An Empirical Examination on External Consumer Information Search on the Internet

ABSTRACT - There is little doubt that the Internet contributes to, and will continue to affect, consumer information search. However, a comprehensive understanding of what motivates and mediates information search behavior on the Internet is relatively lacking. This study sought to identify antecedent factors affecting external search on the Internet in a multivariate setting. The study found that subjects with high situational involvement, low objective knowledge, and no purchase experience were likely to spend more time and consider a larger number of brands during search. The study also found that subjects with high objective knowledge examined a larger set of attributes than those with low objective knowledge.

Citation:

Byung-Kwan Lee, TaiWoong Yun, and Wei-Na Lee (2005) ,"An Empirical Examination on External Consumer Information Search on the Internet", in AP - Asia Pacific Advances in Consumer Research Volume 6, eds. Yong-Uon Ha and Youjae Yi, Duluth, MN : Association for Consumer Research, Pages: 21-27.

Asia Pacific Advances in Consumer Research Volume 6, 2005      Pages 21-27

AN EMPIRICAL EXAMINATION ON EXTERNAL CONSUMER INFORMATION SEARCH ON THE INTERNET

Byung-Kwan Lee, The University of Texas at Austin, U.S.A.

TaiWoong Yun, The University of Texas at Austin, U.S.A.

Wei-Na Lee, The University of Texas at Austin, U.S.A.

ABSTRACT -

There is little doubt that the Internet contributes to, and will continue to affect, consumer information search. However, a comprehensive understanding of what motivates and mediates information search behavior on the Internet is relatively lacking. This study sought to identify antecedent factors affecting external search on the Internet in a multivariate setting. The study found that subjects with high situational involvement, low objective knowledge, and no purchase experience were likely to spend more time and consider a larger number of brands during search. The study also found that subjects with high objective knowledge examined a larger set of attributes than those with low objective knowledge.

INTRODUCTION

Information search is an integral part of consumer decision making process. The Internet contributes to this function by providing an efficient and convenient tool to search for vast amount of product or service related information (Haubl and Trifts, 2000). While the Internet is quickly becoming a major source of information, it is also evolving into a significant channel for business transaction and distribution (Peterson, Balasubramanian, and Bronnenberg, 1997). A recent report estimates that online retail revenue will grow from $95.7 billion in 2003 to $229.9 billion in 2008 which will account for 10% of total retail sales (Forrester Research, 2003).

With the increasing popularity of the Internet, more consumers are using the Internet for information search than before. In a national survey with online consumers, Burke (2002) found that those consumers used the Internet mainly for information search (93%) and comparing and evaluating alternatives (83%). Most recently, Ratchford, Lee, and Talukdar (2003) showed that among those who recently purchased a new car, 39% used the Internet to obtain product information. As more and more consumers search for information online, marketers need to better understand this phenomenon in order to help aid consumers in their decision-making. Although several attempts have been made to provide a conceptual model for examining consumer online information search (Klein, 1998; Peterson and Merino, 2003; Rowley, 2000), empirical studies in this area are still lacking.

Hence, the purpose of the study is to empirically examine what factors affect consumer online information search. These factors include Internet specific variables (i.e., Internet skills, online purchase experience, Internet attitude) along with other personal, situational, and media-related variables.

LITERATURE REVIEW

Information Search Behavior

Consumers engage in both internal and external search for product information (Newman, 1977). Internal information search involves consumer retrieval of memory that stores product knowledge. External information search refers to activities other than memory, such as consulting with salespeople, friends, reading other sources, looking at ads, direct observation and so on. Consumers often employ both types of search in a sequential and iterative fashion when making purchases.

External information search encompasses both goal-directed, prepurchase activities and ongoing search activities (Peterson and Merino, 2003). Most research on information search has focused on prepurchase search which involves consumer’s cognitive effort to reduce uncertainty (Beatty and Smith, 1987; Punj and Staelin, 1983). Meanwhile, ongoing search is generally considered to be related to nonfunctional motives such as entertainment (Bellenger and Korgoankar, 1980; Holbrook and Hirschman, 1982) and product interests (Bloch, Sherrell, and Ridgway, 1986). For example, Bloch et al. (1986) found that the perceived enjoyment of shopping and enduring involvement are related to ongoing search and that heavy ongoing searchers tend to be heavy spenders within the product class.

However, few researchers have attempted to develop a causal model to delineate the relationships among these factors for information search behavior (Moore and Lehmann, 1980; Punj and Staelin, 1983; Schmidt and Spreng, 1996). Most of the empirical studies have examined direct effects of various antecedents on information search in bivariate situations (Guo, 2001; Lee and Hogarth, 2000). While the direct relationship between external search and its determinants is in and of itself important to understanding consumer information search behavior, the complex nature of information serch behavior will require investigating the relationships between various factors and information search in a multivariate setting. External information search is influenced by a number of determinants and, in a multivariate setting, the magnitude and direction of the relationships between search and antecedents will vary. Several researchers have argued that ability, motivation, and cost and benefit mediate the effects of various antecedent factors on information search activities and suggested models for testing (Punj and Staelin, 1983; Srinivasan and Ratchford, 1991).

Information Search on the Internet

The Internet provides benefits to consumers by offering powerful search and screening tools (Haubl and Trifts, 2000), an abundance of product information (Dholakia and Bagozzi, 2001; Peterson and Merino, 2003), and a wide range of product selections and prices (Bakos, 1997). With the increasing popularity of the Internet as a viable information source and a transaction channel, researchers have begun to turn their attention to the nature of information search on the Web either by examining information search patterns on the Web (Holscher and Strube, 2000) or by exploring factors affecting online information search (Klein and Ford, 2002; Liang and Huang, 1998). These two streams of research suggest that there are different patterns of information search on the Web and, more importantly, there seem to be several additional factors (e.g., shopping attitude, experience, perceived control and skills, Internet availability) that influence consumer information search activities on the Web. These researchers, however, have focused on a limited set of factors and therefore fall short of providing a comprehensive understanding of what motivates consumers to navigate the Web for shopping purposes.

Although the general models from traditional information search studies provide a good starting point for investigating information search behavior in an online environment, several factors of particular relevance to the Internet may need to be developed. These factors include characteristics of the Internet (e.g., accessibility, interactivity, flow, customization) and Internet skills and experience (Liang and Huang, 1998; Ratchford, Lee, and Talukdar, 2003).

As discussed, there are a number of factors that may affect online consumer information search including situational, individual, environmental, product-related, and media factors. This study attempts to empirically test the effects of a complete set of factors on online information search activity with a special focus on the effect of Internet-related factors. Figure 1 provides a proposed theoretical model examined in the study.

FIGURE 1

A THEORETICAL MODEL

HYPOTHESES

Internet Skills

Novak, Hoffman, and Young (2000) assert that consumer online navigation and interaction are influenced by his/her online skills. They found that the higher the levels of online skills, the more positive the experience consumers achieve from the Internet. Based on the economic perspective on information search, Ratchford, Talukdar, and Lee (2001) posit that increases in Internet skills will reduce the marginal cost of acquiring a predetermined level of benefit of search, making external search more likely to increase. Schmidt and Spreng (1996) also suggest that skills are positively related to perceived ability which in turn is likely to increase external search. Therefore, the following hypothesis is proposed:

H1: Internet skills increase search time on the Internet.

Objective and Subjective Knowledge

Researchers suggest that objective knowledge and subjective knowledge will have different effects on information search (Park, Mothersbaugh, and Feick, 1994). Objective knowledge is related to specific product information and consumers with high objective knowledge have a well-organized information structure and rich product information which enable them to comprehend and process external information easier (Brucks, 1985). Experienced consumers have product knowledge about various alternatives and they do not need to search more information from external sources (Raju, Lonial, and Mangold, 1995). Two hypotheses are suggested to test these relationships:

H2a: Objective knowledge decreases search time on the Internet.

H2b: Objective knowledge decreases the number of brands examined.

It is likely that prior knowledge facilitates search in a more efficient way by making it easier to process new information in a prepurchase situation (Brucks, 1985; Ozanne, Brucks, and Grewal, 1992). For example, Brucks (1985) found that objective knowledge is positively related to the number of attributes examined. Therefore, the following hypothesis is provided:

H2c: Objective knowledge increases the number of attributes examined.

Subjective knowledge is related to perception of and confidence in the ability to do product-related tasks and past product experience (Park et al., 1994). Consumers with high subjective knowledge will recognize heightened confidence in their ability when performing information search for product purchase (Duncan and Olshavsky, 1982). Consumers who are confident in product purchase are likely to engage in less external search because they feel less need for any more information (Johnson and Russo, 1984). Lee et al. (1999) found that high knowledge consumers examined less information than low knowledge consumers. Consequently, the following hypotheses are suggested:

H3a: Subjective knowledge decreases search time on the Internet.

H3b: Subjective knowledge decreases the number of brands examined.

H3c: Subjective knowledge decreases the number of attributes examined.

Prior Purchase Experience

Consumers with prior purchase experience tend to have procedures for simplifying the decision and reducing the amount of information (Newman and Staelin, 1972; Punj and Staelin, 1983; Srinivasan and Ratchford, 1991). For example, Newman and Staelin (1972) found that when purchasing a new car or appliances, consumers with prior purchase experience tended to spend less time to make a decision. It seems that previous purchase experience on the Internet will reduce perceived benefits of search, which will consequently decrease external search effort on the Internet for information source. Thus, it is suggested that:

H4a: Prior purchase experience decreases search time on the Internet.

Purchase experience also leads to less amount of information examined during search. Moore and Lehmann (1980) found that as the number of purchasing bread increases people tend to search for less information. I a recent study, experience seems to lead to a slight decrease in the number of Web sites visited for air travel (Johnson, Moe, Fader, Bellman, and Lohse, 2002). However, Moorthy et al. (1997) found that as purchase experience increases the number of attributes based on which consumers compare brands increases whereas the number of brands decreases. It seems that consumers become experts with experience who can make fine distinction based on a large set of attributes. It is therefore hypothesized that:

H4b: Prior purchase experience decreases the number of brands examined.

H4c: Prior purchase experience increases the number of attributes examined.

Situational Involvement

Many researchers agree on the important role of involvement in determining consumer prepurchase search for brand information and suggest that situational involvement will increase processing effort (Beatty and Smith, 1987). If the personal relevance of a specific purchase under consideration is increased, consumers tend to allocate more cognitive resources and are more motivated to process or search for relevant information extensively.

Beatty and Smith (1987) suggest that consumers who perceive more relevancy and importance in decision-making tend to exert more search effort than those with low involvement. They found that across several product categories, consumers with high purchase involvement spent more time and examined more brands. Lee et al. (1999) found that consumers with high issue involvement searched for more product information than low involvement consumers. In their study, when asked to find information about laptop PCs, subjects in high situational involvement situation examined a larger number of brands and attributes than subjects in low involvement. Hence it is hypothesized that:

H5a: Situational involvement increases search time on the Internet.

H5b: Situational involvement increases the number of brands examined.

H5c: Situational involvement increases the number of attributes examined.

Enduring Involvement

Enduring involvement refers to the persistent interest in an object and its importance (Zaichkowsky, 1994). Prior research (Srinivasan and Ratchford, 1991) suggests a positive relationship between interest and search. With respect to the effect of involvement on search behavior, Beatty and Smith (1987) found that a higher level of ego involvement led to a greater amount of information search. Celsi and Olson (1988) also found that consumers spent more time attending to information as their involvement increased. Hence, enduring involvement might be positively related to external search for information from various sources. Thus, the following hypotheses are proposed:

H6a: Enduring involvement increases search time on the Internet.

H6b: Enduring involvement increases the number of brands examined.

H6c: Enduring involvement increases the number of attributes examined.

Attitude toward the Internet

Li, Kuo, and Russell (1999) found that frequent online shoppers tended to have more positive perception of channel attributes than non users. They assert that frequent online users perceive the Web to be significantly higher in the three aspects of channel attributes (communication, distribution, accessibility). One of the important attributes of the Internet is its easy access and ubiquitousness. The ease of gathering product information n the Internet is likely to increase consumer intention to search and process because the more available the information is to consumers, the lower the cost of search will be (Schmidt and Spreng, 1996). Similarly, in his interaction model of information search, Klein (1998) posits that characteristics of the Internet such as user control and interactivity, customizability, and accessibility will influence perceived benefits of search and external search activity. Hence, the following hypothesis is proposed:

H7: Positive attitude toward the Internet increases search time on the Internet.

METHODOLOGY

A convenient sample of 72 undergraduate students from a southwestern state university was recruited for this study. The students were given extra credit points as incentives for their participation in the study. Students participated in the study by completing an online information search task in small group sessions during a two-week period. The sessions were held in a laboratory. The laboratory room was equipped with desktop computers and Ethernet connection. Each session was run by an administrator.

Participants were randomly assigned to either high situational involvement (SI) or low SI conditions. SI was manipulated by varying the level of personal relevance and task importance through instruction. High SI group was told that their college would require all the students to purchase a laptop computer. They were told that their input was needed to have a better understanding of students’ preference for laptops. On the other hand, low SI group was told that they were being surveyed to provide helpful information for a distant university that is considering requiring its students to purchase a laptop computer soon.

Measures

In addition to the SI manipulation, there were six independent variables examined in this study; Internet skills (number of items; 4), objective knowledge (5), subjective knowledge (2), prior online purchase experience (1), enduring involvement (10), and perception of the Internet attributes (12). Items yielded a moderate to high level of reliability. The primary dependent variable was the amount of external search. Many studies have introduced different measures of information search activity (Srinivasan, 1990). However, most of the studies have relied on self-report (Li et al., 1999; Ratchford et al., 1997; Srinivasan and Ratchford, 1991) or search intention (Shim, Eastlick, Lotz, and Warrington, 2001), which seems to be subject to distortion and unreliability. To overcome this difficulty of measuring search and provide a more accurate and objective measure, this study utilizes a measure of accurate time spent for external search on the Internet using a software (WinWhatWhere Investigator) that captures real time search activity. In addition, the number of brands and attributes considered, satisfaction, and choice confidence were measured.

Data Collection

At the beginning of each session, participants were told that this was a study to collect information that would help the Dean of their college (high SI) or a distant university (low SI) to make a decision on which laptop to require students to purchase. A study administrator explained the procedures to make sure that all participants followed the instructions properly.

First, study participants were asked to fill out Part I of an online survey questionnaire that asked about their online experience (skills, objective and subjective knowledge, past online purchase experience, enduring involvement, perceived Interne attitude).Afterwards, participants were told that they could go online and shop for a full size laptop computer within a given price range. During this phase of the study, participants were given no specific direction for information search and they were free to visit any Web sites with no time limit. A software program that monitors computer activities and records the data in Excel-like file was turned on for each participant’s PC. Upon completion of their shopping task, participants filled out Part II of the online survey which asked for their recommended brand choice, number of brands and attributes during search, motivation to search, and demographic information. Upon completion of all sessions, subjects were briefed on the purpose of the study.

RESULTS

Manipulation Check

A seven-point Likert-type item checked the manipulation of situational involvement: "To what degree do you think information search on the Internet was interesting?" The ANOVA result shows that subjects in high SI condition were more interested in information search on the Internet than subjects in low SI condition (M_{high SI=}5.3 and M_{low SI=}4.4; F(1, 70)=6.46, p<.05).

Time Spent on Search

Multiple regression analysis was performed to assess the effect of various antecedents on time spent on search on the Internet. These antecedents include Internet skills, objective and subjective knowledge, prior purchase experience, situational and enduring involvement, and Internet attitude. The relative importance of each of the antecedents of external search was examined by regressing all the antecedents on time spent on search. As shown in Table 1, search time was influenced by the composite of the antecedents entered (F=2.90, df=(7, 64), p,.05). According to this model, the antecedents explains 24% of total variation in search time (R²⁼.24). A closer examination of standardized coefficients of the antecedents shows that situational involvement, objective knowledge, and prior purchase experience were significantly related to search time. Situational involvement was most highly related to external search on the Internet (B=.401, p,.05). This indicates that consumers in high situational involvement condition spent more time on search on the Internet than those in low situational involvement condition. Therefore, H5a is supported. Objective knowledge was negatively related to search time (B=-.251, p,.10). Subjects with high objective knowledge about laptop PCs spent less time on search on the Internet, which supported H2a. Also as expected, purchase experience (B=-.240, p,.05) showed a negative relationship with external search time. Subjects who have purchased any laptop PC during the last 12 months tended to spent less time on information search than those who have no online purchase experience. H4a is therefore supported. However, the other variables such as Internet skills, subjective knowledge, enduring involvement, and Internet attitude were not significantly related to external search amount.

Number of Brands Examined

To test what determines the number of brands considered during search on the Internet, a multiple regression analysis was conducted with objective and subjective knowledge, prior purchase experience, situational and enduring involvement entered as independent variables. The regression result indicates that the antecedents significantly affect the number of brands considered during search (F=2.28, df=(5, 66), p,.05). An examination of standardized coefficients of the antecedents reveals that situational involvement, objective knowledge, and purchase experience are significant determinants of the number of brands considered. As shown in Table 1, situational involvement was the most important determinant of the number of brands considered (B=.282, p,.05), indicating that subjects in high situational involvement condition examined more brands than those in low situational involvement condition. Therefore, H5b is supported.

Objective knowledge and purchase experience were found to be marginally negatively related to the number of brands considered. Subjects with high objective knowledge tended to consider less number of brands during search than low objective knowledge subjects. Therefore H2b is supported. As expected, subjects with prior purchase experience were likely to consider less number of brands than those with no purchase experience, which supported H4b. However, the regression result found that subjective knowledge and enduring involvement were not significantly related to the number of brands, failing to support H3b and H6b, respectively.

Number of Attributes Examined

Similarly, objective and subjective knowledge, prior purchase experience, situational and enduring involvement were regressed on the number of attributes considered during search. Results in Table 1 reveal that there is a marginally significant relationship between the composite antecedents and the number of attributes considered (F=1.88, df=(5, 66), p=.10). The regression analysis also found that objective knowledge is the only variable which is significantly related to the number of attributes considered (B=.320, p,.05). Therefore, H2c is supported. This indicates that subjects with high objective knowledge considered more attributes than subjects with low objective knowledge.

TABLE 1

MULTIPLE REGRESSION RESULTS

SUMMARY AND DISCUSSION

This study sought to identify antecedent factors affecting external search on the Internet in a multivariate setting. It was postulated that various factors such as personal, situational, and Internet related variables would affect external search on the Internet in terms of search time, number of brands and attributes examined. Some of the relationships hypothesized were supported and they are provided in Figure 2.

Situational involvement, objective knowledge, and purchase experience were found to influence search time on the Internet. When subjects were highly involved in the search task, they spent more time searching for information. This result is consistent with prior research suggesting that if the personal relevance of the issue or task is increased, people are motivated to exert more effort and process information more thoroughly (Beatty and Smith, 1987; Petty, Cacioppo, and Schumann, 1983). Information search on the Internet, however, is negatively related to objective knowledge and prior purchase experience. That is, subjects with high objective knowledge and prior purchase experience spent less time on information search than those with low objective knowledge and purchase experience. This appears reasonable because increased product knowledge and purchase experience reduce perceived benefit of search resulting in less search effort (Newman and Staelin, 1972; Schmidt and Spreng, 1996).

Similarly, the number of brands considered during search was determined by situational involvement, objective knowledge, and purchase experience. Situational involvement has the strongest impact on the number of brands considered. It seems that high purchase concern increases benefits of search, which results in increased motivation to examine a larger set of brands (Lee et al., 1999). The number of brands seems related to the lack of objective knowledge and purchase experience. This suggests that subjects with high objective knowledge and purchase experience examined fewer brands than those with low knowledge and purchase experience. Consumers who are knowledgeable and have purchased a certain product tend to have rich information about various alternatives and feel less need for additional information.

This study found that objective knowledge was the only variable that determined the number of attributes examined during search. This result is consistent with Brucks’ (1985) finding that, in searching for information about sewing machines, high objective knowledge consumers examined a larger number of attributes than those with low objective knowledge. Given the result that high objective knowledge subjects consider a smaller number of alternatives but examine a larger set of product attributes, it seems that consumers become more expert-like as they gain more objective knowledge in that they feel more certain about individual brands (thus they feel less need for information about various alternatives) and the ability to consider a larger set of attributes (thus they tend to process product information deeply by considering more attributes).

The lack of effects of other variables such as subjective knowledge, enduring involvement, Internet skills, and Internet attitude on external search in this study needs further discussion. For instance, the null effect of subjective knowledge might be in part due to a relatively high correlation between objective and subjective knowledge. Or, a finer conceptualization of different types of knowledge should have been used in this study. As discussed, the role of knowledge on external search is equivocal and further elaboration on the effect of different types of knowledge seems necessary to resolve this. For example, rather than relying on objective and subjective knowledge distinction, Fiske, Luebbehusen, Miyazaki, and Urbany (1994) suggest a distinction between general product category knowledge and specific brand knowledge for explaining mixed results. They assert that general product category knowledge tends to increase external search whereas specific brand knowledge (purchase experience) decreases search effort. In a similar vein, additional work is required on conceptualizing and operationalizing enduring involvement, Internet skills, and Internet attitude.

In summary, this study found that people who were more involved in the purchase task, those who had lower objective knowledge and had not purchased a laptop PC were likely to spend more time and consider a larger number of brands during search. This study also found that people with higher objective knowledge examined a larger set of attributes than those with lower objective knowledge.

FIGURE 2

A WORKING MODEL

LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH

In an attempt to increase our understanding of what determines information search on the Internet, an experimental study was carried out. Although useful results have been presented, there are several limitations in this study. First, this study used laptop PCs as a target product category for which subjects searched for information. This limits the generalization of the findings. Future research needs to replicate this type of study using a more general population with different product categories.

Second, some of the variables used in this study did not show any significant influence on external search on the Internet. A finer conceptualization and operationalization of these variables needs to be explored in order to provide meaningful insights on external information search. Alternatively, qualitative indices of external search along with quantitative measures may prove to be better indicators of relationship with these variables. For example, people with high Internet skills are able to search the Internet by visiting more relevant Web sites and examining product information more efficiently than those with less Internet skills using the same amount of time. Further research will need to investigate how people with different Internet usage and skills navigate the Internet to search for product information.

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