Measuring Factors that Influence the Success of Internet Commerce

Measuring Factors that Influence the Success of Internet Commerce Gholamreza Torkzadeh • Gurpreet Dhillon Department of MIS, College of Business, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, Nevada 89154-6034 [email protected] • [email protected]

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fforts to develop measures of Internet commerce success have been hampered by (1) the rapid development and use of Internet technologies and (2) the lack of conceptual bases necessary to develop success measures. In a recent study, Keeney (1999) proposed two sets of variables labeled as means objectives and fundamental objectives that influence Internet shopping. Means objectives, he argues, help businesses achieve what is important for their customers— fundamental objectives. Based on Keeney's work, this paper describes the development of two instruments that together measure the factors that influence Internet commerce success. One instrument measures the means objectives that influence online purchase (e.g., Internet vendor trust) and the other measures the fundamental objectives that customers perceive to be important for Internet commerce (e.g., Internet product value). In phase one of the instrument development process, we generated 125 items for means and fundamental objectives. Using a sample of 199 responses by individuals with Internet shopping experience, these constructs were examined for reliability and validity. The Phase 1 results suggested a 4-factor, 21-item instrument to measure means objectives and a 4-factor, 17-item instrument to measure fundamental objectives. In Phase 2 of the instrument development process, we gathered a sample of 421 responses to further explore the 2 instruments. With minor modifications, the Phase 2 data support the 2 models. The Phase 2 results suggest a 5-factor, 21-item instrument that measures means objectives in terms of Internet product choice, online payment, Internet vendor trust, shopping travel, and Internet shipping errors. Results also suggest a 4-factor, 16-item instrument that measures fundamental objectives in terms of Internet shopping convenience, Internet ecology, Internet customer relation, and Internet product value. Evidence of reliability and discriminant, construct, and content validity is presented for the hypothesized measurement models. The paper concludes with discussions on the usefulness of these measures and future research ideas. (Internet Commerce; Customer Behavior; Customer Value; Success Factors; Means Objectives; Fundamental Objectives; Instrument Development; Construct Validity)

1. Introduction Effective use of Internet technology is considered a major determinant of competitive advantage, market penetration, innovation, technology transfer, and even management competency. In the B2C arena, customers will engage in Internet commerce when they feel that 1047-7047/02/1302/0187$05.00 1526-5536 electronic ISSN

they are getting a better deal. The ultimate question about Internet commerce success may be more a function of customers' belief and perception of the net value of the benefits and costs of both a product and the processes of finding, ordering, and receiving it (Keeney 1999). In an attempt to understand what © 2002 INFORMS Vol. 13, No. 2, June 2002, pp. 187-204

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TORKZADEH AND DHILLON Measuring Factors that Influence the Success of Internet Commerce

customers value most, this paper evaluates a set of constructs that influence customers in Internet commerce. This exploratory study is based on concepts proposed by Keeney (1999). The value of Internet commerce to the customer is an important construct for academics and practitioners alike. This concept can be used as a dependent or independent variable in the system-to-value chain that links Internet commerce with preceding and ensuing constructs. Its measurement can help our understanding of the impact of this technology and its management. As Zmud and Boynton (1991) suggest, information systems researchers have paid too little attention to measurement development issues and theoretical advancement has been constrained by the absence of reliable measures. This assertion is even more relevant with respect to Internet commerce metrics. This paper reports on the development of two multidimensional scales that together measure factors that influence customers in Internet commerce. The specific research goals were to develop instruments that: (1) measure factors that influence Internet shoppers, (2) identify the multidimensional nature of these factors, (3) demonstrate reliability and construct validity, and (4) are appropriate for use by academics and practitioners alike. These goals influenced the methods used to generate items, the nature of the sample, and procedures used to eliminate items to create shorter and easier to use instruments.

ating and designing products and increasing the value to customers. He also suggests that the objectives identifled in his study could provide a foundation for developing a quantitative model of customer values. Prior to describing the method used in this study to develop measures of factors that influence Internet shoppers, it would be useful to understand the manner in which Keeney developed his initial framework. Keeney's (1999) research in understanding the value of Internet commerce to the customer was based on the concept of "value propositions." He characterizes the value proposition as benefits and costs of what the Internet offers to the customer in terms of products and services and how this offering can be better than what is currently available through conventional means. To operationalize the value proposition and develop the framework, Keeney (1999) uses a value-focused thinking approach. The intent behind value-focused thinking is to focus on activities that occur prior to a decision problem being solved. As a result, value-focused thinking helps in uncovering hidden objectives (Keeney 1994). In a recent study, Leon (1999) supports this contention. To understand value-focused thinking, three classes of definitions need to be considered. These are the decision context, values, and fundamental objectives. The de-

cision context presents alternatives appropriate for a given decision situation and is specified by the range of activities being contemplated. For example in Keeney (1999), the decision context is whether or not to make purchases over the Internet. Values according to Keeney (1994) are principles used for evaluating the 2. Literature Review and Bases for desirability of possible alternatives in a specific deciStudy Measures sion situation. Values come into play prior to a given In a recent study published in Management Science, "decision problem." If purchasing on the Internet or through conventional shopping is to be construed as a Keeney (1999) interviewed more than 100 individuals decision problem, then values afforded by the cusabout their values in using Internet commerce and protomer will form the basis for evaluating the posed a framework that helps our understanding of alternatives. this phenomenon. Keeney identifies 91 objectives that might influence a consumer to purchase online. He Fundamental objectives make explicit the values classifles the 91 objectives into 25 categories, 9 of which that one cares about and define the consequences of are termed as "fundamental objectives" and the rest as concern (Keeney 1992). Therefore, fundamental objec"means objectives." The fundamental and means obtives are the end objectives as opposed to the means jectives are then presented as a means-ends network. objectives. Fundamental objectives concern the ends Keeney suggests that the results are useful in not only that a decision maker may value in a given context. On designing Internet commerce systems, but also in crethe other hand, means objectives are the methods to INFORMATION SYSTEMS RESEARCH

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achieve the ends. Keeney (1994) suggests that means objectives are differentiated from the fundamental objectives by using the "Why is that important?" (WITI) test. He postulates two possible types of answers. First, that the given objective is one of the essential reasons for interest in a given case. Such an objective is fundamental. Second, a given objective is important because of its implication for some other objective, in which case it's a means objective. For means objectives, a response to the WITI question identifies another objective. Repeated application of the WITI test progressively identifles a single fundamental objective for a given decision context. For instance, in assessing the value of Internet commerce to the customer, Keeney (1999) identifies the ultimate fundamental question as "maximize customer satisfaction." The notion of values and value propositions to identify ultimate objectives has been used in other domains as well, particularly to determine preferences (Krischer 1976). Keeney's conceptualization of the value proposition is particularly attractive because it goes beyond the purchase of a product or service in appraising the net value of an Internet purchase to the customer. As mentioned earlier, the value proposition is operationalized through the value-focused thinking approach. Keeney (1999) used the value-focused thinking concepts in three steps. First, a list of customer values was developed. This was carried out through personal interviews. The purpose of the interviews was clarifled and individuals were asked to identify the pros and cons of purchasing on the Internet versus conventional shopping. Each individual was then asked to write personal values that they thought would influence their purchasing behavior. Each response was then suitably probed. In the second step, each of the values identified was expressed in a common form by converting each item into a corresponding objective. An objective is defined as "something one wants to strive towards" (Keeney 1999) and is constituted of a decision context, an object and direction of preference. In the third step, values were organized so as to indicate their relationships. However, because there may be a large number of objectives, it is useful to classify these into categories. For example, Keeney clustered the value of Internet commerce to the customer objectives into 25 categories. At this stage, the WITI test is

applied to classify the objectives into means and fundamental. The means and fundamental objectives for the value of Internet commerce to the customer together would "describe the bottom line consequences of concerns to customers" (Keeney 1999). Keeney contends that each category of objectives is important to the customer as he or she considers whether or not to purchase over the Internet. Means objectives such as "minimize misuse of credit card" or "minimize misuse of personal information" are important because they influence fundamental objectives such as "minimize cost" or "minimize personal hassle." Keeney (1999, p. 535) suggests that means objectives are important to flrms because they suggest "mechanisms for how companies can improve their product or delivery system for customers" and consequently enhance fundamental objectives for the customer such as "maximize product value." Keeney's work provides a useful list of constructs that can be used as a basis for measuring factors that influence Internet shoppers. He encourages the development of specific measures for the recommended means and fundamental objectives. This study operationalizes the proposed constructs through measurement of two categories of means and fundamental objectives. We expect these two categories of objectives to be closely related because they are components of the same overall construct. However, to help respondents distinguish between the two categories, the first set of questions was called "issues" and the second set of questions was called "objectives" that influence the customer. Consistent with Keeney's description of this construct, we define Internet commerce as the sale and purchase of products and services over the Internet. The factors associated with Internet commerce relate to the net value of both the benefits and costs of a product, and the processes of finding, ordering, and receiving it. This broad definition seems to be consistent with the current practice of Internet commerce and appropriate for this evolving construct (Riggins and Rhee 1998).

3. Research Methods Marketing research has traditionally dealt with issues pertaining to customer behavior and mail-order

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shopping. How^ever, researching mail-order customer behavior does not necessarily equate to that of Internet commerce customer behavior. Clearly, the evolving nature of Internet technologies and the way that these technologies are used and adopted poses unique features, which need careful consideration (Zmud and Boynton 1991). Although it may seem prudent to use existing measurements or borrow relevant scales from marketing to study Internet commerce, there is a real danger associated with indiscriminate adoption, as Benbasat (1991) points out. Unlike traditional variables that have been defined and measured in the marketing field, it is difficult to develop reliable and valid measures for information technology constructs (Segars 1997). Development of valid and reliable measures of information technology constructs requires careful analysis, as they become the building blocks for generating valid relationships among a system of variables (Straub 1989). For this reason our research focuses exclusively on Internet commerce and is based on constructs developed qualitatively by Keeney (1999). 3.1. Phase 1 Phase 1 of this research involved generating and categorizing questions and gathering and analyzing data to eliminate items. A total of 125 items were generated to measure factors proposed by Keeney to influence customers in the context of Internet commerce. Consistent with Keeney's approach, the 125 items were grouped into 2 categories of means and fundamental objectives. There were 72 questions in the means objectives category measuring 15 constructs (e.g., minimize fraud, maximize product information, minimize misuse of credit card, assure reliable delivery, maximize product variety, minimize shopping travel). There were 53 questions in the fundamental objectives measuring 9 constructs (e.g., maximize convenience, maximize privacy, minimize cost, minimize environmental impact). Our 125 questions were a larger number than the items identified by Keeney. This is, because at times it was important to have more than one question for each item proposed by Keeney to capture the essence of all the objectives, as suggested by Churchill (1979). We also wanted to ensure that we were measuring all aspects of the proposed constructs. This resulted in more

items than Keeney listed. Although this may have led to redundancy, it helped content validity because we were drawing on a larger universe of possible items (Boudreau et al. 2001). A five-point Likert-type scale was used, with a range from one (strongly disagree) to five (strongly agree). The instructions asked respondents to think about their engagement with Internet commerce and circle the response that best described their belief. The composite self-administered questionnaire appears in the appendix. The survey was administered to graduate and undergraduate students at a state university in the southwestern region of the United States. Students with experience in shopping through the Internet participated in the study, and 199 usable responses were obtained for Phase 1 of the study. Almost all respondents were working students with careers in such fields as sales, hotel, banking, finance, law, human resources, telecommunications, information systems, education, engineering, healthcare, and government. Data were analyzed with several objectives in mind: purification, unidimensionality, reliability, brevity, and simplicity of factor structure. First, we wanted to purify the items before factor analysis. Churchill (1979) describes the need to purify the items (i.e., eliminate "garbage items"). He suggests that when factor analysis is conducted before purification, it produces many more dimensions than can be conceptually identified, confounding the interpretation of the factor analysis. Two independent criteria were used to eliminate items. First, items were eliminated if their corrected item-total correlation (the correlation of each item with the sum of the other items in its category) were less than 0.50. The domain-sampling model proves a rationale for this procedure. The key assumption in the domain-sampling model is that all items, if they belong to the domain of the concept, have an equal amount of common core. If all the items in a measure are drawn from the domain of a single construct, responses to those items should be highly inter cor related. The corrected item-total correlation provides a measure of this (Churchill 1979). Items were also eliminated using internal consistency reliabilities. The reliability of items comprising each dimension was examined using Cronbach's a to INFORMATION SYSTEMS RESEARCH

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see if additional items could be eliminated without substantially lowering reliability. This criterion for item purification has been used in other management information systems (MIS) studies (Torkzadeh and Doll 1999). In the current study, items were eliminated if the reliability of the remaining items was at least 0.90. Where deleting either of two items that would have the same impact on Cronbach's a, the item with the higher correlated item-total correlation was retained. After these deletions, an exploratory factor analysis of the remaining items in each category was conducted to assess the unidimensionality of the retained items for each group and, where appropriate, eliminate items that were not factorially pure (Weiss 1970). We tried to avoid the use of imprecise and ambiguous terms to label the factors (Bagozzi 1981). The items in each category were assumed to be measures of the same construct. If the factor analysis revealed more than one factor, we had to determine whether to eliminate the additional factors or conclude that the construct was more complex than originally anticipated. Items that were not factorially pure (loading on more than one factor at 0.30 or above) were also eliminated. 3.2. Phase 2 To further explore these two instruments, a sample of 421 (58% male, 42% female) was gathered. Respondents were students with working experience in a variety of professions; including banking, insurance, human resources, sales, healthcare, hotel, government, information systems, telecommunications, engineering, law, education, and other areas. Participation in the study was voluntary and instructions for respondents were exactly the same as those used in Phase 1 of the study. The age of the participants ranged from 20 to 60 with a mean of 28.2. More than 80% of the respondents stated that they had direct experience with Internet commerce. Respondents were asked to identify their primary use of the Internet; they responded as follows: information gathering and research (57%), shopping and looking for product (11.7%), education (10.7%), entertainment (6.4%), sport (2.4%), sales (1%), and other (10.7%). A great majority of the respondents (92.4%) owned, or had easy access to, a computer.

Using the sample of 421 respondents, we conducted an exploratory factor analysis for the two sets of items to identify the simplest factor structure and the most interpretable solution. The ratio of sample size to number of items (20:1 for means objectives items or 24:1 for fundamental objectives items) was well above the minimum 10:1 ratio suggested for factor analysis by Kerlinger (1978). Finally, the correlation matrix for each instrument was analyzed for convergent and discriminant validity (Doll and Torkzadeh 1988). This approach to convergent validity tests if the correlations between measures of the same theoretical construct are different than zero and large enough to warrant further investigation (Campbell and Fiske 1959).

4. Data Analysis 4.1. Phase 1—Means Objectives The item purification procedure, described above, allowed us to eliminate 34 of the 72 items for the means objectives category because they had corrected itemtotal correlation below 0.5. Reliability analysis resulted in elimination of three more items. Data analysis revealed that respondents had difficulty relating to certain product issues (e.g., being able to feel the product, being able to see the product, being able to try the product, and being able to test the product). Also, respondents did not seem concerned about before-sale customer support (e.g., talking with a salesperson, having the opportunity for personal interaction) or impulse buying (e.g., purchasing more than needed, making unreasonable or unnecessary purchases). These constructs seem less relevant to Internet commerce, in that Internet shoppers do not expect to talk with a salesperson or be able to feel the product. Items related to these constructs were deleted. Next, we examined the dimensionality of the remaining constructs. Bartlett's test of sphericity was 2816.3 (p < 0.001). This suggests that the intercorrelation matrix contains enough common variance to make factor analysis worth pursuing. Because some of the constructs in this category were similar (e.g., minimizing fraud, minimizing misuse of credit cards), we combined more than one set of items to examine dimensionality. A factor analysis of the remaining items resulted in the elimination of 14 items. The results suggest a four-factor model with eigenvalues


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greater than one. The factor structure was not difficult to interpret, corresponding with online payment (seven

tion was 0.59 to 0.86 for online payment, 0.64 to 0.79 for Internet product choice, 0.73 to 0.83 for Internet vendor items), Internet product choice (seven items), Internet ventrust, and 0.72 to 0.78 for shopping travel. Reliability dor trust (four items), and shopping travel (three items). statistics were 0.93, 0.90, 0.90, and 0.87 for online payThe 4-factor model explained 72.9% of the variance. ment, Internet product choice, Internet vendor trust, The range for factor loadings was 0.75 to 0.84 for online and shopping travel, respectively. Overall reliability for payment, 0.73 to 0.87 for Internet product choice, 0.77 the 21-item scale was 0.90. The Hotelling test was sigto 0.86 for Internet vendor trust, and 0.84 to 0.90 for nificant for all 4 subscales (p < 0.001) with f values shopping travel. Table 1 reports the simple four-factor ranging from 18.7 (for online payment) to 3.8 (Internet solution (loadings greater than 0.3 are reported). The product choice). Hotelling tests for differences among means objectives scale includes 21 items. The ratio of the entire set of dependent variables. Table 1 provides sample size to number of items (9.5) was considered corrected item-total correlation for each item. adequate for exploratory analysis. Using this data set, the corrected item-total correla4.2. Phase 1—Fundamental Objectives tion and Cronbach's as for the four-factor model were The same item purification procedure allowed us to calculated. The range for corrected item-total correlaeliminate 36 of the 53 items for the fundamental objecTable 1

IVIeasures of Means Objectives (Phase 1) Factor Corrected Item-Total Correlation

Online payment am concerned about unauthorized use ot my credit card. am concerned about misuse of my credit card. am worried about who will have access to my credit card number. am concerned about my personal information being shared. am concerned about misuse ot personal information. worry about receiving wrong products. am concerned about shipping errors. Internet product ctioice like broad choice ot products. like having greater product seiection. iike to have maximum product availability. like to have maximum range ot quality product options. like having maximum product variety. like to have greater product choice. like the ease ot comparison shopping. Internet vendor trust am concerned about am concerned about am concerned about am concerned about

vendor legitimacy. seller legitimacy. how much i can trust the vendor. security tor Internet commerce.

Shopping travel like to travel as little as possible to purchase, like to minimize travel for purchase, like to drive as iittle as possible to shop.

0.84 0.83 0.82 0.82 0.81 0.81 0.75

0.86 0.84 0.85 0.79 0.81 0.68 0.59

0.87 0.87 0.82 0.77 0.77 0,73 0.73

0.79 0.78 0.75 0.71 0.70 0.64 0.64

0.86 0.85 0.82 0.77

0.81 0.83 0.73 0.73

0.90 0.87 0.84

0.78 0.75 0.72

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tives category because they had corrected item-total correlation below 0.5. Reliability analysis supported the remaining 17 items. No item was deleted because it would improve reliability. Data analysis revealed that respondents did not perceive some constructs in this category to be important. For example, safety issues (e.g., maximize driving safety, minimize risk of product use), shopping enjoyment (e.g., make shopping a social event, minimize worry, minimize regret), or time to receive product (e.g., minimize delivery time, minimize shipping time, minimize dispatch time). Again, these constructs seem less relevant to customers shopping over the Internet. Items related to these constructs were dropped. Next, an exploratory factor analysis was conducted for the remaining 17 items using principal components as the means of extraction and Varimax as the method of rotation.^ Bartlett's test of sphericity was 1735.8 ip < 0.001). Without specifying the number of factors, the factor analysis of the 17 items suggested a 4-factor model with eigenvalues greater than 1. The ratio of sample size to number of items (11:7) was greater than the accepted standard (10:1). The factor structure was easy to interpret corresponding with Internet shopping convenience (seven items), Internet ecology (three items), Internet customer relation (three items), and Internet product value (four items). The 4-factor model explained 68.1% of the variance. The range for factor loadings was 0.68 to 0.84 for Internet shopping convenience, 0.75 to 0.88 for Internet ecology, 0.71 to 0,82 for Internet customer relation, and 0.63 to 0.76 for Internet product value. This fundamental objectives scale includes 17 items. Table 2 reports the simple fourfactor solution (loadings greater than 0.3 are reported). Using this data set, the corrected item-total correlation and Cronbach's as for the four-factor model were calculated. The range for corrected item-total correlation was 0.65 to 0.79 for Internet shopping convenience, 0.71 to 0.79 for Internet ecology, 0.58 to 0.80 for Internet customer relation, and 0.51 to 0.62 for Internet product value. Reliability scores were 0.90, 0.88, 0.80, 0.76 for Internet shopping convenience, Internet ecology, Internet customer relation, and Internet product value, respectively. Overall reliability for the 17-item 'This procedure was used for all factor analysis in our studies.

scale was 0.91. The Hotelling test was significant (p < 0.005) for all 4 subscales of fundamental objectives with F values ranging from 8.53 (for Internet product value) to 5.1 (for Internet shopping convenience). Table 2 provides corrected item-total correlation for each item. The results of the Phase 1 study provide evidence of reliability and construct validity for the 4-factor, 21item measures of means objectives category and the 4factor, 17-item measures of fundamental objectives category. These results encouraged us to further explore reliability and validity of the two instruments. A total of 38 items was used in Phase 2 of the study. 4.3. Phase 2—Means Objectives Assuming no correlation among factors, Varimax rotation was used. However, the procedure using oblique rotation produced similar results. First, analyses were carried out for the 21-item means objectives instrument resulting from Phase 1 of the study. Without specifying the number of factors, there were five factors with eigenvalues greater than one. This fivefactor model was unproblematic to interpret. A scree plot supported a five-factor model. Four of these factors correspond with those resulting from Phase 1 of the study. Two items, initially loaded with items of online payment, separated, forming a new factor. The new factor is simply interpreted as Internet shipping errors and the items read: "I worry about receiving wrong products" and "I am concerned about shipping errors." An exploratory factor analysis suggests a 5-factor, 21-item solution for measuring means objectives. The five-factor solution reported in Table 3 represents a simple structure that is straightforward to interpret. The items are grouped by their highest (primary) factor loading and are listed in descending order. Loadings greater than or equal to 0.30 are reported. The 5 factors explained 77.3% of the variation in the 21 items and were interpreted as Internet product choice (7 items), online payment (5 items), Internet vendor trust (4 items), shopping travel (3 items), and Internet shipping errors (2

items), respectively. Table 3 also provides a factor correlation matrix and means and standard deviations for the five factors. As these results suggest, product choice and online payment in this category of means


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Table 2

IVIeasures of Fundamental Objectives (Pbase 1) Factor Corrected Item-Total Correlation

Internet shopping convenience It is important to make shopping easy. It is important to minimize time to select a product. It is important to minimize queuing time. It is important to minimize effort ot shopping. it is important to minimize personai hassle. it is important to minimize payment time. it is important to minimize time pressure when shopping.

0.84 0.79 0.76 0.76 0.74 0.70 0.68

Internet ecology It Is important to minimize pollution. It is important to minimize environmental impact. it is important to reduce environmentai damages. Internet customer relation It is important to assure an easy return process. It is important to provide quality atter-sale service. It is important to provide an easy return process. Internet product value It is important to minimize product cost. It is important to minimize tax cost, it is important to maximize product vaiue. it is important to ensure quality ot product.

objecfives explain most of fhe variance (53.3%). Overall reliability for fhe 21-item scale is 0.90. The irtsfrument's correlafion matrix was analyzed for convergent and discriminant vaUdify. The smallest within variable (factor) correlations are: Internet product choice = 0.38, onhne payment = 0.65, Internet vendor frusf = 0.58, shopping travel = 0.68, and Internet shipping errors = 0.80. For a sample of 421, these are significantly (p < 0.01) different than 0 and large enough fo encourage further investigation (Campbell and Fiske 1959). Discriminant validity can be fesfed for each item by counting the number of fimes it correlates more highly with an item of another variable (factor) than wifh items of its own theoretical variable (factor). Campbell and Fiske (1959) suggest determining whether this count is higher than one-half the potential comparisons. However, because of fhe use of a single method

0.79 0.77 0.74 0.70 0.66 0.70 0.65

0.88 0.87 0.76

0.79 0.78 0.71

0.82 0.71 0.71

0.80 0.59 0.58

0.76 0.74 0.65 0.63

0.62 0.51 0.55 0.60

in fhis case, common method variance is definitely present, so it is unclear how large a count would be acceptable. An examination of the matrix for the 21item instrument reveals only fwo violations (out of 420 comparisons) of fhe condition for discriminant validity. Each of fhe 21 items is more highly correlated with the other items in its group than with any of the items measuring other variables. 4.4. Phase 2—Fundamental Objectives Next, we analyzed data for the 17-item fundamental objectives instrument resulting from Phase 1 of the study. Without specifying the number of factors, there were four factors with eigenvalues greater than one. This four-factor model was readily interpretable. A scree plot supported a four-factor model. One item had factor loading above 0.3 on additional (nonprimary) factor. The item, "It is important to ensure quality of INFORMATION SYSTEMS RESEARCH

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Table 3

Factor Pattern for Measures of Means Objectives(/7 = 421) Factor 1

Internet product choice 1 like having greater product seiection. 1 like broad choice of products. 1 like to have maximum range of quality product options. 1 like having maximum product variety. 1 like to have maximum product availability. 1 like the ease of comparison shopping. i iike to have greater product choice.

2

3

4

0.90 0.86 0.85 0.84 0.75 0.67 0.66

Online payment 1 am worried about who will have access to my credit card number. 1 am concerned about unauthorized use of my credit card. 1 am concerned about misuse of personal information. 1 am concerned about misuse of my credit card. 1 am concerned about my personal information being shared.

0.88 0.87 0.87 0.86 0.85

Internet vendor trust 1 am concerned about vendor legitimacy. i am concerned about seller legitimacy. 1 am concerned about how much 1 can trust the vendor. 1 am concerned about security for Internet commerce.

0.87 0.87 0.84 0.68

Shopping travel 1 like to travel as iittie as possible to purchase. 1 like to drive as little as possible to shop. 1 like to minimize travel for purchase.

0.93 0.89 0.86

internet shipping errors 1 worry about receiving wrong products. 1 am concerned about shipping errors.

Factor 2 Factor 3 Factor 4 Factor 5 Mean (scale) Standard deviation Eigenvalue % Variance

5

0.91 0.87

Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

0.04 0.21 * 0.32* 0.08 28.24 5.21 6.43 30.61

0.54* 0.05 0.45* 19.75 5.26 4.76 22.69

0.08 0.28* 15.02 3.88 2.10 9.98

0.03 10.83 3.06 1.70 8.10

5.94 2.28 1.24 5.91

Note. "Significance less than or equal to 0.01.

product," loaded primarily with product value items. Items with multiple loadings may be excellent measures of overall construct, but including them in the

scale blurs the distinction between factors. To improve the distinction between factors, this item was deleted and data were analyzed for the remaining 16 items.


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The 4-factor, 16-item model, reported in Table 4, repThe process of determining content validity is judgresents an uncomplicated solution. The items are mental (Straub 1989) and somewhat unique to each regrouped by their highest (primary) factor loading and searcher (Emory and Cooper 1991). Nunnally (1978, p. listed in descending order. Loadings greater than or 93) suggests that "content validity rests mainly on apequal to 0.30 are reported. The 4 factors explained peals to reason regarding the adequacy with which im69.2% of the variation in the 16 items and were interportant content has been sampled and on the adequacy preted as Internet shopping convenience (7 items), Inter- with which the content has been cast in the form of test net ecology (3 items), Internet customer relation (3 items),items," He suggests there are differences in values and Internet product value (3 items). Table 4 also pro- among people and usually there is some disagreement vides factor correlation matrix, means, and standard about the proper content coverage of particular deviations for the four factors. As these results suggest, measures. shopping convenience and ecological issues in this It is difficult to find a procedure that taps the critical category of fundamental objectives explain most of the dimensions of the variables being measured (Davis variance (52.72%), Based on this sample of 421 reand Consenza 1985, Nunnally 1978). One way to desponses, overall reliability for the 16-item scale is 0.90. termine content validity is to use a panel of persons to Analysis of the correlation matrix of the instrument judge how well the instrument meets the standards suggests convergent and discriminant validity. The (Emory and Cooper 1991). Emory and Cooper give an smallest within factor correlations are: Internet shopoperational example of how an instrument is content ping convenience = 0.42; Internet ecology = 0.71; Invalidated in the employment setting. A panel indepenternet customer relation = 0.30; and Internet product dently assesses the items for a performance test. They value = 0.30. For this sample of 421, these within facjudge each item to be essential, useful but not essential, tor correlations are significantly (p < 0.01) different or not necessary in assessing performance of a relevant than 0. An examination of the correlation matrix for behavior. It is important to make it clear to the panel the 16-item instrument reveals only 2 violations (out of persons judging the content what the constructs are of 240 comparisons) of the Campbell and Fiske (1959) supposed to be measuring, the items that comprise conditions for discriminant validity. Each of the 16 each construct, and what guided the construction of items is more highly correlated with the other items in items (Krathwohl and Payne 1971, Nunnally 1978). its group than with any of the items measuring other factors. 5.1. Content Validity Procedure The content validity procedure had two objectives in 5. Content Validity of the Resulting mind. First, to ensure that the identified constructs and their respective measures adequately cover relevant diInstrument mensions of factors that influence customers in the doThe instrument development process was extended to main of Internet commerce. Second, to determine include content validity, which examined whether the whether the identified constructs and their respective identified constructs and the items measuring them items cover relevant dimensions of factors that influadequately covered factors that influence customers in ence customers in the domain of Internet commerce Internet commerce. This process will also confirm what more closely than any other domain. These objectives was initially proposed by Keeney's study, that is, the influenced the content validity procedure and the sambasis of item generation for this study. The analysis of ple. We designed a two-step content validity procedure. psychometric properties suggests 9 constructs and 37 In the first instance, we conducted independent initems for measuring means and fundamental objecterviews with two marketing professors who were tives for Internet commerce. If the measures adeheavily involved in market research. We first described quately cover the topics that have been defined as the the objectives of our study, Keeney's study and its relevant dimensions, then it can be concluded that an findings as the basis of our item generation, and the instrument has good content validity (Kerlinger 1978). INFORMATION SYSTEMS RESEARCH

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Tabie 4

Factor Pattern for Measures of Fundamental Objectives(n = 421) Factor

Internet shopping convenience It is important to make shopping easy. It is important to minimize effort of shopping. it is important to minimize queuing time. it is important to minimize time to seiect a product. it is important to minimize personai hassie. it is important to minimize payment time. It is important to minimize time pressure when shopping.

0.82 0.78 0.77 0.73 0.72 0.69 0.57

internet ecoiogy It is important to minimize pollution. It is important to minimize environmentai impact. it is important to reduce environmentai damages.

0.92 0.90 0.83

internet customer reiation it is important to assure an easy return process, it is important to provide an easy return process, it is important to provide quaiity after-sale service.

0.87 0.79 0.68

internet product vaiue It is important to minimize tax cost. It is important to minimize product cost. It is important to maximize product vaiue.

Factor 2 Factor 3 Factor 4 Mean (scale) Standard deviation Eigenvalue % Variance

0.83 0.83 0.51

Factor 1

Factor 2

0.38* 0.53* 0.47* 28.52 4.84 6.54 40.90

0.40* 0.33* 12.25 2.73 1.89

11.82

Factor 3

Factor 4

0.48* 13.38 1.84 1.48 9.22

12.81 1.98 1.16 7.23

Note. *Significance iess than or equai to 0.01.

resultant constructs. We then asked the experts to evaluate the content of each item with respect to the two objectives described above. The specific instructions asked participants to: (1) evaluate whether item content covered relevant dimensions in the domain of Internet commerce and (2) suggest whether, relative to other domains, these items were most applicable to the domain of Internet commerce. Responses to these questions were recorded for the 37 items. We also asked these respondents for suggestions to improve or

"fine tune" our content validity procedure. They both suggested that the procedure was appropriate. In a follow-up procedure, we sought to confirm the findings of the pilot study using a larger sample of "experts." We selected a sample of 12 individuals who were experienced in both shopping over the Internet and through mail-order catalogs. To each individual, we described the objectives of the study, mentioned that the interview would take approximately 45 minutes, and asked his or her participation with the study.


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Nine of these individuals agreed to participate with the study. Four respondents were university professors, one was an engineer working for a government agency, one was a homemaker, one was a working student, and two were self-employed. All respondents were experienced Internet shoppers who also purchased through mail-order catalogs. Again, for each participant, we described the objectives of the study, the basis of item generation, and the resultant constructs. Then each participant was asked to judge: (1) whether each construct and its respective items adequately cover relevant dimensions of the factors that influence customers in the domain of Internet commerce and (2) whether each construct and its respective items relate more closely to the domain of Internet commerce or to the domain of catalog shopping. In all cases, respondents were clear about the intent of the study and their role in assessing the content of the identified dimensions of Internet commerce. 5.2. Content Validity Results Content validity results can be summarized as follows: • All 37 items cover relevant dimensions of the factors that influence customers in the domain of Internet commerce. • These items also cover relevant dimensions of the factors that influence customers in "catalog shopping." • The majority of dimensions are more closely related to the domain of Internet commerce than that of catalog shopping; "shopping travel" and "Internet shipping errors" factors in the means objectives category and "Internet customer relation" in the fundamental objectives category were perceived to be equally relevant to Internet commerce and catalog shopping. One respondent felt that the two items related to effort of shopping and queuing time in the "Internet shopping convenience" factor were also equally relevant to both domains. Both respondents noted the benefits of an instrument specifically developed in the context of Internet commerce. Another respondent suggested that these measures explain a higher level of variance in the context of e-commerce. Results of the follow-up procedure using a larger sample of "experts" were consistent with those obtained in step one. There was little variance in response

patterns. Respondents felt that all of the 37 items covered relevant dimensions of factors that influence customers for Internet commerce. Respondents also felt that while the identified factors and their respective items related to both domains of Internet commerce and catalog shopping, several factors were more closely related to the domain of Internet commerce. These factors are "online payment," "Internet vendor trust," "Internet shopping convenience," "Internet ecology," and "Internet product value." There were mixed responses regarding the "Internet product choice" factor. Respondents felt most items of this factor were equally applicable to both domains. However, they felt that the two items for product availability and the ease of comparison shopping relate more closely to Internet commerce. One respondent suggested, "In-stock information is up to date on the Internet." Another respondent said, "If an item is out of stock, I will know from [the] Internet quickly, while I won't know from [a] catalog." On the ease of comparison shopping, respondents particularly liked the interactive nature of the search help. One other respondent said, "You are getting answers to questions as you surf that you did not have when you started to search for a product." Respondents to both step one and the follow-up study made numerous comments that support the appropriateness of these 37 items for Internet commerce, and suggest a perceived difference in the suitability of these items for Internet commerce versus catalog shopping. Overall, respondents felt more strongly about factors of "online payment," "Internet vendor trust," "Internet shopping convenience," "Internet ecology," and "Internet product value" for Internet commerce. The question on misuse of personal information generated this comment: "If you ponder on page 8 or 10, for instance, in a catalog, no one can see what you are doing, but on the Internet, they can check every keystroke. You leave a trail, including the complete image of the page." On the issue of online payment, most respondents talked about significant anxiety with the Internet; however, some suggested that it is declining. One respondent said, "You use a credit card for a catalog, but on the Internet, you don't know where that is going." Another respondent said, "It is easier for a third party to INFORMATION SYSTEMS RESEARCH

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violate personal informatiori on the Internet than through the regular mail." On the Internet vendor trust, respondents felt that catalog companies are well established as compared with the Internet vendors. On the factor for Internet shopping convenience, one respondent gave the example of shopping for a home and said, "With the Web you can drill to the level of precision for this purpose." Another respondent said, "To look for a unique product, the Internet makes it easier. You may, in fact, not be able to get that "table cloth" through the catalog." On the ecological issues, most respondents referred to the paperless feature of Internet shopping. Regarding the Internet product value issue, respondents generally referred to more bargain possibilities on the Internet. In summary, the authors feel that the two sets of measures presented in this article represent substantial progress toward development of standard instruments for measuring factors that influence customers in Internet commerce. The data from Phases 1 and 2 of the study support the construct and discriminant validity of the measures and adequate reliability. Furthermore, the content validity analysis also appears to support the instrumentation. However, continuing efforts should certainly be made to validate the recommended instruments.

6. Discussion The ultimate questions about the success of Internet commerce depends on how customers perceive its value. As Keeney (1999, p. 534) says, "The best way to find out what customers value is to ask them." MIS researchers have appropriately increased attention to research issues related to Internet technologies. However, with a notable exception (Keeney 1999), these studies do not directly address factors that influence the Internet customer. This study makes the following contributions to Internet commerce metrics. • It generates a comprehensive list of items that cover different dimensions proposed by Keeney. • It employs an exploratory approach for instrument development and follows widely accepted methodologies. • It carries out a two-phase study to more carefully eliminate items and test selected items for reliability and validity.

• It gathers large samples to enhance generalizability of the findings. • It develops a procedure for content validity of the proposed measures using a panel of experts. Together, these steps enhance accuracy of outcomes and help academics and practitioners to view the resultant instruments with confidence in that this method of developing instruments provides the degree of confidence in the content and construct validity (Moore and Benbasat 1991). Phase 1 of the study generated substantive results and lent confidence that the dimensions identified made sense in terms of perceived value to the customer. Phase 2 clearly provided evidence of reliability and construct validity for the tentative findings of Phase 1. The result is a parsimonious 5-factor, 21-item instrument for measuring means objectives and a 4-factor, 16-item instrument for measuring fundamental objectives, both with high levels of reliability. 6.1.

Further Research

The findings of this study discovered multidimensional measures of factors that influence Internet shoppers that are intuitively appealing and psychometrically reliable and valid. These measures can be used to evaluate what influences Internet shoppers and to provide insight for making decisions about Internet commerce. We encourage confirmatory studies of the proposed measurements. Though this study reports a significant progress toward the development of multidimensional measures of factors that influence customers of Internet commerce, the validity of an instrument cannot be firmly established on the basis of a single study. Other studies and samples should be used for the validation and assessment of measurement properties. Samples of specific context or industry, such as manufacturing or service, should be gathered to further examine the applicability of these measures. Further research can examine how comprehensive the list of constructs proposed by Keeney is and how it is operationalized in this study. For example, comparative studies of the resultant instruments with measures and constructs that are obtained through the use of methods such as case studies provide opportunity for future research. Research to examine the secondorder nature of these factors is also appropriate. It is


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plausible to expect a second-order model for the proposed constructs, and this study clearly suggests the five components for "means objectives" and the four components for "fundamental objectives" constructs. However, only further research employing confirmatory factor analysis can provide a clearer picture of these concepts. The instruments derived in this study provide research opportunities into a priori factors that influence the value of Internet commerce to the customer. They also provide opportunities for research into posterior factors. Straub and Watson (2001) relate the core competency of IS researchers to the study of the antecedents or effects of IS variables. There is a need for research that helps to describe a "system to value chain" of Internet commerce success factors from belief, to attitudes, to behavior, to the social and economic impacts (Torkzadeh and DoU 1999). Such taxonomy will help cumulative research efforts to examine cause and effect relationships in this domain. The recommended measures can also be applied to a specific product or to develop the value proposition viewed by prospective customers. These measures can be used to prioritize objectives and identify individual value trade-offs. For example, what is the value trade-off to the customer between Internet customer relation and Internet ecology? These measures also help operationalize some of the research ideas that recent literature appropriately suggested (e.g., Orlikowski and Iacono 2001, Sambamurthy and Zmud 2000, Brynjolfsson and Hitt 1998, Gefen and Straub 2000, Venkatraman 1997). In the postindustrial setting, scholars contend that information technology can be used in ways that have substantial implications for the nature of work, productivity, and economic growth. Information technology has the potential to create new ideas, influence values, affect environment, and more effectively meet internal and external customer needs. Our validated measures provide a tool for assessing whether information technology is, indeed, being used in new ways. 6.2. Building on the Scholarly Implications The conceptualization of constructs in this study was based primarily on Keeney's work. Keeney (1999) followed a predefined methodology for determining: (1) the set of constructs and (2) the classification of "means and fundamental objectives." His study makes a significant contribution by specifically addressing cus-

tomer issues in the context of Internet commerce. We, thus, operated within the confines of this framework. We did not want to operationalize the propositions made by Keeney and simultaneously modify them. This could be the subject of a follow-up study, and this section intends to provide some thoughts on likely opportunities. Keeney used the concept of a value proposition to arrive at benefits to a prospective customer of purchasing a product or services on the Internet. The endresult benefits and costs of a product and the processes of finding, ordering, and receiving it characterize this value proposition. Different customers may consider different benefits and costs in judging the net value of Internet purchases, and the potential value to the customer is influenced by the means of purchasing it. The alternatives for the customer include purchase through other means, such as mail order or store shopping. The customer can also decide to purchase from another site or not purchase. The two classifications of means and fundamental objectives proposed by Keeney (1992) were developed using concepts of what he calls value-focused thinking, where a list of customer values is developed and then expressed in a common form and finally organized to indicate their relationships. Keeney contends that it is useful to relate categories by means-ends relationships. He suggests that there are generally two responses when we ask why it is important in the decision context of whether or not to make purchases over the Internet. "It is important because it helps to achieve one or more of the other objectives, or it is one of the fundamental reasons for purchasing on the Internet or not. Objectives with the former response are referred to as means objectives and those with the latter response are referred to as fundamental objectives" (Keeney 1992, p. 535). This suggests that the constructs of means and fundamental objectives must be related. Table 5 provides correlations for subscales in each category and between categories. Subscales of means objectives correlate (range 0.44 to 0.71) more closely with the overall 21-item means objectives than with subscales outside of their category. Similarly, subscales of fundamental objectives correlate (range 0.68 to 0.88) more closely INFORMATION SYSTEMS RESEARCH

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Table 5

Correlations Among Constructs (n = 421)

1

2

3

4

5

6

7

8

9

10

11

(1) Overall means objectives (21 items) (2) Internet product choice (7 items)

0,61**

(3) Online payment (5 items)

0.71**

0,04

(4) Internet vendor trust (4 items)

0,71**

0,21**

0,54**

(5) Shopping travel (3 items)

0,44**

0,32**

0,05

0,08

(6) Internet shipping errors (2 items)

0,51**

0,08

0,45**

0,28**

0,03

(7) Overall fundamental objectives (16 items)

0,36**

0,35**

0,14**

0,14**

0,33**

0,13**

(8) Internet shopping convenience (7 items)

0,29**

0,29**

0,07

0,05

0,38**

0,12*

0,88**

(9) Internet ecology (3 items)

0,23**

0,17**

0,17**

0,13**

0,10*

0,09

0,68**

0,38**

(10) Internet customer relation (3 items)

0,32**

0,31**

0,15**

0,15**

0,18**

0,11*

0,73**

0,53**

0,40**

(11) Internet product value (3 items)

0,29**

0,33**

0.09

0,12*

0,24**

0,07

0.69**

0.47**

0.33**

0.48**

Mean (standardized)

3,80

4,03

3,96

3,75

3,61

2,97

4,18

4,07

4.08

4.46

4.26

Standard deviation

0,58

0,75

1,04

0,97

1,02

1,14

0,56

0,69

0,91

0.61

0.67

Note. *Significance less than or equal to 0,05; **Significance less than or equal to 0,01.

with the overall 16-item fundamental objectives than with subscales outside of their category. All subscales of fundamental objectives category correlate strongly with each other (range 0.33 to 0.53). Subscales of means objectives category also correlate strongly with each other except for cases that do not conceptually make sense (e.g., correlations between Internet product choice and online payment or between shopping travel and Internet shipping errors). While these relationships are plausible, we do not know how means objectives influence fundamental objectives. Or, we do not know the interaction effects among variables of means and fundamental objectives. We know, for example, that the variance explained by items of "Internet shipping errors" is significantly lower than the variance explained by items of "Internet product choice" or "online payment" (see Table 3). An appropriate question for the management might be how much the Internet product choice should be improved or the concerns over online payment should be reduced to account for the concerns regarding Internet shipping errors. These and other questions suggest research opportunities for theory development and refinement. Theoretical advancement in MIS has been constrained by the absence of reliable measures. The resultant instruments with demonstrated reliability

and validity make a contribution toward new theory development. Depending on their needs, researchers and practitioners may find all nine factors essential to their use or may select a subset of these factors. 6.3. Study Limitations Like most survey studies of this nature, this study is subject to limitations. Some of these limitations relate to the exploratory nature of the study and some relate to the framework development issues. Measurement instruments are not "set in stone;" rather, they evolve through an ever-extending process of development, examination, and refinement (Zmud and Boynton 1991). Initial instrument development efforts often contain some ambiguity concerning the appropriate model of underlying data structures (Doll et al. 1994). This exploratory study is an extension of work done in the decision context by Keeney (1999). As described earlier, further confirmatory studies are necessary to complete the instrument development process. In terms of data collection, subjects were all either Internet shoppers or familiar with issues of shopping on the Internet. The survey questionnaire asked that questions be answered by individuals who had purchased products or services over the Internet or understood the difference between Internet commerce and conventional purchasing methods. We felt that


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was necessary for the items to be clearly understood. While there is no evidence that this introduced systematic bias, another large sample of non-Internet shoppers is useful to test the recommended measures. This study did not include a test-retest analysis of the proposed constructs and measures. A test-retest analysis will help to establish stability of these constructs over time. The two independent criteria used in this study (reliability and corrected item-total correlation) are frequently used for item purification and considered adequate for this exploratory study. This study did not use a criterion measure for validity test or item purification. We are not aware of any reason that this lack of an additional criterion produced systematic bias. However, a criterion measure could be added in follow-up studies by forming an item, usually an overall measure, for each construct. The cutoff level of 0.5 for corrected item-total correlation and reliability used in this study to eliminate items might be too strict and may have resulted in potentially good items being dropped during Phase 1 of the study. However, the large number of items in the initial list, as well as overlapping constructs that cause repeat items, made this a practical necessity. Furthermore, reliability and construct validity results, based on the subsequently large sample and the content validity procedure using a panel of experts, support the item-selection procedure used in Phase 1 of the study.

7. Conclusions This article reports on the development of two instruments for measuring "means fundamental objectives" for Internet commerce. The first instrument is a 5factor, 21-item scale that measures means objectives in terms of Internet product choice, online payment, Internet vendor trust, shopping travel, and Internet shipping

errors. The survey data suggest the relative strength of product choice and online payment in the means objectives category. The second instrument is a 4-factor, 16-item scale that measures fundamental objectives in terms of Internet shopping convenience, Internet ecology, Internet customer relation, and Internet product value. The

survey data suggest the relative strength of shopping convenience and ecological issues in the fundamental

objectives category. These instruments are reliable, valid, and can be used with confidence by academics and practitioners. As measures of effectiveness, these instruments should be of great interest to executives, who need to distinguish between effective and ineffective Internet sites. As outcome measures, these instruments should stimulate new research that has practical implications for how e-businesses are designed, developed, and implemented. Appendix. Measures of Factors that Influence Internet Commerce Success (125 Items Used in Phase 1) Means Objectives I am concerned about fraud when I want to purchase a product over the Internet. I am concerned about vendor legitimacy. I am concerned about how much I can trust the vendor. I am concerned about security for Internet commerce. I am concerned about seller legitimacy. I am concerned about how much I can trust the seller. I am worried about unauthorized access to my personal information. I am concerned about hackers. I like greater accessibility to product and service information. I feel there is sufficient transaction protection for Internet commerce. I feel that Internet transactions are speedy. I feel there is sufficient security built into the Internet. I like more information about promotions. I am satisfied with transaction speed. I feel that safety of credit card use should be increased. I like to be able to feel the product. I like to be able to see the product. I like to be able to test the product. I like to be able to try the product. I like to get as much information about products as possible. I am worried about abuse of personal information. I feel Internet commerce transaction is fast. I feel the accuracy of product information is important. I am concerned when I use my credit card. I like assurance of delivery of purchased product. I am concerned about misuse of my credit card. I am worried about who will have access to my credit card number. I like as accurate product information as possible. I am concerned about unauthorized use of my credit card. I am concerned about unnecessary purchase. I am concerned about misuse of personal information. I am concerned about receipt of unsolicited material. I am concerned about receiving junk mail. I am concerned about my personal information being shared. I feel that credit card safety should be increased. I worry about reliable delivery. I am concerned about timely arrival of purchased products. INFORMATION SYSTEMS RESEARCH

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I like to enhance comparison shopping. I am concerned I might purchase more than I need to. I am concerned about impulsive buying. I like assurance of arrival of purchased products. I am concerned about unreasonable purchases. I am concerned about timely delivery of purchased items. I am concerned about accuracy of transaction. I am concerned about transaction error. I worry about receiving wrong products. I am concerned about shipping errors. 1 am concerned about charging errors. I like being able to compare products. I worry about being charged inaccurately. I like to have greater product choice. I feel Internet commerce helps me make better purchase decisions. I feel Internet commerce minimizes my disappointment in purchase experience. I feel confident in making right purchase choices. I like having maximum product variety. I like to have maximum range of quality product options. I feel there is sufficient opportunity for comparison shopping. I like having greater product selection. I like a broad choice of products. I like the ease of comparison shopping. I like to have maximum product availability. I like to minimize travel for purchase. I feel ease of access is important for Internet commerce. I like to travel as little as possible to purchase. I like easy-to-use interface for Internet commerce. I like simple systems for product search. I feel human customer support is important. I feel there should be opportunity for personal interaction. I like to be able to talk with a salesperson. I feel computer-based customer support is sufficient. I like to drive as little as possible to shop. 1 will be satisfied with computer-based customer support alone. Fundamental Objectives It is important to maximize product value. It is important to ensure the quality of a product. It is important to maximize functionality of a purchased item. It is important to minimize dispatch time. It is important to minimize product cost. It is important to minimize tax cost. It is important to maximize purchasing convenience. It is important to minimize shipping cost. It is important to minimize Internet cost. It is important to minimize travel cost. It is important to get the best product for the buck. It is important to minimize search time. It is important to minimize time to receive product. It is important to minimize shipping time. It is important to maximize convenience. It is important to minimize processing time. It is important to minimize delivery time.

It is important to minimize disappointment. It is important to minimize waiting time. It is important to maximize time flexibility in purchasing. It is important to minimize time pressure when shopping. It is important to provide quality after-sale service. It is important to assure an easy return process. It is important to reduce time spent interacting with the vendor. It is important to minimize the effort of shopping. It is important to make shopping easy. It is important to minimize personal hassle. It is important to maximize ease of finding a product. It is important to minimize purchase time. It is important to minimize payment time. It is important to minimize queuing time. It is important to avoid getting on electronic mailing hsts. It is important to minimize time to find a product. It is important to minimize time to order a product. It is important to minimize regret of shopping. It is important to minimize time to gather information. It is important to minimize time to select a product. It is important to provide an easy return process. It is important to reduce environmental damages. It is important to maximize privacy. It is important to make shopping a social event. It is important to minimize the worry of shopping. It is important to inspire customers. It is important to maximize driving safety for shopping. It is important to enhance customer productivity. It is important to minimize pollution. It is important to give customers new ideas. It is important to minimize regret of online shopping. It is important to maximize customer confidence. It is important to minimize shopping effort. It is important to maximize a safe shopping experience. It is important to minimize the risk of product use. It is important to minimize environmental impact.

References Bagozzi, R. P. 1981. An examination of the validity of two measures of attitude. Multivariate Behavioral Res. 16 323-359. Benbasat, I. 1991. Commentary. K. L. Kraemer, ed. The Information Systems Research Challenge: Survey Research Methods, vol. 3. Har-

vard Business School, Boston, MA. Boudreau, M., D. Gefen, D. W. Straub. 2001. Validation in IS research: A state-of-the-art assessment. MIS Quart. 25(1) 1-24. Brynjolfsson, E., L. M. Hitt. 1998. Beyond the productivity paradox. Comm. ACM 41(8) 49-55. Campbell, D. T., D. W. Fiske. 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psych. Bull. 56(1) 81-105. Churchill, G. A., Jr. 1979. A paradigm for developing better measures of marketing constructs. /. Market Res. 16 64-211. Davis, D., R. M. Cosenza. 1985. Business Research for Decision Making.

Kent Publishing Company, Boston, MA.


Vol. 13, No. 2, June 2002

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Doll, W. J., G. Torkzadeh. 1988. The measurement of end-user comately seeking the "IT" in IT research—A call to theorizing the puting satisfaction. MIS Quart. 12(2) 259-274. IT artifact. Inform. Systems Res. 12(2) 121-134. , W. Xia, G. Torkzadeh. 1994. A confirmatory factor analysis of Riggins, F. J., H. Rhee. 1998. Toward a unified view of electronic the end-user computing satisfaction instrument. MIS Quart. commerce. Comm. ACM 41(10) 88-95. 18(4) 453^61. Sambamurthy, V., R. W. Zmud. 2000. Research commentary: The Emory, C. W., D. R. Cooper. 1991. Business Research Methods. Irwin, organizing logic for an enterprise's IT activities in the digital Boston, MA. era—A prognosis of practice and a call for research. Inform. Gefen, D., D. Straub. 2000. The relative important of perceived ease Systems Res. 11(2) 105-114. of use in IS adoption: A study of e-commerce adoption. /. AsSegars, A. H. 1997. Assessing the unidimensionality of measuresociation for Inform. Systems 1(8) 1-30. ment: A paradigm and illustration within the context of inforKeeney, R. L. 1992. Value-Focused Thinking: A Path to Creative mation systems. Omega 25(1) 107-121. Decision-Making. Harvard University Press, Cambridge, MA. Straub, D. W. 1989. Validating instruments in MIS research. MIS . 1994. Creativity in decision making with value-focused thinkQuart. 13(2) 147-169. ing. Sloan Management Rev. (Summer) 33-41. , R. T. Watson. 2001. Research commentary: Transformational ——. 1999. The value of Internet commerce to the customer. Manissues in researching IS and net-enabled organizations. Inform. agement Sci. 45(4) 533-542. Systems Res. 12(4) 337-345. Kerlinger, F. N. 1978. Foundations of Behavioral Research. McGraw, J. C. Wetherbe. 1989. Information technologies for the 1990s: Hill, New York. An organizational impact perspective. Comm. ACM 32(11) Krathwohl, D. R., D. A. Payne. 1971. Defining and assessing edu1328-1339. cational objectives. R. L. Thorndike, ed. Educational MeasureTorkzadeh, G., W. J. Doll. 1999. The development of a tool for meament. American Council of Education, Washington, D.C. suring the perceived impact of information technology on Krischer, J. P. 1976. Utility structure of a medical decision-making work. Omega 27 327-339. problem. Oper. Res. 24 951-972. Venkatraman, N. 1997. Beyond outsourcing: Managing IT resources Leon, O. G. 1999. Value-focused thinking versus alternative-focused as a value center. Sloan Management Rev. 38(3) 51-64. thinking: Effects on generation of objectives. Organ. Behavior and Weiss, S. 1970. Factor analysis in counseling research. /. Counseling Human Decision Processes 80(3) 213-227. Psych. 17 477-485. Moore, G. C, 1. Benbasat. 1991. Development of an instrument to Zmud, R. W., A. C. Boynton. 1991. Survey measures and instruments measure the perceptions of adopting and information technolin MIS: Inventory and appraisal. K. L. Kraemer, ed. The Inforogy innovation. Inform. Systems Res. 2(3) 192-222. mation Systems Research Challenge: Survey Research Methods, vol. Nunnally, J. C. 1978. Psychometric Theory. McGraw-Hill, New York. Orlikowski, W. J., C. S. Iacono. 2001. Research commentary: Desper3. Harvard Business School, Boston, MA. Bruce Weber, Special Issue Editor. This paper was received on December 10, 2000, and was with the authors 5 months for 2 revisions.


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