Consumer Behavior

An Empirical Study of Consumer Switching from Traditional to Electronic Channel: A Purchase Decision Process Perspective
Alok Gupta* ([e-mail]) Bo-chiuan Su ([e-mail]) Zhiping Walter ([e-mail]) ALOK GUPTA ([e-mail]) is an Associate Professor of the Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, USA. He received his PhD in Management Science and Information Systems from the University of Texas at Austin in 1996. His research has been published in various information systems, economics, and computer science journals such as Management Science, ISR, CACM, JMIS, Decision Sciences, Journal of Economics Dynamics and Control, Computational Economics, Decision Support Systems, IEEE Internet Computing, International Journal of Flexible Manufacturing Systems, Information Technology Management, and Journal of Organizational Computing and Electronic Commerce. He received prestigious NSF CAREER award for his research in Online Auction in 2001. His current and teaching interest are in the area of economic modeling and analysis of electronic commerce. He serves on the editorial board of ISR, DSS, and Brazilian Electronic Journal of Economics. BO-CHIUAN SU ([e-mail]) is an Assistant Professor of the Department of Information Management at the National Central University, Taiwan (R.O.C.). He received the Ph.D. degree in Business Administration, specialized in information systems, from the School of Business, University of Connecticut, U.S.A. Dr. Su’s research interests include economic issues in electronic commerce, Internet marketing, and Enterprise Resources Planning (ERP). His research will appear in Decision Support Systems. Zhiping Walter ([e-mail]) received the Ph.D. degree in Business Administration, specializing in Management Information System from the Simon School of Business, University of Rochester. She is currently an Assistant Professor of Management Information System at the School of Business, University of Colorado at Denver, USA. Dr. Walter’s research interests are in the areas of economics of information systems, Internet marketing, and Information Technology in Healthcare. Her research has been published or has been accepted to publish in Communications of the ACM, European Journal of Operational Research, Decision Support Systems, International Journal of Healthcare Technology Management, Technology Analysis and Strategic Management, ICIS proceedings and HICSS proceedings.

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Author names in alphabetical order.

An Empirical Study of Consumer Switching from Traditional to Electronic Channel: A Purchase Decision Process Perspective
Abstract This paper examines the relationships between the operating characteristics of the consumer purchase decision process and the channel switching intentions of consumers. A theoretical model that explains consumer channel switching intentions is constructed and tested based on a sample of 337 actual consumers. The analysis indicates that the overall channel-switching tendency from offline to online is approximately 52% across four product categories, including books, flight tickets, wine, and stereo systems. The order of switching tendency (flight tickets, books, stereo systems, wine) is consistent with their search and experience attributes: flight tickets and books are search goods whereas wine and stereo systems are experience goods. The logistic regression analysis across product categories shows that consumers’ differences in channel risk perceptions, price search intentions, evaluation effort, and waiting time between online and offline channels have significant impacts on their tendency of switching from offline to online shopping. The results also indicate that those attracted to purchase online perceive significantly lower channel risk, search effort, evaluation effort, and waiting (delivery) time online than offline and express significantly higher price search intentions online than offline. Although consumers attracted to offline channels also perceive lower search cost and higher price search intentions online than offline, their perceived online search effort and price search intentions are significantly lower than those attracted to online channels. These results provided further support to the importance of the factors examined in influencing consumer channel switching. It also suggests that demographics might not be effective bases for market

segmentation.

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Keywords and Phrases: Electronic commerce, retail channel switching, risk perceptions, purchase decisions

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1. Introduction
Many researchers and practitioners have recognized the emergence of online shopping as a new retail format. Online retailing sales reached $51.3 billion in 2001 and were expected to reach $72.1 billion in 2003 [13]. In a time-constrained world, online stores allow consumers to shop from the convenience of remote locations. However, online retailing sales just accounted for one percent of total retail spending in 2001 and most of online stores were losing money [83, 89]. An International Council of Shopping Centers’ report [44] also forecasted that in the U.S. only 4.7% of retail sales would be online by 2005 and 5.3% by 2010. Currently the dollar amount of products purchased via the Internet in the U.S. is about $45 billion [89]. But forecasters expect this amount to grow to more than $100 billion, and possibly exceed $200 billion, by 2004 [35, 36]. These forecasts represent average annual growth rates of 40-80%. While this would still amount to under 5% of all retail sales in 2004, it nonetheless represents a dramatic increase in Internet retailing. To investigate this phenomenon, it is critical to study what drive or inhibit the consumer’s intentions to shop online. Specifically, an issue of particular interest to both practitioners and academics is in understanding the consumer’s channel switching behavior (from offline to online) and identifying the factors influencing such behavior. Based on the consumer purchase decision process, this study identifies five major factors that potentially influence a consumer’s switching tendency from shopping offline to online. These factors are: channel risk perceptions, price search intentions, search effort, evaluation effort, and delivery time. The anticipated effects of these factors are incorporated into a model, which is tested with data of four products possessing various product characteristics both aggregately and separately: books, flight tickets, wine, and stereo systems. Wine and stereo systems are experience goods whereas books and flight tickets

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are search goods.1 Information economics theory distinguishes between search and experience goods typically in terms of consumers’ ability to know quality before and after buying [23]. Search goods are those products of which quality can be assessed prior to purchase, whereas the quality of experience goods cannot be assessed without being ascertained by product use.2 It is clear that the taste of a bottle of wine and the sound of a stereo system are difficult to examine and compare online because of their sensory attributes.3 One of the primary objectives of this study then is to determine whether the impacts of these factors on consumer channel switching are similar across search and experience goods. While many researchers have focused on consumer demographics, the technological characteristics of virtual stores, and the unique capabilities of the Internet medium to provide interactive and personalized online shopping experiences [18, 47, 54, 55, 56, 74, 81], no prior studies have focused on how the factors associated with the consumer purchase decision process can affect their channel switching tendency differentially. Further, there is an increased interest in understanding the attitude differences between consumers who prefer online shopping versus traditional stores. The central questions are: are there attitude and perceptual differences between the consumers attracted to shop online and those attracted to traditional stores, and if so, what are

Darby and Karni [23] add a third category, namely “credence” goods. The quality of such products cannot be determined reliably even after usage. They attribute wine to be one of the examples. However, researchers in information economics generally still consider wine as experience goods. 2 We recognize that all goods have some combination of search and experience attributes. A search good is simply one for which the consumption benefits most important to consumers are predicted reliably by attribute information available to them before buying. This reasoning implies that the same product can be a search or experience good, depending on the benefits that are important to consumers and the inferences consumers make about how well those benefits are predicted by information available prior to purchase. In the present study, we still adopt the general definition by information economics for search and experience goods. 3 In some cases, experiential attribute information could still be conveyed effectively electronically. For example, customers or chat community post their own reviews, with positive word of mouth clearly facilitating evaluating sensory or experiential product attributes. Assurance from the expertise or certification of third parties can also facilitate evaluating experiential attributes prior to purchase, such as Bizrate.com and Epinions.com. However, we do not expect such business to be commonly used by consumers and in some occasions consumers have to subscribe by paying fees.

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such differences?

Understanding the effect of such differences on consumers’ choice of

channels should be useful in devising marketing and channel strategies, since if the consumers attracted to shopping online are different on these attitude differences from those attracted to shopping in traditional stores, the design and marketing strategies for the two environments should be tailored to fit the profiles of their target consumers. However, many online marketing decisions regarding product assortment, pricing and promotional strategies rely only on what are observed in the online environment, without knowing the exact causal explanation for the consumer’s channel choice behavior. Consequently, our research is an important first step. Overall, to fill these research gaps, this research has two main objectives: to analyze consumer channel switching behavior and identify which operating characteristics of the consumer purchase decision process lead them to switch channel; to investigate what kinds of consumers are more likely to be attracted to

shopping online instead of to traditional stores and their attitude differences. This article is organized as follows. Next section provides the theoretical background for explaining the positive relationship between purchase intentions and actual purchase behavior. It then follows that the difference in purchase intentions between online and offline channels is a reasonable indicator of channel switching from offline to online. Section 3 identifies and justifies why the factors identified from the consumer purchase decision process are relevant to consumer channel switching. Then, the research hypotheses and model are proposed accordingly. Data collection procedures and measures are explained in Section 4. After presenting and discussing the results of analysis in Section 5, Section 6 concludes the paper.

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2. Theoretical Background 2.1 Purchase Intentions and Actual Purchase Behavior
In this study, the difference in purchase intentions between online and offline channels is used as an indicator of channel switching tendency. A consumer tends to switch to online channels if his online purchase intentions are higher than offline purchase intentions. Essentially, consumers express preferences (stated purchase intentions) based on utility maximization in terms of the costs and benefits of the retail formats presented to them [2]. It implies that a consumer’s utility obtained from online shopping needs to exceed the utility provided by the traditional format to cause the consumer to switch to an online environment. The theory of reasoned action (TRA) asserts that behavior is influenced by behavioral intentions [3]. Research in social psychology suggests that intentions should be the best predictor of an individual’s behavior because they allow each individual to independently incorporate all the relevant factors that may influence his or her actual behavior [34]. Several studies have

examined the relationship between purchase intentions and actual purchase behavior for durable goods [1, 19, 32, 39, 62, 72] and for nondurable goods [37, 46, 88, 92]. The observed

relationship between intentions and purchase is generally positive and significant. Since Internet shopping behavior shares the volitional nature of the phenomena that TRA tries to explain and predict [48], the degree to which people express their intentions to purchase should therefore be a reasonable predictor of their actual purchase behavior. It then follows that consumers’

differences in purchase intentions between online and offline channels should be a reasonable indicator of their tendency to switch from offline to online channels. That is, a consumer’s

offline purchase intensions are used as a point of reference for assessing his tendency to switch to online channels.

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2.2 Factors Relevant to Purchase Decision Process
Based on the consumer purchase decision process, five factors that potentially affect consumers’ intentions to shop online and offline are identified. These factors are: channel risk perceptions, price search intentions, search effort, evaluation effort, and delivery time. Again, using

consumers’ offline perceptions on these factors as points of reference, the differences in these factors between online and offline channels are incorporated into a model for explaining their channel switching tendency. It is well established in marketing and consumer behavior literature that the consumer purchase decision process includes five stages: problem recognition, information search, evaluation of product options, purchase decision, and post-purchase support [29, 53]. The

purchase process starts when a consumer recognizes a problem or a need. Since the desire to buy a product/service is largely subconscious (e.g., thirst, hunger, or admiration of a neighbor’s new car) and the utility from consuming the product/service itself is the same no matter whether the consumer obtains this product/service from a physical store or from an online store, it should play a very minor, if any, role in driving the consumer to purchase online. The next stage is information search. Information search (including price and product information) usually incurs search effort. When purchasing a product from a brick-and-mortar store, a consumer has to spend time browsing the aisles. If the consumer cannot find a suitable product at the store (e.g., high prices and/or no favorable product attributes), he must keep spending effort on additional searches. In contrast, online shopping can dramatically reduce search effort for price and product information with just a few clicks. Specifically, the relative ease of online search for better prices motivates consumers to shop online. Consequently,

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consumers who have stronger price search intentions may find online shopping more attractive than offline shopping. The third stage of the purchase decision process is the evaluation of product options, which incurs evaluation effort. It involves examining and comparing product attributes such as price, brand, quality, and others. Despite the reduced search costs for price information,

consumers may feel troubled in evaluating non-price attributes online. The color and style of a product may not be exactly as it appears when displayed on the computer screen. Product quality may be hard to evaluate online as well. It is especially true for the “feel and touch” product categories. For example, consumers may be apprehensive about buying something without touching or feeling it because of quality uncertainty [9, 33]. Therefore, the online medium can facilitate information search but impede evaluation of product options in terms of non-price attributes. During the evaluation stage, consumers will also evaluate their perceived risk associated with online shopping. Risk perceptions are considered to influence consumers’ evaluation and choice behavior [28, 79]. Research has shown that a consumer’s decision to modify, postpone, or avoid a purchase decision is heavily influenced by his perceived risk [7, 87]. Online shopping might be perceived to be riskier, thus reducing the overall utility that a consumer can obtain from shopping online. However, a consumer perceiving a certain amount of online shopping risk may or may not avoid the risk. Researchers define perceived risk in terms of uncertainty and

consequences [8, 10, 70, 79]; these two components of risk, uncertainty and consequences, have been found in research on risk perceptions in non-marketing contexts as well [84, 85]. According to risk theory, perceived risk increases with a higher level of uncertainty and/or a greater chance associated with negative consequences [66]. For example, if a consumer is considering buying an unfamiliar bottle of wine for a dinner party, the perceived risk associated with that purpose 8

could arise because he does not know how the wine will taste (uncertainty) and is worried about his guests’ reactions if it is not a good wine (negative consequences). Thus, whether a consumer is willing to bear a particular risk depends on his perceptions on the likelihood of the occurrence of the risk and on the importance, or severity of the possible negative consequences, of such risk. Consequently, this study conceptualizes a consumer’s channel risk perceptions as the interaction effect of the likelihood and the importance of a risky situation perceived by the consumer when buying through that channel. After a purchase decision has been made, the product still has to be physically delivered (except digitized products/services, of course) if the purchase is made online. Since consumers tend to maximize utility subject to time constraints [8], the efficiency of delivery becomes a real concern to both consumers and online retailers. Online retailers often experience low customer satisfaction due to their poor fulfillment of on-time delivery [50]. Since different consumers value the speed of delivery differently, time-sensitive consumers may favor a traditional channel simply because it saves delivery time. To account for the effects of waiting problem associated with delivery on channel preference, delivery time is included in our model.

3. Research Hypotheses
As discussed earlier, our analysis is derived from the consumer purchase decision process, and five constructs potentially influencing the consumer’s channel switching tendency are identified. This section analyzes the relationships between these constructs and the consumer’s tendency to switch from offline to online channels, and then the research hypotheses are proposed accordingly.

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3.1 Channel Risk Perceptions
While the Internet allows consumers to shop at the convenience of remote locations, they may be apprehensive about making purchase online if they perceive risk associated with online shopping. Interestingly, some models of online buying behavior have excluded perceived risk as a predictor of online shopping [58, 59, 58, 61], although others see risk reduction as a key to increase consumers’ participation in e-commerce [11, 68]. Consumers’ perceived risk associated with online shopping has received limited attention despite its potentially important implications for online shopping. Some early research suggests that risk perceptions may play a minor role in the adoption of online shopping [47], but several recent industry and government-related studies have nevertheless considered consumers’ risk perceptions to be a primary obstacle to the future growth of e-commerce [22, 30, 31]. Five components of perceived risk have been proposed in the literature: financial, performance, physical, psychological, and social [45, 51, 78]. Financial risk stems from paying more for a product than being necessary or not getting sufficient value for the money spent [76]. Consumers generally address this problem by shopping around for a more satisfactory price. Performance risk, sometimes referred to as quality risk, is based on the belief that a product will not perform as well as expected or will not provide the benefits desired [8]. Physical risk involves the potential threat to consumer safety or physical health and well-being. Psychological risk arises from the likelihood that the purchase fails to reflect one’s personality or self-image. Social risk is concerned with an individual’s ego and the effect that the consumption is observable by others and has on the opinions of reference groups. For shopping, the effect of physical risk is minimum, since shopping activities usually do not involve physical risk. Note that perceived security of online transactions and concerns for privacy should be included as

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elements of performance risk in online shopping. Thus, except for physical risk, the other four types of risk represent the four critical sources of channel risk perceived by consumers. Of course, different individuals have different levels of risk perceptions toward online shopping. Here, these risk perceptions are associated with the Internet as a purchasing medium rather than the consequences of purchasing a particular product. Research has indicated that perceptions of risk can be extended beyond the product to the shopping medium itself [20, 86]. Such concerns are likely to affect consumer behavior on the Internet and may help to explain why most consumers still use the Internet for browsing rather than buying [12, 93]. The amount of risk perceived has been suggested to be a major factor in deciding whether a consumer would shop via a certain retail channel [20, 49, 71, 86]. Based on this perceptive, consumers who perceive lower risk online than offline are more likely to switch to online channels than those risk-laden consumers, resulting in the following hypothesis:

H1 (Channel Risk Perceptions): Consumers who perceive lower risk in conducting purchases online than offline are more likely to switch to online channels.

3.2 Price Search Intentions
With the advent of the Internet, consumers expect to find lower prices more easily in the online environment than in the offline environment. Search engines and agent technologies would dramatically reduce search costs, prices would plummet, and deep discounting is prevalent online [6, 9, 15]. Because of this heightened expectation for lower prices online, consumers would demonstrate higher price search intentions over the Internet than when shopping in traditional stores. This would have a positive impact on consumers’ tendency to switch from offline to

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online channels, because the Internet provides a single source for consumers to evaluate their price consideration sets, instead of searching for prices in many traditional stores. Thus, it is

held that consumers with greater positive perceived differences in price search intentions between online and offline channels would exhibit a higher tendency to switch to online channels. H2 (Price Search Intentions): Consumers who perceive higher price search intentions in conducting purchases online than offline are more likely to switch to online channels.

3.3 Search and Evaluation Efforts
On the Internet, search effort (for price and non-price information) is dramatically reduced. This should have a positive impact on consumers’ intentions to switch to online shopping. The widespread availability of information on the Internet is one of the reasons why many buyers view search and purchase on the Internet as a utilitarian activity [94]. Many online buyers revel in the fact that they can get information directly without having to go through a salesperson who usually has very limited information compared to a web site [94]. On the other hand, it may be fairly difficult to evaluate certain types of product information online, thus impeding consumer channel switching. This is especially true for shopping “look and feel” products [12, 33, 61].

Pictures and animation certainly help but only to a very limited extent. Alternative technologies such as online customization tools will also assist, but, for example, unless the consumer feels the swatch of the fabric for the suit, a purchase decision is difficult [33]. Thus, it is proposed that when consumers perceive increasingly less search and evaluation efforts required for shopping online than offline, they also exhibit a higher tendency to switch to online shopping.

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H3 (Search Effort): Consumers who perceive lower search effort in conducting purchases online than offline are more likely to switch to online channels. H4 (Evaluation Effort): Consumers who perceive lower evaluation effort in conducting purchases online than offline are more likely to switch to online channels.

3.4 Delivery Time
Most online transactions still involve physical product delivery, and the efficiency of delivery can become a real burden for both consumers and online retailers. 4 Among the dot-coms, their inexperience in real-world operations, marketing, and administration has left many online shoppers complaining about late or nonexistent deliveries. Online retailers often experience low customer satisfaction due to their poor fulfillment of on-time delivery [50]. The speed at which ordered items are delivered is important. If timing is so important and one of the major benefits offered by e-commerce is its “convenience” (time-related) for shopping, then shortening delivery time should increase the utility (benefits) of consumers (especially for time-sensitive consumers) and thereby motivate them to purchase online. Thus, it is expected that consumers with less

concern about delivery time will be more inclined to switch to shopping online, as suggested by the following hypothesis. H5 (Delivery Time): Consumers who perceive shorter delivery time in conducting purchases online than offline are more likely to switch to online channels.

The five-factor model of channel switching is shown in Figure 1.

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*** Figure 1 about here ***

4. Data and Methodology
Four product categories, books, flight tickets, wine, and stereo systems, were chosen and available in both online and offline channels. Wine and stereo systems are experience goods whereas books and flight ticket are search goods. It is clear that the quality and taste of wines and the sound quality of stereo systems are difficult to evaluate and compare online. On the other hand, the quality of books is relatively easier to evaluate over the Internet, since once a book title has been chosen, the book itself is basically identical across retailers. The quality of flight tickets is also easy to evaluate prior to purchase, e.g., number of connections, length of connection, flexible departure/return time, and same day/staying overnight. Note that these products were also selected on the basis of price level. Books and wine are low-priced products while flight tickets and stereo systems are relatively high-priced ones.5

4.1 Pilot Study
A pilot study was conducted via a paper survey. For the pilot study, the questionnaire was tested and modified before using for online data collection. Possible question misunderstanding was also considered and the questionnaire was revised accordingly. A convenient student sample was used for this pilot study. One hundred and seven business-major undergraduates and M.B.A. students from a major university in the northeastern U.S.A. participated in the study on a

In July 2000, seven online retailers-including CDnow, KBkids.com, and Toysrus.com agreed to pay civil penalties totaling $1.5 million for variously failing to provide customers notice of delayed deliveries [41]. 5 We realize that the respondents may not be familiar with wine, resulting in a low level of involvement. The level of involvement a consumer has with a product is based on the relevance of that product to the consumer’s inherent needs, value and interests [94]. Involvement is known to be an important element of Internet purchasing behavior

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voluntary base. All the 107 respondents completed the survey. Approximately 94% of the 107 respondents reported experience with online purchase in the last 12 months, and therefore are actual online buyers. The use of business school students as surrogates of actual online buyers might raise the issue of external validity [14, 38, 40]. However, it has been suggested that college students are influential and representative gatekeepers for filtering/spreading opinions and information about Internet content to broader society [24]. Besides, in the absence of a theory or evidence showing why college students would react in a peculiar way, the use of students should not represent a threat to applicability [64]. Thus, for the purpose of pilot testing, it is believed that students are appropriate for refining and validating the survey instrument.

4.2 Main Study
The data used in the main study were collected via a Web survey. The Web survey is a visual stimulus and under the respondent’s complete control with regard to whether and/or how each question is read and comprehended. Therefore, it is expected that responses to Web

questionnaires will closely resemble those observed via mail questionnaires [26]. In addition, the inherent characteristics of Web surveys are similar to those of online shopping. Web surveys are therefore particularly suitable for conducting research in the online shopping realm. Further, Web surveys also have significant advantages such as speed, the ability to collect large amount of data without interviewers, the elimination of postage and stationary costs, and low processing costs without separate data entry.

[17]. But as discussed earlier, we intentionally chose to study wine and wished to demonstrate that the effects of certain factor (channel risk perceptions) may be pronounced and outweigh those of other factors.

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Considerable effort was exerted to carefully design and extensively tests the instrument to make it as user friendly as possible. The main purpose of respondent-friendly design for the Web survey is to decrease measurement and non-response errors. Such design may also have

coverage benefits by helping ensure an equal chance for people with various types of browsers, computer equipment, and operating systems to receive and complete the Web questionnaire. We followed the principles for the design of Web surveys as suggested in Dillman [26]. Meanwhile, the Web survey was also designed to eliminate those possible causes of respondent frustration observed by Dillman and Bowker [27]. In order to reduce the possibility of multiple submissions by an individual, cookie technology was used to ensure all respondents answered the questionnaire only once. 6 In addition, we restrained the size of response categories within tables to reduce measurement errors due to attitude scales with physical distances between points on the scale, which changes as a result of (1) changing the screen configuration from 800*600 to one of the other used sizes (e.g., 640*480 or 1024*768) or (2) using a lower level browser. Further, the Web questionnaire was constructed to emulate visual aspects of the paper questionnaire. Thus, a similarly colored background and placement of questions on the page were designed to ensure a very similar stimulus to traditional mail surveys. The invitations to participate in the study were distributed through an email list. A list of 50,000 email addresses was purchased from a private company. This sample frame has known characteristics (consists of individuals who are at least 20 years of age and had sent in registration

A cookie simply consists of a text-only string that enters into the memory of a browser. This string contains the domain, path, lifetime, and value of a variable that a website sets. If the lifetime of this variable is longer than the time the user spends at that site, then this string (completion state variable) is saved to file in client’s computer for future reference.

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cards of warranties for a wide variety of computer hardware and software) in comparison with many of the Web surveys having no specific sample frames and soliciting responses from a variety of sources, such as advertising, announcement on Web pages, and message boards of commercial and academic Web sites. It is clearly impossible to calculate the response rate for these kinds of Web surveys that do not have a pre-selected sample frame. Although it was not our objective to have the sample limited to those purchasers of computer hardware and software, the availability of a sample frame with known characteristics led us to adopt this list. Moreover, the subjects in our sample frame most likely have adequate equipment to conduct online shopping. Indeed, approximately 98% of the participants in our study had made purchases on the Internet in the previous 12 months. The survey was administered over a period of four weeks (4/3 – 5/1/02). It is important to note that many other Web surveys were conducted over a period of several months to a year, in contrast to the four weeks for this research. Email invitations were actually delivered to 18,988 addresses (38%) of the original 50,000-email list, due to returned emails as undeliverable and other unexpected delivery problems. To protect the anonymity of the survey receipts, special coding was developed to send out email individually; i.e., concealing the names of survey recipients and place one’s own email in the regular “send to” field. In so doing, the survey recipients only saw one email address – that of the individual who sent it. The email included an explanation of the purpose of the survey and confidentiality. The email also included the link to the survey URL and contained “institution.edu,” the names of professors, and contact phone, sending a signal that we are members of a higher education institution (and thus increasing our credibility). Response rates of Web surveys are likely to be very low [90]. The response rate in this study is not an exception and hovers around 2% (a total of 337 complete responses were 17

collected). This might lead to the possibility of a non-response bias, that is, if the respondents are different from the non-respondents on the questions that are being asked. However, non-response error is not conceptually equivalent to response rate [27].7 Also, calculating response rates can be challenging, because calculating the total numbers who received the email and “open” it is questionable (the denominator in the response rate). Although to test actual delivery and opening of the email to non-respondents, a read receipt acknowledge can be attached. In this way, it is the “confirmed” email “contact” rate. But not all mail systems are compliant with this read receipt acknowledge attached and many people may choose to return the receipt without even reading the email, still causing difficulty in computing the “confirmed” response rate. Nonetheless, we attribute the occurrence of low response rate to five factors: (1) survey fatigue, (2) lengthy questionnaire, (3) the use of Java technology, (2) no inclusion of incentives, and (5) methodological differences. Survey fatigue is common, as public opinion polls have become more popular with the media and telemarketers using surveys for data mining research. The email list that we purchased from a private company might be a weak response pool. Many individuals on the list should have been contacted by a variety of surveys and are sensitive to “spamming.” Given the possibly large amount of spamming and unwanted solicitations via email, a low email survey response rate is likely. However, we believe that this might have a relatively little impact on our study, as our email contained “institution.edu” and other contact information for increasing our credibility. The survey URL was also clearly located in the university domain. Another factor that undoubtedly contributes to the low response rate is the lengthiness of the questionnaire. In addition to four demographic questions, the questionnaire contains 38 questions

But we recognize that there still is a possibility that the higher the proportion of sampled respondents who respond to a survey, the lower the likelihood of non-response error.

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for each product category.

Since the survey requires the subjects to assess four product

categories, the total number of questions is 156, which is likely to significantly reduce the subjects’ interest in responding to the survey. Our analysis showed that the respondents on average spent 28 minutes answering the survey, with a standard deviation of 4 minutes. Apparently, completing this questionnaire requires considerable effort and patience, thus leading to a low response rate. We also found that the use of Java Script technique for the survey might make it impossible for many subjects to access the survey and to submit the completed Web questionnaire. This would reduce the response rate as well. Further, nonresponse might occur because some subjects were prevented from accessing some aspects of the questionnaire due to the incompatibilities of hardware and software (e.g., various levels and types of Web browsers). Such incompatibilities might cause some of the response features to be disabled or perhaps rearranged on the screen. No inclusion of incentives could be a factor of the low response rate as well. Past research has shown that survey response rates can be increased by 15-20% by including such incentives as cash and gift certificates [57]. But because of the budget constraints, we were unable to offer such incentives. Besides, unlike mailed surveys, it is impossible to

include prepaid incentives such as a dollar bill with an email invitation to the Web survey, even though we recognized that incentives paid upon competition (post-payment) are possible with Web surveys. Lastly, a methodological difference may also help explain the low response rate of this study. This study used a separate Web survey to capture respondent data, rather than an email survey [63]. Using the Web form requires respondents to access another computermediated communication medium and perform an addition task (use a Web browser), in order to participate in the survey. Additional steps and tasks with the Web survey might further reduce the survey response rate.

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Finally, with the deluge of emails from various ListServs and advertising campaigns, many individuals are sensitive to “spamming” and thus may take offense at receiving multiple emails from a researcher they do not know. Consequently, we did not send out a reminder email after the first email to increase the response rate. The questionnaire is provided in Appendix A, including the measures of the theoretical factors. The questions pertaining to the factors

examined are common to both online and offline shopping. By having both sets of questions (online and offline), we were able to compare the attitudes and behavior of consumers in comparable scenarios where the only difference was the purchase channel.

4.3 Reliability of Instrument
The reliabilities of the multi-item scales, assessed by Cronbach’s alpha, for measuring channel risk perceptions and price search intentions are satisfactory (both online and offline) and provided in Appendix A. For the two-item measure of purchase intentions, the Pearson

correlation between the two items was over 0.76. The alphas for price search intentions (4 items) were 0.92 and 0.9 for online and offline, respectively. Although the alphas were 0.61 and 0.60 for online and offline channel risk perceptions (5 items), they still indicated acceptable reliability [21]. The validity of these results is also supported by [80], which found that Cronbach’s alpha should be in the range of 0.56 – 0.94 for factors measured by 3-7 items. For all the multi-item measures, the scales were obtained by adding the item scores. The means, standard deviations, and intercorrelations of the scales are given in Table 1. *** Table 1 about here ***

4.4 Demographics

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The respondents’ ages range from 21 to 91 years old, with a mean of 47 (median = 47). Not surprisingly, examining the respondents by gender revealed a male dominance (72% male, 28% female); perhaps, this is in part due to that the respondents are purchasers of computer hardware and software. Analysis of the respondents by educational background revealed a relatively high level of education. A significant number of the respondents (67%) have a four-year college degree. This compares favorably with the national average of 24.4% for the same educational level [89]. Annual household gross income ranges from $0 - $20,000 to over $120,000, with a median of $70,000 - $90,000. Comparisons of the respondents with the Internet customers of other third-party data are shown in Table 2. The last column in Table 2 is the U.S. population demographics according to the latest U.S. Census. Compared to those of the survey data, the demographic characteristics of our respondents appear to be somewhat older, more educated, wealthier, and male predominant. Appendix B presents the respondents’ online activity profiles,

indicating that these nonstudent respondents are comprised of not only actual but also frequent online buyers: 98% of respondents reported experience with online purchasing in the last 12 months, and 73% of respondents made at least 7 purchases online, 36% at least 12 purchase, and 17% at least 24 purchases in the last 12 months. This sample, therefore, cannot be thought to represent the population of online shoppers. But this frequent online buyers segment is important [54] as online retailers are striving for determining what features attract frequent online buyers to return and what they can do to further encourage repeat customers to become loyal customers. For example, in a survey by Yahoo! Store, a store hosting service, more than 85 percent of stores received fewer than 10 percent of their orders from frequent buyers [4].

*** Table 2 about here ***

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5. Results 5.1 Channel Switching Tendency
To analyze consumers’ tendency of switching from offline to online channels, a binary switching variable was coded 1 if online purchase intentions were greater than offline purchase intentions and zero otherwise. At the aggregate level, the analysis indicated that the overall channelswitching tendency from offline to online was approximately 52% across the four product categories. As Table 3 shows, the order of switching tendency (flight tickets, books, stereo systems, wine) is consistent with the products’ search and experience attributes. These results suggest that books and flight tickets should be more successful in alluring consumers to switch from physical to online channels, whereas wine and stereo systems dominated by experience attributes (taste and sound quality) should fare less well in inducing channel switching. Our findings support that merchandises purchased on the basis of search attributes are more amenable to electronic retailing, whereas merchandises purchased on the basis of experience attributes are more likely to be purchased in physical stores. To examine the effects of the five factors

identified in the study on consumer channel switching, five logistic regression models are tested and discussed in the following section. *** Table 3 about here ***

5.2 Factors Influencing Channel Switching
A logistic regression model was constructed to predict consumer channel switching as a function of the difference scores between online and offline channels of the five factors. The channelswitching model is represented as a logistic regression model with the following structure:
Switchingonline,i = γ 0 + γ 1 RISKPDi + γ 2 PRSERACHD i + γ 3SEARCHD i + γ 4 EVALUATIOND i + γ 5 WAITINGDi + ε i

22

(1)

Switchingonline,i is a binary variable such that consumer i chooses to switch from offline to online channels if his online purchase intentions are greater than his offline purchase intentions.

RISKPDi , PRSERACHDi , SEARCHDi , EVALUATIOND i , and WAITINGDi are consumer i 's respective perceived differences in channel risk perceptions, price search intentions, search effort, evaluation effort, and delivery time between online and offline channels, and ε i is random error. The model was tested first with the aggregate data and then with the separate data for each product, as shown in Table 4. Overall, the differences in channel risk perceptions, price search intentions, evaluation effort, and delivery time between online and offline channels appeared to have a significant impact on the respondents’ tendency to switch from offline to online channels. No substantive changes in the results were observed even when the effects of the demographics variables had been controlled for. These results are consistent with the findings in consumer behavior that demographics and lifestyle variables only explain a small fraction of people’s choice behavior [75]. The classification matrices showed high hit ratios of the correctly classified switching and non-switching cases with the models. The overall “hit ratios” are 74%, and 79%, 88%, 83%, and 71% for the models of books, flight tickets, wine, and stereo systems, respectively. Crossvalidation by splitting the sample randomly into halves to produce separate “analysis” and “holdout” samples still suggested high hit ratios and the same significant factors selected.

*** Table 4 about here ***

23

As Table 4 shows, the impact of these factors varies across product categories. First, the difference in channel risk perceptions between channels showed generally a negative association with channel switching tendency, regardless of whether the analysis was based on aggregate data or on separate data for individual products. Separate analyses for individual products revealed that the negative influences of the differences in channel risk perceptions were quite consistent across the four products, yet the magnitude of the affects was small. Thus, even though

Hypothesis 1 was supported statistically at the significance level of 0.05, the impact of the differences in channel risk perceptions between online and offline channels might lack practical significance. When analyzed aggregately, the difference in price search intentions between online and offline channels showed positive influence on channel switching tendency. Separate analyses for individual products all indicated that the difference in price search intensions had significant effect on consumer channel switching. Consequently, Hypothesis 2 was supported. This result thus suggests that price is a major concern to consumers when they buy from online channels. As consumers have an increasingly greater desire to search for lower priced products online than offline, their tendency to purchase online also increases. The implication is obvious that if online retailers want to be successful, they should stock sufficient lower-price product alternatives or offer more sales promotions. In particular for flight tickets, the difference in price search intentions appeared to be a very influential factor for consumer channel switching, indicating that seeking low priced tickets could be the main objective of those consumers who shop flight tickets online. The difference in search effort between channels showed no significant effect on channel switching tendency, regardless of whether the analysis was based on aggregate or separate data. Thus, Hypothesis 3 was not supported. This result indicated that the difference in search effort 24

between channels played largely no role in motivating consumers to shop online.

Further

analysis showed that not all the respondents consider online search effort to be low (mean: 4.1, median: 4, and standard deviation: 1.8 on a 1(very low)-7(very high) scale), which might be contradictory to the conventional wisdom. Alba et al. [2] present the case for consumers; the main attraction of interactive electronic retailing is a reduction in search costs for product and product-related information. However, we argue that, although the search engine allows

consumers to search for a product and product-related information across stores more easily than in a conventional store, it nevertheless requires consumers to spend time going through pages of search results. Actually, search costs for obtaining information sensory attributes (e.g., how a wine tastes to a given consumer or how to judge the quality of the sound of a stereo system) might be higher online than offline. Further analysis showed that the mean search effort is significant higher for wine or stereo systems (experience goods) than for books or flight tickets (search goods) (all p-values < 0.05), a result consistent with the products’ attributes. As hypothesized, the difference in evaluation effort between channels showed a significant negative effect on consumer intentions to switch to online channels, regardless of whether it was analyzed aggregately across the products or separately for individual products. Thus, Hypothesis 4 was supported, indicating the difficulty of evaluating products online could be a major obstacle for online retailers to overcome. Except for flight tickets, the difference in delivery time between channels appeared to affect consumer channel switching, and thus Hypothesis 5 was partially supported. This result indicated that for most products a longer waiting time for the delivery of products ordered online could reduce consumers’ incentives to switch to online channels. Since consumers can always pick up the ordered flight tickets at the airport before departure, it seems reasonable to see that delivery time played little role in discouraging consumers from buying flight tickets online. 25

Overall, when aggregating across the four products investigated, only the difference in search effort between channels failed to show the hypothesized effect on the consumer’s tendency to switch to online channels. This might indicate that the Internet has provided an environment that greatly facilitates consumers to search for desired products so that the search effort incurred online no longer inhibits them from shopping online. In fact, many technologies, such as intelligent agents, have been developed to facilitate consumer search on the Internet. Even though the Internet is unstructured and fragmented, consumer search efforts can still be greatly reduced with such technologies. On the other hand, it has been articulated that

technologies such as intelligent agents that can scour the Internet for the best deals on products and facilitate product evaluations. Yet, as long as information on the Internet is unstructured, the potential for these automated shopping assistants to reduce evaluation effort is limited. The current inadequacy in information representation may also undermine consumers’ confidence in making informed purchase decisions and impinge on their switching to online shopping. Same as analyzed aggregately, the difference in search effort was the only factor failed to motivate consumer channel switching.

5.3 Demographics Differences and Attitudes Differences between Consumers Attracted to Online vs. Offline Channels
One of the primary goals in this research is to investigate what kinds of consumers are attracted to shopping online vs. a traditional store and their attitude differences. As mentioned earlier, the attitudes of interest here are those associated with the consumer purchase decision process; that is, channel risk perceptions, price search intentions, search effort, evaluation effort, and delivery time. By separating the respondents into two groups based on whether their intentions to

26

shopping online are higher than shopping in traditional stores, ANOVA tests were conducted to check the perceptual differences between these two groups of respondents, as shown in Table 5. The results indicated that those attracted to purchasing online perceived significantly lower channel risk, search effort, evaluation effort, and waiting (delivery) time online than offline (all p-values < 0.001) and expressed significantly higher price search intentions online than offline

(p-value < 0.001). In contrast, those respondents attracted to offline channels (48% of the respondents) perceived significantly higher channel risk, evaluation effort, and waiting (delivery) time online than offline (p-value < 0.001); although they perceived lower search cost and higher price search intentions online than offline, their perceived reduction in online search effort and their online price search intentions were significantly lower than those attracted to online channels (all p-values < 0.001). Table 6 presents the demographics differences between The demographics of online shoppers

consumers attracted to online vs. offline channels.

appeared to be indifferent from those of offline shoppers. These results provided further support to the importance of the factors examined in influencing consumer channel switching. It also suggests that demographics might not be effective bases for market segmentation.
*** Table 5 about here *** *** Table 6 about here ***

6. Conclusion
Both researchers and practitioners have recognized the emergence of online shopping as a new retail format. They have stressed the potential for a dramatic increase in Internet retailing and for a cannibalization of electronic channels over traditional channels. In this study, we first examine consumers’ channel switching tendency (from offline to online), and then based on the consumer purchase decision process, we identify and study the drivers and inhibiters of such tendency. The

27

results of this study help us understand the attitude differences between consumers who have different preferences over the shopping channels. At the aggregate level, the result indicated that the overall channel-switching tendency from offline to online is approximately 52% across the four product categories examined. The 52% switching tendency suggests that online shopping is still in the early stage of development and has the potential of thriving continuously. For example, fifty-six percent of retailers in U.S. reported profitable online retail in 2001, up from 43 percent in 2000 and further profitability and growth is anticipated in 2002 [13]. For the four product categories investigated, the results indicated that the order of channel switching tendency (flight tickets (83%), books (40%), stereo systems (18%), wine (6%)) is consistent with their search and experience attributes: flight tickets

and books are search goods whereas wine and stereo systems are experience goods.

These

results support the conjecture that merchandises purchased on the basis of search attributes are more amenable to electronic retailing (because direct experience is not required), whereas merchandises purchased on the basis of experience attributes are not. This implies that the limitations of relying on old paradigms become apparent when they consider the “virtually every type of products can be sold on the Internet” logic [91]. The difference in channel risk perceptions of consumers between channels showed generally a negative association with channel switching tendency, yet the magnitude of the impact was small. So, the impact of the difference in channel risk perceptions between online and offline channels might not be so significant in practice, indicating that consumer risk perceptions might not be a primary obstacle to the future growth of online retailing. The result also concurs with some early research, which suggests that risk perceptions may play a minor role in the adoption of online shopping [47].

28

We showed that the difference in price search intentions between online and offline channels has a positive influence on consumer channel switching tendency, indicating that the relative ease of online price search is one of the motivators for consumers to shop online. Findings also showed that subjects who prefer to switch to online channels have significantly higher price search intentions than those who prefer to remain with traditional channels (45% percentage difference on average). The respondents appeared to value the potential for finding lower prices if they shop through the electronic channel. Online shoppers therefore tend to be more sensitive to price, possibly leading to a more price-sensitive segment in retailing. Many brick-and-mortar companies should be advised to set up Web-based outlets just so they can discriminate the price-sensitive consumers from the price-insensitive ones. Another important implication of the result is that, if price-shopping pays and shoppers are always just “one click away” from a better deal, it will be difficult to make and maintain the Internet shoppers royal to a particular store. Competition might thus be based on other transaction attributes. The difference in search effort between channels showed no significant effect on channel switching tendency, regardless of whether the analysis was based on aggregate data or on separate data for individual products. This suggests that the conventional wisdom that online channels allow consumers to incur lower search costs, and thus motivate them to shop online, does not hold. It implies that searching online might not be that easy. Thus, although online search engines allow consumers to search for a product and the related information across stores more easily than in conventional stores, they nevertheless require consumers to spend time evaluating pages of search results. There is much evidence of visitors to sites becoming

frustrated and abandoning their search for information or even their desire to buy something. Abound of customers simply gave up on their attempts to do business with companies online 39% of online shoppers failed to make a purchase because the sites were too difficult to navigate; 29

56% of attempts to search for information failed; 62% had an "I give up" experience within the past 60 days [42]. The obvious conclusion is that many companies make it very difficult for visitors to use their sites for searching the desired information and to conclude the business. Thus, the Internet retailers should exert greater effort to make their sites more search-friendly with easy navigation. Additionally, as the number of sellers in the electronic marketplace increases, consumer search efficiency might suffer as well, because an additional seller could make consumers incur extra search and evaluation cost. Besides, there typically are strong diminishing returns in terms of search. As the cost of searching for and evaluating new alternatives continues to increase, a point is reached at which the expected cost of considering additional alternatives is greater than the expected increase in benefits. At this point, the consumer would terminate the search for additional alternatives. Because the search costs of Internet shopping are not trivial, the extent to which the Internet can help the consumer making purchase decisions that provide greater value is, therefore, an open question. Some consumers may consider online search effort to be low but others may not. It partly explains why search effort does not significantly affect consumer channel switching in our study. Future research may examine this effect with broader product categories. Moreover, the results showed that the difference in evaluation effort between channels appeared to have a significantly negative effect on consumer tendency to switch to online channels. It implies that the Internet retailers need to facilitate product evaluation by providing more detailed product information. Another implication is that the Internet retailers should not expand the range of their product offerings without accounting for the consumer’s online evaluation capacity. According to the conventional wisdom, the Internet retailers could offer a much wider range of product options (without worrying about shelf space) than offline retailers. 30

But our findings suggest that probably due to the lack of physical looking and feeling, consumers may feel troubled when evaluating and comparing a variety of product options online. Since an increase in product variety generally requires more effort from consumers to learn about and evaluate varieties, perhaps not all consumers are well disposed toward more choices because they are unwilling to devote more of their precious time to decision making, even though the products are offered online. Additionally, frequent radical redesigns of a Web site are a bad idea, which would force the consumers to relearn and reevaluate the site, raising the possibility that they might switch to another site. The difference in delivery time between channels appeared to affect consumer channel switching, except for flight tickets. This is not surprising, since most of ticketing online is electronic without generating physical tickets. The finding suggests that the Internet retailers should provide timely delivery and may offer a free express delivery of some time-sensitive goods. Providing more prompt and timely delivery should not be too cumbersome to the Internet retailers, since they usually rely on those established express and package carriers (e.g., USPS, FedEx, UPS, DHL, and Airborne Express) to deliver the orders with the delivery option desired by the customers. The study has several limitations. First, the available sample frame consists of people who had sent in computer hardware or software warranty information. Although it is not our objective to have the sample limited in this way, a suitable sample frame for a Web survey is always difficult to obtain, especially a sample frame with known characteristics. Because of this constraint, our sample largely consists of not only actual but also frequently online buyers. Consequently, the results obtained in the current study might not be the same as those that could be obtained from a sample frame that represents the general online shoppers more closely. The perceptual measures and the difference score approach adopted in the study also require further 31

refinements and validation. Future research may extend the generalizability of the results by employing larger, more representative samples and enhance the validity of the results by replication studies with broader ranges of product categories. Although the demographic data of our sample closely match the demographic data of the Internet shopper population, the sample nevertheless contains disproportionately more male respondents (72%). The design of the study is therefore somewhat limiting in that women may in fact represent a more important and rapidly growing segment of the Web shopper base. Clearly, women still do the majority of shopping for most households and are the primary buying decision maker in the households [25], making them play a more critical role in current as well as future online retailing activities. Thus, future research may include more female subjects and explore the effect of gender difference on consumer online shopping intentions and behavior. Although the empirical evidence is supportive to the model proposed in the study, many other factors excluded from the current study are also likely to affect consumer channel switching. Offering utility-based value, e.g., lower prices or lower search effort, represents only one of the dimensions by which companies can gain competitive advantage [73]. Consumer behavior is not only determined by purchase cost but also by psychological motivations [67], such as trust. Consumer evaluations of trust in online merchants have been shown to influence their attitudes toward shopping on a site as well as their purchase intentions [48]. Nonetheless, the impact of trust may vary across products sold online. For example, trust may be more important in buying “high-touched” products (i.e., ones that consumers would rather physically inspect before buying) such as clothes, antiques, or some foods [69]. Factors inherent in high-touched products, such as the degree to which they may reflect on the consumer’s self-image (psychology risk) or the risk associated with their quality, may heighten the importance of trust in the seller. Indeed, it is interesting to note that most of the leading product categories in online shopping involve “low32

touch” products and “no-touch” services, e.g., computing hardware, software, financial services, music, videos, books, electronic goods, travel, and tickets. The role of trust is also likely to vary across countries and cultural regions. For example, trust may prove to be less significant to North American buyers who have made a disproportionate share of past purchases over the Web [66]. Understanding cultural and gender-based trust perceptions of consumers should also

improve our understanding of their channel preference and thereby their actual behavior. Finally, consumer purchase decisions do not always include all five stages, depending in part on the complexity of the product. Their buying behavior is also determined by their level of involvement and the intensity of their interest in the product in a particular situation. The level of involvement determines why a consumer is motivated to seek more information about a certain product or a merchant. Hence, buying a product of low involvement (e.g., a commodity) or with sufficient repeated purchase experience needs very little search and decision effort. Thus, caution should be taken when generalizing the findings of this study to the cases of online commodity shopping and product repurchasing.
Acknowledgement. The authors would like to thank the three anonymous reviewers for

their helpful comments on earlier drafts of the paper; it has been greatly improved as a result. The financial support provided by the MOE Program for Promoting Academic Excellence of Universities under the grant number 91-H-FA07-1-4 is gratefully acknowledged.

33

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Appendix A
Measurement Items Used in the Study (in a 7-Point Likert-type Scale) Channel Risk Attitudes (physical channel) ( α = 0.61) Risk Perceptions ( α = 0.6):
(anchored from very unlikely to very likely)

Risk Importance ( α = 0.63):
(anchored from not important to very important)

Financial Risk

A1. What is the likelihood that you would find this product in a physical store more expensive than you thought?

How important is this issue when you consider buying this product from a physical store?

Performance Risk

A3. What is the likelihood that something may go wrong when you buy this product from a physical store (such as a long checkout line)?

How important is this issue when you consider buying this product from a physical store?

Psychological Risk

A5. What is the likelihood that buying this product from a physical store would not fit well with how you view yourself?

How important is this issue when you consider buying this product from a physical store?

Social (ego-related) Risk

A7. What is the likelihood that buying this product from a physical store would affect unfavorable how others view you?

How important is this issue when you consider buying this product from a physical store?

Overall Risk

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A9. On the whole, considering all factors, how risky do you think it is to buy this product from a physical store? (anchored from no risk to very high risk)

How important is the overall risk when you consider buying from a physical store?

Channel Risk Attitude (Internet channel) ( α = 0.64) Risk Perceptions ( α = 0.61):
(anchored from very unlikely to very likely)

Risk Importance ( α = 0.62):
(anchored from not important to very important)

Financial Risk

A2. What is the likelihood that you would find this product online more expensive than you thought?

How important is this issue when you consider buying this product online?

Performance Risk

A4. What is the likelihood that something may go wrong when buying this product online, such as interception of personal and credit information?

How important is this issue when you consider buying this product online?

Psychological Risk

A6. What is the likelihood that online shopping of this product may not fit well with how you view yourself?

How important is this issue when you consider buying this product online?

Social (ego-related) Risk

A8. What is the likelihood that buying this product online would affect unfavorable how others view you?

How important is this issue when you consider buying this product online?

Overall Risk

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A10. On the whole, considering all factors, how risky do you think it is to buy this product online? (anchored from no risk to very high risk)

How important is the overall risk when you consider buying from an online store?

Offline Price Search Intentions (Modified from [60]) ( α = 0.9)

(anchored from strongly disagree to strongly agree) B1. I put in a lot of effort, in physical stores, to find lower prices for this product. B3. I shop around several physical stores to take advantage of low prices for this product. B5. I consider the money saved by finding low prices, in physical stores, to be worth the effort. B7. I consider the time taken to find the low prices, in physical stores, to be worth the effort.
Online Price Search Intentions ( α = 0.92)

(anchored from strongly disagree to strongly agree) B2. I put in a lot of effort to find lower prices online for this product. B4. I shop around several online sites to take advantage of low prices. B6. I consider the money saved by finding low prices online to be worth the effort. B8. I consider the time taken to find the low prices online to be worth the effort.

Intentions to Purchase Offline (Modified from [16]) (Pearson correlation = 0.76)

C1. Suppose you need to buy this product in the next month, how probable is it that you would buy it from a physical store? (anchored from very improbable to very probable) C2. Suppose you need to buy this product in the next month, how willing would you be to buy it from a physical store? (anchored from very unwilling to very willing)

Intentions to Purchase Online (Pearson correlation = 0.78)

C3. Suppose you need to buy this product in the next month, how probable is it that you would buy it online? (anchored from very improbable to very probable) C4. Suppose you need to buy this product in the next month, how willing would you be to buy it online? (anchored from very unwilling to very willing)

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Offline Search Effort

C5. When buying this product from a physical store, what is your assessment of the effort needed to search relevant information? (anchored from very low to very high)

Offline Evaluation Effort

C6. After searching and collecting information, we often need to evaluate a product in terms of its price, quality, and other product attributes. Do you perceive information gathered from physical stores adequate for you to evaluate this product? (anchored from very inadequate to very adequate)

Offline Waiting Time

C7. We usually need to wait in line to check-out in order to buy products from a physical store. Do you perceive waiting in line for this product a big problem for you? (anchored from not a problem to enormous problem)

Online Search Effort

C10. When buying this product online, what is your assessment of the effort needed to search relevant information? (anchored from very low to very high)

Online Evaluation Effort

C11. After searching and collecting information, we often need to evaluate a product in terms of its price, quality, and other product attributes. Do you perceive information gathered online adequate for you to evaluate this product? (anchored from very inadequate to very adequate)
Online Delivery Time

C12. We usually need to wait for an online purchase to be delivered after we 've placed order. Do you perceive waiting for the delivery of this product a big problem for you? (anchored from not a problem to enormous problem)

Demographics

Q1. Gender Q2. Age 44

Q3. Education Q4. Annual total household income

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Appendix B - Respondents’ Online Activity Profiles
Approximately 98% of the 337 respondents reported experience with online purchase in the last 12 months. This compares to 31.1 percent (in a previous six-month period) reported by Rohm and Milne [77], 48 percent (in an indefinite period) of America Online subscribers reported by Holstein et al. [43], 24 percent for the National Consumers League [65] study, and 49 percent (in an indefinite period) for 1999 reported by Sefton [82]. The sample of our main study, therefore, comprises actual online buyers. Further, 73% of the respondents made at least 7 purchases online, 36% at least 12 purchase, and 17% at least 24 purchases in the last 12 months. Therefore, most of the respondents are not only actual but also frequent online buyers. Of the respondents who recently made online purchases, 73% bought book in the last 12 months, 67% in flight ticket, 3% in wine, and 10% in stereo system. The respondents on average spent $1,000 to $2,000 on online shopping in the previous 12 months (median $1,000-$2,000). The average number of online purchases they made in the previous 12 months is 7-12. The average money spent is $146 (median $135). With respect to Internet experience (except for checking email), the respondents reported an average frequency of 16-20 days for monthly World Wide Web usage (median 16-20 days), with an average duration (except for checking email) of 6-10 hours per week (median 6-10 hours). Table B-1 summarizes the respondents’ online activities.

*** Table B-1 about here ***

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Perceived Difference in Price Search Intentions

+

(from offline to online)

Perceived Difference in Delivery Time

Perceived Difference in Search Effort

Figure 1: Channel Switching Model

47

-

-

-

Perceived Difference in Channel Risk Perceptions

-

Channel Switching

Perceived Difference in Evaluation Effort

Table 1: Means, Standard Deviations and Intercorrelations (n = 1348)
Mean S.D. 1. 2. 1. Channel Risk 9.59 33.25 1.000 Perceptions 2. Price Search Intensions 4.50 7.25 1.000 −.327 3. Search Effort 2.27 .193 −.51 −.137 4. Evaluation Effort 2.51 .307 −.33 −.370 5. Waiting Time 2.58 .378 −.13 −.241 6. Switching Tendency .51 .50 .380 −.365 All intercorrelations are significant at the 0.01 level (two-tailed). 3. 4. 5. 6.

1.000 .212 .210 −.162

1.000 0.199 −.367

1.000 −.282

1.000

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Table 2: Comparison of Demographics in the Current Study with Other Third-Party Data Sources This Study Ernst&Young * InsightExpress * U.S. Population * 35.3 49.1%

Age (Median) 47 40-49 Gender (Male) 72% 69% Income $70,000$50,000 (Median) $70,000 $90,000 Education 67% N.A. (College Grad) * Ernst & Young Internet Shopping Study 19998 * InsightExpress Study 20019 * Latest U.S. Census 200010

38 49% $49,800 N.A.

$50,046 24.4%

8

The second annual Ernst & Young Internet shopping study: the digital channel continues to gather steam, 1999/11/12, available at http://www.ey.com/publicate/consumer/pdf/Internetshopping.pdf. 9 InsightExpress e-RDD Study, 2001, available at http://www.insightexpress.com/audiences/methodology.asp. 10 U.S. Census 2000, available at http://www.census.gov/prod/2002pubs/c2kprof00-us.pdf.

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Table 3: Channel Switching Tendency (from offline to online) Across Categories Books 66% Flight Tickets 83% Wine 18% Stereo Systems 40%

Switching Tendency

52%

50

Table 4: The Relationship between Channel Switching and Purchase Decision Variables (Logistic Regression) Across Categories (n = 1,348) −.909** n.s. n.s. n.s. n.s. Books (n = 337) .774 n.s. n.s. n.s. n.s. Flight Tickets (n = 337) .638 n.s. n.s. n.s. n.s. −.019** Wine (n = 337) −2.375** n.s. n.s. n.s. n.s. −.017* Stereo Systems (n = 337) −1.734* n.s. n.s. n.s. n.s. −.019***

Constant Gender Age Education Family Income Channel Risk −.018*** −.011* Perceptions Price Search .103*** .097*** Intentions Search Effort −.037 −.1 *** Evaluation Effort −.251 −.299*** Delivery Time −.142*** −.238*** .38 .37 Nagelkerke R 2 11 Hit Ratio 74% 79% Dependent variable: switch = 1, non-switch = 0 Based on one-tailed test * p < 0.05 ** p < 0.01 *** p < 0.001

.175***
−.186 −.183* −.089 .48 88%

.056*
−.046 −.172* −.217** .26 83%

.094***
−.037 −.225*** −.140* 0.36 71%

11

Nagalkerke’s R-square is comparable to the R-square in multiple regressions, also ranging from 0 to 1 with higher value indicating greater model fit. In contrast, the Cox and Snell R-square measure is limited and it cannot reach the maximum value of 1.

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Table 5: Comparison of Consumers Attracted to Online Shopping vs. Traditional Stores Consumers Attracted to Online Shopping (n = 172) Consumers Attracted to Traditional Stores (n = 165)

Channel Risk Perception Channel Risk Attitudes Price Search Intention Search Effort Evaluation Effort

- 10% *** - 7% ***
54% ***

18% *** 43% *** 13% ***

Waiting Time 17% *** * p < 0.05 *** p < 0.001 Note: Mean difference is presented as percentage difference (online – offline)

- 21% *** - 18% *** - 27% ***

- 4% *
15% ***

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Table 6: Comparison of Consumers Attracted to Online Shopping vs. Traditional Stores Consumers Attracted to Online Shopping (n = 172) 47 71% $70,000 - $90,000 Consumers Attracted to Traditional Stores (n = 165) 47 73% $70,000 - $90,000

Age (Median) Gender (Male) Income (Median) Education (College Grad.)

66%

65%

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Table B-1: Respondents’ Online Activities % Online Shopping Experience (previous 12 months) Books Flight Tickets Wine Stereo Systems # of Purchases /Buyer (previous 12 months) Online Spending /Buyer (previous 12 months) Average Purchase /Buyer (previous 12 months) Days Online / Month

98% 73% 67% 3% 10% 7-12 $1,000-$2,000 $158 16-20 days

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