Ethical Decision-Making in an IT Context - IEEE Computer Society

Proceedings of the 39th Hawaii International Conference on System Sciences - 2006

Ethical Decision-Making in an IT Context: The Roles of Personal Moral Philosophies and Moral Intensity Carlos Alberto Dorantes, Barbara Hewitt, Tim Goles University of Texas at San Antonio [email protected], [email protected], [email protected] Abstract Information technologies (IT) have spread throughout all areas of modern society. However, the evolution of ethics that guide their use lags behind technological advances [2]. A promising approach to this problem involves identifying factors associated with ethical decision-making in an IT context. This study tests a model of ethical decision-making based on the argument that an individual’s perception of ethical issues inherent in a specific situation is fundamental to the decision-making process, and is shaped by the moral intensity of the situation [1]. Findings suggest that moral intensity: is influenced by the individual’s personal moral philosophy, age, gender, and religiosity; and subsequently influences various stages of the decision-making process. Results support the use of the moral intensity model of ethical decision-making in IT contexts, and suggest the need to further explore antecedents of the ethical decisionmaking process.

. 1. Introduction The rapid and widespread diffusion of information technology (IT) has provided remarkable benefits for people, businesses, and the general public. Unfortunately, unethical behavior using these technologies (e.g. unauthorized access, creation of virus and worms, software piracy) has undermined the realization of those benefits [2, 3]. This deliberate misuse of information systems and technology has a significant adverse effect on individuals, organizations, and society. The U.S. Congressional Research Service estimated that worldwide losses in 2003 ranged from US$13 billion (for virus and worms only) to US$226 billion (for all forms of cyber attacks) [4]. Worldwide losses due to pirated software were approximately at US$33 billion in 2004 [5]. Efforts to curb the spread of IT-related unethical behavior have not been successful. On the technology

side, the development of information systems security tools has been unable to keep up with the proliferation of IT exploitations [6]. On the ethics side, professional associations have proposed ethical codes of conduct, including the Association for Computing Machinery (ACM) [7] and the Computer Ethics Institute [8]. However, organizations and individuals are often left to develop their own standards of ITrelated conduct and ethics. Even when standards of conduct are developed and widely adopted, individuals will still stray from the established standards [9]. This is especially problematic, given that (1) the evolution of IT-related ethics lags behind the increasing use of information technologies [2] and (2) the development of security tools and policies trails the development of new information systems and technologies [6]. Educational initiatives can significantly contribute towards diminishing this problem. However, before implementing educational programs it is necessary to understand why individuals behave unethically in an IT context. Therefore, it is of paramount importance to understand the decision-making process that individuals undertake when encountering ethically problematic situations in the IT realm. That is the focus of this study. While numerous ethical decision-making models have been proposed in the general literature, a much smaller number have been empirically tested in the context of information technology. This paper proposes and tests a comprehensive ethical decisionmaking model in an IT context. The model draws on behavioral theories from ethics, moral philosophy, and social psychology. It also takes into account individual and situational factors that influence ethical decisions in IT scenarios. The model proposes that certain demographic variables (gender, age, and religiosity), along with personal moral philosophies (relativism and idealism), serve as antecedents to the perceived moral intensity of a specific situation. Moral intensity shapes the recognition of the situation as ethically problematic or not. Both moral intensity

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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006

and perceptions of the ethical problem inherent in the situation go on to influence the actor’s intention to behave ethically. Using structural equation modeling, the model is analyzed and evaluated. An important contribution of this paper is the integration of theories of ethical behavior, and the application of the resulting model in the context of IT. The structure of the paper is as follows. A literature review of ethical decision-making models in both general business and IT contexts is provided, followed by development of the model. The methodology, measures, and data collection are then described. The results are analyzed and discussed. The paper closes with implications of the findings for managers and researchers.

2. Literature review 2.1. Ethical decision-making business (non-IT studies)

models

in

One approach to ethical decision-making explores the cognitive process used by individuals when faced with an ethical dilemma. This perspective originated with the thought that there are distinct levels of moral development through which individuals progress. As they mature and develop, their ethical decisionmaking process becomes more refined [10]. This notion became the basis for more complex decisionmaking models. Rest [11] introduced a processoriented model wherein the individual (1) recognizes that a moral issue exists within a given situation; (2) makes an ethical judgment influenced by the individual’s level of moral development [10]; (3) forms an intention to behave morally (or not); and (4) acts on that intention. A somewhat similar model begins with the recognition of an ethical dilemma, moves on to a cognitive process that includes Kohlberg’s [10] moral development model, and moderates the resulting ethical decision based on individual and situational factors. The ensuing moral judgment influences behavior [12]. Concurrently, marketing researchers developed contingency theories of ethical decision-making. In general, these include both individual and environmental factors that affect the perception of the ethical issue, which in turn helps shape intentions and subsequent behavior [13, 14]. Studies have focused on aspects of the individual decision maker [15], the issue or situation itself [16, 17], and the environment in which the decision is made [18]. Several studies have examined a combination of these factors [19, 20]. A review of this research leads to the conclusion that, in general, ethical behavior is context-dependent: that is, an individual must recognize a given situation

as presenting an ethical dilemma before engaging in an ethical decision-making process. Furthermore, while studies may differ in selecting, defining, or operationalizing various factors, there is general agreement that an individual’s ethical decisionmaking process is related to certain personal characteristics, and is influenced by personal normative beliefs or philosophies. However, metaanalyses of these studies expose a lack of consistency in their detailed findings, and recommend further investigation into moral intensity, the ethical decisionmaking process, and resultant intentions [10, 15].

2.2. Moral Intensity Jones [1] synthesized prior research from the cognitive process and marketing fields to create an issue-contingent model of ethical decision-making that introduces the concept of moral intensity. Moral intensity is defined as the “extent of issue-related moral imperative in a situation” [16, p. 372]. It is based on the premise that intentions and ensuing behavior arise from an individual’s perception of whether a set of ethical components exists in a given situation, and if so, to what extent. Moral intensity is a multidimensional construct that varies according to: 1) magnitude of consequences (the aggregate harm or benefits of the act); 2) probability of effect (the likelihood that the act will cause harm or benefits); 3) temporal immediacy (the length of time between the act and its consequences); 4) concentration of effect (the number of people affected by the act); 5) proximity (the social distance between the decisionmaker and the people affected by the act); and 6) social consensus (the degree to which others think the act is good or evil). The more these components are present, the more likely it is that an individual will recognize the existence of an ethical problem, with a subsequent effect on judgment, intentions, and behavior. Jones [1] proposed that the level of moral intensity helps shape each of the four stages in Rest’s [11] process model (recognizing a moral issue, making a moral judgment, establishing moral intent, and engaging in moral behavior). The extent to which an individual perceives, evaluates, forms intentions, and behaves in an ethically uncertain situation is governed by the moral intensity of the situation itself. Hence moral intensity is exclusive of individual or organizational factors1; it focuses on the situationspecific moral issue. 1

Jones agrees with other researchers that in an organizational setting, certain organizational factors influence intention and behavior in the decision-making process.

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Jones’ [1] model is used as the theoretical foundation for more than 240 empirical studies. Numerous demographic and organizational factors have been included in these studies, but results for these factors have been mixed [21]. Few studies have integrated the concept of moral intensity with moral philosophy (idealism and relativism), and individual and situational characteristics. A notable exception is one study that constructed an integrated model based on these concepts, and then tested it using marketing scenarios that presented the decision-maker with ethically questionable situations [15]. The results indicate that personal moral philosophy does affect moral intensity, which in turn helps shape the individual’s perceptions of an ethical problem. These perceptions influence intentions, which are also influenced, directly and indirectly, by moral intensity.

2.3. Ethical decision-making in the context of IT It has been argued that the rapid and widespread deployment of information technology has led to the emergence of new ethical issues, or at the very least has exacerbated existing ones (for a review see Tavani [22]). Examples abound: debates over privacy; the spread of worms and viruses; pirating of intellectual property; Internet pornography; websites that promote violence targeted at individuals or groups; and many more. Claims that IT-related ethical issues are unique are rooted in three basic arguments; moral distancing, cultural lag, and context-specific norms. Moral distancing enables an individual to dissociate himself from an act and the consequences of that act [23]. Cultural lag is the time between the diffusion of a new technology and the emergence or evolution of laws, ethics, and philosophy that addresses issues raised by that technology [2, 24]. Context-specific norms refer to the development of ethical norms in different milieus. While there may be relative consensus on norms within a given milieu, this consensus may not extend to other milieus. Furthermore, individual, professional, organizational, and regional/national milieus often overlap, requiring the individual to engage his or her individual ethical decision-making process [25]. Thus the question is raised, “Are individual models of ethical decisionmaking from other areas valid in an IT setting?” As shown in Table 1, prior research into this question has employed several theoretical foundations. However, research utilizing the concepts of moral intensity, personal moral philosophy, and religiosity has been limited. This could be attributed to the complexity of the issue (ubiquitous computing) and the immature state of research in this area.

Table 1: Empirical ethics studies in IT Author (year)/ Study purpose

Theoretical base, model and concepts/ Factors studied/ Sample Theory of Reasoned Action, Theory of Planned Behavior Factors: computer attitudes, material consequences, norms (behaviors expected by others), social-legal attitudes

Findings

Logsdon et al [27] Examine the relationship between moral judgment and software piracy

Rest’s [11] model of moral development Factors: Moral development level and attitudes toward software piracy Sample: 363 students

Not significant correlation between moral development and software piracy attitudes; Moral intensity might be a better model

Loch and Conger [28] Understand ethical decisions in computer use

Theory of Reasoned Action Factors: Attitudes, Social Norms, Self-image, Deindividuation, computer literacy Sample: 174 Graduate students

Only 10% of the variance was explained, and TRA did not explain women’s computer decisions

Harrington [9] Examines whether a code of ethics affects computer misuse by IS employees

Code of Ethics Factors: Code of ethics, denial of responsibility, computer abuse judgment and intentions Sample: 219 IS employees from 9 firms

Generic code of ethics impacts IS personnel who avoid accountability and ISspecific code of ethics effected sabotage intentions/judgments

Barnejee et al [29] Identify individual and situational factors that influence IS professionals to act ethically/unethically

Theory of planned behavior, Rest’s [11] moral judgment theory Factors: Moral judgment, attitudes, normative beliefs, ego strength, locus of control, organizational climate Sample: 261 IS employees

Not strong support for the theoretical factors; organizational control variable was more important

Cappel and Windsor [30] Explored the ethical decision making differences between IS professionals and students

Bommer et. al.[31] ethical decision making model and Rest’s [11] Defining Issues Tests Factors: ethical issues (scenario) individual characteristics, social and religious values, professional codes of conduct, company goals and culture Sample: 76 IS professionals and 71 IS senior-level students

Found that IS professionals and students differ in ethical decision making including in terms of hat decision is made and ho the decision is justified.

Kreie and Cronan [32] Identify factors that influence judgments of acceptable behavior and determine if awareness of consequences effects ethical behavior

Bommer et. al.[31] ethical decision making model Factors: Societal, Belief System, individual, professional, legal, business, personal values, individual characteristics, moral obligation, awareness of consequences, ethical issue (scenario) Sample: 307 IS students

Moral obligation, awareness of consequences, gender and scenario/issue impacted one’s judgment on a person’s behavior

Leonard et al. [33] Propose and test a two-stage model for IT ethics (one stage for attitudes and other for intentions)

Theory of Planned Behavior, Theory of Reasoned Action, Robin et al’s [16] concept of Perceived Importance of an Ethical issue (PIE) Factors: Attitude, Normative beliefs, moral judgment, perceived behavioral control, ego strength, locus of control, gender, age, PIE, org. climate

PIE, ego strength and gender were the more important factors. Attitude and normative beliefs were also significant in all scenarios

Eining and Christensen [26] Explored a psychosocial ethical model to explain software piracy behavior

All factors were significant in explaining software piracy behavior except social-legal attitude

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Sample: 423 Students Winter et al. [34] Investigate what factors influence programmers and R&D workers, their attitudes toward privacy, and intellectual property

Forsyth’s [35] moral philosophy and Machiavellism Factors: Idealism, Relativism, Computer literacy, Machiavellism Sample: 290 workers with e-mail

Machiavellism and idealism were significant for both privacy and intellectual property attitudes. Relativism and computer literacy were significant for intellectual property attitudes

Cronan et. al [36] Examine the effect of Perceived Importance of ethical issue (PIE) on Intentions to act ethically/unethically

Robin et al’s [16]concept of PIE Factors: PIE, Attitudes, Age, Gender Sample: 202 students

PIE and Gender are the most influential factors

3. Model Development The underlying premise of this research is that the decision-making process an individual employs in IT related situations is consistent with that used in nonIT related situations. In other words, when individuals confront ethical dilemmas in an IT context, they apply the same cognitive process they use when they confront ethical dilemmas in other contexts. In general, an individual’s intention to behave ethically or unethically is influenced by their perception of the moral intensity inherent in a given situation. The perceived moral intensity affects recognition of the moral issue, the ethical judgment associated with that issue, and behavioral intentions. Antecedents of moral intensity include age [33, 36], gender [33, 36], and religiosity [30], along with personal moral philosophy [34]. These characteristics also have a direct effect on intentions, as does the recognition of the moral issue. These hypothesized relationships are shown in the research model (Figure 1 in Appendix B) and discussed in more detail in the following sections.

3.1 Moral Intensity The research model portrays ethical decision making as a process that begins with an evaluation of a situation in terms of its perceived moral intensity [16]. Moral intensity is based on the premise that intentions and subsequent behavior arise from an individual’s perception of whether a set of ethical components exists in a given situation, and if so, to what extent. Moral intensity is a multidimensional construct that varies according to: 1) magnitude of consequences; 2) social consensus; 3) probability of effect; 4) temporal immediacy; 5) proximity; and 6) concentration of effect. The more these components are present, the more likely it is that an individual will recognize the existence of an ethical problem. There is a large body of research supporting Jones’ [1] argument that moral intensity influences ethical

decision-making. Empirical studies [16] indicate that moral intensity influences perceptions of ethical issues. Furthermore, moral intensity significantly affects moral judgment [37] and intentions [9, 15, 19].

3.2 Moral Judgment Moral judgment arises from a person’s careful and considered evaluation of what is the morally correct course of action when facing an ethical dilemma [11]. Intentions to behave ethically (or not) are shaped by the individual’s judgment pertaining to the moral issue or situation in question. Prior research suggests that moral judgments precede intentions [13, 15, 38, 39].

3.3 Recognition of an Ethical Problem The ethical decision-making process begins with recognition that an ethical problem exists in a specific situation. If an ethical issue is not recognized, an alternate decision-making process will be employed (e.g., economic rationality) [1]. Once the moral issue is recognized, the decision-maker moves on to evaluate alternative courses of action; that is, make a moral judgment. Prior studies show an additional relationship between recognition of an ethical problem and intentions to behave ethically [15].

3.4 Personal Moral Philosophy (idealism and relativism) Personal moral philosophy is an individual characteristic that shapes one’s moral beliefs and influences one’s moral judgments. Forsyth [35] proposed that an individual’s personal moral philosophy consists of two independent dimensions; relativism, and idealism. Relativism is the extent to which the individual rejects universal moral rules and bases his or her moral judgment on the situation itself and the people involved, more than on ethical principles. Idealism is the degree to which the individual bases his or her moral judgment on values related to the principle that harm to others should be avoided. It should be noted, however, that idealism is not moral absolutism, but rather a belief in values such as altruism and a sense of optimism. Idealism and relativism have shown explanatory power related to ethical judgment and decisions in business [40]. In addition, idealism and relativism are significantly related to moral intensity [41]. Idealism tends to heighten perceptions of moral intensity, while relativism tends to reduce them [15].

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3.5 Gender, Age and Religion Gender is perhaps the most studied demographic factor in ethics [42]. A meta-analysis of gender differences in ethical business studies indicates that women are more likely than men, in general, to recognize that a situation involves a moral issue [43]. On the other hand, men, in general, tend to behave less ethically than women [44-46]. Gender-related findings in IS ethical studies have been consistent with the business ethics literature [33, 36, 47]. Regarding age, several studies suggest that older individuals have greater ethical concerns and tend to behave more ethically than younger individuals [48]. In the context of IT, age seems be important since it is generally believed that, overall, younger individuals are more familiar with computers than older individuals [49]. Recent empirical studies in IT ethics have shown that younger individuals have a greater tendency to act unethically than older individuals [36]. For that reason, it is expected that in IT scenarios, younger individuals will have lower levels of moral intensity and more intent to act unethically than older individuals. Extending this reasoning, it is expected that younger individuals will be less idealistic and more relativistic than older individuals. Religiosity is a key personal variable [13]. Some studies have shown that more religious individuals will have higher ethical concerns than less religious individuals [50]. Other studies have found that more religious individuals are more idealistic and less relativistic than less religious individuals [15]. Although the religion construct has not been widely studied in IT ethics, it is expected that in the context of IT the influence of religion will be consistent with previous studies in business ethics. Hence we anticipate that religiosity will be positively related to idealism and moral intensity, and negatively related to relativism and intentions to act unethically. To summarize, it is hypothesized that age, gender, and religion will have direct effects on moral philosophy (idealism and relativism), moral intensity, and behavioral intentions. Further, in accordance with previous research [15], moral intensity is modeled as a mediating variable that will capture a significant effect of demographic variables and moral philosophy on moral intentions.

4. Methodology 4.1 Sample The current study uses students enrolled in an Information Systems course required for all students within a Business School in the southwest United States. The subjects self-identified themselves as

general IS users, and as such are suitable for research that examines IS-related ethical issues [51]. The final number of usable responses was 318. Table 2 shows characteristics of the respondents. Factor Gender Male Female

Table 2: Descriptive Statistics #

Age 19-24 25-30 31-40 41 + Mean Age = 24.74

%

138 176

43.4 55.3

216 60 26 14

68.4 19.9 7.3 4.4

4.2 Operationalization An oft-used research methodology for examining ethical judgments and intentions involves the use of scenarios that present different ethical situations. This allows researchers to evaluate responses in standardized contexts with a relatively high level of reliability [17]. This approach has been used in previous IT-related ethics research [9, 29, 30, 51]. Consistent with this approach, six scenarios developed by Ellis and Griffith [51] to examine ethics in various IS-related situations were used. The scenarios are included in Appendix A. Measures of moral intensity components were adapted from Singhapakdi et al. [15], as were items that measured recognition of ethical problems, and intentions. For each of the six scenarios moral intensity was measured with six items, while ethical problem recognition, moral judgment, and moral intent were measured with one item each (Appendix A). Forsyth’s [35] scales for moral philosophy were used. Religiosity was measured using the 3 item scale developed by McDaniel and Barnett [52].

4.3 Analysis Item reliability was calculated using SPSS 13.0. In general, all measures exhibited acceptable reliability (see Table 3). Path analysis was conducted using LISREL 7.51. Table 3: Cronbach’s Alpha for Measures Factors Cronbach’s alpha Idealism .814 Relativism .735 Religiosity .784 Moral Intensity Scenario 1 .787 Moral Intensity Scenario 2 .849 Moral Intensity Scenario 3 .807 Moral Intensity Scenario 4 .790 Moral Intensity Scenario 5 .804 Moral Intensity Scenario 6 .760

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5. Findings and Discussion

6. Implications and Limitations

As recommended by Kline [53], non-significant relationships were dropped on an individual basis from the original model for each scenario. A review of the results indicates that, generally speaking, the model showed satisfactory fit (see Table 5 in Appendix B and Figure 1 in Appendix B). All the hypothesized relationships that involve moral intensity, recognition of an ethical problem, moral judgment, and intentions were strongly supported in almost all the scenarios (Table 5). The hypothesized negative relationship between relativism to moral intensity was significant in five of the six scenarios. The hypothesized positive relationship between idealism to moral intensity was significant only in two scenarios. This is congruent with previous studies suggesting that individuals with low levels of relativism perceive higher levels of moral intensity than individuals with high levels of relativism [15]. The finding that idealism was significantly linked to moral intensity in only two scenarios may be attributable to the relatively young age of the subjects. As expected, the relationship of age to relativism was found to be significant. This suggests that older individuals are less relativistic than younger individuals. The effect of age on idealism was not significant. The hypothesized relationship between religion and idealism was significant and consistent with prior research. Thus more religious individuals have a higher level of idealism than less religious individuals. The relationship between religion and relativism was not significant. There is a significant link from gender to moral intensity in three of the scenarios, indicating that females tend to perceive higher levels of moral intensity than males. The relationship between gender and intentions was significant in three scenarios, suggesting that males have more intentions to act unethically than females. The direct effect of gender on intentions was noticeably greater than its indirect effect through moral intensity. In summary, gender has a significant direct effect on intentions, and it also has a mediated effect through moral intensity. The effect of age on intentions is mediated by relativism and moral intensity. Age does not show a consistent direct effect on intention throughout the scenarios, but a consistent indirect effect on intentions through relativism and moral intensity. The effect of religiosity on intentions is totally mediated by idealism. The effect of idealism on intentions is weak and mediated by moral intensity. Finally, the effect of relativism and idealism on intentions was primarily mediated by moral intensity.

The present study is one of the first to test the concept of moral intensity in IT contexts. Results indicate that an ethical decision-making model based on moral intensity is appropriate to use in IT-related situations. Another contribution of this study is the incorporation of personal moral philosophies and religiosity in IT ethical decision-making. Looking at the full model, the goodness-of-fit statistics are marginally satisfactory in all the scenarios. Even though this supports the argument that moral intensity can be applied to IT contexts, alternative models that examine more closely the individual components that make up the moral intensity construct might provide deeper insight into ethical decision-making in an IT context. For example, it can be argued that individuals employ more simplified moral reasoning when they confront ethical situations in IT contexts than they use in faceto-face situations. This is consistent with the concept of moral distancing proposed by Rubin [23], based on the anonymity that virtual environments promote. This is analogous to the proximity component of moral intensity. This line of reasoning can be extended to focus on factors that differentiate ethical decision making in an IT context (moral distancing, cultural lag, and context-specific norms). This may be a fruitful avenue for future research. Our study provides limited support for prior studies in other disciplines that found individual differences in religiosity, age and gender influence the way people assess and ascertain ethical issues. The study also indicates that relativism sways one’s perception of moral intensity. These findings imply that managers should be aware of the potential impact of these factors on an individual’s perception of ethically questionable situations, and subsequent intentions. This argues for training programs that include diverse groups of individuals, and the development of training material that reflects different viewpoints. More specifically, our model supports claims that females tend to be more aware of the moral intensity inherent in ethically uncertain situation than males, and tend to have greater intentions to behave ethically than males. Furthermore, our findings suggest that younger individuals tend to be more relativistic than older individuals, and less sensitive to a situation’s moral intensity than older individuals. Managers should be aware of these trends as more women enter the workforce, and as the average age of the workforce decreases as more baby boomers reach retirement age.

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There are some limitations of the present study. The sample is composed basically of student IS users, which may not be representative of the general population. This also lowered the mean age of the sample. The inclusion of a broader group of users, and the identification of certain types of users (e.g., IS professionals, IS security professionals, and “hackers”) might result in more generalizability. In addition, the use of single-item measures for recognition of an ethical problem, moral judgment, and intentions may be perceived as a limitation. Although this is consistent with prior research [e.g., 15], future researchers may wish to develop composite measures for these variables. Nevertheless, the results from the present study make a meaningful contribution to our knowledge concerning ethical decision-making in an IT context.

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40. Arrington, C.E. and P.M.J. Reckers, A SocialPsychological Investigation into Perceptions of Tax Evasion. Accounting and Business Research, 1985. 15(59): p. 163. 41. Douglas, P.C., R.A. Davidson, and B.N. Schwartz, The Effect of Organizational Culture and Ethical Orientation on Accountants' Ethical Judgment. Journal of Business Ethics, 2001. 34: p. 101-120. 42. Ford, R. and W. Richardson, Ethical Decision Making: A Review of the Empirical Literature. Journal of Business Ethics, 1994. 13(3): p. 205-221. 43. Franke, G., D. Crown, and D. Spake, Gender Differences in Ethical Decision Making. Woman in Management Review, 1997. 17(5/6): p. 217-227. 44. Glover, S.H., et al., Gender differences in ethical decision making. Women in Management Review, 2002. 17(5/6): p. 217. 45. Kidwell Jr, R.E. and S.M. Kochanowski, The Morality of Employee Theft: Teaching about Ethics and Deviant Behavio in the Workplace. Journal of Management Education. 29(1): p. 135. 46. Betz, M., L. O'Connell, and J.M. Shepard, Gender Differences In Proclivity For Unethical behavior. Journal of Business Ethics, 1989. 8(5): p. 321. 47. Kreie, J. and T.P. Cronan, How Men and Women View Ethics. Communication of the ACM, 1998. 41(9): p. 7076. 48. Muncy, J.A. and S.J. Vitell, Consumer Ethics: An Investigation of the Ethical Beliefs of the Final Consumer. Journal of Business Research. 24(4): p. 297. 49. Arief, B. and D. Besnard, Technical and Human Issues in Computer-Based Systems Security. 2004, University of Newcastle, DIRC Project. 50. McCabe, D.L. and L. Trevino, K., Academic Dishonesty: Honor Codes and Other Contextual Influences. Journal of Higher Education, 1993. 64(5): p. 523-538. 51. Ellis, T. and D. Griffith, The Evaluation of IT Ethical Decision Making in Marketing. The DATA BASE for Advances In Information Systems, 2001. 32(1): p. 7585. 52. McDaniel, S.W. and J.J. Burnett, Consumer Religiosity and Retail Store Evaluation Criteria. Journal of the Academy of Marketing Science, 1990. 18(2): p. 101112. 53. Kline, R.B., Principles and Practice of Structural Equation Modeling. Methodology in Social Sciences, ed. D.A. Kenny. 1998, New York, NY: Guilford Publications. 354.

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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006

8. Appendix 8.1 Appendix A. IS Ethics Scenarios (adapted from Ellis and Griffith [51]) Scenario 1: A programmer developed a tool that would contact corporate sites, scan their networks, and find flaws in their security system. The programmer made the software available to everyone over the Internet. Corporations felt the programmer was assisting hackers and cyber-criminals. The programmer felt that he was providing a tool for network managers to troubleshoot their security systems. Scenario 2: A popular Internet Service Provider (ISP) offers online registration. Any user with an Internet connection can access the Hookyouup Network and register for Internet service. What the users do not know is that as part of registration, the ISP scans their hard drive assessing their system for potential new software marketing opportunities. Scenario 3: Ruth likes to play practical jokes on friends. Once she tried to log on to Jim’s account, guessing his password was his wife’s name. Once she had access, she installed a program that would flash the message “There is no Escape” every time the escape key was pressed. Jim discovered the joke after a few days and was upset. Scenario 4: Joe is giving an on-line demonstration in which he uses software that was licensed for a 90-day trial period. Prior to giving the seminar, he noted that the license would expire. Rather than pay the licensing fee, he changes the date on his computer, effectively fooling the software into believing it is at the beginning of the licensing period. Scenario 5: Anna needs software to convert TIFF formatted images to GIF format. She found an excellent piece of shareware and has used it once to convert the images. The shareware developer requests that she send $5 if she likes and uses the software. She has not sent a check to the developer to date. Scenario 6: Joan is a programmer at XYZ, Inc. While working late one night, she notices that her boss has left his computer on. She enters his office to turn it off and finds that he is still connected to his email. She scans the messages briefly, noticing whom they are from and what the topics are. One message catches her eye. It is regarding herself in an unflattering way.

Table 4: Items used in the survey (Adapted from Singhapakdi, 1999). Construct Recognition of Ethical Problem Moral Judgment Moral Intentions MI – magnitude of consequences MI – social consensus MI – temporal immediacy MI – probability of effect MI – proximity MI – concentration of effect MI = Moral Intensity

Question 1. The situation above involves an ethical problem. 2. The action taken by (the actor) was ethical. 3. I would act in the same manner as (the actor) did in the above scenario. 4. The overall harm (if any) done as a result of (the actor’s) action would be very small. 5. Most people would agree that (the actor’s) actions are wrong. 6. (The actor’s) actions will not cause any harm in the immediate future. 7. There is a very small likelihood that (the actor’s) actions will actually cause any harm. 8. If (the actor) is a personal friend of (the victim), the action is wrong. 9. (The actor’s) actions will harm very few people (if any).

8.2 Appendix B. Table 5. Measurement Model for the Six Scenarios Paths (Numbers in parenthesis indicate number of scenarios where the indicated paths were significant out of the 6 scenarios)

Standard Coefficient (t-value) Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Scenario 6 0.36 (5.17)

Moral Intensity Æ Recognition of Ethical Problem (6)

0.43 (6.29)

0.47 (7.08)

0.52 (7.85)

0.31 (4.59)

0.44 (6.53)

Moral Intensity Æ Moral Judgment (4)

0.40 (5.27)

0.38 (5.11)

NS

NS

0.14 (1.93)

0.23 (3.16)

Moral Intensity Æ Intentions (5)

-0.35 (-5.00)

-0.35 (-4.77)

NS

-0.39 (-5.83)

-0.28 (-4.30)

-0.21 (-2.93)

Moral Judgment Æ Intentions (4)

-0.58 (-7.87)

-0.3 (-3.45)

NS

NS

-0.48 (-4.16)

-0.30 (-3.62)

0.19 (2.29)

0.32 (3.88)

0.66 (9.82)

0.63 (9.02)

0.68 (8.59)

0.34 (4.21)

-0.13 (-1.89)

-0.25 (-3.07)

-0.71 (-12.12)

-0.47 (-5.83)

-0.23 (-1.96)

-0.19 (-2.28)

NS

0.2 (2.83)

NS

0.14 (1.96)

NS

NS

Recognition of Ethical Problem Æ Moral Judgment (6) Recognition of Ethical Problem Æ Intentions (6) Idealism Æ Moral Intensity (2)

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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006

Relativism Æ Moral Intensity (5)

NS

Gender (female) Æ Moral Intensity (3)

NS

NS

Gender (female) Æ Intentions (3)

NS

-0.13 (-2.37)

Age Æ Ideal (0)

NS

NS

-.27 (-3.45) NS

Age Æ Relativism (6) Age Æ Moral Intensity (3)

-0.17 (-2.04)

-0.17 (-2.02)

-0.22 (-2.64)

-0.17 (-2.10)

-0.25 (-3.11)

0.10 (1.67)

0.19 (3.24)

.16 (2.53)

NS

-0.21 (-3.86)

-0.12 (-2.03)

NS

NS

NS

NS

NS

NS

-0.27 (-3.44)

-0.27 (-3.44)

-0.27 (3.48)

-0.27 (-3.49)

-0.29 (-3.70)

0.11 (1.63)

0.15 (2.19)

0.14 (2.22)

NS

NS -0.21 (-3.36)

Age Æ Intentions (1)

NS

NS

NS

NS

NS

Religiosity Æ Moral Intensity (0)

NS

NS

NS

NS

NS

NS

0.32 (4.38)

0.32 (4.42)

0.31 (4.32)

0.32 (4.45)

0.32 (4.40)

0.32 (4.40)

Religiosity Æ Relativism (0)

NS

NS

NS

NS

NS

NS

Relativism Æ Intentions (1)

NS

NS

NS

0.12 (1.57)

NS

NS

Idealism Æ Intentions (1)

NS

NS

-0.13 (-1.98)

NS

NS

NS

Gender Æ Relativism (0)

NS

NS

NS

NS

NS

NS

Gender Æ Idealism (0)

NS

NS

NS

NS

NS

NS

Religiosity Æ Idealism (6)

Goodness of fit test results (significant indicator value) Chi-square (Degrees of Freedom)

220.04 (97)

212.17 (96)

330.67 (96)

273.87 (96)

318.06 (98)

282.90 (98)

Chi-square/degrees of freedom (< 3)

2.27

2.21

3.37

2.85

3.25

2.89

GFI (> .9)

0.92

0.92

0.89

0.90

0.89

0.90

AGFI (> .9)

0.89

0.89

0.84

0.86

0.85

0.86

RMSEA (< .08)

0.063

0.062

0.087

0.075

0.084

0.077

SRMR (< .10)

0.057

0.060

0.084

0.077

0.080

0.077

(G - 3) (A - 3) (R - 0)

Gender (G) Age (A)

(G - 3) (A - 1) (R - 0)

Religiosity (R)

(G - 0) (A - 6)* (R - 0)

Moral Intensity Magnitude of Consequences Social Consensus Probability of Effect Temporal Immediacy Proximity Concentration of Effect

(G - 0) (A - 0) (R - 6)

(5)* (2)

(6)

(5)* (4)

Relativism

Idealism

Recognition of Ethical Problem

(6)

Moral Judgment (6)*

(1)

(4)*

Moral Intention

(4)

(1)

Figure 1: IT Ethical Decision Making Model: Based on Jones’ Moral Intensity Model Jones’ [1] model is in the dotted box. Numbers in parenthesis indicate number of scenarios (out of 6) where the path as significant (See Table 5). * A negative relationship was found. Moral intention is measured as one’s intent to behave unethically.

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