Moral Intensity and Ethical Decision-Making: A Contextual Extension

Transcription

1 Moral Intensity and Ethical Decision-Making: A Contextual Extension Tim Goles University of Texas at San Antonio Gregory B. White University of Texas at San Antonio Nicole Beebe University of Texas at San Antonio Carlos A. Dorantes University of Texas at San Antonio Barbara Hewitt University of Texas at San Antonio Acknowledgements The authors would like to thank the anonymous reviewers for their insightful and helpful comments on this paper, and Sue Brown for her advice and support in her role as Guest Editor of this special issue. The paper greatly benefited from their contributions. Abstract This paper explores the role of an individual s perception of situation-specific issues on decisionmaking in ethical situations. It does so by examining the influence of moral intensity on a person s perceptions of an ethical problem, and subsequent intentions. Moral intensity (Jones, 1991) is an issuecontingent model of ethical decision-making based on the supposition that situations vary in terms of the moral imperative present in that situation. An individual s decision is guided by his or her assessment of six different components that collectively comprise the moral intensity of the situation. The relationship between the components of moral intensity and the decision-making process is tested through the use of scenarios that present ISrelated ethical situations. The results indicate that moral intensity plays a significant role in shaping the perceptions and intentions of individuals faced with IS-related ethical situations. The conclusion drawn from this is that, consistent with prior research, the decision-making process is influenced by an individual s perception of situation-specific issues; that is, the moral intensity of the situation. ACM Categories: K.4.1, K.7.4 Keywords: Ethics, Ethical Decision-making, Intentions, Moral Intensity, Perceived Ethical Problems, PLS Introduction Concern over ethical decision-making in business has dramatically increased recently due to incidents such as the Enron and Global Crossing scandals. This is paralleled by mounting apprehension over the misuse of information systems and technology, leading to calls for more research that explores ethical decision-making in an information systems (IS) context (Banerjee et al., 1998; Cappel & Windsor, 1998; Ellis & Griffith, 2001). This paper answers that call by extending research investigating ethical decision-making in the marketing field to generate new knowledge in the IS arena. It does so by employing the concept of moral intensity, or the extent of issue-related moral imperative in a situation, (Jones, 1991, p. 372) to examine an individual s perception of IS-based ethical scenarios, and subsequent behavioral intentions. Moral intensity suggests that ethical decisions are primarily contingent upon the perceived characteristics of the issue at stake, and therefore ethical decision-making involves the collective assessment of those characteristics. For example, using a companyowned electronic mail system to send innocuous personal messages (e.g., sending a shopping list to 86 The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3)

2 one s spouse) would be rated low in moral intensity by many people. On the other hand, using that same system to send harmful or malicious messages (e.g., child pornography or hate literature) would probably be ranked high in moral intensity. A basic premise of this study is that ethics are largely situation-specific. This is in line with the argument that hypernorms, or fundamental ethical principles, cannot effectively address all behaviors because there are a large number of diverse milieus in which people live, work, and play, and these milieus often have different ethical norms (Conger & Loch, 2001). A second premise is that the proliferation of IS in societal, business, and personal settings has spawned new ethical issues, or at least exacerbated existing ones. Thus the purpose of this paper is to explore the impact of moral intensity on two significant components of ethical decision-making in an IS context: an individual s perception of an ethical problem, and ensuing intentions. Prior Research Much prior research into ethical decision making has focused on personal characteristics (gender, age, education, level of moral development) and social or organizational factors (corporate ethical climate, influence of peer groups, codes of ethics). Metaanalyses of this research reveals mixed results, and recommends further empirical testing, particularly in the area of ethical decision-making intentions and moral intensity (Ford & Richardson, 1994; Loe et al., 2000). These meta-analyses also suggest that ethical decision-making is situation-specific, a conclusion that is seconded by empirical studies in marketing (Singhapakdi et al., 1996) and IS (Banerjee et al., 1998). Moral intensity (Jones, 1991) is often used to examine ethical decision-making in different circumstances (Chia & Mee, 2000; Frey, 2000; Harrington, 1996; Morris & McDonald, 1995; Paolillo & Vitell, 2002; Singer, 1996; Singhapakdi et al., 1996). In brief, this theory postulates that moral issues can be viewed in terms of underlying characteristics that influence the various stages of the decision making process. This paper draws from a previous study that investigated the impact of moral intensity on two components of ethical decision-making, ethical perceptions and behavioral intentions, in a marketing context (Singhapakdi et al., 1996). Their findings are consistent with prior research indicating that situation-specific issues influence the ethical decision-making process, and that moral intensity helps to shape perceptions and intentions. We extend their study by testing the model using a confirmatory approach in place of the exploratory approach they used, and by changing the context to IS-related situations. This is a valid and necessary step in expanding the ethical decision-making body of knowledge, due to the emergence of new and unanticipated issues brought about by the rapid development and deployment of information systems and technology (Maner, 1996; Moor, 1985). These issues arise from factors that add an element of uniqueness to IS-related situations. The unique factors that are salient in shaping the ethics of individuals in an IS setting include cultural lag (Ogburn, 1966), moral distancing (Rubin, 1994), and context-specific norm development (Conger & Loch, 2001). The notion of cultural lag (Ogburn, 1966) argues that the evolution of material culture (e.g., technological inventions, innovation, and diffusion) outpaces non-material culture (e.g., ethics, philosophy, and law). This is reflected in current debates revolving around the interplay between the Internet and issues related to privacy, intellectual property rights, and pornography (Marshall, 1999). Moral distancing suggests that information systems and technologies may increase the propensity for unethical behavior by allowing an individual to dissociate himself from an act and its consequences (Rubin, 1994). Development of ethical norms often varies across different contexts or milieus. Consensus on ethical norms within each milieu helps define acceptable behavior for that setting, and may provide a guide to ethical behavior in an IS context (Conger & Loch, 2001). Furthermore, it has been argued that IS ethical issues are philosophically interesting in their own light, and deserving of recognition as a legitimate domain of applied ethics that merits further investigation (Tavani, 2002). Thus it is appropriate and desirable to evaluate the applicability of existing theory to ethical decisionmaking in an IS context. Model Development Moral intensity is multidimensional, consisting of six components: 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 decision maker and those affected by the act; and 6) social consensus the degree to which others think the act is good or evil (Jones, 1991). The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3) 87

3 Perceived Ethical Problem Moral Intensity Behavioral Intentions Figure 1. Research Model (based on Singhapakdi et al., 1996). A person s collective assessment of these characteristics results in a given situation s moral intensity. This influences the individual s moral judgment, intentions, and subsequent behavior. In general, issues with high moral intensity will be recognized as ethical dilemmas more often than those with low moral intensity, leading to a positive relationship between moral intensity and perception of an ethical problem. Furthermore, issues with high moral intensity have a positive relationship with an individual s intention to behave in an ethical manner (Singhapakdi et al., 1996). Consistent with existing ethical theories (Dubinsky & Loken, 1989; Ferrel et al., 1989; Hunt & Vitell, 1986; Singhapakdi et al., 1999) we postulate an additional positive relationship between perception of an ethical problem and intention to behave ethically. This is reflected in the research model shown in Figure 1. Methodology Sample Participants were solicited from all sections of the core IS course required for business majors at a large southwestern state university. Participation was voluntary. Details are provided in Table 1. Operationalization Data was collected via a scenario-based questionnaire. Scenarios are commonly used to examine ethical judgments and intentions in many different areas, including IS (Banerjee et al., 1998; Cappel & Windsor, 1998; Ellis & Griffith, 2001; Harrington, 1996). Consistent with this approach, scenarios developed by Ellis & Griffith (2001) were adopted (Appendix A). Measures of moral intensity were adapted from prior research, as were items that measured ethical perceptions and intentions (Singhapakdi et al., 1996) (Appendix B). Factor # % Gender Male Female Age Mean age = Major Accounting Economics Finance IS Management Marketing Mgt. Science Other Employment Status Part time Full time Not employed Number of Surveys Distributed 511 Usable Responses Response Rate 86% Table 1. Demographics Scenario Validation It is crucial that scenarios used in an empirical test of moral intensity be perceived as presenting an ethical problem (Hunt & Vitell, 1986). 1 Our predetermined criteria were to reject any that answered every question with the same number (e.g., all 5 ) or any that were missing entire sections of data. There was one survey rejected for identical answers, and two for incomplete data, resulting in a total of 442 usable surveys. Every respondent did not answer each demographic question, so the totals for each factor may not equal The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3)

4 Scenario Moral Intensity Components Magnitude of Mean Consequences Std. Deviation Social Mean Consensus Std. Deviation Temporal Mean Immediacy Std. Deviation Probability of Mean Effect Std. Deviation Proximity Mean Std. Deviation Concentration of Mean Effect Std. Deviation Perceived Ethical Mean Problem Std. Deviation t-value Intentions Mean Std. Deviation t-value For the Moral Intensity components, a higher mean indicates a higher level of moral intensity. For Perceived Ethical Problem, a lower mean indicates the scenario is perceived to present a greater ethical problem. All the t-values are significant at the.05 level. For Intentions, a higher mean indicates a greater intention to behave in a different (more ethical) manner than the actor in the scenario. All the t-values are significant at the.05 level. Table 2. Scenario Means, Standard Deviations, and t-values Furthermore, to test the underlying premise of moral intensity, the scenarios should vary in terms of the different components. To ensure this, the scenarios were evaluated through a three-stage process. First, an independent panel of researchers reviewed the scenarios to determine whether or not they presented an ethical problem. There was consensus that the first six did indeed involve an ethical situation. Scenario seven was judged to be marginal. However, it was included to determine if respondents would differentiate between what the panel felt were clear ethical problems and one that was deemed to be borderline. Next, the means and standard deviations of the responses for each scenario were calculated (Table 2). The results suggest that the respondents perceive each of the moral intensity components as varying between scenarios. Likewise, the results varied for perceived ethical problem, again suggesting that the respondents viewed each scenario as differing in ethical considerations. Finally, t-tests were conducted comparing the mean responses of perceived ethical problem and intentions for each scenario to the neutral value of 5 (the midpoint of the 9-point Likert scale). As Table 2 indicates, there was a significant difference between the perceived ethical problem mean and the neutral value for each scenario, indicating the respondents viewed each scenario as involving an ethical problem. As expected, the results for scenario 7 suggest that the respondents viewed this scenario as less intense than the others. Additionally, the means and t-values for intentions vary according to scenario, and the t-values indicate a significant difference between the respondents and the actors in the scenarios. This suggests that the respondents are ethically sensitive, in that they can differentiate between scenarios, and are inclined to behave differently that is, more ethically than the scenario actors. Again, the differences are much less for scenario 7. Analysis PLS Graph version 3.0 was used to analyze the research model for two reasons. First, the model contains both formative constructs (moral intensity) and reflective constructs (perceived ethical problem; intentions). Constructs measured by formative indicators cannot be adequately evaluated using covariance-based structural equation modeling techniques such as LISREL or AMOS. PLS has the ability to deal with both types of indicators (Chin, 1998a). Second, within each individual scenario the reflective constructs are measured using single-item The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3) 89

5 indicators. Consequently, their measurement error cannot be estimated. However, PLS provides results identical to multiple regression analysis in the case of single-item indicators (Chin et al., 2003). Formative indicators are defining characteristics of the construct; they form or cause the construct, as opposed to reflective indicators that are manifestations of the construct. Formative indicators are not necessarily correlated with each other, nor can unidimensionality be assumed. Thus commonly used methods to assess individual item reliability and convergent validity are irrelevant (Chin, 1998a; Gefen et al., 2000). Instead, item weights are used in place of loadings and are interpreted similar to beta coefficients in multiple regression, indicating the relevance of the items to the research model (Chin, 1998b). Statistical significance was determined by using the bootstrap option with 200 resamples. Each scenario was analyzed individually in accordance with the research model (Figure 1). In addition, a mega-scenario was created, consisting of the aggregated responses to all seven individual scenarios. The weights and t-statistics for the formative indicators are presented in Table 3. The structural model is assessed by examining the r 2 values, path values, and associated t-statistics, as shown in Tables 4 and 5. Results and Discussion Table 3 indicates that, as expected, moral intensity is situation specific; that is, the significance of each component varies by scenario. For example, social consensus is significant in all seven of the scenarios, while concentration of effect is significant in only one. This is not entirely unexpected, nor without precedent. Previous research suggests that the impact of individual components on overall moral intensity varies according to the characteristics of the scenario, both in IS (Banerjee et al., 1998; Cappel & Windsor, 1998; Ellis & Griffith, 2001) and non-is (Chia & Mee, 2000; Frey, 2000; Morris & McDonald, 1995) contexts. In addition to differences in scenario characteristics, there is another possible explanation for the inconsistent showing of two specific components, probability of effect and concentration of effect. There exists a cognitive bias in risk perception that often leads people to underestimate potential future risky or negative implications of a situation (Jones, 1991). Although Jones downplays this bias, it may have weakened the recognition of these two components. Nevertheless, this does not invalidate the concept of moral intensity. Overall, Table 3 reinforces the notion that moral intensity is truly issue-contingent. 90 The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3)

6 Perceived Ethical Problem Intentions Scenario Scenario Scenario Scenario Scenario Scenario Scenario Mega-Scenario Table 4. r 2 Values Also of interest are the implications drawn from Tables 4 and 5. The r 2 values signify that, overall, the model explains 41% of the variance in the perceived ethical problem construct, and 44% of the variance in behavioral intentions. The variance explained is less in the individual scenarios, due to the uniqueness of each scenario. Table 5 indicates that all the paths from moral intensity to perceived ethical problem, and from moral intensity to intentions, exceed the suggested minimum standard of 0.20 (Chin, 1998b), and are statistically significant. This suggests that moral intensity does indeed help to explain the relative influence of situational factors on the ethical decision-making process. There is, however, less support for the expected relationship between perceived ethical problem and intentions. The overall path coefficient, although statistically significant, falls below the 0.20 threshold, as do the path coefficients for scenarios 1, 2, 6, and 7. This is not fully in accordance with suggestions in the literature that perceptions of an ethical problem precede intentions (Dubinsky & Loken, 1989; Ferrell et al., 1989; Harrington, 1996; Hunt & Vitell, 1986). To explore this further, the indirect effect of moral intensity on intentions was investigated. The indirect effect is calculated by multiplying the path coefficient from moral intensity to perceived ethical problem (0.637) by the path coefficient from perceived ethical problem to intentions (0.144). The resulting indirect effect (0.637 * = 0.092), when added to the direct effect of moral intensity on intentions (0.564) results in a total effect of moral intensity on intentions of ( = 0.656). This suggests that moral intensity strongly influences intentions, both directly and indirectly, through an individual s perception of the ethical ramifications of a given situation. One reason for the lack of direct effect between perceived ethical problem and intentions might be that individuals assess a specific situation in terms of moral intensity without consciously realizing that they are going through an ethical evaluation process. In other words, they base their behavioral intentions on their own egocentric value system, oversimplifying the situation and bypassing a formal assessment of the ethical implications (Cappel & Windsor, 1998). Conclusion This paper examined the role of moral intensity in the ethical decision-making process in an IS context. It contributes to the body of knowledge by extending existing theory to a new context, and by employing a confirmatory analytical approach. The results support the basic concept underlying moral intensity; the decision-making process is influenced by the individual s perception of situation-specific issues. From: Moral Intensity path To: Perceived Ethical Problem From: Moral Intensity path To: Intentions From: Perceived Ethical Problem path To: Intentions coefficient t-statistic coefficient t-statistic coefficient t-statistic Scenario *** *** * Scenario *** *** * Scenario *** *** *** Scenario *** *** *** Scenario *** *** *** Scenario *** *** *** Scenario *** *** Mega- Scenario *** significant at the.05 level *** significant at the.01 level *** *** *** Table 5. Path Coefficients and t-statistics The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3) 91

7 This is consistent with previous research, and extends research in other areas into an IS context, providing additional assurance that moral intensity is a viable theoretical concept across varying settings. The results also highlight both the direct and indirect role of moral intensity in shaping behavioral intentions. It is intriguing to speculate as to what extent the factors (cultural lag, moral distancing, and contextspecific norm development) that make IS-related ethical issues different from other contexts may have influenced the results of this study. For example, concentration of effect was significant in only one scenario. This may be due, at least in part, to a lack of consensus on ethical norms regarding technological innovations, arising in turn from the time lag between technological evolution and norm development, the ability of technology to diffuse or spread out the impact of an act, or a combination of the two. These factors may also have undermined any direct effect between perceived ethical problem and intentions. Our opinion is that these factors do in fact differentiate IS-related ethical issues from those in other fields. Obviously, however, much work remains to be done to confirm or refute this belief. As with any study, this one is subject to certain limitations, one of which is the use of students as research subjects. Previous research has shown that students views on ethical dilemmas vary from professionals. Students tend to view workplacebased ethical situations from an individual perspective, while professionals tend to take the firm s perspective into account. Professionals are also more accepting of authority, and generally adopt a more sophisticated level of moral reasoning in the ethical decision-making process (Athey, 1993; Cappel & Windsor, 1998). Other researchers, however, argue that the use of students is reasonable in instances where their response can be linked to their 'real world' context (Sambamurthy & Chin, 1994). Our position is that the use of students in this study is justified because we are not claiming that students are surrogates for professionals. Rather, we contend that students are IS users, and as such are suitable subjects for examining IS-related ethical issues (Ellis & Griffith, 2001). Furthermore, a strong argument can be made that the respondents can identify with the scenarios used, based in part on the fact that over 75% of the respondents work either full or part time (Table 1). Finally, students are the employees and managers of tomorrow. Their outlook towards IS-related ethical issues is at least partially shaped prior to their entry into the workforce, and is likely to be carried forward into their business careers (Cheng et al., 1997; Sims et al., 1996). Consequently, insights into students perceptions and judgment of ethical issues can provide guidance to organizations seeking to foster an ethical corporate climate. Nevertheless, it would be prudent to extend this study to include IS professionals. If moral intensity is indeed a key component in ethical decision-making, as suggested by this study, then a significant implication is that both students and professionals may benefit from a better understanding of moral intensity s individual components. Educating individuals on potential consequences and implications of ethical problems could sharpen their perception and decision-making skills when they encounter ethically sensitive situations. This may be accomplished through a mix of instruction at the university level, and on-going education and training at the professional level. Discussion of various scenarios, such as the ones used in this paper, can serve as a vehicle to aid individuals in evaluating their ethical reasoning, and comparing it to others. This can be supplemented with establishing guidelines for individual accountability (Banerjee et al., 1998), publishing and enforcing codes of ethics (Harrington, 1996), and implementing detective, preventive, and deterrence measures (Straub et al., 1993). In short, the findings from this study furnish insight into the ethical decision-making process. Initiatives based on these insights offer an opportunity to increase the comfort level and decision-making capability of individuals when confronted by ethically complex situations. References Athey, S. (1993). Comparison of Experts and High Tech Students Ethical Beliefs in Computer Related Situations, Journal of Business Ethics, Vol.12, No.5, pp Banerjee, D., Cronan, T., and Jones, T. (1998). Modeling IT Ethics: A Study in Situational Ethics, MIS Quarterly, Vol.22, No.1, pp Cappel, J. and Windsor, J. (1998). A Comparative Investigation of Ethical Decision Making: Information Systems Professionals versus Students, The Database for Advances in Information Systems, Vol.29, No.2, pp Cheng, H., Sims, R., and Teegen, H. (1997). "To Purchase or Pirate Software: An Empirical Study," Journal of Management Information Systems, Vol.13, No.4, pp Chia, A. and Mee, L. (2000). The Effects of Issue Characteristics on the Recognition of Moral Issues, Journal of Business Ethics, Vol.27, No.3, pp The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3)

8 Chin, W.W. (1998a). Issues and Opinion on Structural Equation Modeling, MIS Quarterly, Vol.22, No.1, pp. vii-xvi. Chin, W.W. (1998b). The Partial Least Squares Approach for Structural Equation Modeling, in Marcoulides, G.A. (Ed.), Modern Methods for Business Research, Mahwah, NJ: Lawrence Erlbaum Associates, pp Chin, W.W., Marcolin, B.L., and Newsted, P.R. (2003). A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study, Information Systems Research, Vol.14, No.2, pp Congor, S. and Loch, K. (2001). Invitation to a Public Debate on Ethical Computer Use, The DATA BASE for Advances in Information Systems, Vol.32, No.1, pp Dubinsky, A. and Loken, B. (1989). Analyzing Ethical Decision Making in Marketing, Journal of Business Research, Vol.19, No.2, pp Ellis, T. and Griffith, D. (2001). The Evaluation of IT Ethical Scenarios Using a Multidimensional Scale, The DATA BASE for Advances in Information Systems, Vol.32, No.1, pp Ferrell, O.C., Gresham, L.G., and Fraedrich, J. (1989). A Synthesis of Ethical Decision Models for Marketing, Journal of Macromarketing, Vol.9, No.2, pp Ford, R. and Richardson, W. (1994). Ethical Decision Making: A Review of the Empirical Literature, Journal of Business Ethics Vol.13, No.3, pp Frey, B. (2000). The Impact of Moral Intensity on Decision Making in a Business Context, Journal of Business Ethics, Vol.26, No.3, pp Gefen, D., Straub, D.W., and Boudreau, M.C. (2000). Structural Equation Modeling and Regression: Guidelines for Research Practice, Communications of the AIS, Vol.4, No.7, pp Harrington, S. (1996). The Effects of Codes of Ethics and Personal Denials of Responsibility on Computer Abuse Judgments and Intentions, MIS Quarterly, Vol.20, 3, pp Hunt, S. and Vitell, S. (1986). A General Theory of Marketing Ethics, Journal of Macromarketing, Vol.8, No.1, pp Jones, T. (1991). Ethical Decision Making by Individuals in Organizations: An Issue- Contingent Model, Academy of Management Review, Vol.16, No.2, pp Loe, T., Ferrell, L., and Mansfield, P. (2000). A Review of Empirical Studies Assessing Ethical Decision Making in Business, Journal of Business Ethics, Vol.25, No.3, pp Maner, W. (1996). Unique Ethical Problems in Information Technology, Science and Engineering Ethics, Vol.2, No.2, pp Marshall, K.P. (1999). Has Technology Introduced New Ethical Problems? Journal of Business Ethics, Vol.19, No.1, pp Moor, J.H. (1985). "What Is Computer Ethics?" Metaphilosophy, Vol.16, No.4, pp Morris, S. and McDonald, R. (1995). The Role of Moral Intensity in Moral Judgments: An Empirical Investigation, Journal of Business Ethics, Vol.14, 9, pp Ogburn, W.F. (1966). Social Change with Regard to Cultural and Original Nature, New York: Dell Publishing Co. Paolillo, J. and Vitell, S. (2002). An Empirical Investigation of the Influence of Selected Personal, Organizational and Moral Intensity Factors on Ethical Decision Making, Journal of Business Ethics, Vol.35, No.1, pp Rubin, R. (1994). Moral Distancing and the Use of Information Technologies: The Seven Temptations, Proceedings of the ACM Conference on Ethics in the Computer Age, Gatlinburg, TN, pp Sambamurthy, V. and Chin, W. (1994). The Effects of Group Attitudes toward Alternative GDSS Designs on the Decision-Making Performance of Computer-Supported Groups, Decision Sciences, Vol.25, No.2, pp Sims, R., Cheng, H., and Teegen, H. (1996). Toward a Profile of Student Software Piraters, Journal of Business Ethics, Vol.15, No.8, pp Singer, M. (1996). The Role of Moral Intensity and Fairness Perception in Judgments of Ethicality: A Comparison of Managerial Professionals and the General Public, Journal of Business Ethics, Vol.15, No.4, pp Singhapakdi, A., Vitell, S., and Kraft, K. (1996). Moral Intensity and Ethical Decision-Making of Marketing Professionals, Journal of Business Research, Vol.36, No.3, pp Singhapakdi, A., Vitell, S., and Franklin, G.R. (1999). Antecedents, Consequences, and Mediating Effects of Perceived Moral Intensity and Personal Moral Philosophies, Journal of the Academy of Marketing Science, Vol.27, No.1, pp Straub, D.W., Carlson, P.J., and Jones, E.H. (1993). Deterring Cheating by Student Programmers: A Field Experiment in Computer Security, Journal of Management Systems, Vol.5, No.1, pp Tavani, H.T. (2002). The Uniqueness Debate in Computer Ethics: What Exactly is at Issue, and Why Does it Matter? Ethics and Information Technology, Vol.4, No.1, pp The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3) 93

9 About the Authors Tim Goles is Assistant Professor at the University of Texas at San Antonio. He earned his Ph.D. from the University of Houston. He has over fifteen years management experience in the information technology arena, including evaluating, developing, and implementing strategic and operational information systems, outsourcing contract management, and IS security. His research interests and publications parallel his work experience. Dr. Gregory White obtained his Ph.D. in Computer Science from Texas A&M University in 1995 and has been involved in computer and network security research since He currently serves as the Director for the Center for Infrastructure Assurance and Security and is an Associate Professor of Computer Science at The University of Texas at San Antonio (UTSA). Nicole Lang Beebe is a doctoral candidate in Information Technology at the University of Texas at San Antonio. She has over a decade of experience in information security in both the corporate and government sector. She is a Certified Information Systems Security Professional (CISSP) and holds degrees in electrical engineering and criminal justice. Carlos Alberto Dorantes is a doctoral student in Information Technology at the University of Texas at San Antonio. He obtained his M.S. in Computer Science from the Tecnológico de Monterrey. He has over a decade of experience in enterprise systems implementation in a multi-campus university. His work has appeared in IS conferences such as HICSS and AMCIS. Barbara Hewitt is a doctoral candidate in Information Technology at the University of Texas at San Antonio. She has over a decade of experience in system analysis and software development in the corporate, education, and government sectors. She holds degrees in computer science and business administration. Appendix A: IS Ethics Scenarios (adapted from Ellis and Griffith, 2001) 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 . 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. Scenario 7: Jim was recently fired from The Spot, a national discount department store. Jim is a techno-savvy individual who felt he was wrongfully fired. In protest, he created a web page called This Spot Unfair in order to state his case to the world about The Spot s unfair treatment. 94 The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3)

10 Appendix B: Measures (adapted from Singhapakdi et al., 1996) Strongly.... Neutral.... Strongly Agree Disagree 1. The situation above involves an ethical problem I would act in the same manner as (the actor) did in the above scenario The overall harm (if any) done as a result of (the actor s) action would be very small Most people would agree that (the actor s) actions are wrong (The actor s) actions will not cause any harm in the immediate future There is a very small likelihood that (the actor s) actions will actually cause any harm If (the actor) is a personal friend of her boss, the action is wrong (The actor s) actions will harm very few people (if any) The DATA BASE for Advances in Information Systems - Spring-Summer 2006 (Vol. 37, Nos. 2 & 3) 95