University of Texas at Austin, USA
University of Texas at Austin, USA
Information search is an integral part of the consumer decision making process. There is no doubt that the Internet contributes to, and will continue to affect, this function. However, a comprehensive understanding of what causes, motivates, and mediates information search behavior on the Internet is relatively lacking. Based on an in-depth review and critical critique of past research on information search behavior and, in particular, online information search, this chapter offers a causal model of online information search with 16 specific research propositions outlined. It argues that information search on the Internet should be investigated by considering Internet specific factors (i.e., skills, prior online purchase experience, attitude toward the Internet) as well as various antecedents including situational, product-related, and individual factors. Contribution and implications of the model for further understanding of information search behavior in the context of the Internet are also discussed.
Information search is an integral part of the consumer decision making process. The Internet contributes to this function by providing an efficient and convenient tool to search for a vast amount of product or service related information (Alba, Lynch, Weitz, Janiszewski, Lutz, Sawyer, & Wood, 1997). 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, & 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). It seems apparent that with the increasing popularity of the Internet, more consumers are using the Internet for information search than before. In a national survey with 2,120 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 get product information. As more and more consumers search for information on the Internet, marketers need to better understand what causes consumers to search for information on the Internet in order to help aid consumers in their decision-making. The goal of this chapter is to provide a theoretical model to facilitate that understanding.
This chapter is divided into the following sections. First, a comprehensive review and critique of past research on information search behavior in general and online information search in particular is provided. Second, a causal model of online information search is presented with specific research propositions outlined. Third, potential theoretical and managerial implications drawn from the proposed model are suggested.
Consumers engage in both internal and external search for product information (Beales, Mazis, Salop, & Staelin, 1981; Bettman, 1979; DeSarbo & Choi, 1999; Moore & Lehman, 1980; 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 who make purchases often employ both types of search in a sequential and iterative fashion. First, consumers consult information stored in memory. If they do not have sufficient information, then they search for additional information from external sources. This information is then accumulated in memory for later use. The amount of information search exerted for a specific purchase is therefore a function of both internal and external resources available to the consumer. When both internal and external search are carried out, results that are conflicting will need to be resolved properly.
External information search encompasses both goal-directed, pre-purchase activities and ongoing search activities (Lee & Hogarth, 2000b; Peterson & Merino, 2003). Most research on information search has focused on pre-purchase information search which involves the consumer’s cognitive effort to reduce uncertainty for specific purchases (Beatty & Smith, 1987; Punj & Staelin, 1983; Srinivasan, 1990). Meanwhile, other researchers assert that ongoing search is not specifically related to imminent purchases and is more related to nonfunctional motives including entertainment (Bellenger & Korgoankar, 1980; Holbrook & Hirschman, 1982; Tauber, 1972) and product interests (Bloch & Richins, 1983; Bloch, Sherrell, & 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.
Consumers can gather information from various external sources. Many researchers have reported different types of information sources (Andeasen, 1968; Beatty & Smith, 1987; Freiden & Goldsmith, 1989; Lee & Hogarth, 2000b; Newman & Staein, 1973; Schmidt & Spreng, 1996). Often used information sources can be categorized into several types: seller provided information (e.g., stores, catalogs, salespeople), media (e.g., television, newspaper, magazine, radio, the Internet), interpersonal sources (e.g., friends, family, relatives), third party sources (e.g., Consumer Reports, J. D. Powers), and direct inspection (e.g., direct observation, product trial).
There has been a stream of research on the categorization of different modes of information search or browsing based on both empirical evidence and theoretical propositions (Claxton, Fry, & Portis, 1974; Furse, Punj, & Stewart, 1984; Kiel & Layton, 1981; Newman & Staelin, 1972, 1973; Westbrook & Fornell, 1979; Wilkie & Dickson, 1985). These researchers have sought to identify the pattern of information search by focusing on distinct groups of consumers. There appear to be several factors (situational, environmental, and individual) based on which distinct patterns of information search are categorized. However, few researchers (Maute & Forrester, 1991; Moore & Lehmann, 1980; Punj & Staelin, 1983; Schmidt & Spreng, 1996; Srinivasan & Ratchford, 1991) have attempted to develop a causal model to reveal the relationships among these factors for information search behavior.
In theory, there are three approaches to modeling information search behavior (Schmidt & Spreng, 1996; Srinivasan, 1990). Some researchers applied the economics of information theory to behavioral aspects of search (Avery, 1996; Goldman & Johansson, 1978; Miller, 1993; Urbany, 1986). According to this approach, consumers acquire and process information until the marginal cost of information search exceeds the expected marginal benefit of information search (Ratchford, 1982; Stigler, 1961). In other words, perceiving increased benefits will lead to more information search and perceiving increased costs will lead to less search effort. Thus, investigating variables affecting the perception of costs and benefits of search would be necessary for understanding external search behavior.
A psychological or motivational approach, on the other hand, assumes motivation to be the driving force for external information search (Howard & Sheth, 1969). Motivation refers to the desire to exert effort for a certain task (Bettman, 1979) or the arousal for achieving goals (Park & Mittal, 1985). Different search goals (i.e., optimizing vs. satisficing) and different levels of product involvement (i.e., relevance or interests) may affect search direction and intensity. Therefore, any variables that increase the motivation to search will yield more search effort and vice versa.
Finally, an information processing approach emphasizes consumer memory and cognitive ability (Bettman, 1979). This approach has led to extensive research on search behavior affected by prior knowledge, familiarity, experience (Brucks, 1985; Johnson & Russo, 1984; Punj & Staelin, 1983), expertise (Alba & Hutchinson, 1987), and prior belief (Duncan & Olshavsky, 1982; Urbany, 1986).
Most of the empirical studies have examined direct effects of various antecedents on information search in bivariate situations (Guo, 2001; Lee & Hogarth, 2000b). While 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 search 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 be different. 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 (Maute & Forrester, 1991; Moore & Lehmann, 1980; Punj & Staelin, 1983; Schmidt & Spreng, 1996; Srinivasan & Ratchford, 1991).
In summary, empirical studies on consumer information search behavior to date have provided some general implications and guidelines for further research in this area. First, past research has found four different relationships between antecedent variables and information search: positive, negative, inverted U, and null relationship (see Guo, 2001, for a detailed discussion). Second, different studies have adopted different measures of information search from single measures to multiple measures to aggregate (weighted/ unweighted) indices of information search effort. Third, most of the relationships have been studied in bivariate settings with few exceptions.
These general models from traditional information search studies provide a good starting point for investigating information search behavior in an online environment. However, several factors particularly relevant to the Internet may need to be developed. Factors such as characteristics of the Internet media (e.g., accessibility, interactivity, flow, customization) and Internet skill and experience (Liang & Huang, 1998; Ratchford, Talukdar, & Lee, 2001) will need to be incorporated as we move into an increasingly online shopping environment.
The Internet provides a number of benefits to consumers in several ways. The Internet offers powerful search and screening tools (Alba et al., 1997; Haubl & Trifts, 20000), an abundance of product information (Alba et al., 1997; Dholakia & Bagozzi, 2001; Papacharissi & Rubin 2000; Peterson & Merino, 2003), and a wide range of product selections and prices (Bakos, 1997; Brynjolfsson & Smith, 2000). The cost-effective nature and easy access to a vast information resource have generated great interest in this new medium (Donthu & Garcia 1999).
Many researchers have attempted to better understand Web users and online shopping (Jarvenpaa & Todd, 1997; Korgaonkar & Wolin, 1999). Studies have found that online users are convenience seekers, time starved (Bellman, Lohse, & Johnson, 1999; Donthu & Garcia 1999; Kaufman-Scarborough & Lindquist, 2002; Szymanski & Hise, 2000), more educated (Burke, 2002; Ratchford, Lee, & Talukdar, 2003), and older and with a higher income (Donthu & Garcia 1999) than non online shoppers. While some researchers have focused on the functional aspects of shopping motivation such as price, convenience, and accessibility (Alba et al., 1997; Degeratu, Rangswamy, & Wu, 2000), others have suggested the need for studying non functional motivations for online shopping such as fun and recreation (Girard, Korgaonkar, & Silverblatt, 2003; Parker & Plank, 2000; Parsons, 2002; Wolfinbarger & Gilly, 2001).
The Internet can facilitate information search because it reduces search costs by providing bountiful price and product related information (Bakos, 1997; Brynjolfsson & Smith, 2000). Online information search is particularly useful for search goods (i.e., computers, books, travels) due to the low perceived costs of offering and assessing objective data (Klein, 1998).
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 (Catledge & Pitkow, 1995; Choo, Detlor, & Turnbull, 1999; Hölscher & Strube, 2000; Tauscher & Greenberg, 1997a, 1997b) or by exploring factors affecting online information search (Klein & Ford, 2002; Liang & Huang, 1998; Ratchford, Talukdar, & Lee, 2001; Shim, Eastlick, Lotz, & Warrington, 2001). For example, Tauscher and Greenberg (1997a, 1997b) identified seven Web browsing patterns based on the rate that Web pages were visited. Shim et al. (2001) found that shopping attitude, perceived control of information or skill, and prior purchase experience are crucial factors influencing online search and purchase intention. Also, Ratchford et al. (2001) revealed that Internet accessibility and skill are important in determining consumer search activity in an online environment. Most recently, Ratchford et al. (2003) examined what kind of factors affect consumer’s using various sources of automobile information such as retailers, friends/relatives, non-advocate sources in print media (i.e., Consumer Reports), and the Internet. They found that overall search is decreased with increasing Internet usage, education, age, and income. The Internet substantially reduces the time with dealer/manufacturer sources, which leads to a reduction in total search.
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 activity on the Web. These researchers, however, have focused on a limited set of factors and therefore do not provide a comprehensive understanding of what motivates consumers to navigate the Web for shopping purposes. Hence, the following section of this chapter will present a model with a complete set of factors already integrated to explain online information search behavior fully. In addition, testable research propositions for future empirical investigation of consumer information search on the Internet are suggested.
The proposed causal model is primarily based on Schmidt and Spreng’s (1996) conceptual framework with significant modifications by incorporating Internet specific factors such as Internet skill, online purchase experience, and Internet attributes. Different types of antecedents including personal factors (skill, knowledge, experience, enduring involvement, need for cognition, shopping attitude, perceived risk), product factors (product type, attribute type, price and price dispersion), media factors (interactivity, customization, accessibility), and situational factors (situational involvement, time pressure) are included and their inter-relationships are explained in detail (Figure 2-1).
Figure 2-1: A proposed causal model of online information search
Perceived ability to search is defined here as “the perceived cognitive capability of searching for and processing information” (Schmidt & Spreng, 1996). Bettman and Park (1980) assert that search ability will increase search activity. Locander and Hermann (1979) found that self confidence tended to increase external information search for five different product categories. Also, Duncan and Olshavsky (1982) found that the perceived ability to judge the television category resulted in increased external search. Most recently, Selnes and Howell (1999) showed that perceived cognitive ability increased information search for portable stereos. Klein and Ford (2002) suggested that in an online environment the ability to navigate on the Internet will facilitate information gathering and source evaluation. Therefore, the following proposition is suggested:
Research Proposition 1: Perceived ability to search increases external search on the Internet.
In an online environment, however, Internet skills and prior purchase experience will become increasingly important for consumers to repeatedly use the Internet as an information source and shopping outlet. It is therefore proposed that perceived online search ability may be determined by Internet skills and prior purchase experience as well as consumer knowledge.
Skill. Novak, Hoffman, and Young (2000) assert that consumer online navigation and interaction are influenced by his or her online skills. They found that the higher the level of online skills, the more positive experience users achieved from the Internet. Based on the economic perspective on information search, Ratchford, Talukdar, and Lee (2001) posit that an increase in Internet skill will reduce the marginal cost of acquiring a predetermined level of benefit of search, making external search more likely to increase. Shim et al. (2001) also found that perceived skill is positively related to consumer online search intention.
Research Proposition 2: Internet skills increase the perceived ability to search on the Internet.
Prior Purchase Experience. Researchers have argued that prior purchase experience on the Internet has a positive effect in predicting the consumer’s use of the Internet for external search (Klein, 1998; Liang & Huang, 1998; Shim et al., 2001). Experienced consumers might be more likely than inexperienced consumers to perceive increased ability to search on the Internet, which will eventually affect external search online in a positive manner. Hence, the effect of prior experience on information search on the Internet will depend on the consumer’s perceived ability of external search.
Research Proposition 3: Prior purchase experience increases the perceived ability to search on the Internet.
Product Knowledge. The relationship between product knowledge and the amount of external search is mixed. Some researchers have reported a positive relation between knowledge and search (Brucks, 1985; Duncan & Olshavsky, 1982; Jacoby, Chestnut, & Fisher, 1978; Schmidt & Spreng, 1996; Selnes & Troye, 1989; Srinivasan & Ratchford, 1991; Urbany, Dickson, & Wilkie, 1989) whereas others have found negative effects of knowledge on external search (Beatty & Smith, 1987; Claxton, Fry, & Portis, 1974; Lee et al., 1999; Moore & Lehmann, 1980; Newman & Staelin, 1971, 1972; Urbany, 1986) and still other studies indicate an inverted U relationship between knowledge and search (Bettman & Park, 1980; Johnson & Russo, 1984; Park & Lessig, 1981; Raju, Lonial, & Mangold, 1995; Srinivasan & Agrawal, 1988; Urbany et al., 1989). Several researchers further suggest that objective knowledge and subjective knowledge will have different effects on information search (Brucks, 1985; Park, Mothersbaugh, & Feick, 1994; Schmidt & Spreng, 1996).
Consumers with high objective knowledge have well-organized information structure and rich product information which enable them to comprehend and process external information easier (Brucks, 1985). This indicates that high objective knowledge will influence the consumer’s perceived ability to search for product information (Schmidt & Spreng, 1996). Subjective knowledge is related to confidence in the ability to do product-related tasks and past product experience (Park et al., 1994). Consumers with high subjective knowledge will have heightened confidence in their ability when performing information search (Duncan & Olshavsky, 1982).
Research Proposition 4: Product knowledge increases the perceived ability of information search on the Internet.
The perceived benefits and costs of search have been examined in the economic analysis framework (Duncan & Olshavsky, 1982; Guo, 2001; Punj & Staelin, 1983; Urbany, 1986). Srinivasan and Ratchford (1991) found that perceived benefits of information search were positively related to external search activity. Schmidt and Spreng (1996) proposed that perceived benefits increase external information search effort. More recently, Heaney and Goldsmith (1999) found that consumers perceiving more benefits of search for bank service information did more external search than those who perceived external search as less beneficial.
The choice and use of the Internet will largely depend on the perceived benefits of the information provided on the Internet. The Internet makes a large amount of information accessible at any time in any location. In addition, the Internet enables consumers and marketers to interact with each other regarding product information, transaction, and delivery. All of these should improve the perceived benefits of online information search and external search effort.
Perceived costs of search for information refers to the consumer’s evaluation of financial, psychological, physical, and time expenses. Increased costs will yield less search effort (Bucklin, 1966; Miller, 1993; Moorthy, Ratchford, & Talukdar, 1997; Punj & Staelin, 1983; Srinivasan, 1987; Stigler, 1961). In a grocery shopping setting, Putrevu and Ratchford (1997) found that perceived time cost significantly decreased external search effort. In a similar vein, DeSarbo and Choi (1999) found that perceived costs of search, time, and evaluation were negatively related to external search.
It appears reasonable to suggest that the Internet is capable of decreasing search costs by offering rich product information (Bakos, 1997; Liang & Huang, 1998). Higher accessibility to information and lower information and time costs bring consumers to the Internet with the possibility of becoming fully informed about products (Bakos, 1997; Brynjolfsson & Smith, 2000; Dickson, 2000).
There exist a number of antecedents that influence either the benefit or the cost of search or both. For instance, prior online purchase experience may decrease the perceived benefit of search but does not change the perceived cost. However, the Internet’s easy access and convenience may decrease search cost and increase search benefit at the same time. Information gathered is therefore a function of the perceived benefits of search and cost of information (Kiel & Layton, 1981; Ratchford et al., 2003; Srinivasan & Ratchford, 1991). Thus, it is suggested that the determinants of external search efforts depend on the net effect of the perceived benefit and cost (perceived benefit minus perceived cost).
Research Proposition 5: The perceived net benefits of search increases external search on the Internet.
The key determinants of net benefits of search on the Internet involve prior purchase experience, product knowledge, perceived risk, situational involvement, media attitude, time pressure, and product characteristics, among others.
Prior Purchase Experience. Consumers with prior purchase experience tend to have procedures for simplifying decisions and reducing the amount of information sought (Kiel & Layton, 1981; Newman & Staelin, 1972; Punj & Staelin, 1983; Srinivasan & Ratchford, 1991). For example, Newman and Staelin (1971) found that when purchasing a new car or appliances, consumers with prior purchase experience tended to spend less time to make a decision. Prior purchase experience is closely related to what is termed as specific brand knowledge (Fiske, Luebbehusen, Miyazaki, & Urbany, 1994). Fiske et al. (1994) suggest that this knowledge (experience) tends to decrease external search effort. It could be that previous purchase experience on the Internet will reduce the perceived benefits of search, which will consequently decrease external search effort on the Internet for information. A recent study found that experience led to a slight decrease in the number of visited sites for air travel (Johnson, Moe, Fader, Bellman, & Lohse, 2002). Thus, it is proposed that:
Research Proposition 6: Prior purchase experience decreases the net benefits of information search on the Internet.
Product Knowledge. Several researchers have suggested a negative relationship between subjective knowledge and the benefits of external search (Brucks, 1985; Urbany et al., 1989; Schmidt & Spreng, 1996). Consumers who are confident in product purchase are likely to engage less in external search because they feel less need for information, which is associated with lower perceived benefits of search (Johnson & Russo, 1984). However, other researchers argued that consumer knowledge facilitates information search by recognizing a purchase problem properly and locating relevant information (Brucks, 1985; Selnye & Troye, 1989). This should reduce the cognitive costs of information search. Hence, the effect of consumer knowledge will depend on its relative effect on perceived costs and benefits of information search.
Research Proposition 7: Product knowledge will have a positive, negative, or inverted U relationship with the amount of external search depending on its relative effect on the perceived net benefits of information search on the Internet.
Perceived Risk. Perceived risk is consumer uncertainty in a purchase context about financial, performance, social, psychological, safety, and time/convenience gain or loss (Murray, 1991). When faced with increasing perceived risk, consumers tend to seek more product information from various sources in order to diminish purchase uncertainty (Mitra, Reiss, & Capella, 1999; Taylor, 1974). For example, Lutz and Reilly (1973) found that consumers used more information sources when there was a higher level of perceived performance risk than when performance risk was low. Similarly, Hugstad, Taylor, and Bruce (1987) found that in a high risk purchase situation such as buying major appliances, consumers used more sources of information than they did in low risk situations. The role of personal sources of information (friends, family, and salespeople) appears to be more important in high than low risk situations. When purchasing computer and audio equipment in an in-home shopping context, consumers perceiving a high risk of purchase increased external search effort (Sundaram & Taylor, 1998). Because Internet shopping is a relatively new mode of shopping involving various kinds of perceived risks due to its nature of virtuality, consumers will likely put more importance on information search when using the Internet (Shim et al., 2001).
Research Proposition 8: Perceived risk increases the net benefits of information search on the Internet.
Situational Involvement. Many researchers have agreed on the important role of involvement in determining consumer prepurchase search for brand information and suggested that situational involvement will increase processing effort (Beatty & Smith, 1987; Greenwald & Leavitt, 1984; Lee, Herr, Kardes, & Kim, 1999; Petty, Cacioppo, & Schumann, 1983; Swoboda, 1998). If the personal relevance of a specific purchase is increased, consumers tend to allocate more cognitive resources and are more motivated to process or search for relevant information.
Celsi and Olson (1988) proposed that consumers in high involvement state (felt involvement) are more likely to process information thoroughly than those in low involvement state. Their results indicate that involvement state has a positive relationship with the amount of attention, the number of thoughts, and proportion of product-related thoughts. Lee et al. (1999) found that consumers with high issue involvement searched for more product information than low involvement consumers. It was also found that high product knowledge (both subjective and objective) produced less external search than low knowledge.
Research Proposition 9: Situational involvement increases the net benefits of information search on the Internet.
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 shoppers. They argued that frequent online shoppers perceive the Internet to be significantly higher in the three aspects of channel attributes (communication, distribution, accessibility) than non-shoppers. One of the important attributes of the Internet is its easy access and ubiquitousness. The ease of gathering product information on the Internet is likely to increase consumer intention to search and to process because the more available the information is to consumers, the lower the cost of search will be (Bettman, 1979; Schmidt & 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.
Research Proposition 10: Positive attitude toward the Internet increases the net benefits of information search on the Internet.
Time Pressure. Time pressure, or consumer’s perception of time availability, affects the amount of information gathered in a pre-purchase situation (Beatty & Smith, 1987; Claxton et al., 1974; Katona & Mueller, 1955; Newman & Staelin, 1972; Sundaram & Taylor, 1998). For example, for the purchase of analgesics and shoes, consumers decrease external search when they feel pressured by the urgency of the purchase situation (DeSarbo & Choi, 1999). Similarly, Weenig and Maarleveld (2002) found that under time constraint, subjects adopted selective search strategy by decreasing the number of attributes inspected. Time pressure, it seems, increases the perceived cost of external search in a general prepurchase setting (Schmidt & Spreng, 1996). However, time pressure may affect information search on the Internet to a lesser extent. Because Internet users are generally time starved and convenience seekers (Donthu & Garcia, 1999), under time pressure, they are more likely to use the Internet for information search. With various interactive tools and agents providing more information efficiently (Haubl & Trifts, 2000), time pressed consumers will be able to search without increased search cost.
Research Proposition 11: Time pressure does not decrease the net benefits of information search on the Internet when compared to other information sources.
Product Characteristics. Most information search studies have focused on products, especially consumer durable goods such as automobiles (Kiel & Layton, 1981; Ratchford et al., 2003; Srinivasan & Ratchford, 1991) and appliances (Beatty & Smith, 1987; Newman & Staelin, 1971; Urbany, 1986) whereas information search in the service area has not been well documented (Heaney & Goldsmith, 1999). Researchers recently began to devote more attention to studying information search effort for services (Chang & Hanna, 1992; Iglesias & Guillen, 2002; Lee & Hogarth, 1998, 2000a, 2000b; Maute & Forrester, 1991; Menon, Deshpande, Perri, & Zinkhan, 2002; Murray & Schlacter, 1990).
Compared to consumer products, service is characterized as intangible, heterogeneous, inseparable, and perishable (Venkatraman & Dholakia, 1997). Research suggests that differences between products and services may yield different consumer search and acquisition behavior. Because information about banking, phone, medical and veterinary services is experiential and intangible in nature, consumers tend to seek less information for services than for products (Venkatraman & Dholakia, 1997). Freiden and Goldsmith (1989) found that for professional services such as medical, dental, legal, and veterinary services, consumers sought personal sources of information (i.e., friends, coworkers) more frequently than non-personal sources (i.e., advertisements).
However, differences in information search behavior for products and services are likely to be less pronounced on the Internet. Due to its virtual capability of providing a limitless amount of information quickly, the Internet is able to substitute various types of personal and non-personal information sources such as media, word of mouth communication, consumer opinions, expert comments, and even retail-like displays which are beneficial for both products and services.
Research Proposition 12: Consumer products as well as services increase the net benefits of information search on the Internet.
Motivation to search refers to “the desire to expend effort in the collection and processing of information, which is characterized by both direction and the intensity of effort” (Schmidt & Spreng, 1996, p. 250). Recently, Ramaswani, Strader, and Brett (2001) found that willingness to use the Internet was positively related to increase in information search on the Internet when financial services were considered.
Research Proposition 13: Higher motivation to search increases consumer external search on the Internet.
Among individual difference variables, enduring involvement, need for cognition, and shopping attitude are likely to influence consumer motivation to search for information on the Internet.
Enduring Involvement. Enduring involvement refers to the persistent interest in an object and its importance (Zaichkowsky, 1994). Prior research (Srinivasan, 1987; Srinivasan & Ratchford, 1991) suggests a positive relationship between interest and search. Consumers who are interested in the product category will be more likely to engage in information search effort for the product category (Howard & Sheth, 1969). With respect to the effect of involvement on search behavior, high level of ego involvement leads to a greater amount of information search (Beatty & Smith, 1987). Celsi and Olson (1988) found that consumers spent more time attending to information when their involvement was increased. Hence, enduring involvement might be positively related to motivation to perform external search.
Research Proposition 14: Higher enduring involvement increases consumer motivation to engage in information search on the Internet.
Need for Cognition. Need for cognition (NFC) is defined as “the tendency for an individual to engage in and enjoy thinking” (Cacioppo & Petty, 1982) which emphasizes individual differences in cognitive efforts. High NFC individuals tend to elaborate more extensively on information and exert more cognitive effort on processing product information than low NFC individuals. NFC is an important motivational antecedent of information search, and several studies have suggested that high NFC individuals are more likely to engage in effortful search processes than low NFC individuals (Verplanken, 1993; Verplanken, Hazenberg, & Palenwen, 1992). For example, Inman, McAlister, and Hoyer (1990) found that in a grocery shopping setting, high NFC consumers tended to search for more information than did low NFC consumers. Also Verplanken (1993) found that when asked to search for information about refrigerators, high NFC subjects expended more cognitive efforts than low NFC subjects. All of these seem to suggest that high NFC individuals could be motivated to expend more effort on cognitive tasks than low NFC individuals in an information rich online environment.
Research Proposition 15: Higher need for cognition increases consumer motivation to engage in information search on the Internet.
Shopping Attitude. Consumers have different goals and motivations when shopping for products (Babin, Darden, & Griffin, 1994; Darden & Dorsch, 1990; Stone, 1954; Tauber, 1972; Westbrook & Black, 1985, for retail shopping; Akaah, Korgaonkar, & Lund, 1995; Eastlick & Feinberg, 1999, for in-home shopping; Eastlick & Lotz, 1999; Parsons, 2002; Wolfinbarger & Gilly, 2001; Wolin, Korgaonkar, & Lund, 2002, for Internet shopping). In general, shoppers pursue either utilitarian, functional, goal-directed (Donthu & Garcia, 1999; Papacharissi & Rubin, 2000) or hedonistic, non-functional, recreational activities (Bellenger & Korgaonkar, 1980; Holbrook & Hirschman, 1982).
A number of researchers argued that attitudes and beliefs about shopping should have a positive influence on external search behavior (Beatty & Smith, 1987; Duncan & Olshavsky, 1982; Klein, 1998; Schmidt & Spreng, 1996; Shim et al., 2001). For example, Li et al. (1999) found that frequent Internet shoppers are more convenience seekers and are lower in the experiential shopping orientation than less frequent Internet shoppers. Donthu and Garcia (1999) also found that Internet shoppers tend to seek more convenience than non-shoppers. More recently, Shim et al. (2001) found a positive relationship between attitude and intention to use the Internet for information search. In terms of the effect of price and brand (economic shopping orientation), Shim et al. (2001) posit that consumers who highly evaluate the economic aspect of shopping will be more likely to use the Internet for an information source.
As discussed above, the Internet is capable of providing a variety of product information with low cost and effort, thus increasing the convenience and economic value of online information search. The Internet, however, seems relatively less effective in fulfilling experiential aspects of shopping motivation due to its inability to provide direct examination or trial of the product. Therefore, the following propositions are proposed:
Research Proposition 16a: Consumers with high convenience shopping orientation have higher motivation to engage in online information search than consumers with low convenience shopping orientation.
Research Proposition 16b: Consumers with high economic shopping orientation have higher motivation to engage in online information search than consumers with low economic shopping orientation.
Research Proposition 16c: Consumers with high experiential shopping orientation do not have higher motivation to engage in online information search than consumers with low experiential shopping orientation.
Several important issues related to the role of the Internet in information search are discussed in this chapter. First of all, is information search on the Internet different from traditional information search behavior? A group of researchers suggest that consumer search behavior on the Internet will be similar to traditional search behavior and will follow the same decision making perspective (Hoffman & Novak, 1996). Others indicate that online information search behavior will change because the Internet is able to provide iterative screenings with respect to an individual consumer’s attribute preference (Alba et al., 1997) and consumers can easily adjust their preferences for product attributes (Jaillet, 2002; Peterson et al., 1997). This issue can be better understood by considering characteristics of the Internet as an information source and transaction channel more thoroughly and by adopting a comprehensive causal framework such as the one proposed in this chapter.
Second, the Internet is but one among various information sources available to consumers (Kaufman-Scarborough & Lindquist, 2002; Schoenbachler & Gordon, 2002). Consumers may not utilize the Internet exclusively for their search and purchases. In today’s multi-source and multi-channel environment, it is necessary to understand what factors influence consumers to choose different channels and information sources for products and services.
Lastly, information search on the Internet does not necessarily increase the amount of search but rather it may substitute for other external information search (Peterson & Merino, 2003; Ratchford et al., 2003). Ratchford et al. (2003) found that Internet users’ total search for automobiles reduced because the Internet replaced search effort dedicated to other sources such as dealer visits. It is also important to recognize that using the Internet for product information search does not necessarily lead to product purchase online (Burke, 2002; Kaufman-Scarborough & Lindquist, 2002). For example, Burke (2002) found that among Internet users, about half of them (55%) used the Internet for product purchase. However, a majority of them tended to purchase products using conventional retailers (91%). Marketers may need to improve transaction and checkout processes such as account setups, delivery and tracking as well as product information quality in order to transform online searchers into online shoppers.
The increasing use of the Internet as a business transaction channel and information source has attracted attention from researchers and practitioners alike. The model proposed in this chapter is a comprehensive framework to understand what motivates, drives, and mediates online information search behavior. The proposed model may facilitate empirical tests of online search behavior in several ways.
First of all, the proposed model presents boundary conditions for the relationships between antecedents and external information search. For example, prior research suggests that the information-rich and interactive nature of the Internet affects consumer information search (Huang, 2000; Li et al., 1999). It appears that information complexity tends to decrease information search whereas information novelty increases the amount of search. However, this effect will depend on other factors such as consumer ability and motivation to search, or situational factors such as the ones indicated in the proposed model. It is therefore plausible that consumers with prior online purchase experience or high product knowledge will be less affected by information complexity and novelty.
Second, the proposed model may provide theoretical grounds for the interactive role of several important constructs in explaining information search behavior. For example, some researchers found that product knowledge decreases external search (Betty & Smith, 1987) while other researchers demonstrated that higher situational involvement facilitates information search (Celsi & Olson, 1988). The proposed model suggests that situational involvement increases external search only for low product knowledge consumers because high knowledge consumers will utilize their existing knowledge regardless of situational involvement.
Third, the proposed model provides implications for practitioners in terms of how to deliver product and service information on their Web sites in an effective way. Practitioners are reminded of consumer ability, motivation to search, and perceived benefits and costs of search when designing their Web sites. In addition, marketers need to consider consumer characteristics (i.e., knowledge, experience, skill, involvement, shopping attitudes, need for cognition), media and product characteristics simultaneously so that their Web sites can provide information that is properly tailored for the consumer’s abilities and motivations.
Akaah, I.P., Korgaonkar, P.K., & Lund, D. (1995). Direct marketing attitudes. Journal of Business Research, 34(3), 211-219.
Alba, J.W., & Hutchinson, J.W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13(4), 411-454.
Alba, J.W., Lynch, J., Weitz, B., Janiszewski, C., Lutz, R., Sawyer, A., & Wood, S. (1997). Interactive home shopping: Consumer, retailer, and manufacturer incentives to participate in electronic marketplaces. Journal of Marketing, 61(3), 38-53.
Andeasen, A.R. (1968). Attitudes and customer behavior: A decision model. In H. H. Kassarjian & T. S. Robertson (Eds.), Perspectives in consumer behavior. Glenview, IL: Scott, Foreman & Co.
Avery, R.J. (1996). Determinants of search for nondurable goods: An empirical assessment of the economics of information theory. Journal of Consumer Affairs, 30(2), 390-420.
Babin, B.J., Darden,W.R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644-656.
Bakos, J.Y. (1997). Reducing buyer search costs: Implications for electronic marketplaces. Management Science, 43(12), 1676–1692.
Beales, H., Mazis, M.B., Salop, S.C., & Staelin, R. (1981). Consumer search and public policy. Journal of Consumer Research, 8(1), 11-22.
Beatty, S.E., & Smith, S.M. (1987). External search effort: An investigation across several product categories. Journal of Consumer Research, 14(1), 83-95.
Bellenger, D.N., & Korgaonkar, P.K. (1980). Profiling the recreational shopper. Journal of Retailing, 56(3), 77-92.
Bellman, S., Lohse, G.L., & Johnson, E.J. (1999). Predictors of online buying behavior. Communications of the ACM, 42(12), 32-38.
Bettman, J.R. (1979). An information processing theory of consumer choice. Reading, MA: Addison-Wesley.
Bettman, J.R., & Park, C.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(3), 234-248.
Bloch, P.H., & Richins, M.L. (1983). Shopping without purchase: An investigation of consumer browsing behavior. Advances in Consumer Research, 10, 389-393.
Bloch, P.H., Sherrell, D.L., & Ridgway, N.M. (1986). Consumer search: An extended framework. Journal of Consumer Research, 13(1), 119-126.
Brucks, M. (1985). The effects of product class knowledge on information search behavior. Journal of Consumer Research, 12(1), 1-16.
Brynjolfsson, E., & Smith, M.D. (2000). Frictionless commerce? A comparison of Internet and conventional retailers. Management Science, 46(4), 563-585.
Bucklin, L.P. (1966). Testing propensities to shop. Journal of Marketing, 30(1), 22-27.
Burke, R.R. (2002). Technology and the customer interface: What consumers want in the physical and virtual store. Journal of the Academy of Marketing Science, 30(4), 411-432.
Cacioppo, J.T., & Petty, R.E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42, 116-131.
Catledge, L.D., & Pitkow, J.E. (1995). Characterizing browsing strategies in the World Wide Web. Retrieved from: http://www.igd.fhg.de/www/www95/papers/80/userpatterns/UserPatterns.Paper4.formatted.html
Celsi, R.L., & Olson, J.C. (1988). The role of involvement in attention and comprehension processes. Journal of Consumer Research, 15(2), 210-224.
Chang, Y.R., & Hanna, S. (1992). Consumer credit search behaviour. Journal of Consumer Studies and Home Economics, 16, 207-227.
Choo, C.W., Detlor, B., & Turnbull, D. (1999). Information seeking on the Web - An integrated model of browsing and searching. Proceedings of the Annual Meeting of the American Society for Information Science (ASIS). Retrieved from: http:// choo.fis.utoronto.ca/fis/respub/aisis99/
Claxton, J.D., Fry, J.N., & Portis, B. (1974). A taxonomy of prepurchase information gathering patterns. Journal of Consumer Research, 1(3), 35-42.
Darden, W.R., & Dorsch, M.J. (1990). An action strategy approach to examining shopping behavior. Journal of Business Research, 21(3), 289-308.
Degeratu, A.M., Rangaswamy, A., & Wu, J. (2000). Consumer choice behavior in online and traditional supermarkets: The effects of brand name, price, and other search attributes. International Journal of Research in Marketing, 17(1), 55-78.
DeSarbo, W.S., & Choi, J. (1999). A latent structure double hurdle regression model for exploring heterogeneity in consumer search patterns. Journal of Econometrics, 89, 423-455.
Dickson, P.R. (2000). Understanding the trade winds: The global evolution of production, consumption, and the Internet. Journal of Consumer Research, 27(1), 115-122.
Dholakia, R.R., & Bagozzi, R.P. (2001). Consumer behavior in digital environment. In J. Wind & V. J. Mahajan (Eds.), Digital marketing (pp. 163-200). New York: John Wiley & Sons.
Donthu, N., & Garcia, A. (1999). The Internet shoppers. Journal of Advertising Research, 39(3), 52-58.
Duncan, C.P., & Olshavsky, R.W. (1982). External search: The role of consumer beliefs. Journal of Marketing Research, 19(1), 32-43.
Eastlick, M.A., & Feinberg, R.A. (1999). Shopping motives for mail catalog shopping. Journal of Business Research, 45(3), 247-304.
Eastlick, M.A., & Lotz, S.L. (1999). Profiling potential adopters and non-adopters of an interactive electronic shopping medium. International Journal of Retail & Distribution Management, 27(6), 209-223.
Fiske, C.A., Luebbehusen, L.A., Miyazaki, A.D., & Urbany, J.E. (1994). The relationship between knowledge and search: It depends. Advances in Consumer Research, 21, 43-50.
Forrester Research, Inc. (2003). US eCommerce overview: 2003 to 2008.
Freiden, J.B., & Goldsmith, R.E. (1989). Prepurchase information-seeking for professional services. Journal of Services Marketing, 3(1), 45-55.
Furse, D.H., Punj, G.N., & Stewart, D.W. (1984). A typology of individual search strategies among purchasers of new automobiles. Journal of Consumer Research, 10(4), 417-431.
Girard, T., Korgaonkar, P., & Silverblatt, R. (2003). Relationship of type of product, shopping orientations, and demographics with preference for shopping on the Internet. Journal of Business and Psychology, 18(1), 101-120.
Goldman, A., & Johansson, J.K. (1978). Determinant of search for lower prices: An empirical assessment of the economics of information theory. Journal of Consumer Research, 5(3), 176-186.
Greenwald, A.G., & Leavitt, C. (1984). Audience involvement in advertising: Four levels. Journal of Consumer Research, 11(1), 581-592.
Guo, C. (2001). A review on consumer external search: Amount and determinants. Journal of Business and Psychology, 15(3), 505-519.
Haubl, V., & Trifts, G. (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 19(1), 4-21.
Heaney, J.G., & Goldsmith, R.E. (1999). External information search for banking services. The International Journal of Bank Marketing, 17(7), 305-323.
Hoffman, D.L., & Novak, T.P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50-68.
Holbrook, M.B., & Hirschman, E.C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132-140.
Hölscher, C., & Strube, G. (2000, June). Web search behavior of Internet experts and newbies. Computer Networks, 33, 337-346.
Howard, J.A., & Sheth, J.N. (1969). The theory of buyer behavior. New York: John Wiley.
Huang, M. (2000). Information load: Its relationship to online exploratory and shopping behavior. International Journal of Information Management, 20, 337-347.
Hugstad, P., Taylor, J.W., & Bruce, G.D. (1987). The effects of social class and perceived risk on consumer information search. The Journal of Consumer Marketing, 4(2), 41-46.
Iglesias, M.P., & Guillen, M.J.Y. (2002). Searching for information when selecting a restaurant. Food Service Technology, 2, 35-45.
Inman, J.J., McAlister, L., & Hoyer, W.D. (1990). Promotion signal: Proxy for a price cut? Journal of Consumer Research, 17(1), 74-81.
Jacoby, J., Chestnut, R.W., & Fisher, W.A. (1978). A behavioral process approach to information acquisition in nondurable purchasing. Journal of Marketing Research, 15(4), 532-44.
Jaillet, H.F. (2002). Web metrics: Measuring patterns in online shopping. Journal of Consumer Behaviour, 2(4), 369-381.
Jarvenpaa, S.L., & Todd, P.A. (1996-1997). Consumer reactions to electronic shopping on the World Wide Web. International Journal of Electronic Commerce, 1(2), 59-88.
Johnson, E.J., Bellman, S., & Lohse, G.L. (2002). Cognitive lockin and the power law of practice. New York: Department of Marketing, Columbia Business School, Columbia University.
Johnson, E.J., Moe, W.W., Fader, P.S., Bellman, S., & Lohse, G.L. (2002). On the depth and dynamics of online search behavior. New York: Department of Marketing, Columbia Business School, Columbia University.
Johnson, E.J., & Russo, J.E. (1984). Product familiarity and learning new information. Journal of Consumer Research, 11(1), 542-550.
Katona, G., & Mueller, E. (1955). A study of purchasing decisions. In L.H. Clark (Ed.), Consumer behavior: The dynamics of consumer reaction (pp. 30-87). New York: New York University Press.
Kaufman-Sarborough, C., & Lindquist, J.D. (2002). E-shopping in a multiple channel environment. Journal of Consumer Marketing, 19(4), 333-350.
Kiel, G.C., & Layton, R.A. (1981). Dimensions of consumer information seeking. Journal of Marketing Research, 18(2), 233-239.
Klein, L.R. (1998). Evaluating the potential of interactive media through a new lens: Search versus experience goods. Journal of Business Research, 41(3), 195-203.
Klein, L.R., & Ford, G.T. (2002). Consumer search for information in the digital age: An empirical study of pre-purchase search for automobiles. Advances in Consumer Research, 29, 100-101.
Korgaonkar, P.K., & Wolin, L.D. (1999). A multivariate analysis of Web usage. Journal of Advertising Research, 39(2), 53-67.
Lee, H., Herr, P.M., Kardes, F.R., & Kim, C. (1999). Motivated search: Effects of choice accountability, issue involvement, and prior knowledge on information acquisition and use. Journal of Business Research, 45(1), 75-88.
Lee, J., & Hogarth, J.M. (1998). Shopping for a credit card: Does information search pay off? Consumer Interests Annual, 44, 30-35.
Lee, J., & Hogarth, J.M. (2000a). Shopping for a credit card: The search for information. Consumer Interests Annual, 46, 115-120.
Lee, J., & Hogarth, J.M. (2000b). Relationships among information search activities when shopping for a credit card. Journal of Consumer Affairs, 34(2), 330-360.
Li, H. , Kuo, C., & 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(2). Retrieved from: http://www.ascusc.org/jcmc/vol5/issue2/hairong.html
Liang, T., & Huang, J. (1998). An empirical study on consumer acceptance of products in electronic markets: A transaction cost model. Decision Support Systems, 24(1), 29-43.
Lohse, G.L., & Spiller, P. (1998). Electronic shopping. Communications of the ACM, 41(7), 81-87.
Lutz, R.J., & Reilly, P.J. (1973). An exploration of the effects of perceived social and performance risk on consumer information acquisition. Advances in Consumer Research, 1, 393-405.
Maute, M.F., & Forrester, Jr., W.R. (1991). The effect of attribute qualities on consumer decision making: A causal model of external information search. Journal of Economic Psychology, 12(4), 643-666.
Menon, A.M., Deshpande, A.D., Perri III, M., & Zinkhan, G.M. (2002). Trust in online prescription drug information among Internet users: The impact on information search behavior after exposure to direct-to-consumer advertising. Health Marketing Quarterly, 29(1), 17-35.
Miller, H.J. (1993). Consumer search and retail analysis. Journal of Retailing, 69(2), 160-192.
Mitra, K., Reiss, M., & Capella, L.M. (1999). An examination of perceived risk, information search and behavioral intentions in search, experience and credence services. Journal of Services Marketing, 13(3), 208-228.
Moore, W.L., & Lehmann, D.R. (1980). Individual differences in search behavior for a nondurable. Journal of Consumer Research, 7(3), 296-307.
Moorthy, S., Ratchford, B.T., & Talukdar, D. (1997). Consumer information search revisited: Theory and empirical analysis. Journal of Consumer Research, 23(4), 263-277.
Murray, K.B. (1991). A test of services marketing theory: Consumer information acquisition activities. Journal of Marketing, 55(1), 10-25.
Murray, K.B., & Schlacter, J.L. (1990). The impact of services versus goods on consumers’ assessment of perceived risk and variability. Journal of the Academy of Marketing Science, 18(1), 51-65.
Newman, J.W. (1977). Consumer external search: Amount and determinants. In A.G. Woodside, J.N. Sheth, & P. Bennett (Eds.), Consumer and industrial buying behavior (pp. 79-94), New York: Elsevier North Holland.
Newman, J.W., & Staelin, R. (1971). Multivariate analysis of differences in buyer decision time. Journal of Marketing Research, 8(2), 192-198.
Newman, J.W., & Staelin, R. (1972). Prepurchase information seeking for new cars and major household appliances. Journal of Marketing Research, 9(3), 249-257.
Newman, J.W., & Staelin, R. (1973). Information sources of durable goods. Journal of Advertising Research, 13(2), 19-29.
Novak, T.P., Hoffman, D.L., & Young, Y. (2000). Measuring the flow construct in online environments: A structural modeling approach. Marketing Science, 19(1), 22-44.
Papacharissi, Z., & Rubin, A.M. (2000). Predictors of Internet use. Journal of Broadcasting & Electronic Media, 44(2), 175-196.
Park, C.W., & Lessig, V.P. (1981). Familiarity and its impact on consumer decision biases and heuristics. Journal of Consumer Research, 8(3), 223-230.
Park, C.W., & Mittal, B. (1985). A theory of involvement in consumer behavior: Problems and issues. Advances in Consumer Research, 12, 210-231.
Park, C.W., Mothersbaugh, D.C., & Feick, L. (1994). Consumer knowledge assessment. Journal of Consumer Research, 21(1), 71-78.
Parker, B.J., & Plank, R.E. (2000, June). A uses and gratifications perspective on the Internet as a new information source. American Business Review, 43-49.
Parsons, A.G. (2002). Non-functional motives for online shoppers: Why we click. Journal of Consumer Marketing, 19(5), 380-392.
Peterson, R.A., Balasubramanian, S., & Bronnenberg, B.J. (1997). Exploring the implications of the Internet for consumer marketing. Journal of the Academy of Marketing Science, 25(4), 329-346.
Peterson, R.A., & Merino, M.C. (2003). Consumer information search behavior and the Internet. Psychology & Marketing, 20(2), 99-121.
Petty, R.E., Cacioppo, J.T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10(2), 135-146.
Punj, G.N., & Staelin, R. (1983). A Model of consumer information search behavior for new automobiles. Journal of Consumer Research, 9(4), 366-380.
Putrevu, S., & Ratchford, B.T. (1997). A model of search behavior with an application to grocery shopping. Journal of Retailing, 73(4), 463-486.
Raju, P. S., Lonial, S.C., & Mangold, W.G. (1995). Differential effects of subjective knowledge, objective knowledge, and usage experience on decision making: An exploratory investigation. Journal of Consumer Psychology, 4(2), 153-180.
Ramaswami, S.N., Strader, T.J. & Brett, K. (2000-2001). Determinants of on-line channel use for purchasing financial products. International Journal of Electronic Commerce, 5(2), 95-118.
Ratchford, B.T. (1982). Cost-benefit models for explaining consumer choice and information seeking behavior. Management Science, 28(2), 197-212.
Ratchford, B.T., Lee, M., & Talukdar, D. (2003). The impact of the Internet on information search for automobiles. Journal of Marketing Search, 40(2), 193-209.
Ratchford, B.T., Talukdar, D., & Lee, M. (2001). A model of consumer choice of the Internet as an information source. International Journal of Electronic Commerce, 5(3), 7-21.
Schmidt, J.B., & Spreng, R.A. (1996). A proposed model of external consumer information search. Journal of the Academy of Marketing Science, 24(3), 246-256.
Schoenbachler, D.D., & Gordon, G.L. (2002). Multi-channel shopping: Understanding what drives channel choice. Journal of Consumer Marketing, 19(1), 42-53.
Selnes, F., & Howell, R. (1999). The effect of product expertise on decision making and search for written and sensory information. Advances in Consumer Research, 26, 80-88.
Selnes, F., & Troye, S.V. (1989). Buying expertise, information search, and problem solving. Journal of Economic Psychology, 10(3), 411-428.
Shim, S., Eastlick, M.A., Lotz, S.L., & Warrington, P. (2001). An online prepurchase intentions model: The role of intention to search. Journal of Retailing, 77(3), 397-416.
Srinivasan, N. (1987). A path analytic model of external search for information for new automobiles. Advances in Consumer Research, 14, 319-322.
Srinivasan, N. (1990). Pre-purchase external search for information. In V.E. Zeithaml (Ed.), Review of marketing (pp. 153-189). Chicago: American Marketing Association.
Srinivasan, N., & Agrawal, J. (1988). The relationship between prior knowledge and external search. Advances in Consumer Research, 15, 27-31.
Srinivasan, N., & Ratchford, B.T. (1991). An empirical test of a model of external search for automobiles. Journal of Consumer Research, 18(2), 233-242.
Stigler, G.J. (1961). The economics of information. Journal of Political Economy, 69(3), 213-223.
Stone, G.P. (1954). City and urban identification: Observations on the social psychology of city life. American Journal of Sociology, 60(1), 36-45.
Sundaram, D. S., & Taylor, R.D. (1998). An investigation of external information search effort: Replication in in-home shopping situations. Advances in Consumer Research, 25, 440-445.
Swoboda, B.S. (1998). Conditions of consumer information seeking: Theoretical foundations and empirical results of using interactive multimedia systems. The International Review of Retail, Distribution and Consumer Research, 8(4), 361-381.
Szymanski, D.M., & Hise, R.T. (2000). E-satisfaction: An initial examination. Journal of Retailing, 76(3), 309-322.
Tauber, E.M. (1972). Marketing notes and communications. Journal of Marketing, 36(4), 46-49.
Tauscher, L., & Greenberg, S. (1997a). How people revisit Web pages: Empirical findings and implications for the design of history systems. International Journal of Human-Computer Studies, 47, 97-137.
Tauscher, L., & Greenberg, S. (1997b). Revisitation patterns in World Wide Web navigation. In S. Pemberton (Ed.), Proceedings of CHI 97 Human Factors in Computing Systems (pp. 399-406), Atlanta, Georgia.
Taylor, J.W. (1974). The role of risk in consumer behavior. Journal of Marketing, 38(2), 54-60.
Urbany, J.E. (1986). An experimental examination of the economics of information. Journal of Consumer Research, 13(2), 257-271.
Urbany, J.E., Dickson, P.R., & Wilkie, W.L. (1989). Buyer uncertainty and information search. Journal of Consumer Research, 16(2), 208-215.
Venkatraman M., & Dholakia, R.R. (1997). Searching for information in marketspace: Does the form - product or service - matter? Journal of Services Marketing, 11(5), 303-316.
Verplanken, B. (1993). Need for cognition and external information search: Responses to time pressure during decision-making. Journal of Research in Personality, 27(3), 238-253.
Verplanken, B., Hazenberg, P.T., & Palenewen, G.R. (1992). Need for cognition and external information search effort. Journal of Research in Personality, 26, 128-136.
Weenig, M.W.H., & Maarleveld, M. (2002). The impact of time constraint on information search strategies in complex choice tasks. Journal of Economic Psychology, 23(6), 689-702.
Westbrook, R.A., & Black, W.C. (1985). A motivation-based shopper typology. Journal of Retailing, 61(1), 78-103.
Westbrook, R.A., & Fornell, C. (1979, August). Patterns of information source usage among durable goods buyers. Journal of Marketing Research, 16, 303-312.
Wilkie, W.L., & Dickson, P.R. (1985). Shopping for appliances: Consumers’ strategies and patterns of information search. Cambridge, MA: Marketing Science Institute.
Wolfinbarger, M., & Gilly, M.C. (2001). Shopping online for freedom, control, and fun. California Management Review, 43(2), 34-55.
Wolin, L.D., Korgaonkar, P.K., & Lund, D. (2002). Beliefs, attitudes and behavior towards Web advertising. International Journal of Advertising, 21(1), 87-114.
Zaichkowsky, J.L. (1994). The personal involvement inventory: Reduction, revision, and application to advertising. Journal of Advertising, 23(4), 59-70.
Section I - Consumer Behavior in Web-Based Commerce
Section II - Web Site Usability and Interface Design
Section III - Systems Design for Electronic Commerce
Section IV - Customer Trust and Loyalty Online
Section V - Social and Legal Influences on Web Marketing and Online Consumers