Marketing Letters 14:4, 257–272, 2003 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.
A Model Integrating the Multidimensional Developmental Theory of Privacy and Theory of Planned Behavior to Examine Fabrication of Information Online MAY O. LWIN
[email protected] Department of Marketing, School of Business, National University of Singapore, 1 Business Link, #02-18 BIZ 1 Building, Singapore 117592
[email protected] JEROME D. WILLIAMS ∗ Department of Advertising, University of Texas, 1 University Station A1200, Austin, TX 78712-1092, USA
Abstract We investigate the antecedents behind online consumers’ attempt to disguise their identities through fabrication. We first develop a general conceptual model that draws on two extant theoretical frameworks: (1) Laufer and Wolfe’s Multidimensional Approach to Privacy, and (2) Ajzen’s Theory of Planned Behavior (TPB) with Perceived Moral Obligation. Next we conduct an empirical study using SEM to test the portion of the conceptual model based on the TPB framework. Results demonstrate that Attitudes, Perceived Behavioral Control, and Perceived Moral Obligation are significant drivers of fabrication, while Subjective Norms are not. Anonymity, one of the unique characteristics of the Internet compared to in-store environments, likely contributed to the intention to fabricate information. In the concluding section we discuss the implications of our empirical results, industry self-regulation and public policy considerations, and how future research can draw upon the conceptual Laufer and Wolfe framework, particularly the “calculus of behavior” construct, to further enrich our understanding of fabrication behavior on the Internet. Keywords:
personal information privacy, online behavior, fabrication, Internet ethics
Introduction The impact of Internet marketing on both consumers and society has been an issue facing marketers and public policy makers for over a decade. Researchers have identified a number of issues that are particularly challenging from a legal and ethical standpoint. One such issue involves privacy and online consumer behavior (Caudill and Murphy, 2000; Culnan, 1993). In the realm of privacy research, a dichotomy of views has emerged on what constitutes the ethical treatment of consumer information. Some feel that industry self-regulation is sufficient while others advocate greater government intervention. To help contribute to our understanding of these divergent viewpoints, there are key areas that ∗ Corresponding author.
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researchers still need to address. It is particularly essential for researchers to understand the tradeoffs between the rights of Internet marketers to the identity of consumers versus the rights of consumers to protect and maintain their privacy as individuals, and how this relationship influences online consumer behavior. Cited as one of the top inhibiting factors for e-commerce growth (Bolin, 1998), privacy concerns result in the consumers being less likely and less willing to buy over the Internet (Business Week, 2000). Privacy invasions arise when consumers’ control over their private and individual-specific data is unwillingly reduced (Culnan, 1993; Milne and Gordon, 1993; Nowak and Phleps, 1995). Such data include names, addresses, lifestyle characteristics, and purchasing habits that can be traced to individuals. Increasing consumer privacy concerns have led some consumers to adopt counter measures to protect their personal information. Consumers are especially reluctant to provide full names, as well as other information such as phone numbers and demographic information (Costello, 2001). A number of researchers have identified consumer responses ranging from flaming (Sheehan and Hoy, 1999a) to withholding (Culnan and Milne, 2001). Of these, fabrication of information has emerged as a major consumer response strategy to privacy concerns, with over 44% of online consumers polled having resorted to fabrication of personal information (Fox et al., 2000). Compared to concerned consumers participating in lobbying efforts of organizations such as the Electronic Privacy Information Center (EPIC) (Wijnholds and Little, 2001), fabrication is a less costly and more convenient alternative. Fabrication of information on the Internet protects privacy by restricting marketers’ access, and limits the power of firms’ to distribute their personal information. Unlike other consumer response strategies that may merely be an inconvenience to marketers, fabrication can have a dramatic impact. The collection and use of such erroneous online information can result in the wrong analyses of consumer trends, and to a greater extent, incorrect target market analysis, leading marketers to incur higher costs. In other words, it can wholly undermine online marketers’ efforts. Online marketers, government regulators, policy makers, and consumer advocates are concerned that such behavior may inhibit the growth of the Internet, with estimated loss of revenue running into billions of dollars (Hiller and Cohen, 2002). Adopting an exploratory approach, we investigate the factors that drive consumers to fabricate their personal information by exploring the question: What leads consumers to routinely provide false information on the Internet? To better understand the drivers behind this behavioral phenomenon, we develop a general conceptual model (see Figure 1) that draws on two extant theoretical frameworks: (1) Laufer and Wolfe’s (1977) Multidimensional Developmental Approach to Privacy, which relates to an individual’s understanding of privacy within a particular environment, and (2) Ajzen’s (1987, 1991) Theory of Planned Behavior (TPB) with Perceived Moral Obligation (PMO), an extension of the Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1975, 1980). As noted in Figure 1, the Laufer and Wolfe framework incorporates the dimensions of self-ego, environmental, interpersonal/calculus of behavior, and control/choice, as factors that should enable one to predict types of situations that can potentially create privacy or invasion experiences. On the TPB side of our general conceptual model, the reasoning factors behind information fabrication are identified as the three main dimensions in traditional reasoning models,
Figure 1. An Integrative Model for Fabrication of Information.
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namely, attitude, subjective norms, and perceived behavioral control, with the addition of perceived moral obligation. In the next section we describe each of the factors from the Laufer and Wolfe framework, followed by a section describing each of the factors from the TPB framework. Although this is an exploratory study, we felt that it would be useful to go beyond a conceptual model and provide an empirical test of some components of our general model. Therefore, we conducted a test using SEM for the TPB portion of the model, as TPB represented the more established theoretical framework. Also, we felt that we could develop more definitive hypotheses for each TPB component by drawing upon extant literature. In describing the components from the TPB framework, we have also included the hypotheses that we test in the empirical study that follows.
1. Privacy Theoretical Framework Laufer and Wolfe’s (1977) Multidimensional Developmental Theory of Privacy proposed that there are three major elements of situations that must be taken into account to understand an individual’s perception and experience of privacy and invasions of privacy. First, the Self-Ego Dimension refers to a developmental process that focuses on individuation (autonomy) and by implication, personal dignity (Laufer and Wolfe, 1977). Individuals have varying degrees of privacy concerns (Caudill and Murphy, 2000), with the level of threshold based on personal experiences. A consumer who is more concerned for his/her general privacy (which is multi-faceted – information privacy, bodily privacy, communications privacy and territorial privacy) is likely to bring along these concerns to the online world. Second, the Environmental Dimension suggests that environmental elements can influence the individual’s ability to perceive, have, and utilize available options. When purchasing on the Internet, regulations materialize as an environmental factor in addressing privacy concerns as individuals are dependent on public policies and corporate policies to guard against any possible privacy violations. Past research has shown that 91% of individuals surveyed believe that businesses and the government are not doing enough to protect their privacy (Nowak and Phleps, 1992). If the government and firms are not seen to be exercising responsible measures to protect consumers’ privacy, consumers are likely to be more concerned, thus leading to defensive measures such as fabrication. Finally, the Interpersonal Dimension, constituting the core of the privacy phenomenon as experienced in daily life (Laufer et al., 1974; Laufer and Wolfe, 1977), is made up of two elements of information management and interaction management. Interaction management relates to the choice of non-interaction with specified others, while information management pertains to the disclosure or non-disclosure of personal information. This latter concept suggests that the exchange of personal information between individuals and an organization is subject to a “calculus of behavior” (Laufer and Wolfe, 1977). That is, when an individual is considering whether to disclose personal information, consideration of future consequences is an important determinant. An individual who perceives potential future (negative) consequences to be minimal will be more willing to disclose their personal information. Culnan and Armstrong (1999) have shown that trust in a Website, operationalized through prior experience with the site, allows a consumer to feel more
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secure when divulging their personal information. In a sense, the risk of unforeseeable outcomes is mitigated through past experience. However, people are willing to disclose personal information if the exchange for some economic or social benefit outweighs the risks (Hansell, 2003; Milne and Gordon, 1993). Within an environment where trust between the consumer and Website is lacking, fabrication becomes a means of receiving the benefits without exposure to risk. The notion of “privacy calculus” is thus especially relevant in the consideration of fabrication of personal information in an online context. Our adaptation of these dimensions is shown in the conceptual model in Figure 1.
2. Reasoned Action Theoretical Framework TPB is an improved version of the basic TRA model with the addition of perceived behavioral control as a new variable (Ajzen, 1991). The theory asserts that intention to act is determined by the individual’s attitudes towards performing the behavior, the subjective norms held by the individual, and the individual’s perceived behavioral control over the act. Past research provided precedence for TPB’s suitability in the study of and the prediction of ethical and unethical behavior (Chang, 1998; Randall and Gibson, 1991). TPB with PMO has especially been found to be suitable for investigating behaviors involving lying and dishonesty (Beck and Ajzen, 1991). Attitude (ATT) towards performing a particular behavior is the degree to which an individual has a favorable or unfavorable evaluation of the behavior. TPB predicts that the more favorable an individual evaluates a particular behavior, the more likely he/she will intend to perform that behavior (Ajzen, 1987). For example, if an individual believes that the advantage gained from the provision of false information over the Internet will help protect personal privacy (Culnan and Milne, 2001), then his/her ATT towards fabricating information is likely to be positive. Furthermore, if the perceived benefits of giving false information to secure one’s privacy exceeded the perceived costs of the action, the likelihood of engaging in such behavior would be high. Hence, positive ATT is likely to increase one’s propensity to fabricate information. H1: The more positive the Attitude towards fabricating information the more likely the intention to fabricate information online. Subjective Norm (SN) refers to an individual’s perception of whether people who are important to him/her think that he/she should or should not perform the behavior in question (Ajzen and Fishbein, 1980). The more an individual perceives that significant others think he/she should engage in the behavior, the greater an individual’s level of motivation to comply with those others. This is also known as the perceived prevalence effect or the “everyone does it concept” (Simpson et al., 1994), where individuals act according to the expectations of how others expect them to conduct themselves. Thus, if an individual thought that everyone around him/her might not perceive fabrication as unethical, the likelihood of the person engaging in fabricating information would be high.
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H2: The greater the Subjective Norms towards accepting fabrication of information the more likely the intention to fabricate information online. Perceived Behavioral Control (PBC) is a function of control beliefs and perceived facilitation, which respectively refer to the perception of the presence or absence of requisite resources and opportunities needed to carry out the behavior, and one’s assessment of the importance of those resources to the achievement of the behavior (Ajzen and Madden, 1986). This perception of volitional control or the perceived difficulty towards completion of the act will affect an individual’s intent as well as the successful performance of that behavior (Chang, 1998). The Internet’s anonymous nature provides individuals with the opportunity to fabricate their personal information. As in the case of popular Internet chat lines such as the Internet Relay Chat (IRC), many Internet users adopt ‘nicks’ which are fictitious representations of the self, portraying as someone else and even to the extent of masquerading as the opposite sex (Wallace, 1999). In this situation, the perceived ease of fabricating information online is likely to create a greater intent to fabricate. H3: The greater the Perceived Behavioral Control the more likely the intention to fabricate information online. Kohlberg (1969) suggested that the maturity of moral judgment could be a powerful and meaningful predictor of action. Given this, Schwartz and Tessler (1972) suggested that moral issues might take on an added salience with respect to ethical behaviors. Gorsuch and Ortberg (1983) supported this proposition when their study showed that the inclusion of personal moral norms significantly explained variation in intent. Utilizing TPB to investigate three unethical situations (cheating in an examination, shoplifting and lying to get out of taking a test), Beck and Ajzen (1991) proposed the addition of PMO to the original TPB model. Thus, PMO could be seen as the level of morality that one has over the behavior in question. Therefore, if the level of PMO online is low, there will be an increased possibility of the intent to fabricate. We propose PMO to negatively influence one’s intention to fabricate information. H4: The greater the Perceived Moral Obligation the less likely the intention to fabricate information online. The original antecedent factors of TPB, namely ATT, SN and PBC, along with PMO, stipulate that actual behavior is determined by an individual’s intention (BI) to perform (or not to perform) the behavior in question. The theory puts forward that the stronger the intent to perform a behavior, the greater the likelihood of the individual engaging in that behavior. In this paper, BI is defined as an individual’s subjective probability that he/she will engage in the fabrication of online information.
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3. Method and Analysis of Model Based on Reasoned Action 3.1. Online Survey and Scale Development Our research plan was to undertake a step-by-step evaluation of the two theoretical approaches before arriving at a full model. Due to the limitations of the current paper, we only report on the first phase of this evaluation, namely, the reason factors in Figure 1 (see shaded area). Future research will address an evaluation of the privacy factors. We felt that it was more appropriate to start with a test of the reason factors as the TPB with PMO model has been more routinely applied in understanding behaviors related to fabrication, e.g., lying to get out of taking a test (Beck and Ajzen, 1991). We utilized Structural Equation Modeling (SEM) to study the reason factors. A pretest, a pilot test and a final online survey were implemented. While pre-tests provided an initial insight into issues regarding online fabrication, the pilot test identified any ambiguous items and assessed the length of time taken to complete the questionnaire. The use of an online survey was justified as online surveys are increasing in popularity as they are easy to administer (Sheehan and Hoy, 1999b). Secondly, greater design flexibility and data control, and third, ability to create skip-patterns and interactions with media-rich content (Bowers, 1998). The online survey was set up and hosted at commercial servers. In anticipation of potential server problems, a mirrored site was also put up at another commercial server. The Website link was e-mailed to 600 randomly generated e-addresses obtained from a commercial survey research company. Incomplete survey forms were excluded from the data analysis. Additional incentives in the form of a lottery were provided. Within approximately 4 weeks, 341 responses were received, returning a response rate of 56.8%. The survey Website consisted of four HTML Web pages that gave participants a formal introduction to the study, its objective and a brief description of the lottery prizes, in the form of incentivized gift vouchers. Participants were not requested to provide any sensitive information (e.g., social security numbers). A link was provided for respondents to contact the relevant persons in view of any inquiries about the survey. All constructs for this section of the study were adapted from TPB. The domain of the relevant construct was specified and items subsequently drafted on the basis of their mapping with the construct’s definition. Eight items were adapted from Ajzen and Fishbein (1980) for the ATT construct (e.g., “By providing fake personal information, I am able to protect my personal privacy”). For the SN construct, pretest elicited referent groups such as family, best friend, ordinary friend and the general public (other people). These were indicated by a six-item scale, which indexed one’s normative beliefs and the likelihood to comply with those beliefs (e.g., “Where giving personal information is concerned, I usually do what my friends are doing”). PBC was assessed by a five-item scale tapping on one’s perception of opportunities, difficulties and control over fabrication of information (e.g., “Providing false information is easy”). PMO was assessed using four-item scale adapted from Beck and Ajzen (1991). Modifications were made to elicit specific responses (e.g., “The act of providing false information online is not unethical”).
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3.2. Measure Validation Procedures An analysis of the structural relationships was performed using SEM Package Amos 4.01, which allowed multiple relationships to be analyzed simultaneously while maintaining statistical efficiency (Arbuckle, 1999). Prior to hypothesis testing, a path model, similar to the conceptual model, was created and the measures specified were subjected to a series of validity checks. An exploratory factor analysis (EFA) was conducted to identify items that were of relatively low reliability when compared to items within the same constructs. Next, confirmatory factor analysis (CFA) was done to ensure convergent validity among the multi-item measures. Finally, SEM was carried out to test the null structural model with the measurement model. The initial 23 items measuring the 5 constructs were subjected to item-to-total correlations and EFA. Overall, each item appeared to belong to the appropriate domains of ATT, PMO, PBC, and BI. Items that did not load on specified factors, or with loadings less than 0.35 were excluded from subsequent analysis. This resulted in the retention of 4 items for SN, 3 items for ATT, 3 items for PMO, 3 items for PBC and 2 items for BI. The SN construct was removed from subsequent analysis due to very poor item loading. Generally, the Cronbach alpha values for all multi-item scales (ranging from 0.77 to 0.94) displayed sufficient reliability, rendering adequacy for analysis. (See Appendix A for descriptive statistics.) The overall fit of the four-construct CFA was good. The Chi-square statistic was insignificant, χ 2 (341) = 149.973, p < 0.001 as might be expected given the size of the sample (Bagozzi and Yi, 1988). Other measures include goodness of fit index (GFI) = 0.923, normed fit index (NFI) = 0.892, incremental fit index (IFI) = 0.919 and comparative fit index (CFI) = 0.919. All fit indexes indicated a very strong and significant factoring of the items. The root mean square error of approximation (RMSEA) = 0.088 was also within the acceptable range (i.e. between 0.05 and 0.09). To access the fit of the measurement model, Table 1 shows that the indicators were statistically significant for most of the proposed constructs (critical value > 1.645). As evidence of convergent validity, all items loaded on specified constructs and were significant (Fornell, 1982).
3.3. Structural Model Fit A summary of the results from the hypothesis testing in Table 2 shows that all the indicators were statistically significant for most of the proposed constructs (critical value > 1.645). Structural model fit was assessed by looking at the significance of the estimated coefficients. Using one-tailed distribution (p < 0.05, critical value = 1.645), the proposed hypotheses were examined. Evidence revealed that all hypotheses were significant at p < 0.05 level with the exception of H2 where findings showed extremely weak links. Figure 2 shows the path diagram with the standardized coefficient weights and directions of the hypotheses. The standardized coefficients shown in Figure 2 suggest that ATT was significantly and positively associated with intention to give false information. The path linking the two variables had a significant standardized coefficient of 0.35 (p < 0.05),
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Indicator Loadings for Measurement Model
Construct
Standardized parameter
Construct loadings
Critical valuesa
Attitude ATT1 (protect against junk mail) ATT2 (protect privacy) ATT3 (security)
0.413 0.649 0.932
0.085 0.092 1.000
6.170 7.880 –
Behavioral control PBC1 (complete control) PBC2 (ease of usage) PBC3 (difficulty)
0.722 0.695 0.626
0.131 1.000 0.102
8.440 – 8.344
−0.705 −0.975 0.502
0.144 0.222 1.000
−9.022 −8.632 –
0.948 0.811
1.000 0.069
– 13.392
Moral obligations PMO1 (no ethical problems) PMO2 (not against morals) PMO3 (feel guilty) Behavioral intention BI1 (false information) BI2 (fake identity)
a Some critical values were not calculated because loading was set to 1 to fix construct
variance. Table 2. Results of Hypotheses Testing Hypothesis
Standardized estimate (β)
Critical ratio
Significant at p < 0.05
H1: ATT towards fabricating information positively relates to intention to fabricate information online.
0.345
5.649
Yes
H2: SN towards fabricating information positively relates to intention to fabricate information online.
−0.045
−0.569
No
H3: PBC positively relates to intention to fabricate information online.
0.292
3.981
Yes
H4: PMO negatively relates to intention to fabricate information online.
−0.717
−6.920
Yes
which supported H1. H2 hypothesized positive relation between SN and BI, and was insignificant at the p < 0.05 level (β = −0.05). The standardized coefficients showed a significant and positive relationship between PBC and BI (β = 0.29, p < 0.05), supporting H3. H4, which postulated a negative relationship between PMO and the intention to fabricate information, was statistically significant at p < 0.05 (β = −0.72) and supported. 4. Discussion and Conclusion This paper presents an exploratory model predicting BI. Findings lend support to the hypothesized relationships of H1, H3, and H4. ATT and PBC relate to BI positively while
Figure 2. SEM Showing Relationships between Reason Factors towards Intention to Fabricate Information Online. Notes: Standardized Coefficients Are Shown. Attitude, Behavioral Control and Moral Obligations Are the Proposed Drivers for Intention. The Scale Items Are Shown in Table 1.
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PMO relates to BI negatively. Insignificant contribution by SN in predicting BI was partially attributed to the Internet’s unique environmental characteristic of allowing consumers to remain anonymous. This precludes social norms from having any effect on behavior; thus H2 was not supported. In this empirical study, we only assessed the impact of the components from the TPB framework. Future studies should examine the entire general conceptual model, i.e., integrating consumers’ online privacy concerns based on the components from the Laufer and Wolfe framework and assessing their impact on fabrication. Both marketers and public policy makers have vested interest in appreciating the intrinsic working behind consumers’ intention to fabricate. Understanding this intention will enable marketers to preempt monetary loss stemming from misdirected and ineffective marketing strategies conceived through the use of an inaccurate information database. It is hoped that the present research can be used as a springboard to enhance marketers’ understanding of information fabrication. To help the Internet industry establish self-regulatory privacy guidelines and codes of conduct, the Federal Trade Commission (FTC) has advocated the adherence to the “Fair Information Practices” of notice to information practices, consumer choice in the use of information, access by a subject to information held about themselves and reasonable security procedures should protect the information gathered (Pitofsky, 2000). Theory suggests that through the disclosure of an Internet site’s privacy practices, consumer privacy concerns will be significantly eased and as a result a more trusting environment for online transactions will be built (Benassi, 1999). As Culnan and Armstrong (1999) have shown, increasing trust is an effective way to preempt consumer fabrication of information as it mitigates the risks that they perceive to exist when supplying their private information online. Publicly stated privacy policies also have the effect of acting to limit liability for any perceived invasion of an individuals claim to privacy by clearly stating an organization’s position (Goodwin, 1991). Privacy policies also “volunteer” Internet sites for liability under FTC rules if they are not followed (Killingsworth, 1999). Additional efforts in the realms of both industry self-regulation and government public policy will be necessary to provide consumers with the comfort level necessary to ease concerns over significant privacy issues. For example, the IBM Multi-National Consumer Privacy Study found that almost 50% respondents in the U.S. and the United Kingdom (U.K.) respondents look for a privacy statement on Internet sites (Sever et al., 1999). The same study also found that 63% of those respondents who used the Internet had refused to give information to Internet sites that they perceived had unclear privacy policies. However, headway is being made. To standardize privacy policies, several organizations have initiated privacy seal programs which allow Internet sites who meet certain stated criteria to display the seal of the certifying organization. Privacy seal programs bring the credibility of third-party assessment, verification, and dispute resolution to a Website’s information practices (Killingsworth, 1999). Two of the most recognized privacy seals are the BBBOnline and TRUSTe seals. TRUSTe affiliated sites account for 33% of total U.S. Web traffic (DCITA, 2002). Those Internet sites displaying the TRUSTe seal agree to have a stated privacy policy that states what personal information is being gathered about users, how the information will be used, who such information will be shared with, the choices available regarding how collected information is used and how users can update or correct
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inaccuracies in your information (www.truste.org). The BBBOnline Privacy seal stands for fact that the corporation has met the BBBOnline program requirements regarding the handling of any personal information give to the site (Park, 2001). In addition to industry self-regulation progress, public policymakers also are making strides in shaping a regulatory climate conducive to reducing the provision of false information and simultaneously fueling the growth of e-commerce on the Internet. There is currently no comprehensive legislation that governs the Internet. However, there several laws that make up the “sectoral” approach to privacy, e.g., sites targeted at kids and some financial service firms. For example, The Children’s Online Privacy Protection Act (COPPA) requires Internet sites that market to those under the age of 13, and those who know that those in that age group use their Website, institute procedures to obtain explicit parental consent before collecting personally identifiable information from children (Hiller and Cohen, 2002). In addition, federal laws have been enacted in several areas to protect certain personal information (Winn, 2001), e.g., financial records (Right to Financial Privacy Act), credit reports (Fair Credit Reporting Act), video rentals (Video Privacy Protection Act of 1988), cable television (Cable Privacy Protection Act of 1984), educational records (Family Educational Rights and Privacy Act), motor vehicle registrations (Drivers Privacy Protection Act), and telephone records (Telephone Consumer Protection Act) (Banisar and Davies, 1999). Also, the Gramm–Leach–Bliley Act (GLBA) regulates the manner in which banks, insurance companies, and securities firms, as well as other “financial institutions” can collect, use, and disclose personally identifiable financial information (Ambrose and Gelb, 2002). Unfortunately, the enforcement of these privacy protection laws has been patchy. As an example, a study of 162 children’s Websites found 10% in blatant noncompliance with COPPA, and almost half failed to comply with important elements of the COPPA Rule (Turow, 2001). The challenge for policymakers and the courts is to strike a balance between the benefits and harms of restricting consumer personal information. Until consumers are comfortable that this balance has been struck, they may continue to engage in their own protective defense mechanisms, including fabrication. While the present study is not designed to be conclusive on the issue of fabrication on the Internet, we feel our findings can be used as a foundation to further explore this topic. Future research can build upon our integrated model to examine the relationship of fabrication with other variables, such as personal orientation toward privacy (Westin, 1991), and readiness to embrace new technologies (Parasuraman, 2000). In addition, it is important to consider demographic information when looking at consumers since different segments of the population will have varying degrees of privacy concerns based on their personal orientation toward privacy issues, and hence will engage in their own specific “privacy calculus” relative to their concerns. For example, a recent Harris poll (Taylor, 2000) found substantial differences between the concerns of different segments of the population on the use and abuse of personal consumer information. Currently the majority of Blacks (60%) and Hispanics (68%) do not use the Internet (NTIA, 2002). As these numbers increase, minority consumers may find fabrication of certain demographic and other descriptive information online to be a convenient mechanism for shielding themselves from the type of discriminatory behavior they encounter in in-store retail environments (Williams et al., 2001). This documented non-Internet consumer discrimination in the marketplace can weigh heavily in
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the “privacy calculus” of ethnic minorities online. Already court cases have resulted from such concerns, e.g., see Harris (2003) for consumer racial profiling in cyberspace case, and Petty et al. (2003) for Website discrimination case. Finally, future research could investigate other common Internet behaviors such as withholding and boycotting, and consider other environmental contexts that might affect the perceived sensitivity of personal information, e.g., credit card applicants who routinely disclose income information but who might refrain from such disclosure (or even fabricate) in another context. Such additions to the present model and the exploration of other linkages would no doubt provide for a more thorough understanding of the underlying factors leading to the fabrication of information.
Appendix A. Questionnaire Items Table 3. Questionnaire Items Construct
Label
Attitude
ATT1 ATT2 ATT3
Perceived behavioral control
PBC1 PBC2
Questionnaire
Standardized parameter
Construct loadings
Critical valuesa
By providing false personal information, I would receive less junk mail
0.413
0.085
6.189
By providing false personal information, I am able to protect my personal privacy By providing false personal information, I feel secure
0.650
0.091
7.923
0.932
1.000
–
When providing false information, I have complete control Providing false information is easy
0.722
0.131
8.440
0.694
1.000
–
0.625
0.102
8.345
PBC3
Website features make it difficult for me to provide false information
Perceived moral
PMO1
The act of providing false information online is not ethical
−0.704
0.144
−9.018
obligations
PMO2
The act of providing false information does not go against my moral value There is nothing wrong in providing false information
−0.977
0.223
−8.599
0.501
1.000
–
In the future, I intend to provide false information to Websites In the future, I will not give up my true identity
0.950
1.000
–
0.810
0.069
13.351
(i) It is very important that my best friend approves of my falsifying personal information (ii) When giving personal information is concerned, I usually do what my best friend is doing
0.938
0.176
11.039
PMO3 Behavioral Intentions
BI1 BI2
Social normsb
SN1
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Table 3. (Continued.) Construct
Label
Questionnaire
Standardized parameter
Construct loadings
Critical valuesa
SN2
(i) It is very important that my friends approve of me falsifying personal information (ii) When giving personal information is concerned, I usually do what my friends are doing
0.983
0.165
11.194
SN3
(i) It is very important that my parents approve of me falsifying personal information (ii) When giving personal information is concerned, I usually do what my parents are doing
0.870
0.164
10.684
SN4
(i) It is very important that other people (except my family and friends) approve of me falsifying personal information (ii) When giving personal information is concerned, I usually do what other people (except my family and friends) are doing
0.529
1.000
–
a Critical values were not calculated because loading was set to 1 to fix construct variance. b Social norm is defined as the multiplicity summation of (i) and (ii) (i.e. SN1 = (i) ∗ (ii)).
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