Accordingly, dozens of Internet-only companies .... environmental awareness, and doing the right thing/doing good. Empirical studies on Internet marketing ...
Ó Springer 2006
Journal of Business Ethics (2007) 72:131–148 DOI 10.1007/s10551-006-9161-y
The Ethics of Online Retailing: A Scale Development and Validation from the Consumers’ Perspective
ABSTRACT. While e-commerce has witnessed extensive growth in recent years, so has consumers’ concerns regarding ethical issues surrounding online shopping. The vast majority of earlier research on this area is conceptual in nature, and limited in scope by focusing on consumers’ privacy issues. This study develops a reliable and valid scale to measure consumers’ perceptions regarding the ethics of online retailers (CPEOR). Findings indicate that the four factors of the scale – security, privacy, nondeception and fulfillment/reliability – are strongly predictive of online consumers’ satisfaction and trust. The results offer important implications for e-retailers and are likely to stimulate further research in the area of e-ethics from the consumers’ perspective. KEY WORDS: consumers, ethics, Internet, retailing, satisfaction, scale development, trust
The total number of Internet users worldwide passed 1 billion in 2005, up from 45 million in 1995 and 420 million in 2000 (New Media Age, 2006). Accordingly, dozens of Internet-only companies have surfaced in many industries and numerous conventionally operated companies have adopted the Internet (Yang et al., 2004). Nevertheless, as Grewal et al. (2004, p. 712) recently pointed out: ‘‘whether Internet retailing will remain a stagnant business with negligible market share, such as TV
Sergio Roma´n is an Associate Professor of Marketing at the University of Murcia (Spain). He has been a Visiting Scholar at the University of Arizona. His articles have appeared in the Journal of Business Research, International Marketing Review, International Journal of Market Research, European Journal of Marketing and Journal of Marketing Management. His research interests are focused on personal selling and sales management, international marketing and business ethics.
Sergio Roma´n
home shopping, or whether it will become as ubiquitous and enduring as the department store, remains to be seen.’’ Issues such as privacy, unsolicited e-mail, transaction security continue to be hotly debated in the academic and practitioner literature (e.g., Meinert et al., 2006; Stead and Gilbert, 2001; Vijayan, 2005). For example, a recent survey of 1,009 U.S. consumers conducted by Forrester Custom Consumer Research indicated that one in four consumers said they would not shop online because of Internet security concerns (Vijayan, 2005). Internet represents a ‘‘new environment for unethical behavior’’ (Freestone and Mitchell, 2004; p. 126). For instance, findings from Citera et al. (2005) revealed that ethical transgressions are more likely to happen in e-transactions as compared to face-to-face transactions. To begin with, a high degree of physical proximity promotes warmer and closer interpersonal relationships, whereas a low degree of physical proximity leads to psychological distance (Latane´, 1981). Furthermore, in traditional retail settings consumers make inferences about aspects of a store during the service encounter, that period of time when the consumer interacts directly with the firm. Consumers’ impressions of a firm’s ethical conduct can be influenced by employees’ actions during service delivery (McIntyre et al., 1999). In other words, bricks and mortar stores may be able to signal longevity, and ethical behavior, by factors such as their location and their employees, whereas Internet retailing is ‘‘inherently limited in its ability to offer high-trust persuasive communication1’’ (Grewal et al., 2004, p. 707). Internet retailers can easily imitate one another so the signaling value of a sophisticated website is dimin-
132
Sergio Roma´n
ished, and consumers may find it more difficult to distinguish between ‘‘better’’ and ‘‘worse’’ retailers. For example, the Internet can be used effectively by a small company to appear deceptively large, as the webpage on the computer screen does not distinguish between a large and a small company (Petty, 1998). Additionally, Internet transactions are carried out over a public domain. Therefore, personal and financial information that is collected can be easily stored, copied, and shared (Bush et al., 2000). All the above suggests that: (1) consumers’ ethical evaluations and expectations are particularly relevant on the Internet, and (2) they are formed in a different way on the Internet, as compared to traditional retail settings. Furthermore, online consumers tend to have a very different profile compared to traditional retail shoppers (Donthu and Garcia, 1999). Past research in the traditional/offline marketplace has addressed consumers’ ethical believes and practices2 (Fullerton et al., 1996; Muncy and Vitell, 1992; Strutton et al., 1997; Vitell and Muncy, 2005), as well as consumers’ perceptions of retailers ethics (Burns et al., 1994; Lagace et al., 1991; McIntyre et al., 1999; Norris and Gifford, 1988; Roma´n, 2003). Yet, little research has been conducted on the potential ethical issues regarding online retailing from the consumers’ perspective. In addition, the vast majority of earlier research is conceptual in nature, and has primarily focused on privacy issues (e.g., Beltramini, 2003; Caudill and Murphy, 2000; Le Menestrel et al., 2002; Maury and Kleiner, 2002; Palmer, 2005; Pollach, 2005; Sama and Shoaf, 2002; Siplor et al., 2004; Stead and Gilbert, 2001), ignoring other important ethical marketing issues surrounding the Internet such as deception and dishonesty (Bush et al., 2000; Murphy et al., 2005). The main purpose of this research is to develop and validate an instrument to measure consumers’ perceptions regarding the ethics of online retailers (CPEOR). This is, to our knowledge, the first attempt in the literature to specifically measure this construct. Such effort is particularly relevant since online retailers must understand how consumers perceive and evaluate the ethics of their websites in the face of severe competition and continually rising consumer expectations (Anderson and Srinivasan, 2003). Prior research in traditional retail settings has shown that consumers’ perceptions of retailer’s ethical practices
are strongly related to the formation of consumers’ positive store attitudes and repeat buying behavior (e.g., Roma´n, 2003). Accordingly, the second objective of this study is to analyze the influence of CPEOR on consumers’ satisfaction and trust. Although there are many different types of Internet sites, this research is focused on online shopping sites. The article does not deal with other Internet sites – such as online newspapers, portals, free down-load sites, customer to customer sites such as eBay or job sites – that exist for purposes other than online shopping and that are advertiser supported.
Literature review The topic of CPEOR is still an under-researched area, and consequently, previous scales are not available. In what follows, we review the general marketing/business ethics scales developed in the literature. Then we summarize the results of the empirical studies that specifically address ethical issues related to marketing on the Internet. Finally, we focus on specifying the domain of CPEOR.
Marketing/business ethics scales Based on the five major moral philosophies Reidenbach and Robin (1990) developed a business ethics scale using retail store managers. This scale comprised eight semantic differential items distributed across three factors as follows: moral equity, relativistic, and contractualism. Later work by Reidenbach et al. (1991) aimed to test and extend the Reidenbach and Robin’s (1990) scale to selected marketing groups (retail managers, direct marketers, automobile dealers and book representatives). Different marketing practices and groups were used in order to give the business ethics scale a wide variety of exposures. The results showed that the original instrument performed well in all settings and for all practices. Vitell et al. (1993) developed a marketing norms ethics scale in order to assess ethical situations faced by marketers in their decision-making. The American Marketing Association (AMA) code of ethics was used to drive conceptualization and item
The Ethics of Online Retailing generation. Data were gathered from AMA members. The scale had five dimensions: price and distribution norms, information and contract norms, product and promotion norms, obligation and disclosure norms, and general honesty and integrity. Evidence of validity was provided by correlations with idealism and relativism and with two dimensions of the Ethics Dimension Questionnaire. Later, Singhapakdi et al. (1996) developed a scale to measure marketers’ perceptions regarding the importance of ethics and social responsibility. The analysis yielded three factors: social responsibility and profitability, long term gains, and short-term gains. The research by Muncy and Vitell (1992) adopted a different perspective because these authors focused on examining ethical issues in the market place from the perspective of the consumer ethics. They specifically developed a consumer ethics scale that examined ethical beliefs regarding various questionable behaviors. Their research resulted in a four dimensional solution: actively benefiting from illegal activities, passively benefiting, actively benefiting from deceptive (or questionable, but legal) practices, and no harm/no foul activities. Recently, the article by Vitell and Muncy (2005) updates the Muncy–Vitell scale with modifications that include rewording and the addition of new items. These new items can be grouped into three distinct categories: downloading/buying counterfeit goods, recycling/ environmental awareness, and doing the right thing/doing good.
Empirical studies on Internet marketing ethics Bush et al. (2000) assessed the perceptions of the ethical issues concerning marketing on the Internet among a sample of 292 marketing executives. The authors used an open ended question ‘‘due to the lack of published research from which scaled items could be developed’’ (Bush et al., 2000; p. 240). The ethical concerns most often mentioned regarding marketing on the Internet was the security of transactions. The next three most often mentioned ethical concerns were illegal activities (e.g., fraud, hacking), privacy, and honesty/truthfulness of the information on the Internet. Miyazaki and Fernandez (2001) evaluated consumers’ concerns regarding online shopping. Four
133
major concerns emerged from a sample of 189 consumers, three of these concerns were related to ethical issues. The first category, privacy, contained a variety of concerns, such as unauthorized sharing of personal information, unsolicited contacts from the online retailer, and undisclosed tracking of shopping behavior. The second category, system security, included concerns about potentially malicious individuals who breach technological data protection devices to acquire consumers’ personal, financial, or transaction-oriented information. The third category, online retailer fraud, focused on concerns regarding fraudulent behavior by the online retailer, such as purposeful misrepresentation or non-delivery of goods. Singh and Hill’s (2003) study focused on consumers’ concerns regarding online privacy in Germany. Their results suggested that consumers’ views about Internet use and online behaviors are affected, among other things, by their views regarding privacy in general, and how they view the role of the government and the role of companies in protecting consumer privacy. Other scholars have focused their attention on analyzing online retailers’ disclosures of privacy and security policies on websites, and their effects on consumers’ perceived risks for online shopping. For example, findings from Miyazaki and Fernandez (2000) showed that a positive relationship exist between the percentage of privacy- and security-related statements on websites and consumers’ online purchase intentions. Later, Milne and Culnan (2004) investigated why online consumers read privacy notices across a variety of situations. They found that reading privacy notices is only one element in an overall strategy consumers use to manage the risks of disclosing personal information online. Pollach (2005) examined privacy policies of online retailers from a linguistic angle to determine whether the language of these documents is adequate for communicating data-handling practices in a manner that enables informed consent on the part of the user. Her findings highlighted that corporate privacy policies obfuscate, enhance and mitigate unethical data handling practices and use persuasive appeals to increase online retailers’ trustworthiness. Recently, Meinert et al. (2006) explored the willingness of graduate students to provide personal information given various degrees of protection offered by privacy policy statements. Their results revealed
134
Sergio Roma´n
that the willingness to provide information to online retailers increased as the level of privacy guaranteed by the statements increased. Their findings also demonstrated that while most individuals were aware of privacy policy statements, less than half of the respondents had ever read a privacy statement.
Specification of the domain of the construct The first step in the procedure for developing new measures involves specifying the domain of the construct (Churchill, 1979). Typical definitions of business ethics refer to the rightness or wrongness of a business action (e.g., Bartels, 1967; Barry, 1979; Beauchamp and Bowie, 1983). Accordingly, as previously shown, several of the business ethics scales have used this notion to drive item generation. Nevertheless, not everyone agrees on what is morally right or wrong, good or bad, ethical or unethical (Tsalikis and Fritzsche, 1989). There are numerous standard operational models that people use when confronted with ethical dilemmas and each model approaches the issue differently. For example, the Utilitarian Model emphasizes the consequences an action may have on all people directly or indirectly affected by this action; the Golden-Rule Model embraces the basic principle that one should treat others as he or she would liked to be treated. To complicate the problem, most of the available definitions of business ethics exist at highly abstract levels. Yet, it is generally agreed that business ethics is related to widely ‘‘recognized’’ societal norms such as fairness, responsiveness, honesty and integrity (Lewis, 1985; Camenisch, 1991). In the marketing field, the protection/satisfaction of consumers’ interests/needs is especially relevant from an ethical perspective. For example, the Dictionary of Marketing Communications defines ethics in marketing and marketing communications as ‘‘the moral standards, principles, and values underlying and surrounding the marketer’s efforts toward the target audience, the realm of right and wrong’’ (Govoni, 2004, p. 68). In the personal selling arena, Roma´n and Munuera (2005, p. 474) define ethical sales behavior as ‘‘fair and honest actions that enable the salesperson to foster long-term relationships with customers based on customer satisfaction and trust’’
(emphasis added in both definitions). Earlier research in advertising and retailing has identified the following examples of unethical practices: selling a product to a customer that he/she does not need through high-pressure selling techniques, implementing deceptive or misleading influence tactics such as embellishment (to make something appear better than it is) or exaggerating the features and benefits of a product (also known as puffery) (e.g., Hyman et al., 1994; Lagace et al., 1991; Levy and Dubinsky, 1983; Roma´n and Munuera, 2005; Roma´n and Ruiz, 2005). Drawing on the above studies, in this research, CPEOR is defined as consumers’ perceptions about the integrity and responsibility of the company (behind the website) in its attempt to deal with consumers in a secure, confidential, fair and honest manner that ultimately protects consumers’ interests. At this stage of the scale development, the lack of prior empirical research in this area prevented us from hypothesizing the dimensions of CPEOR. However, based on conceptual contributions on internet ethics (Stead and Gilbert, 2001; Tavani, 2000;) as well as research on online trust (Bart et al., 2005; Belanger et al., 2002; Miyazaki and Fernandez, 2001) it was expected that CPEOR would have at least two dimensions – namely, privacy and security. It is important to note that the concepts of security and privacy are closely related and that certain issues associated with these two concepts frequently overlap. However, some important distinctions can and should be drawn. Privacy is defined in terms of individual control over disclosure and subsequent uses of their personal information (Westin, 1967). Accordingly, privacy concerns often arise because online users are concerned about losing control over personal information about themselves to organizations (Tavani, 2000). Security concerns, on the other hand, refer to ‘‘the safety of the computer and credit card or financial information’’ (Bart et al., 2005, p. 135).
Scale development Item generation A total of 38 different items were identified in this first step from the review of the literature.3 Five in-depth interviews and three focus group
The Ethics of Online Retailing interviews (with 6–8 members each) were conducted with convenience samples of online consumers (i.e., people who have purchased items online) in order to (1) help in the process of defining the dimensions of the construct, (2) generate new items, (3) perform a thorough evaluation of the item wording and (4) eliminate any redundant, ambiguous, or poorly worded items. Focus groups lasted 2 hours approximately. Initial questions were related to participants’ typical online shopping experiences, where and when they did their online shopping, what were their concerns (e.g., privacy, security, not having the product delivered, etc.) when buying from an online retailer, and if there were goods or services they will not purchase online. Next, they were requested to focus on the website where they made the last purchase. Then, first the CPEOR definition, and second a list of CPEOR items were shown to the group for discussion. Participants were encouraged to identify dimensions of the CPEOR construct that were present in the website where they had made the last purchase. Finally, they were asked to add any item they thought could be considered as CPEOR and not included in the aforementioned items and eliminate not representative items.4 Overall, 40 scale items were finally generated from the literature and interviews. These items were submitted to a panel of expert judges (marketing professors) in order to assess its content validity. The panel of experts checked the scale items for ambiguity, clarity, triviality, sensible construction and redundancy, as well as to make sure that the items reflected the definition of CPEOR. After the elimination of 12 redundant items or ‘‘not representative’’ items, the experts agreed that the scale items of CPEOR adequately represented the construct. The revised CPEOR scale had 28 items ranging from 1 ‘‘strongly disagree’’ to 5 ‘‘strongly agree.’’
135
retailer’s website. Following the procedure by Milne and Culnan (2004), early data collection for item refinement was undertaken with members of the community of a southeastern university in Spain (the survey was conducted by email). The e-mail message described the research purpose and invited each receiver to participate in the survey by filling in the attached e-questionnaire.5 They were requested to respond to the questionnaire based on their latest online purchase. Surveying by email possesses numerous advantages over conventional interviewing methods. Email surveys offer a more efficient and convenient form of data collection (Best and Krueger, 2002). In addition, an online approach can be more effective for identifying and reaching online shoppers. After the elimination of missing data, 153 observations remained in our database. This sample size exceeded the conventional requirement that around five observations per scale item are needed for conducting factor analyses (Hair et al., 1998; Stevens, 1996). About 63% of the respondents were faculty and university staff, whereas the remaining 37% were students. The sample consisted of more males (64%) than females. The mean age was 32.4 years. The respondents were representative of online customers across numerous e-retailers, having purchased a variety of items (e.g., travel, books, CDS, computers). Convenience samples are considered valid under two conditions: if the study is exploratory in nature, and if the items on the questionnaire are pertinent to the respondents who answer them (Ferber, 1977). This study satisfies both of them. Since this is one of the first attempts to develop a scale to measure CPEOR, this study can clearly be considered exploratory. Also, since it was a necessary condition to fill in the questionnaire to have purchased an item online in the last 4 months, the scale items are certainly relevant to these respondents. The Kaiser– Meyer–Olkin (KMO) measure of sampling adequacy was 0.83 indicating that the variables belong together (Malhotra, 2004).
First study Sample and data collection The unit of analysis in this study is the individual consumer who had purchased at least an item online in the last 4 months. This condition to facilitate consumers’ evaluations of the online
Factor and item analyses Results of the initial exploratory, principal components factor analysis using varimax rotation yielded five factors. Items were retained if (1) they loaded 0.50 or more on a factor, (2) did not load more than 0.50 on two factors, and (3) if the reliability analysis
Sergio Roma´n
136
indicated an item to total correlation of more than 0.40 (Hair et al., 1998). In addition, strong evidence was found for deletion of Factor 5 which had three items, two of which had split loadings, thus making it difficult to interpret (Singhapakdi et al., 1996). Moreover, the reliability of this subscale was only 0.52, which was at the low end of the normally acceptable range for exploratory research. Overall, 9
items were eliminated. As shown in Table I, final exploratory analysis yielded four factors accounting for a total of 65.2% of the variance. Factor loadings ranged from 0.87 to 0.56. Coefficient alpha had acceptable levels ranging from 0.87 to 0.80 (Nunnally and Bernstein, 1994). The first factor that CPEOR measures is ‘‘security’’ (a = 0.87). This factor explains 19.1% of the
TABLE I Items retained based on exploratory factor analysis (Study 1; n=153) Item The security policy is easy to understand The site displays the terms and conditions of the online transaction before the purchase has taken place It provides information about the company behind the site The site appears to offer secure payment methods You can confirm the details of the transaction before paying This site has adequate security features The site clearly explains how user information is used Only the personal information necessary for the transaction to be completed needs to be provided Information regarding the privacy policy is clearly presented The site shows that it complies with the rules and regulations governing online data protection The site exaggerates the benefits and characteristics of its offerings* It is not entirely truthful about its offerings The site uses misleading tactics to convince consumers to buy its products This site takes advantage of less experienced consumers to make them purchase This site attempts to persuade you to buy things that you do not need The price shown on the site is the actual amount billed You get what you ordered from this site The products I looked at were available Promises to do something by a certain time, they do it *Items in italic are reverse-scored.
Security
Privacy
Non-deception
Fulfillment/Reliability
0.70 0.75
0.60 0.81 0.71 0.79 0.82 0.73
0.87 0.80
0.60 0.56 0.83 0.87 0.80 0.75 0.82 0.80 0.63
The Ethics of Online Retailing variance and consists of 6 items that refer to consumers’ perceptions about the security of the online transaction (i.e., the safety of the payment methods) along with the protection of financial information from unauthorized access. To a minor extent, it also refers to the set of concerns involving a computer system’s vulnerability to viruses, worms, and other ‘‘rogue’’ programs that can attack a system and its resources. The relevance of this factor is consistent with the information gathered in the focus group interviews with consumers. When asked why they refrain from buying online, consumers unanimously expressed the concern that their credit card information will be stolen while being transmitted to the seller. In fact, some participants said that: ‘‘I only purchase online when the website gives me the option to pay cash on delivery.’’ Other focus group members pointed out that: ‘‘There is a concern because you are dealing with online companies of unknown location whilst not knowing if they are legal or not. That’s why it feels more comfortable purchasing from websites that provide contact information about the company who own the site. Furthermore, there is more confidence in the transaction if the company has a physical presence in Spain.’’ The second factor, ‘‘privacy’’ (a = 0.88), consists of 4 items accounting for 16.7% of the variance. These items refer to consumers’ perceptions about the protection of individually identifiable information on the Internet (Bart et al., 2005). A good starting point for the analysis of this dimension is the AMA Code of Ethics for Marketing on the Internet. This states that: ‘‘information collected from customers should be confidential and used only for expressed purposes.’’ The consumers we interviewed also voiced their concerns about privacy issues: ‘‘I’m not comfortable at all with the idea of the online retailer having my personal information and selling it to other companies for marketing purposes,’’ ‘‘I don’t like websites that ask you for personal information that is not necessary for the purchase to be made’’ and ‘‘All privacy notices contain the same information, and besides, how do I know that the website actually follows the privacy policy.’’ The third factor was labeled ‘‘non-deception6’’ (a = 0.81) and explains 16.1% of the total variance. The 5 items on this factor refer to the extent to which the consumer believes that the online retailer
137
does not use deceptive or manipulative practices with the intent to persuade consumers to purchase the website’s offerings. This may happen, applying Gardner’s (1975, p. 42) words to the online context, when the online retailer ‘‘leaves the consumer with an impression or belief different from what could be expected of the consumer with reasonable knowledge, and that belief or impression is factually untrue or potentially misleading.’’ Deception in the context of marketing practices is ‘‘unethical and unfair to the deceived’’ (Aditya, 2001, p. 737). This dimension focuses on consumer’s perceptions of online retailer’s deceiving/misleading practices, rather than on the act of deceiving itself. Several examples came out in the focus-group interviews. For instance, one participant argued that she had perceived an online retailer to be unfair and deceptive because ‘‘it shows photographs of a woman, before and after having taken a weight loss product for 10 months, making her lose a total of 59 kilos (130 pounds).’’ Even though this might have been true, the participant perceived that ‘‘the images were manipulated,’’ and that ‘‘the claim was an exaggeration.’’ Other participants complained about travel websites because ‘‘some of these sites offer flight tickets at unbelievably low prices, yet once you click on the link, it is impossible to purchase them, either because you just can’t find them, or if you’re lucky enough to find them, they are sold out.’’ The fourth factor, ‘‘fulfillment/reliability’’ (a = 0.80) consists of 4 items accounting for 13.3% of the variance. These items are related to the accurate display and description of a product so that what consumers receive is what they thought they ordered, as well as the delivery of the right product within the frame promised (Wolfinbarger and Gilly, 2003). Earlier research has found fulfillment/ reliability to be one of the key dimensions of online service quality as perceived by consumers (Parasuraman et al., 2005; Wolfinbarger and Gilly, 2003;). The following quotes from our focus groups are illustrative of the relevance of such dimension: ‘‘Products at this site are a bit pricey, but it is worth purchasing from this site since you get what you order and within the promised delivery time,’’ ‘‘I keep purchasing from this site because they always have the items I want in stock.’’
138
Sergio Roma´n
Second study Sample and data collection A second study was carried out in order to further asses the factor structure and reliability of the 19item refined scale as well as to establish convergent, discriminant and nomological validity through confirmatory factor analyses. To have a sufficient number of observations for such analyses a minimum sample size of 200 was set (Parasuraman et al., 2005). Following previous research (Freestone and Mitchell, 2004), a convenience sample of respondents were emailed the questionnaire, and asked to refer others belonging to the target population (consumers that had purchased an item online in the last 4 months) to complete the equestionnaire and send it back to the main author. Again, respondents were encouraged to respond to the questionnaire based on their latest on-line purchase. The e-mail campaign produced 209 complete and usable responses. As in the first study, questionnaires covered a range of sites in terms of product variety (books, CDs, computer
software and hardware, apparel, flight tickets). To assess the representativeness of the sample, demographic data about the respondents, were also collected. 60.8% of the respondents were males. The sample was relatively young (mean age = 33.6 years). Respondent ages ranged from 18 to 70, 55.5% were between 20 and 34 years old. The sample was well-educated, with 74% of the respondents having a university level or and equivalent education, and only 1% having lower education. As for their occupation, 61.7% of them were employed people, 18.2% were self-employed workers and 16.7% were students. The demographic characteristics of the sample were similar to those reported in recent national study conducted by the Spanish Ministry of Industry, Tourism and Commerce (2005). Confirmatory factor analyses7: reliability, convergent and discriminant validity We subjected the purification data set to confirmatory factor analyses (CFA) by means of LISREL 8.72 (Jo¨reskof and So¨rbom, 1996). Initially a four-factor
TABLE II Construct measurement summary: confirmatory factor analysis (Study 2; n=209) Item Description Security The security policy is easy to understand The site displays the terms and conditions of the online transaction before the purchase has taken place The site appears to offer secure payment methods This site has adequate security features Privacy The site clearly explains how user information is used Only the personal information necessary for the transaction to be completed needs to be provided Information regarding the privacy policy is clearly presented Non-deception The site exaggerates the benefits and characteristics of its offerings* This site takes advantage of less experienced consumers to make them purchase This site attempts to persuade you to buy things that you do not need Fulfillment/reliability The price shown on the site is the actual amount billed You get what you ordered from this site Promises to do something by a certain time, they do it
Std. Loading (t-value)
0.81 (13.52) 0.85 (14.69) 0.79 (13.23) 0.74 (12.03) 0.95 (17.68) 0.73 (12.11) 0.84 (14.49) 0.69 (10.59) 0.87 (14.18) 0.76 (12.01) 0.83 (13.97) 0.76 (12.40) 0.82 (13.59)
*Items in italic are reverse-scored. v2(59) = 166.97; p < 0.01; GFI = 0.90 CFI = 0.96; RMSEA = 0.08; RMR = 0.06; TLI (NNFI) = 0.95.
The Ethics of Online Retailing model8 using all 19 indicators was estimated. The fit of this model was poor (v2(146) = 546.26; CFI = 0.89; GFI = 0.79; NNFI = 0.88; RMSEA = 0.10 and RMR = 0.07). Problematic items were deleted one at a time, followed by another round of confirmatory factor analyses. This process resulted in the deletion of 6 items. As shown in Table II, the re-specified four-factor model had an acceptable fit, the CFI and the NNFI are greater than 0.90, and the RMSEA and RMR are not greater than 0.08 and 0.06, respectively (Hair et al., 1998). Reliability of the measures was confirmed with composite reliability index higher than the recommended level of 0.60 (Bagozzi and Yi, 1988) and average variance extracted for each dimension higher than the recommended level of 0.50 (Hair et al., 1998) as shown in Table III. Following the procedures suggested by Fornell and Larcker (1981) and Bagozzi and Yi (1988), convergent validity was assessed by verifying the significance of the t values associated with the parameter estimates (Table II). All t values were positive and significant (p < 0.01).
139
Discriminant validity was tested by comparing the average variance extracted by each construct to the shared variance between the construct and all other variables. For each comparison, the explained variance exceeded all combinations of shared variance (see Table III). Following the method utilized by Wolfinbarger and Gilly (2003), CFA analyses comparing several possible factor structures were performed (see Table IV). We tested a one-factor model, a twofactor model (privacy + security, fulfillment + non-deception), a three-factor model (privacy + security, fulfillment, non-deception), a four factor model, and a four-factor model where CPEOR is considered a reflective second-order factor (see this model in Figure 1). A second-order factor analysis demonstrates the structural relationships between the facets or dimensions of a multidimensional construct. The first-order factors estimated are considered subdimensions of a ‘‘broader and more encompassing construct’’ (Hair et al., 1998, pp. 625–627) – in this case CPEOR.
TABLE III Means, standard deviations, scale reliability, AVEa and correlations
1. 2. 3. 4.
Security Privacy Non-deception Fullfiment/Reliability
Means
s.d.
AVE
1
2
3
4
3.92 3.96 2.82 4.02
0.55 0.61 0.72 0.61
0.78 0.84 0.74 0.78
0.85b 0.58 0.41 0.54
0.33 0.88 0.35 0.56
0.16 0.12 0.82 0.53
0.29 0.31 0.28 0.85
a
Average variance extracted. bScale composite reliability is reported in bold along the diagonal. Correlations are reported in the lower half of the matrix Shared variances are reported in the upper half of the matrix. All correlations are significant at p < 0.01. TABLE IV Comparison of various models of CPEOR factors Model
Four factors, one second-order factor Four factors Three factors (privacy+security, fulfillment, non-deception) Two factors (privacy+security, fulfillment +non-deception) One factor
v2(df)
GFI
CFI
NNFI
RMR
174.04 (61) 166.97 (59) 441.20 (62)
0.90 0.90 0.76
0.95 0.96 0.88
0.94 0.95 0.85
0.06 0.06 0.10
0.08 0.08 0.16
593.77 (64)
0.70
0.84
0.80
0.10
0.18
714.10 (65)
0.65
0.78
0.73
0.11
0.21
RMSEA
Sergio Roma´n
140
Overall ethics
.73 (t=9.11)
Security
Y1
Y2
Y3
.72 (t=9.81)
Y5
Y6
.70 (t=9.52)
Nondeception
Privacy
Y4
.58 (t=6.37)
Y7
Y8
Y9
Fulfillment/ Reliability
Y10
Y11
Y12
Y13
χ2(61)=174.04 p
