An Intention Model-based Study of Software Piracy

Transcription

1 An Intention Model-based Study of Software Piracy Tung-Ching Lin *, Meng Hsiang Hsu **, Feng-Yang Kuo *, Pei-Cheng Sun * * Department of Information Management, National Sun Yat-Sen University, Kaohsiung, Taiwan ** Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan Abstract. The illegal copying of software is commonly referred to as software piracy, which has considered as one of ethical issues on cyberspace. To this end, several measures have been tried to prevent and deter losses. One is to identify the factors that influence individuals piracy act. In this research, a theoretical model is developed to test the factors that would significantly affect piracy intention of information system (IS) professionals. The model is based on the Ajzen s Theory of Planned Behavior (TPB), Ethical Decision Making theory, Self- Efficacy Theory, and M-O-P (Motivation-Object- Protect) criminal theory. Our study shows that that IS professional s piracy acts are directly influenced by their attitudes, subjective norms, and perceived deindividuation. It also shows that the attitude and subjective norms are, in turn, influenced by their ethical perception of piracy issues and organizational ethical climates. Keywords: Piracy, IT ethics, Intention model. I. Introduction The illegal copying of computer software is commonly referred to as software piracy. The costs of software piracy are enormous and have far reaching impact. According to Business Software Alliance's report, the software industry loses more than US$11.2 billion annually worldwide due to piracy. As a result, as many as 130,000 jobs and US$1 billion in tax revenues were lost in The reduction of losses incurred from software piracy warrants investigation into the problem. Accordingly, many measures have been suggested to deter and prevent losses. Deterrent measures (e.g., applying ACM (The Association for Computing Machinery) codes of conduct to piracy decisionmaking; including ethical issues in the curriculum for Information Systems (IS) majors) have been supported in the literature(anderson et al. 1993; Parker 1990). An overlooked but potentially effective deterrent is the identification of individual and situational characteristics of IS personnel who commit the piracy act (Harrington, 1996; Banerjee et al., 1998). Identification of unique characteristics could lead to the formulation of more effective ways of solving the problem of piracy act. While several conceptual models for explaining piracy act have been proposed in the literature, few have been developed based on previous related theories. To this end, this study improved upon previous research efforts by incorporating theories from disparate fields of ethics research to develop a model of piracy intention and by using IS employees to test the proposed model. The model were (1) to identify specific characteristics that associated with and may influence the piracy intention of IS employees and (2) to examine the relationship, if any, between these characteristics and piracy intention of IS employees. It could help us to understand the causal chain from IS employees beliefs to their committing of piracy. IS investigators have suggested intention models from social psychology as a potential theoretical foundation for research on the determinants on user behavior (Swanson 1982; Christie 1981). Ajzen s (1985, 1989) Theory of Planned Behavior (TPB) is an especially well-researched intention model that has been proven successful in predicting and explaining behavior across a wide variety of domains. Thus, TPB is appropriate for studying the determinants of piracy behavior. This study used TPB as a theoretical basis, and incorporated theories on related subject to develop a model especially well-suited for modeling piracy behaviors. After presenting the research model, we discuss a survey study of 246 IS employees which provides empirical data for assessing how well the proposed model. We then discuss the limitations of the study and the implications of its finding. II. Research Model Ethical decision-making models may aid in clarifying relevant variables (Rest 1986). While several ethical-making models for explaining ethical model have been proposed in the literature, few have been empirically validated. After reviewing ethical decision-making models, Jones(1991) integrated the models into one model, largely founded on the work of Rest(1986). This model consists of four main components: awareness, judgment, intention, and 1

2 behavior. Jones s model is consistent with the theory of reasoned action (TRA) (Fishbein and Ajzen 1975), suggesting that a person s decision to act ethically or unethically is determined by his/her intentions. Scholars refer it to as the behavioral intention model (Swanson 1982). As behavioral intention models have been verified by many empirical research, they have been recommended by scholars in IS filed as the skeletal theory to explore the behaviors of individual IS employees (David et al., 1989). Among the intention models, the theory of planned behavior (TPB) (Ajzen, 1985, 1989) is one such model that has undergone theory development and verification. It has become one of the most important behavior intention models to date (Ajzen, 1985, 1989; Mathieson, 1991). In addition, to completely account for and predict the software piracy behaviors, this study integrates the ethical decision theory (Jones, 1989), the self-efficacy theory (Bandura, 1986), and the M-O-P criminological theory (Felson, 1994), to build up a piracy model. Theory of Planned Behavior (TPB) To predict and understand the causes of behavior, Ajzen (1985, 1989) proposes the TPB, believing that behavior must be preceded by behavioral intentions, which in turns are affected by either the attitudes toward the behavior, the subjective norms toward the behavior, the perceived behavioral control, or all of them. Intention refers to the subjective probability of one s engagement in any behavior (Fishbein and Ajzen, 1975). The stronger the behavioral intention, the more likely the execution. As the relationship between behavioral intention and the execution of the behavior is so strong that researchers often replace actual behavior measurement with behavioral intention when studying individual behavior with TPB(Ajzen and Fishbein, 1980). Attitude toward the behavior refers to the degree to which the person has a favorable on unfavorable evaluation of the behavior in question (Ajzen 1989). In an ethical context, if individuals view stealing software as wrong, they are unlikely to intent to steal it. Ethical computing research has shown attitudes to be important predictors of individuals ethical computing behaviors (Loch and Conger 1996). Subjective Norms concerning the behavior refers to the perceived social pressure to perform or not to perform the behavior (Ajzen, 1989). Subjective norms in a business setting include social, organizational, departmental, and peer norms (Mathieson 1991). The closer the affinity of individuals goals with their reference group an any level, the more likely the individuals is to perform according to reference group expectations. For example, an employee may think that the supervisor will approve his/her using unauthorized software to solve the problems at work, that is the subjective norms toward the behavior. Perceived Behavioral Control refers to the individual s belief in the ease to execute a behavior (Ajzen, 1989). The stronger the individual feels his ability to execute the behavior, the more the resources and opportunities the individual possesses to execute the behavior, the higher the perceived behavioral control(ajzen, 1989). For example, an employee may think that it will be difficult for one to discover his using unauthorized software, it means that he has a higher perceived behavioral control over his act of piracy. Depart from attitude, subjective norm and behavioral control, Ajzen and Fishbein (1980) do not deny the presence of other factors, such as character traits, attitudes toward the objects, beliefs concerning the objects, technical factors, and social factors. All these are referred to as the external variables. Ajzen (1985) considers that TPB is the mediator between the external variables and the actual behavior. In other words, external variables affect the actual behavior through TPB, and there are three mediate variables between the external variables and the behavioral intentions, namely, attitude, subjective norm, and behavioral control, which are referred to as the internal variables. In short, TPB divides the formation of the behavior into three sections: external variables of behavior, the internal variables of behavior, and the final action, and has constituted the major internal variables of behavior. Ajzen and Madden (1986) used TPB to predict students decisions about attending class and earning a good grade. Banerjee et al. (1998) applied TPB to explain the individual and situational characteristics that do influence ethical behavior intention of improper computer use. There have been more tests of the theory of reasoned action (TRA), on which TPB is based. In IS domain, Yeaman (1988) found that TRA predicted intention to learn to use a microcomputer. Davis et al. (1988) reported that TRA predicted intention to use a word processing program. Recently, Harwick and Barki (1994) employ the TRA as a theoretical framework within which to explain the effects of user participation and 2

3 involvement in information system development and system use. Software Piracy Model Though the TPB s capability to predict general behaviors has been verified, TPB is a generalized behavioral model. Therefore, to adapt TPB into a piracy behavior, one must specialize the TPB, in order to make it cogent for the piracy behavior. We revise TPB model and then propose the research model as shown in Figure 1 (shown in Appendix). First, attitudes toward the piracy and subjective norms toward the piracy in the model are corresponding to attitudes toward the behavior and subjective norms toward the behavior in TPB, respectively; while perceived behavioral control in TPB has been replaced with the computer deindividuation and computer self-efficacy. Computer deindividuation is developed in accordance with the M-O-P criminological theory and computer self-efficacy with the self-efficacy theory. The definition of the two variables are given below. Perception of Piracy Issue Organizational Ethical Climate Proceedings of the 32nd Hawaii International Conference on System Sciences 1999 H5 H6 H7 H8 Attitude Toward the Piracy Subjective Norms Toward the Piracy Computer Deindividuation Computer Self-Efficacy Perceived Control Toward the Piracy H1 H2 H3 H4 Figure 1. An Intention Model of Software Piracy Piracy Intention Deindividuation. Anonymity is the most common uses of computer or network. Sproull and Kiesler (1991) considers that anonymous use of computer will easily cause to computer deindividuation. When the individual is in the state of deindividuation, Zimbardo (1970) thinks that he will lose the selfawareness toward the society and the sense of selfregulation, where he senses opportunities to carry out anti-society norms behaviors. The effects of deindividuation on piracy are based on the M-O-P (Motivation-Object-Protect) theory. Felson (1994) consider that the M-O-P elements must present when a crime occurs. In M-O-P theory, P represents the opportunities to execute piracy, while computer deindividuation is the catalyst of such opportunities. Furthermore, in TPB, opportunity refers to the element to increase the perceived behavioral control; therefore, computer deindividuation will be used as one of the measuring variables for the perceived behavioral control of TPB in the present study. Self-efficacy. Bandura (1986) defines self-efficacy as people s judgements of their capabilities to organize and execute courses of action requirement to attained designed types of performances (p. 391). This definition indicates that the focus is not on the skills possessed by the individual, it is rather the ability to judgement to use his skills to accomplish the mission. Thus, computer self-efficacy represents an individual s perceptions of his or her ability to use computers in the accomplishment of a task (Compeau and Higgins, 1995). In the self-efficacy theory proposed by Bandura (1982), the individual s belief in his ability and capability (self-efficacy) to achieve a given mission will determine his intention to execute the behavior. Likewise, the individual will determine if he is capable of engaging in a piracy before he really does so. Furthermore, another element in the perceived behavioral control of TPB is the individual s capability to engage in the behavior; therefore, computer self-efficacy will be used as one of the measuring variables for the perceived behavioral control of TPB in the present study. In identifying the external variables that affect (indirectly) the piracy behavior, ethical decisionmaking models may aid in clarifying relevant variables (Trevino 1986). Several ethical decisionmaking models (e.g., Trevino 1986) suggested that a person s decision to act ethically or unethically is determined by environmental characteristics. Current approaches to understanding ethical decision-making behavior in organizations recognize that ethical/unethical behavior occurs in a social context and is heavily influenced by characteristics of the organization s ethical environment (Ferrell and Gresham 1985, Trevino 1986). Thus, Victor and Cullen (1988) argued that organizational ethical climate would be expected to predict an employee s behavior. Furthermore, according to the issue-contingent decision-making model proposed by Jones (1991), individual s perception toward ethical issues will affect the individual s ethical judgement and then his ethical behavior. Jones argues that ethics-related situations vary in terms of moral intensity, and the moral intensity of the issue itself has a significant effect that on moral decision making in organizations. According to Jones, an individual s perception toward ethical issues will affect his/her behavioral decisions. Thus, the deeper the individual s perception of piracy issue, the more possible the individual s concerns for the piracy behavior. Hypotheses in the Research Based on the research model, we proposed the following hypotheses: [H1] Attitudes toward the piracy behavior will directly affect his behavioral intentions to piracy. [H2] Subjective norms toward the piracy behavior will directly affect his behavioral intentions to piracy. [H3] Computer deindividuation will directly affect his behavioral intentions to piracy. [H4] Computer self-efficacy will directly affect his behavioral intentions to piracy. 3

4 [H5] Perception of piracy issue will directly affect his attitude. [H6] Perception of piracy issue will directly affect his subjective norms. [H7] Organizational ethical climate will directly affect the individual s attitude. [H8] Organizational ethical climate will directly affect the individual s subjective norms. III. Method Research Methodologies As piracy is an immoral behavior, no respondent will affirm such an act in the interview. Therefore, the employment of the scenario-based vignette has become one of the major methodologies in the study of computer crime/ethical behavior. (Harrigton, 1996) The advantage of vignettes is that it provides a kind of relaxed, pressure-free, and quasi-realistic decision scenario when a respondent is requested to answer some sensitive questions (Harrington, 1996). Therefore, in the present research, we have used a piracy vignette adapted from Anderson et al. (1993, p. 99) for respondents to read before answering the questionnaires. The present research aims at exploring factors affecting IS employees engaging in piracy behavior, therefore, samples are taken from corporate IS employees. In this research, the employees of the MCSE (Microsoft Certification System Engineer) on-the-job training program have been selected as the source of samples. MCSE is a professional license awarded by the Microsoft Corporation, and trainees attending this program are aiming to obtain such a license. The Measures Proceedings of the 32nd Hawaii International Conference on System Sciences 1999 Intention. After a respondent has finished reading the piracy vignette, he must answer the following question: If I were the character in the vignette, I will have done the same thing. Attitude. Barki and Harwick (1994) announce a set of measures to measure the attitude toward the use of system, including the good/bad, terrible/terrific, valuable/worthless, useful/useless of the behavior. To enable using the measures in the attitude toward the piracy behavior, we have revised the measures in the present research and have verify the reliability and validity of the measures by means of confirmatory factor analysis. Subjective Norm. In a research done by Compeau and Higgins (1995), the pressure groups that affect the behavior of employees include supervisors, colleagues. Furthermore, according to Mathieson (1991), a pressure group may exercise its pressure on the individuals in the following ways: including (1) support, (2) want, and (3) prefer the individuals to engage in a certain behavior. Therefore, we have adopt (1) support from supervisors and colleagues, (2) want from supervisors and colleagues, and (3) preference from supervisors and colleagues as measures for the subjective norms concerning the behavior. Computer deindividuation. There are two measuring items in computer deindividuation, one is the sense of privacy, the other one is the opportunity. The computer deindividuation measure first appears in the research done by Loch and Conger (1996), they discover three measures. Despite the reliability (Cronbach s alpha) of the sense of privacy which is over 0.7, the reliability of the other two is below 0.6. Therefore, the sense of privacy will be used as one of the measures in computer deindividuation in the present research. Furthermore, according to Ajzen (1989), depart from the sense of privacy, he includes opportunity in the perception of behavioral control. Therefore, we have adopted both the sense of privacy and opportunity in the present research as measurement variables for computer deindividuation. Computer self-efficacy. Compeau and Higgins (1995) develop a computer self-efficacy instrument which contains ten measuring questions based on the Guttman scale. Each question aims at measuring the confidence in computer uses of a respondent. Each respondent must choose either yes or no from the answer for each question. If the answer is yes, it means that the respondent is confident to execute the respective behavior. Organizational Ethical Climate. The instrument was a ethical scores of the individual s current company to describe the corporate culture using a system developed by Victor and Cullan(1988). The Victor and Cullan measures characterize companies as caring, instrumental and rule-oriented. Since the caring instrument is enough to measure the ethical extent of organizational climate, this study only used the caring instrument as a method to measure the ethical climate, each item is scored in accordance with the 7-point Likert scale. Perception of Piracy Issue. Though software piracy has been the subject matter of many researchers, it lacks a instrument to measure the perception of piracy issue. Therefore, based on the codes of ACM and DPMA, we have developed a instrument for the perception of piracy issue, including (1) duplication of copyright-protection-free software is morally acceptable behavior; (2) occasional use of piracy software is unavoidable; and (3) we can reject the royalty when using copyright-protection-free software. Sampling Characteristics In the present research, we have sent out 290 copies of questionnaires, and have collected 263 copies, the response rate is as high as 89%. All questionnaires that contain missing value are voided. After deducting the voided questionnaires, the number of valid samples is 246. Over half of the 246 valid samples are answered by male respondents (73%); over half of the respondents are graduated from electronics or information related fields (59%), and about 40% of the respondents are currently working for electronics and information businesses. In addition, most respondents are very young, between 4

5 22-36 years of age; the average age is just 27, the average working seniority is 6 years. IV. Data Analysis In the present research, we used the structural equation model (SEM), which is a multivariate technique that facilitates testing of the psychometric properties of the scales used to measure a variable, as well as estimating the parameters of a structural model that is, the magnitude and direction of the relationships among the model variables (Igbaria et al., 1995). A structural equation model is a regression-based technique, with its roots in path analysis, and often loosely termed as a causal modeling technique (Igbaria et al., 1995). SEM recognizes two components of a causal model: the measurement model and the structural model (Chin and Todd, 1995). The research model shown in Figure 1 represents the structural model being examined. The model described the relationships or path among theorectical constructs. Furthermore, for each construct in Figure 1, there is a related measurement models, which links the construct is the diagram with a set of items. For example, attitude is a composite of four related items. The measurement model consists of the relationships between the manifested variables (measurement items) and the latent variables (constructs) they measure. To test the model, the sample was split into two subgroups (S1 and S2). Researchers (e.g., Chin and Todd, 1995; Compeau and Higgins, 1995; Igbaria et al., 1995) suggested that the initial model was tested using the first split sample (S1). Revisions were made based on the initial results as suggested by Grant (1989). The revised model was tested with the holdout sample (S2). The sample was split by putting all responses from questionnaires with event identification numbers into one sample (S1), and all the responses with odd identification numbers into a second sample (S2). Therefore, S1 and S2, each group contains 123 samples. Testing the Measurement Model The test of measurement model aims at analyzing all sample data (S1 and S2), in order to verify the reliability coefficients (Cronbach s alpha) of the measures and the convergent and discriminant validity of the measure. According to the recommendations made by Bagozzi and Yi (1988), we have selected three most common indices to evaluate the measurement model, including the individual item reliability, latent variable composite reliability, and the average variance extracted. The individual item reliability is the factor loading to manifested variables toward that latent variable, the factor of all individual items within the present research is greater than 0.5, which satisfies the value recommended by Hair et al. (1992). The composite reliability of latent variables is the composition of all individual item reliabilities, aiming to evaluate the internal consistency of the latent variables. According to Table 1 (shown in Appendix), the composite reliability of all latent variables within the model is at or over 0.6, which satisfies the value recommended by Fornell (1992). The average variance extracted of latent variables is used to calculate the average variance of all measures within a latent variable toward that latent variable, it represents the convergent and discriminant validity of a latent variable. According to Table 1, all latent variables in the present research is greater than 0.5, which satisfies the value recommended by Fornell and Larcker (1981). Thus, the measurement model tests were adequate, indicating that the measures achieved the desired effect. Testing the Structural Model The test of structural model consists of two evaluations: (1) model fitness and (2) causal relationship of model variables. The evaluation of model fitness aims at evaluating the explanatory power of the model. The evaluation of causal relationship attempts to confirm the theoretical relationships among the model variables; that is, it evaluates the significance of paths in the structural model. Here is the description of the two evaluations. In the evaluation of the model fitness, we have referenced to the opinions proposed by Bagozzi and Yi (1988) and Joreskog and Sorbom (1989) to evaluate the research model. According to the recommendations made by Joreskog and Sorbom (1989), we have selected ten indices to evaluate the model fitness. Among the ten verification indices, the Chi square (χ 2 ) test, and the ratio between χ 2 and the degree of freedom will be the most important indices. The former is the model fitness index regardless of the data size, the latter is the model fitness index after multiple considerations (Bagozzi and Yi, 1988). Therefore, we must observe these two indices before determining whether or not the model fits the S1 data. The remaining eight indices are for reference only, and the result is shown in Table 2 (shown in Appendix). Table 2 shows that it is not significant in the χ 2 test (P-Value=0.6), which means that the fitness between the research model and the S1 data is adequate. When we also consider the size of sample data, the result of the ratio between χ 2 and the degree of freedom is smaller than 3 (0.94), which means that the fitness of the research model will be unaffected by the sample size. The results of the other eight auxiliary indices do also achieve the desired effect. Thus, the model fitness tests were adequate. The causal effect analysis of model variable aims at verifying if the causal effect among all model variables satisfies the anticipations of the research model. The result shows that the research Hypotheses 4 does not achieve the desired effect, that is, path coefficients between computer selfefficacy and piracy intention does not reach the 5

6 significant level (P < 0.01); while the rest of the research hypotheses do reach the anticipated effect. Therefore, we have removed Hypotheses 4 from the research model and proposed a revised software piracy model subjective norms (L=0.42) is next to that of the attitude, and the regression coefficient of deindividuation also reaches the apparent standard (L=0.21). Furthermore, the perception of piracy issue will affect the attitude (L=0.27) and the subjective norm (L=0.33); and the effect of organizational ethical climate on attitude and on Table 1. Assessing the Measurement Model (Sample S1 and S2) Variables Composite Reliability a Average Variance Extracted b Attitude 0.88 c (0.90) d 0.78(0.80) Subjective norm 0.91(0.88) 0.77(0.75) Compuder deindividuation 0.90(0.87) 0.81(0.81) Computer self-efficacy 0.88(0.85) 0.72(0.75) Perception of piracy issue 0.76(0.74) 0.61(0.62) Organizational ethical climate 0.75(0.76) 0.59(0.61) a Reliability = ( L i ) 2 / (( L i ) 2 + Var(L i )) 2 baverage variance extracted = L i 2/( L i c S1 d S2 + Var(L i )) Testing the Revised Structural Model The original model was revised to remove the construct computer self-efficacy as the result of being shown in Figure 2 (shown in Appendix). Given the revised model, the hypothesized model can be evaluated using a different data set (Igbaria, 1995). The revised model, consisting of the six constructs, was tested using the holdout sample (S2). The path coefficients among all variables shown in Figure 2 represent the direct effect, and all path coefficients in Figure 2 represent standardized regression coefficients. subjective norm is 0.37 and 0.39, respectively. The path coefficients represent the direct effects of each of the antecedent constructs. It is also important, however, to consider the total effect. In total, the model explained 21 percent of the variance in attitude, 27 percent of the variance in subjective norm, and 56 percent of variance in intention. Since the objective of the study was primarily the understanding of piracy intention, the revised model was acceptable in terms of explanatory power. Table 2. Assessing the Model Fitness(Sample S1) Fit Indices Guidelines Result χ 2 (Chi-square) Small is better 63.85(0.6) a χ 2 / df <3 0.94(df=68) Goodness of Fit Index (GFI) > Adjusted for Degrees of Freedom (AGFI) > Bentler & Bonett's (1980) NFI > Bentler & Bonett's (1980) (NNFI) >0, McDonald's (1989) Centrality > Bentler's Comparative Fit Index (CFI) > Root Mean Square Residual (RMR) < RMSEA Estimate < a P-Value Perception of Piracy Issue Organizational Ethical Climate 0.27* 0.33* 0.37* 0.39* Attitude Toward the Piracy Subjective Norms Toward the Piracy Computer Deindividuation 0.561* 0.331* 0.186* Figure 2. Revised Model and Path Coefficients Piracy Intention In the three factors (attitudes, subjective norms, and computer deindividuation) affecting the piracy intentions, the regression coefficient of attitude is the greatest (L=0.49), the regression coefficient of V. Discussion and Conclusion The present research has assumed that piracy is a planned behavior, is a decision that made by IS personnel after reasoned processed. It is not a casual behavior. Under such a circumstance, the finding of this study provide support for the research model and the Theory of Planned Behavior on piracy behavior. Three factors was found to play an important role in shaping IS professional to engage in piracy, including the attitudes toward piracy, the subjective norms toward piracy, and the computer deindividuation. Furthermore, in the present research, we discovered two other external 6

7 variables the perception of piracy issue and the organizational ethical climate, which will indirectly affect the behavioral intention of piracy through attitude and subjective norm. Based on the research model, we proposed the following findings: (1) The attitude is the most important factor of IS personnel s engagement in piracy: according to the analysis of all sample data (N=246), among the factors affecting piracy intention, the attitude factor is the most important, then the subjective norm factor, and computer deindividuation. It means that respondents will decide on the piracy behavior according to the outcome of the behavior. (2) Influence of executives and colleagues is important: in the present research the ethical pressure from higher level executives and colleagues has been used to measure the subjective norms of IS personnel. The result shows that both the opinions and practices of executives and colleagues will be an example of the subjective norms of IS personnel. (3) Computer deindividuation improves the perceived control of piracy: the present research discovers that the deindividuation of IS personnel gives chances for IS personnel to execute unethical computer behaviors. Such a result not only satisfies the TPB theory and the M-O-P criminological theory but also coincides with the result of deindividuation research. (Loch and Conger, 1996). (4) Perception of piracy issue and organizational ethical climate indirectly affect piracy intention: both the perception of piracy and the organizational ethical climate will directly affect the attitudes and subjective norms toward the piracy, the result is consistent with TRA. According to Ajzen and Fishbein (1980), these two variables are referred to as the external variables which will indirectly affect the behavioral intentions through attitudes and subjective norms. The Internet Piracy Web sites are files containing information which consumers can access. In this respect, the Internet web site may causes more practices of software piracy issues than that of traditional piracy issue. SPA (Software Publishers Association) has launched a comprehensive Internet Anti-Piracy Campaign (IAPC). Modeled after our highly successful cooperative audit program, the IAPC maintains SPA's longstanding tradition of balancing enforcement with education. These pages are dedicated toward educating the Internet community about software piracy and how to prevent it. As such, the research results in this study can provide implications to SPA. References Proceedings of the 32nd Hawaii International Conference on System Sciences Ajzen, I. From Intentions to Actins: A theory of Planned Behavior, in J. Kuhl and J. Bechmann(Eds.), Action-Control: From Cognition to Behavior, Springer, Heidelberg, 1985, Ajzen, I., Attitude Structure and Behavior, in A.R. Pratkanis, S.J. Breckler, and A.G. Greenwald (Eds.). Attitude Structure and Function, Lawrence Erlbaum Associates, Hillsdale, NJ, 1989, Ajzen, I. and Fishbein, M. Understanding Attitudes and Predicting Social Behavior, Prentice-Hall, Englewood Cliffs, NJ, Ajzen, I. and T. Madden, Prediction of Goal- Directed Behavior: Attitude, Intentions, and Perceived behavioral Control, J. Exp. Soc. Psy., 22, 1986, Anderson, R.E., Johnson, D.G.,Gotterbarn, D. and Perrole Judith. Using the New ACM code of ethics in Decision Making, Comm. of the ACM, 36, 2, Feb. 1993, Banerjee, D., Cronan, T.P., and Jones, T.W. Modeling IT Ethics: A Study in Situational Ethics, MIS Quarterly, 22, 1, 1998, Harwick, J. And Barki, H. Measuring User Participation, User Involvement, and User Attitude, MIS Quarterly, 18, 1, 1994, Bagozzi, R.P. and Yi, Y. On the Evaluation of Structural Equation Mdels, Academy of Marking Science, 16, 1988, Bandura, A. Self-efficacy Mechanism in Human Agency, American Psychologist, 37,2, February 1982, Bandura, A., Social Fundations of Thought and Action, Prentice Hall, Englewood Cliffs, NJ, Barki, H. and Harwick, J., Measuring User Participation, User Involvement, and User Attitude, MIS Quarterly, 18, 1, 1994, Chin, W.W. and Todd, P. On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research : A Note of Caution, MIS Quarterly, June 1995, pp Christie, B., Face to File Communication: A Psychological Approach to Information System, Wiley, New York, Compeau, D.R. and Higgins, C.A. Computer Self-Efficacy: Development of a Measure and Initial Test, MIS Quarterly, 19,2, June 1995, Davis, F.D., Bagozzi, R.P., and Warshaw P.R. User Acceptance of Computer technology: A Comparison of Two Theoretical Models, Management Science, 35,8, 1989, Felson, M., Crime and Everyday life : insight and implication for society, Newbury park, Calif : Pine Forge Press, Ferrel, O.C., Gresham, L.G. A Contingency Framework for Understanding Ethical Decision Making in Marketing, Journal of Marketing, 49, 3, 1985, Fishbein, M. A Theory of Reasoned Action: Some Application and Implications, in H. Howe and M. Page(Eds.) Nebraska Symposium on 7

8 Motivation, 27, University of Nebraska Press, Lincoln, NB, Fishbein, M. and Ajzen, I., Beliefs, Attitude, Intentions and Behavior: An Introduction to theory and Research, Addition-Wesley, Boston, MA, Ford, R.C. and Richardson, W.D. Ethical Decision Making: A Review of the Empirical Literature, Journal of Business Ethics, 13, 1994, Fornell, C. And Larcker, D.F. Evaluating Structural Equation Models with Unobervables and Measurement Error, Journal of Marketing Research, 1981, Grant, R.A., Building and testing a causal models of an information technology s impact, Proceedings of the Tenth International Conference on Information Systems. Boston, December 4-6, 1989, pp Hair, J.F., Anderson, R.E., Tatham, R.L., and Black, W.C. Multivariate Data Analysis with Reading. 3 rd New York: Macmillan, Harrington, S. The Effect of Codes of Ethics and Personal Denial of Responsibility oncomputer Abuse Judgments and Intention, MIS Q., Sep. 1996, Igbaria, M., Guimaraes, T., and Davis, G.B. Testing the Determinants of Microcomputer Usages via a Structual Equation Model, Journal of Management Information System, 11, 4, 1995, pp International Planning and Research Corporation (IPR), Overview: Global Software Piracy Report: Facts and Figures, , Jones, T.M. Ethical Decision Making by Individuals in Organizations: An Issues- Contingent Model, Academy of Management Review, 16, 2, 1991, Joreskog, K.G. and Sorbom, D. LISEREL : A Guide to the Program and Applications, 1989, 2 nd Edition. 29. Loch, K.D. and Conger, S. Evaluating Ethical Decision Making and Computer Use, Comm. ACM, 39, 7, 1996, MacCallum, R.C., Roznowski, M., Mar, C.M., and Reith, J.V. Alternative Strategies for Cross- Validation of Covariance Structure Models, Multivariate Behavioral Research, 29,1 1994, Mason, R.O. Ethics to Information Technology Issues, Comm. ACM 38, 12, Dec. 1995, Mathieson, K. Predicting User Intentions: Comparing the technology Acceptance Model with the Theory of Planned Behavior, Infomation Sys. Res., 2, 3, 1991, Oz, E. Ethical Standards for Information System Professionals: A Case for a Unified Code, MIS Quarterly, December 1992, Parker, D., Swope, D., and Baker, B. Ethical Conflicts in Information and Computer Science, Technology, and Business, Wellesley, MA: QED Information Sciences, Rest, J.R. Moral development: Advances in Research and Theory, Praeger Publishers, New York, Schifter, B. Organizational Climate: An Essay, Personnel Psychology, 28, 1975, Schifter, D.B. and Ajzen, I. Intention, Perceived Control and Weight Loss: An Application of the Theory of Planned Behavior, Journal of Personality and Social Psychology, 49, 1985, Singhapakdi, A. Ethical Perceptions of Markers: The Interaction Effects of Machiavellianism and Organizational Ethical Culture, Journal of Business Ethics, 12, 1993, Sproull, L. and Kiesler, S. Connections. MIT Press, 1991, Boston. 40. Swanson, E.B., Measuring User Attitude in MIS Research: A Review, OMEGA, 10, 1982, Trevino, L.K. Ethical decision making in organizations: A person-situation interactionist model, Academy of Management Review, 11, 1986, Victor, B. and Cullen, J.B. The Organizational Bases of Ethical Work Climate, Administrative Science Q. 33, 3, 1988, Yeaman, A.R.J., Attitudes, Learning and Human-Computer Interaction: An Application of the Fishbein and Ajzen Model of Attitudebahavior Consistency, proceeding of the Annual Meeting of the Association for Educational Communications and Technology, New Orleans, LA, January 1988, Zimbardo, P.G. The Human Choice: Individuation, Reason and Order versus Deindividuation, Impulse, and Chaos, in W.J. Arnold & D. Levine (eds.), Nebraska Symposium on Motivation (vol. 19), 1970, Lincoln: U. of Nebraska Press. 8