Skip to main content
Log in

Exploring Factors Affecting Digital Piracy Using the Norm Activation and UTAUT Models: The Role of National Culture

  • Published:
Journal of Business Ethics Aims and scope Submit manuscript

An Erratum to this article was published on 16 January 2015

Abstract

We develop and use an integrated individual-level model to explain the driving forces behind digital piracy (DP) practice in two nations. The proposed model combines the Norm Activation model and Unified Theory of Acceptance and Use of Technology models. This study also explores the effect of culture on intention (INT) to practice DP in two nations: US (individualistic) and India (collectivistic). A survey instrument was used to collect data from 231 US and 331 Indian participants. Use of the integrated model proves to be a powerful and a viable approach to understanding DP across cultures. In each nation, all 10 path coefficients on the research model are statistically significant thereby establishing the fact that personal norm, together with other factors, influences INT to engage in DP, which in turn, may influence the actual practice. The results reveal a support for cross-cultural generalizability and applicability of the proposed model. Culture clearly plays a strong moderating role in two out of the three paths tested. The implications of the findings are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abrahamse, W., Steg, L., Gifford, R., & Vlek, C. (2009). Factors influencing car use for commuting and the intention to reduce it: A question of self-interest or morality? Transportation Research Part F: Traffic Psychology and Behaviour, 12(4), 317–324.

    Article  Google Scholar 

  • Ajzen, I. (2002). Residual effects of past on later behavior: Habituation and reasoned action perspectives’. Personality and Social Psychology Review, 6(2), 107–122.

    Article  Google Scholar 

  • Al-Rafee, S., & Cronan, T. P. (2006). Digital piracy: Factors that influence attitude toward behavior. Journal of Business Ethics, 63, 237–259.

    Article  Google Scholar 

  • Al-Rafee, S., & Rouibah, K. (2010). The fight against digital piracy: An experiment. Telematics and Information, 27, 283–292.

    Article  Google Scholar 

  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

    Article  Google Scholar 

  • Bagchi, K., Kirs, P., & Cerveny, R. (2006). Global software piracy: Can economic factors alone explain the trend? Communications of the ACM, 49(6), 70–75.

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Banerjee, D., Cronan, T. P., et al. (1998). Modeling IT ethics: A study in situational ethics. MIS Quarterly, 22(1), 31–60.

    Article  Google Scholar 

  • Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual strategic and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.

    Article  Google Scholar 

  • Batson, C., & Powell, A. A. (2003). Altruism and prosocial behavior. In T. Millon & M. J. Lerner (Eds.), Handbook of psychology: Personality and social psychology (Vol. 5, pp. 463–484). Hoboken, NJ: Wiley.

    Google Scholar 

  • Bhattacharjee, S., Gopal, R. D., & Lertwachara, K. (2006). Consumer search and retailer strategies in the presence of online music sharing. Journal of Management Information Systems, 23(1), 129–159.

    Article  Google Scholar 

  • Bono, S., Rubin, A., Stubblefield, A., & Green, M. (2006). Security through legality. Communications of the ACM, 49(6), 41–43.

    Article  Google Scholar 

  • Carlo, G., Mestre, M. V., Samper, P., Tur, A., & Armenta, B. E. (2010). Feelings or cognitions? Moral cognitions and emotions as longitudinal predictors of prosocial and aggressive behaviors. Personality and Individual Differences, 48, 872–877.

    Article  Google Scholar 

  • Castaneda, J. A., Munoz-Leiva, F., & Luque, T. (2007). Web acceptance model (WAM): Moderating effects of user experience. Information and Management, 44, 384–396.

    Article  Google Scholar 

  • Chin, W., Marcolin, B., & Newsted, P. R. (1996) A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and voice mail emotion/adoption study. In Proceedings of ICIS (pp. 21–41).

  • Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145–158.

    Article  Google Scholar 

  • Conner, M., & Armitage, C. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28(15), 1429–1464.

    Article  Google Scholar 

  • Corbett, J. (2005). Altruism, self-interest and the reasonable person model of environmentally responsible behavior. Science Communication, 26(4), 368–389.

    Article  Google Scholar 

  • Cronan, T. P., & Al-Rafee, S. (2008). Factors that influence the intention to pirate software and media. Journal of Business Ethics, 78, 527–545.

    Article  Google Scholar 

  • Cross, T. (2006). Academic freedom and the hacker ethic. Communications of the ACM, 49(6), 37–40.

    Article  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.

    Article  Google Scholar 

  • De Groot, J. I. M., & Steg, L. (2009). Morality and prosocial behavior: The role of awareness, responsibility and norms in the norm activation model. Journal of Social Psychology, 149, 425–449.

    Article  Google Scholar 

  • De Groot, J. I. M., & Steg, L. (2010). Morality and nuclear energy: Perceptions of risks and benefits, personal norms, and willingness to take action related to nuclear energy. Risk Analysis, 30(9), 1363–1373.

    Article  Google Scholar 

  • Depken, C. A., & Simmons, L. C. (2004). Social construct and the propensity for software piracy. Applied Economics Letters, 11(2), 97–100.

    Article  Google Scholar 

  • Eberl, M. (2010). Chapter 21: An application of PLS in multi-group analysis—The need for differentiated corporate-level marketing in the mobile communications industry. In V. Vinzi, W. Chin, J. Henseler & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications. Springer handbook of computational statistics (pp. 495–496). Heidelberg: Springer

  • Eckhardt, A., Laumer, S., & Weitzel, T. (2009). Who influences whom? Analyzing workplace referents’ social influence on IT adoption and non-adoption. Journal of Information Technology, 24, 11–24.

    Article  Google Scholar 

  • Eining, M., & Christensen, A. (1991). A psycho-social model of software piracy: The development and test of a model. In R. Dejorie, G. Fowler, & D. Paradice (Eds.), Ethical issues in information systems. Boston: Boyd and Fraser.

    Google Scholar 

  • Ford, D. P., Connelly, C. E., & Meister, D. B. (2003). Information systems research and Hofstede’s culture’s consequences: An uneasy and incomplete partnership. IEEE Transactions on Engineering Management, 50(1), 8–25.

    Article  Google Scholar 

  • Garling, T., Fujii, S., Garling, A., & Jakobsson, C. (2003). Moderating effects of social value orientation on determinants of proenvironmental intention. Journal of Environmental Psychology, 23, 1–9.

    Article  Google Scholar 

  • Gopal, R., & Sanders, G. L. (1997). Preventive and deterrent controls for software piracy. Journal of Management Information Systems, 13, 29–47.

    Article  Google Scholar 

  • Gopal, R., Sanders, G. L., Bhattacharjee, S., Agrawal, M., & Wagner, S. (2004). A behavioral model of digital music piracy. Journal of Organizational Computing and Electronic Commerce, 14, 89–105.

    Article  Google Scholar 

  • Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340–2350.

    Article  Google Scholar 

  • Hagger, M., Chatzisarantis, N., & Biddle, S. (2002). A meta-analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and contribution of additional variables. Journal of Sport and Exercise Psychology, 24, 3–32.

    Google Scholar 

  • Harland, P., Staats, H., & Wilke, H. (1999). Explaining pro-environmental intention and behavior by personal norms and the theory of planned behavior. Journal of Applied Social Psychology, 29(12), 2505–2528.

    Article  Google Scholar 

  • Harland, P., Staats, H., & Wilke, H. (2007). Situational and personality factors as direct or personal norm mediated predictors of pro-environmental behavior: Questions derived from norm-activation theory. Basic and Applied Social Psychology, 29(4), 323–334.

    Article  Google Scholar 

  • He, D., & Lu, Y. (2007). Consumers perceptions and acceptances towards mobile advertising: An empirical study in China. In Proceedings of International Conference Wireless Communications, Networking and Mobile Computing.

  • Henseler, J., & Fassot, G. (2010). Chapter 30: Testing moderating effects in PLS models: An illustration of available procedures. In V. Vinzi, W. Chin, J. Henseler & H. Wang (Eds.), Springer handbook of computational statistics (pp. 713–735). Heidelberg: Springer.

  • Higgins, G. E., Fell, B. D., & Wilson, A. L. (2007). Digital piracy: Assessing the contributions of an integrated self-control theory and social learning theory using structural equation modeling. Criminal Justice Studies, 19(1), 3–22.

    Article  Google Scholar 

  • Higgins, G. E., Wilson, A. L., & Fell, B. D. (2005). An application of deterrence theory to software piracy. Journal of Criminal Justice and Popular Culture, 12, 166–194.

    Google Scholar 

  • Hinduja, S. (2001). Trends and patterns among online software pirates. Ethics and Information Technology, 5, 49–61.

    Article  Google Scholar 

  • Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information and Management, 41(7), 853–868.

    Article  Google Scholar 

  • Huijts, N. M. A., De Groot, J. I. M., Molin, E. J. E., & van Wee, B. (2013). Intention to act towards a local hydrogen refueling facility: Moral considerations versus self-interest. Transportation Research A: Policy and Practice, 48, 63–74.

    Google Scholar 

  • Husted, B. W. (2000). The impact of national culture on software piracy. Journal of Business Ethics, 26, 197–211.

    Article  Google Scholar 

  • Igbaria, M. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605.

    Article  Google Scholar 

  • Jain, S. (2008). Digital piracy: A competitive analysis. Market Science, 27(4), 610–626.

    Article  Google Scholar 

  • Kaiser, F. G., Hubner, G., & Bogner, F. X. (2005). Castrating the theory of planned behavior with the value–belief–norm model in explaining conservation behavior. Journal of Applied Social Psychology, 35, 2150–2170.

    Article  Google Scholar 

  • Keil, M., Tan, B. C. Y., Wei, K. K., Saarinen, T., Tuunainen, V., & Wassenaar, A. (2000). A cross-cultural study on escalation of commitment behavior in software projects. MIS Quarterly, 24(2), 299–325.

    Article  Google Scholar 

  • Kim, Y. J., Chun, J. U., & Song, J. (2009). Investigating the role of attitude in technology acceptance from an attitude strength perspective. International Journal of Information Management, 29(1), 67–77.

    Article  Google Scholar 

  • Kock, N. (2013). WarpPLS 4.0 user manual. Laredo, TX: ScriptWarp Systems.

    Google Scholar 

  • Kock, N. (2014). Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-based SEM. International Journal of E-Collaboration, 10(1), 1–13.

    Article  Google Scholar 

  • Kuisma, T., Laukkanen, T., & Hiltunen, M. (2007). Mapping the reasons for resistance to internet banking: A means-end approach. International Journal of Information Management, 27(2), 75–85.

    Article  Google Scholar 

  • LaRose, R., Lai, Y. J., Lange, R., Love, B., & Wu, Y. (2005). Sharing or piracy? An exploration of downloading behavior. Journal of Computer Mediated Communication, 11(1), 1–21.

    Article  Google Scholar 

  • Lau, E. (2003). An empirical study of software piracy. Business Ethics, 12(3), 233–245.

    Article  Google Scholar 

  • Leidner, D. E., & Kayworth, T. (2006). Review: A review of culture in information systems research: Toward a theory of information technology culture conflict. MIS Quarterly, 30(2), 357–399.

    Google Scholar 

  • Lescevica, M., Ginters, E., & Mazza, R. (2013). Unified theory of acceptance and use of technology (UTAUT) for market analysis of FP7 CHOReOS products. Procedia Computer Science, 26, 51–68.

    Article  Google Scholar 

  • Liable, D., Eye, J., & Carlo, G. (2008). Dimensions of conscience in mid-adolescence. Link with social behavior, parenting, and temperament. Journal of Youth and Adolescence, 37, 875–887.

    Article  Google Scholar 

  • Liang, Z., & Yan, Z. (2005). Software piracy among college students: A comprehensive review of contributing factors, underlying processes, and tackling strategies. Journal of Educational Computing Research, 33, 115–140.

    Article  Google Scholar 

  • Lin, C. P., & Bhattacharjee, A. (2008). Learning online social support: An investigation of network information technology based on UTAUT. Cyber Psychology and Behavior, 11(3), 268–272.

    Article  Google Scholar 

  • Lua, J., Yaob, J. E., & Yua, C. S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. Journal of Strategic Information Systems, 14, 245–268.

    Article  Google Scholar 

  • Miller, P. A., & Eisenberg, N. (1988). The relation of empathy to aggression and externalizing/antisocial behavior. Psychological Bulletin, 103, 324–344.

    Article  Google Scholar 

  • Moores, T. T. (2008). An analysis of the impact of economic wealth and national culture on the rise and fall of software piracy rates. Journal of Business Ethics, 81, 39–51.

    Article  Google Scholar 

  • Moores, T. T. (2010). Untangling the web of relationships between wealth, culture, and global software piracy rates: A path model. Journal of Global Information Technology, 18(1), 1–14.

    Google Scholar 

  • Nitse, P., Parker, K., Krumwiede, D., & Ottaway, T. (2004). The impact of color in the E-commerce marketing of fashions: An exploratory study. European Journal of Marketing, 38(7), 898–915.

    Article  Google Scholar 

  • Onwezen, M. C., Antonides, G., & Bartels, J. (2013). The norm activation model: An exploration of the functions of anticipated pride and guilt in pro-environmental behavior. Journal of Economic Psychology, 39, 141–153.

    Article  Google Scholar 

  • Peace, A. G., Galletta, D. F., & Thong, J. Y. L. (2003). Software piracy in the workplace: A model and empirical test. Journal of Management Information Systems, 20(1), 153–177.

    Google Scholar 

  • Perugini, M., & Bagozzi, R. P. (2001). The role of desires and anticipated emotions in goal-directed behaviours: Broadening and deepening the theory of planned behaviour. British Journal of Social Psychology, 40, 79–98.

    Article  Google Scholar 

  • Peters, A., Cutscher, H., & Scholz, R. W. (2011). Psychological determinants of fuel consumption of purchased new cars. Transportation Research Part F: Traffic Psychology and Behaviour, 14, 229–239.

    Article  Google Scholar 

  • Piquero, N., & Piquero, A. (2006). Democracy and intellectual property: Examining trajectories of software piracy. The Annals of the American Academy of Political and Social Science, 605(1), 104–127.

    Article  Google Scholar 

  • Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13(1), 50–69.

    Article  Google Scholar 

  • Rowley, J. (2006). An analysis of the E-service literature: Towards a research agenda. Internet Research, 16(3), 339–359.

    Article  Google Scholar 

  • Sally, L. P. M. (2006). Prediction of Internet addiction for undergraduates in Hong Kong. Unpublished Degree'sThesis, Hong Kong Baptist University, Hong Kong.

  • Schaupp, L. C., Carter, L., & McBride, M. E. (2010). E-file adoption: A study of U.S. taxpayers intentions. Computers in Human Behavior, 26(4), 636–644.

    Article  Google Scholar 

  • Schultz, P. W., Gouveia, V. V., Cameron, L. D., Tankha, G., Schmuck, P., & Franek, M. (2005). Value and their relationship to environmental concern and conservation behavior. Journal of Cross-Cultural Psychology, 36, 457–475.

    Article  Google Scholar 

  • Schwartz, S. H. (1977). Normative influences on altruism. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 10, pp. 221–279). New York: Academic Press.

    Google Scholar 

  • Schwartz, S. H., & Howard, J. A. (1980). Explanations of the moderating effect of responsibility denial on the personal norm–behavior relationship. Social Psychology Quarterly, 43, 441–446.

    Article  Google Scholar 

  • Shadlen, K. C. A., Schrank, A., & Kurtz, M. J. (2005). The political economy of intellectual property protection: The case of software. International Studies Quarterly, 49(1), 45–71.

    Article  Google Scholar 

  • Shih, H. P. (2004). An empirical study on predicting user acceptance of E-shopping on the web. Information and Management, 41(3), 351–368.

    Article  Google Scholar 

  • Shore, B., Venkatachalam, A. R., Solorzano, E., Burn, J. B., Hassan, S. Z., & Janczewski, L. J. (2001). Softlifting and piracy: Behavior across cultures. Technology in Society, 23, 563–581.

    Article  Google Scholar 

  • Sia, C. L., Lim, K. H., Leung, K., Lee, M. K. O., Huang, W. W., & Benbasat, I. (2009). Web strategies to promote internet shopping: Is cultural-customization needed? MIS Quarterly, 33(3), 491–512.

    Google Scholar 

  • Simpson, P., Banerjee, D., & Simpson, C. L. (1994). Softlifting: A model of motivating factors. Journal of Business Ethics, 13, 431–438.

    Article  Google Scholar 

  • Steg, L., & de Groot, J. (2010). Explaining prosocial intentions: Testing causal relationships in the norm activation model. British Journal of Social Psychology, 49, 725–743.

    Article  Google Scholar 

  • Steg, L., Dreijerink, L., & Abrahamse, W. (2005). Factors influencing the acceptability of energy policies: Testing VBN theory. Journal of Environmental Psychology, 25, 415–425.

    Article  Google Scholar 

  • Stern, P. (2000). Toward a coherent theory of environmentally-significant behaviour. Journal of Social Issues, 56(3), 407–424.

    Article  Google Scholar 

  • Swinyard, W. R., Rinne, H., & Kau, A. K. (1990). The morality of software piracy: A cross-cultural analysis. Journal of Business Ethics, 9, 655–664.

    Article  Google Scholar 

  • Taylor, S. A., Ishida, C., & Wallace, D. (2009). Intention to engage in digital piracy: A conceptual model and empirical test. Journal of Service Research, 11(3), 246–262.

    Article  Google Scholar 

  • Thatcher, A., & Matthews, M. (2012). Comparing software piracy in South Africa and Zambia using social cognitive theory. African Journal of Business Ethics, 6(1), 1–12.

    Article  Google Scholar 

  • Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26(4), 381–396.

    Article  Google Scholar 

  • Thong, J., & Yap, C. (1998). Testing an ethical decision-making theory: The case of softlifting. Journal of Management Information Systems, 15(1), 213–237.

    Article  Google Scholar 

  • Udo, G. J., Bagchi, K. K., & Kirs, P. J. (2010). An assessment of customers’ E-service quality perception, satisfaction and intention. International Journal of Information Management, 30(6), 481–492.

    Article  Google Scholar 

  • Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 7(3), 425–478.

    Google Scholar 

  • Vida, I., Koklic, M. K., Kukar-Kinney, M., & Penz, E. (2012). Predicting consumer digital piracy behavior: The role of rationalization and perceived consequences. Journal of Research in Interactive Marketing, 6(4), 298–313.

    Article  Google Scholar 

  • Wagner, S., & Sanders, G. (2001). Considerations in ethical decision-making and software piracy. Journal of Business Ethics, 29(1/2), 161–167.

    Article  Google Scholar 

  • Wall, D. S. (2005). The internet as a conduct for criminal activity. In A. Pattavina (Ed.), Information technology and the criminal justice system (pp. 78–94). Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  • Wall, R., Devine-Wright, P., & Mill, G. A. (2007). Comparing and combining theories to explain pro-environmental intentions: The case of commuting-mode choice. Environment and Behavior, 39(6), 731–753.

    Article  Google Scholar 

  • Walls, W., & Harvey, P. (2006). Digital pirates in practice: Analysis of market transactions in Hong Kong’s pirate software arcades. International Journal of Management, 23(2), 207–215.

    Google Scholar 

  • Wang, H. I., & Yang, H. L. (2005). The role of personality traits in UTAUT model under online stocking. Contemporary Management Research, 1(1), 69–82.

    Article  Google Scholar 

  • Wold, H. (1985). Encyclopedia of statistical science. New York: Wiley.

    Google Scholar 

  • Wolfe, S. E., Higgins, G. E., & Marcum, C. D. (2008). Deterrence and digital piracy: A preliminary examination of the role of viruses. Social Science Computer Review, 26(3), 317–333.

    Article  Google Scholar 

  • Wu, Y., Tao, Y., & Yang, P. (2008). The use of unified theory of acceptance and use of technology to confer the behavioral model of 3G mobile telecommunication users. Journal of Statistics and Management Systems, 11(5), 919–949.

    Article  Google Scholar 

  • Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42, 719–729.

    Article  Google Scholar 

  • Wu, J. H., Wang, S. C., & Lin, L. M. (2006). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International Journal of Medical Informatics, 76(1), 66–77.

    Article  Google Scholar 

  • Yang, D., Sonmez, M., Bosworth, D., & Fryxell, G. (2008). Global software piracy: Searching for further explanations. Journal of Business Ethics, 87, 269–283.

    Article  Google Scholar 

  • Yoon, C. (2009). The effects of national culture values on consumer acceptance of E-commerce: online shoppers in China. Information and Management, 45, 294–301.

    Article  Google Scholar 

  • Yoon, C. (2011). Theory of planned behavior and ethics theory in digital piracy: An integrated model. Journal of Business Ethics, 100, 405–417.

    Article  Google Scholar 

  • Zhang, L., Smith, W. W., & Mcdowell, W. C. (2009). Examining digital piracy: Self-control, punishment, and self-efficacy. Information Resources Management Journal, 22(1), 24–44.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Godwin Udo.

Additional information

An erratum to this article is available at http://dx.doi.org/10.1007/s10551-015-2543-2.

Appendices

Appendix 1: The NAM–UTAUT Constructs and Source

Awareness of consequences (ACs; 1 = strongly disagree to 7 = strongly agree)

Steg et al. (2005), Schwartz and Howard (1980)

AC1. Digital piracy is a problem for society

AC2. Less copying may help reduce digital piracy

AC3. Copyright infringement is a problem

AC4. The financial loss to the concerned industry is a problem

Ascription of responsibility (AR; 1 = strongly disagree to 7 = strongly agree)

Steg et al. (2005), Schwartz and Howard (1980)

AR1. I am jointly responsible for the “digital piracy” problem

AR2. I feel jointly responsible for the financial loss to the concerned industries

AR3. I feel jointly responsible for copyright infringement

Personal norms (PNs; 1 = strongly disagree to 7 = strongly agree)

Steg et al. (2005), Schwartz and Howard (1980)

PN1. I feel morally obliged not to indulge in digital piracy, regardless of what others do

PN2. I feel guilty when I practice digital piracy

PN3. I feel morally obliged to prevent/refrain from digital piracy instead of using materials obtained through digital piracy

PN4. People like me should do everything they can to decrease digital piracy

PN5. If I buy a new digital good, I feel morally obliged to buy it without taking recourse to digital piracy

PN6. I would be a better person if I refrained from practicing digital piracy

Performance expectancy (PE; 1 = strongly disagree to 7 = strongly agree)

Venkatesh et al. (2003)

PE1. I would find digitally pirated material useful in my job

PE2. Using digitally pirated material enables me to accomplish tasks more quickly

PE3. Using digitally pirated material increases my productivity

PE4. If I use digitally pirated material, I will increase my chances of getting a raise

Effort expectancy (EE; 1 = strongly disagree to 7 = strongly agree)

Venkatesh et al. (2003)

EE1. I have the resources necessary to undertake digital piracy

EE2. It would be easy for me to become skillful at digital piracy

EE3. I could find digitally pirated material easily

EE4. Learning to undertake digital piracy is easy for me

Social influence (SI; 1 = strongly disagree to 7 = strongly agree)

Venkatesh et al. (2003)

SI1. People who influence my behavior think that I should not use digitally pirated material

SI2. People who are important to me think that I should not use digitally pirated material

SI3. The senior management of this business has been helpful in preventing the use of digitally pirated material

SI4. In general, the organization has not supported the use of digitally pirated material

Self-efficacy (SEFF; 1 = strongly disagree to 7 = strongly agree)

Venkatesh et al. (2003)

SEFF1. I could complete a job or task using digitally pirated material if there was no one around to tell me what to do as I go

SEFF2. I could complete a job or task using digitally pirated material if I could call someone for help if I got stuck

SEFF3. I could complete a job or task using digitally pirated material if I had a lot of time to complete the job for which the software as provided

SEFF4. I could complete a job or task using digitally pirated material if I had just the built-in facility for assistance

Intentions (INTs; 1 = strongly disagree to 7 = strongly agree)

Venkatesh et al. (2003), Cronan and Al-Rafee (2008)

INT1. I intend to practice digital piracy in the next 12 months

INT2. I predict I would practice digital piracy in the next 12 months

INT3. I plan to practice digital piracy in the next 12 months

Past behavior (PB; 1 = never, 2 = 1–2, 3 = 3–5, 4 = 6–10, 5 = >10)

Cronan and Al-Rafee (2008), Venkatesh et al. (2003)

AB3. How many times in last 6 months did you practice “digital piracy”?

Appendix 2.1: Normalized Pattern Loadings and Cross-Loadings: US

 

AC

AR

PN

PE

EE

SI

SEFF

PB

INT

AC1

0.968

−0.174

0.077

0.12

−0.039

−0.048

0.023

−0.008

−0.09

AC2

0.855

0.091

−0.168

−0.04

−0.323

0.068

0.142

−0.219

0.23

AC3

0.949

−0.107

−0.22

−0.107

0.118

0.109

0.051

−0.005

−0.011

AC4

0.939

0.171

−0.157

−0.182

0.116

0.091

−0.023

0.026

0.088

AR1

0.274

0.613

−0.34

0.077

0.019

0.265

−0.003

0.375

0.464

AR2

0.155

0.902

−0.189

0.034

0.007

0.146

−0.005

0.205

0.251

AR3

0.125

0.893

−0.176

0.092

0.027

0.145

0.016

0.213

0.283

PN1

0.083

0.417

0.778

−0.211

−0.094

0.034

0.163

−0.013

0.365

PN2

−0.088

0.04

0.94

−0.073

−0.145

−0.101

0.016

−0.14

0.224

PN3

−0.087

0.067

0.957

−0.159

0.045

0

0.04

−0.134

0.16

PN4

−0.096

−0.067

0.983

−0.021

0.053

0.081

0.072

−0.065

−0.035

PN5

0.099

−0.086

0.969

0.125

−0.065

−0.081

−0.123

0.026

−0.032

PN6

−0.039

−0.015

0.987

−0.043

0.04

−0.121

−0.044

−0.056

0.005

PE1

−0.008

0.026

0.067

0.984

−0.124

−0.051

0.068

0.031

0.059

PE2

−0.052

0.014

0.03

0.987

0.004

−0.013

0.06

0.041

−0.128

PE3

−0.005

−0.005

−0.023

0.997

0.05

0.018

−0.019

−0.007

−0.047

PE4

−0.008

0.062

0.009

0.996

−0.033

−0.022

−0.002

−0.002

−0.051

EE1

0.132

0.045

−0.233

0.013

0.938

0.18

0.012

0.014

−0.12

EE2

0.015

−0.162

−0.031

0.045

0.973

0.013

−0.024

−0.067

−0.135

EE3

0.054

−0.015

0.007

−0.133

0.981

−0.117

−0.031

0.033

0.04

EE4

−0.031

−0.033

0.053

0.005

0.997

−0.041

−0.006

−0.013

0.01

SI1

−0.061

−0.083

−0.049

0.097

−0.081

0.979

0.018

−0.099

−0.042

SI2

0.006

−0.089

−0.088

0.093

−0.102

0.979

0.004

−0.031

−0.081

SI3

0.028

0.114

0.084

−0.112

0.087

0.974

−0.012

0.08

0.07

SI4

0.066

−0.184

−0.088

0.054

0.167

0.959

−0.009

−0.043

−0.03

SEFF1

0.018

0.036

−0.034

0.037

0.001

−0.019

0.997

−0.022

−0.008

SEFF2

0.067

0.073

0.041

0.047

−0.034

−0.086

0.981

0.046

−0.115

SEFF3

−0.075

−0.107

0.145

0.049

−0.07

0

0.971

0.054

−0.093

SEFF4

0.033

0.005

0.066

−0.065

−0.007

−0.021

0.992

0.069

−0.04

PB

0

0

0

0

0

0

0

1

0

INT1

−0.03

−0.025

−0.019

−0.008

−0.02

0.028

0.024

−0.027

0.998

INT2

0.036

0.001

−0.007

0.007

0.025

−0.018

−0.014

0.027

0.998

INT3

−0.046

0.047

0.058

−0.005

−0.032

−0.002

−0.007

−0.027

0.995

Appendix 2.2: Normalized Pattern Loadings and Cross-Loadings: India

 

AC

AR

PN

PE

EE

SI

SEFF

INT

PB

AC1

0.986

−0.105

0.118

0.031

0.016

0.019

−0.031

−0.014

−0.026

AC2

0.975

0.066

−0.107

−0.072

−0.041

−0.031

0.076

−0.041

0.131

AC3

0.92

−0.116

−0.136

−0.045

−0.084

0.102

−0.052

−0.031

0.315

AC4

0.956

0.195

−0.171

0.009

0.013

−0.017

−0.009

0.087

−0.105

AR1

0.301

0.582

−0.107

0.045

−0.109

0.081

−0.057

0.376

0.282

AR2

0.299

0.881

−0.238

0.02

−0.061

0.033

−0.035

0.209

0.166

AR3

0.294

0.83

−0.265

0.065

−0.086

0.13

−0.029

0.305

0.179

PN1

−0.009

0.065

0.975

−0.044

−0.019

−0.09

−0.011

0.183

−0.014

PN2

−0.029

0

0.993

−0.057

−0.021

−0.094

−0.001

−0.023

0.003

PN3

−0.115

−0.106

0.962

0.137

0.064

0.026

−0.063

−0.126

−0.088

PN4

−0.116

−0.036

0.97

−0.057

−0.018

−0.04

−0.01

−0.157

0.123

PN5

0.2

−0.077

0.943

0.05

−0.001

0.05

−0.142

−0.043

0.195

PN6

0.073

−0.011

0.986

−0.005

−0.091

−0.08

0.019

0.079

−0.005

PE1

0.097

−0.034

−0.027

0.979

0.018

−0.036

0.081

0.094

−0.112

PE2

−0.034

−0.159

0.017

0.981

−0.067

−0.01

0.072

−0.035

−0.031

PE3

0.025

0.014

−0.026

0.998

−0.011

0.017

−0.038

−0.004

0.027

PE4

−0.157

0.025

0.078

0.975

0.003

0.013

−0.031

−0.101

0.084

EE1

0.03

0.102

−0.089

0.054

0.987

0.029

−0.028

−0.003

0.044

EE2

−0.019

−0.017

−0.043

0.01

0.992

−0.008

0.01

−0.047

−0.104

EE3

−0.004

−0.306

0.256

−0.102

0.903

−0.087

0.057

0.06

−0.032

EE4

−0.099

0.085

0.009

−0.073

0.987

0.029

0.013

−0.031

−0.019

SI1

0.11

0.01

−0.068

0.153

0.057

0.978

0.006

0.002

−0.028

SI2

−0.003

0.036

−0.002

0.047

0.01

0.997

−0.028

−0.025

−0.03

SI3

−0.061

−0.057

0.027

−0.381

−0.112

0.879

0.069

0.114

0.209

SI4

−0.046

−0.106

0.146

−0.24

−0.053

0.745

0.273

0.027

−0.113

SEFF1

0.037

−0.036

0.085

0.141

−0.033

−0.056

0.982

0.05

0.013

SEFF2

0.023

−0.02

0.083

−0.11

0

−0.079

0.986

0.043

−0.006

SEFF3

0.052

0.063

−0.173

−0.084

−0.038

0.068

0.959

−0.147

0.092

SEFF4

−0.115

0

0.003

−0.049

0.082

0.057

0.98

0.049

−0.104

PB

0

0

0

0

0

0

0

1

0

INT1

−0.009

0.011

−0.005

0.081

0.012

0.048

−0.05

−0.14

0.984

INT2

0.006

−0.007

0.003

−0.041

−0.004

−0.025

0.025

0.069

0.996

INT3

−0.066

0.054

−0.022

0.115

−0.049

0.098

−0.049

−0.13

0.973

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Udo, G., Bagchi, K. & Maity, M. Exploring Factors Affecting Digital Piracy Using the Norm Activation and UTAUT Models: The Role of National Culture. J Bus Ethics 135, 517–541 (2016). https://doi.org/10.1007/s10551-014-2484-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10551-014-2484-1

Keywords

Navigation