcayarak

ulaşmak benim için önemlidir. 1 2 3 4 5

 

34- Yaşamınız boyunca toplam kaç yıl/yıldır öğrenim gördüğünüzü belirtiniz.

 (yıl)

35- Yaşınız…………………..

36- Cinsiyetiniz: □ Kadın □ Erkek

37- Medeni Durumunuz: □Evli □Bekar □ Dul\Boşanmış

38- Bir ayda evinize giren net geliri işaretler misiniz? (Lütfen gelir sahibi aile fertlerini de ekleyerek cevap veriniz)

□ 500 YTL nin altında □ 501-1.000 YTL

□ 1.001 YTL-1.500 YTL □ 1.501 -2.000 YTL

□ 2.001-2.500 YTL □ 2.501 YTL-3.000 YTL

□ 3.001-3.500 YTL □ 3.501-4.000 YTL

□ 4.001-4.500 YTL □ 4.501-5.000 YTL

□ 5.001 YTL ve üzeri

39- En son bitirdiğiniz okul itibariyle eğitim durumunuz:

□ İlk öğretim □ Lise □ Üniversite

□ Yüksek Lisans/Doktora

40- Mesleğiniz:

□ Çalışmıyor □ Üst düzey yönetici

□ Öğrenci □ Büyük ya da orta ölçekli işletme sahibi

□ Ev hanımı □ Avukat, dişçi, mimar vb.

□ Emekli □ Büro/Ofis elemanı

□ İşçi □ Memur

□ Zanaatçi □ Diğer (lütfen belirtiniz.) .........

 

CURRICULUM VITAE

Gonca Ulubaşoğlu was born in Denizli, on 03.02.1977. After graduating from Denizli High School in 1994, she has earned B.Sc degree in Management Engineering at Istanbul Technical University with a third degree award in 1999. She attended graduate programme in Management Engineering at Istanbul Technical University in 2000 and earned her Master Degree in 2002. She continued her Ph.D studies on Management Engineering at Istanbul Technical University in 2002. Since 2001, she has been working as a research assistant at the Faculty of Management of Istanbul Technical University.