Advertisement

Lowering the pirate flag: a TPB study of the factors influencing the intention to pay for movie streaming services

  • Domenico Sardanelli
  • Agostino VolleroEmail author
  • Alfonso Siano
  • Gianmaria Bottoni
Article

Abstract

The launch of several movie streaming services has raised new questions about how online consumers deal with both legal and illegal options to obtain their desired products. This paper investigates the factors influencing consumers’ intentions to subscribe to online movie streaming services. These services have challenged the dramatic growth in their illegal counterpart in recent years. Taking the theory of planned behavior as a starting point, we extended existing models in the literature by incorporating factors that are specific to consumer behavior in this particular field. A quantitative survey was conducted for the Italian market, and structural equation modeling was used for data analysis. Attitudes, involvement with products, moral judgement and frequency of past behavior were found to be the most important factors in explaining the intention to pay for movie streaming services. The paper provides insights for policy makers and industry managers on the marketing communication strategies needed to minimize the risk of digital piracy.

Keywords

Streaming services Subscription intention Movie industry Digital piracy Structural equation modeling 

Notes

References

  1. 1.
    Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.Google Scholar
  2. 2.
    Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood, Cliffs, NJ: Prentice-Hall.Google Scholar
  3. 3.
    Ajzen, I., & Fishbein, M. (2000). Attitudes and the attitude-behavior relation: Reasoned and automatic processes. European Review of Social Psychology, 11(1), 1–33.Google Scholar
  4. 4.
    Aldas-Manzano, J., Lassala-Navarre, C., Ruiz-Mafe, C., & Sanz-Blas, S. (2009). The role of consumer innovativeness and perceived risk in online banking usage. International Journal of Bank Marketing, 27(1), 53–75.Google Scholar
  5. 5.
    Al-Rafee, S., & Cronan, T. P. (2006). Digital piracy: Factors that influence attitude toward behavior. Journal of Business Ethics, 63(3), 237–259.Google Scholar
  6. 6.
    Arvola, A., Vassallo, M., Dean, M., Lampila, P., Saba, A., Lähteenmäki, L., et al. (2008). Predicting intentions to purchase organic food: The role of affective and moral attitudes in the theory of planned behaviour. Appetite, 50(2), 443–454.Google Scholar
  7. 7.
    Asparouhov, T. & Muthen, B. (2003). Full-information maximum-likelihood estimation of general two-level latent variable models with missing data. Technical report. Los Angeles: Muthén and Muthén.Google Scholar
  8. 8.
    Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human Relations, 45(7), 659–686.Google Scholar
  9. 9.
    Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184–206.Google Scholar
  10. 10.
    Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.Google Scholar
  11. 11.
    Beck, L., & Ajzen, I. (1991). Predicting dishonest actions using the theory of planned behavior. Journal of Research in Personality, 25, 285–301.Google Scholar
  12. 12.
    Bentler, P. M. (1990). Fit indexes, Lagrange multipliers, constraint changes and incomplete data in structural models. Multivariate Behavioral Research, 25(2), 163–172.Google Scholar
  13. 13.
    Bentler, P. M. (1992). On the fit of models to covariances and methodology to the Bulletin. Psychological Bulletin, 112, 400–404.Google Scholar
  14. 14.
    Borja, K., Dieringer, S., & Daw, J. (2015). The effect of music streaming services on music piracy among college students. Computers in Human Behavior, 45, 69–76.Google Scholar
  15. 15.
    Byers, S., Cranor, L. F., Cronin, E., Korman, D., & McDaniel, P. (2004). An analysis of security vulnerabilities in the movie production and distribution process. Telecommunications Policy, 28(7), 619–644.Google Scholar
  16. 16.
    Carrington, M. J., Neville, B. A., & Whitwell, G. J. (2010). Why ethical consumers don’t walk their talk: Towards a framework for understanding the gap between the ethical purchase intentions and actual buying behaviour of ethically minded consumers. Journal of Business Ethics, 97(1), 139–158.Google Scholar
  17. 17.
    Cesareo, L., & Pastore, A. (2014). Consumers’ attitude and behavior towards online music piracy and subscription-based services. Journal of Consumer Marketing, 31(6/7), 515–525.Google Scholar
  18. 18.
    Choice (2015). Research update: CHOICE Digital Consumers Paying for Content - Behaviour and Attitudes, white paper. Available at: https://www.choice.com.au/electronics-and-technology/internet/internet-privacy-and-safety/articles/choice-piracy-survey-2015. Last accessed June 16, 2016.
  19. 19.
    Cisco (2012). Cisco visual networking index: Forecast and methodology, 2011–2016. White Paper. San Jose, CA: Cisco.Google Scholar
  20. 20.
    Cisco (2016), Cisco visual networking index: Forecast and methodology, 2015–2020. White Paper. San Jose, CA: Cisco.Google Scholar
  21. 21.
    Commuri, S. (2009). The impact of counterfeiting on genuine-item consumers’ brand relationships. Journal of Marketing, 73(3), 86–98.Google Scholar
  22. 22.
    Cronan, T. P., & Al-Rafee, S. (2008). Factors that influence the intention to pirate software and media. Journal of Business Ethics, 78(4), 527–545.Google Scholar
  23. 23.
    Culiberg, B., Koklic, M. K., Vida, I., & Bajde, D. (2016). Examining the effects of utilities and involvement on intentions to engage in digital piracy. Computers in Human Behavior, 61, 146–154.Google Scholar
  24. 24.
    D’Astous, A., Colbert, F., & Monpetit, D. (2005). Music piracy on the web: How effective are anti-piracy arguments? Evidence from the theory of planned behavior. Journal of Consumer Policy, 28(3), 289–310.Google Scholar
  25. 25.
    Dai, B., Forsythe, S., & Kwon, W. S. (2014). The impact of online shopping experience on risk perceptions and online purchase intentions: Does product category matter? Journal of Electronic Commerce Research, 15(1), 13–24.Google Scholar
  26. 26.
    Davidov, E., Meuleman, B., Cieciuch, J., Schmidt, P., & Billiet, J. (2014). Measurement equivalence in cross-national research. Annual Review of Sociology, 40, 55–75.Google Scholar
  27. 27.
    Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.Google Scholar
  28. 28.
    De Corte, C. E., & Van Kenhove, P. (2017). One sail fits all? A psychographic segmentation of digital pirates. Journal of Business Ethics, 143(3), 441–465.Google Scholar
  29. 29.
    Depoorter, B., Parisi, F., & Vanneste, S. (2005). Problems with the enforcement of copyright law: Is there a social norm Backlash? International Journal of the Economics of Business, 12(3), 361–369.Google Scholar
  30. 30.
    Dunlap, T. M., & Kurtz, N. A. (2011). Electronic evidence in torrent copyright cases. Digital Evidence and Electronic Signature Law Review, 8, 171–178.Google Scholar
  31. 31.
    Eagly, A. H., & Chaiken, S. (2007). The advantages of an inclusive definition of attitude. Social Cognition, 25(5), 582–602.Google Scholar
  32. 32.
    End, C. M. (2001). An examination of NFL fans’ computer mediated BIRGing. Journal of Sport Behavior, 24(2), 162–182.Google Scholar
  33. 33.
    Feick, L. F., & Price, L. L. (1987). The market maven: A diffuser of marketplace information. Journal of Marketing, 51, 83–97.Google Scholar
  34. 34.
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.Google Scholar
  35. 35.
    Gefen, D., Karahanna, E., & Straub, D. W. (2003). Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management, 50(3), 307–321.Google Scholar
  36. 36.
    George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet Research, 14(3), 198–212.Google Scholar
  37. 37.
    Giles, D. C. (2002). Parasocial interaction: A review of the literature and a model for future research. Media Psychology, 4(3), 279–305.Google Scholar
  38. 38.
    Glasman, L. R., & Albarracin, D. (2006). Forming attitudes that predict future behavior: A meta-analysis of the attitude-behavior relation. Psychological Bulletin, 132(5), 778–822.Google Scholar
  39. 39.
    Greenberg, M. (2015). The economics of video piracy. PIT Journal. Available at: http://pitjournal.unc.edu/article/economics-video-piracy. Last accessed June 16, 2016.
  40. 40.
    Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River: Pearson Education.Google Scholar
  41. 41.
    Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 80, 197–206.Google Scholar
  42. 42.
    Hennig-Thurau, T., Henning, V., & Sattler, H. (2007). Consumer file sharing of motion pictures. Journal of Marketing, 71(4), 1–18.Google Scholar
  43. 43.
    Higie, R. A., & Feick, L. F. (1989). Enduring involvement: Conceptual and measurement issues. Advances in Consumer Research, 16(1), 690–696.Google Scholar
  44. 44.
    Hox, J. J. (2010). Multilevel analysis: Techniques and applications. London: Routledge.Google Scholar
  45. 45.
    Hox, J. J., Maas, C. J., & Brinkhuis, M. J. (2010). The effect of estimation method and sample size in multilevel structural equation modeling. Statistica Neerlandica, 64(2), 157–170.Google Scholar
  46. 46.
    Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.Google Scholar
  47. 47.
    IFPI (2014). Digital music report 2014. http://www.ifpi.org/downloads/Digital-Music-Report-2014.pdf. Last accessed June 14, 2016.
  48. 48.
    Irdeto (2015), Irdeto piracy and business intelligence quick read report: 2015 Academy Award ® Nominated Films. Available at: http://irdeto.com. Last accessed November 24, 2015.
  49. 49.
    ISTAT (2014). Internet@Italia 2013: La popolazione italiana e l’uso di Internet. Available at http://www.istat.it/it/files/2014/11/Internet@Italia2013-def.pdf. Last accessed April 01, 2016.
  50. 50.
    Kassing, J. W., & Sanderson, J. (2009). “You’re the kind of guy that we all want for a drinking buddy”: Expressions of parasocial interaction on Floydlandis.com. Western Journal of Communication, 73(2), 182–203.Google Scholar
  51. 51.
    Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509–523.Google Scholar
  52. 52.
    Kozinets, R. V. (2010). Netnography: Doing ethnographic research online. Thousand Oaks: Sage Publications.Google Scholar
  53. 53.
    Kwong, S. W., & Park, J. (2008). Digital music services: Consumer intention and adoption. The Service Industries Journal, 28(10), 1463–1481.Google Scholar
  54. 54.
    Liang, A., & Lim, W. M. (2011). Exploring the online buying behavior of specialty food shoppers. International Journal of Hospitality Management, 30(4), 855–865.Google Scholar
  55. 55.
    Lin, H. F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433–442.Google Scholar
  56. 56.
    Lin, T. C., Hsu, J. S. C., & Chen, H. C. (2013). Customer willingness to pay for online music: the role of free mentality. Journal of Electronic Commerce Research, 14(4), 315.Google Scholar
  57. 57.
    Lysonski, S., & Durvasula, S. (2008). Digital piracy of MP3 s: Consumer ethical predispositions. Journal of Consumer Marketing, 25(3), 167–178.Google Scholar
  58. 58.
    Manstead, A. S. R. (2000). The role of moral norm in the attitude–behavior relation. In D. J. Terry & M. A. Hogg (Eds.), Attitudes, behavior and social context: The role of norms and group membership (pp. 11–30). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  59. 59.
    Marsh, H. W., & Bailey, M. (1991). Confirmatory factor analyses of multitrait-multimethod data: A comparison of alternative models. Applied Psychological Measurement, 15(1), 47–70.Google Scholar
  60. 60.
    Melancon, J. P., Noble, S. M., & Noble, C. H. (2011). Managing rewards to enhance relational worth. Journal of the Academy of Marketing Science, 39(3), 341–362.Google Scholar
  61. 61.
    Ming-Shen, W., Chih-Chung, C., Su-Chao, C., & Yung-Her, Y. (2007). Effects of online shopping attitudes, subjective norms and control beliefs on online shopping intentions: A test of the theory of planned behaviour. International Journal of Management, 24(2), 296–302.Google Scholar
  62. 62.
    Mittal, B., & Lee, M. S. (1989). A causal model of consumer involvement. Journal of Economic Psychology, 10(3), 363–389.Google Scholar
  63. 63.
    Nonnecke, B., Andrews, D., & Preece, J. (2006). Non-public and public online community participation: Needs, attitudes and behavior. Electronic Commerce Research, 6(1), 7–20.Google Scholar
  64. 64.
    Oliver, R. L., & Swan, J. E. (1989). Consumer perceptions of interpersonal equity and satisfaction in transactions: A field survey approach. Journal of Marketing, 53, 21–35.Google Scholar
  65. 65.
    Park, C. W., & Young, S. M. (1986). Consumer response to television commercials: The impact of involvement and background music on brand attitude formation. Journal of Marketing Research, 23(1), 11–24.Google Scholar
  66. 66.
    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. The British Journal of Social Psychology, 40, 79–98.Google Scholar
  67. 67.
    Petty, R. E., & Cacioppo, J. T. (1979). Issue involvement can increase or decrease persuasion by enhancing message-relevant cognitive responses. Journal of Personality and Social Psychology, 37(10), 1915–1926.Google Scholar
  68. 68.
    Petty, R. E., Haugtvedt, C. P., & Smith, S. M. (1995). Elaboration as a determinant of attitude strength: Creating attitudes that are persistent, resistant, and predictive of behavior. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength: Antecedents and consequences (Vol. 4, pp. 93–130). London: Psychology Press.Google Scholar
  69. 69.
    Phau, I., Lim, A., Liang, J., & Lwin, M. (2014). Engaging in digital piracy of movies: A theory of planned behaviour approach. Internet Research, 24(2), 246–266.Google Scholar
  70. 70.
    Poort, J., Leenheer, J., van der Ham, J., & Dumitru, C. (2014). Baywatch: Two approaches to measure the effects of blocking access to The Pirate Bay. Telecommunications Policy, 38(4), 383–392.Google Scholar
  71. 71.
    Pucely, M. J., Mizerski, R., & Perrewe, P. (1988). A comparison of involvement measures for the purchase and consumption of pre-recorded music. Advances in Consumer Research, 15(1), 37–42.Google Scholar
  72. 72.
    Randall, D. M., & Gibson, A. M. (1991). Ethical decision making in the medical profession: An application of the theory of planned behavior. Journal of Business Ethics, 10(2), 111–122.Google Scholar
  73. 73.
    Redondo, I., & Charron, J. P. (2013). The payment dilemma in movie and music downloads: An explanation through cognitive dissonance theory. Computers in Human Behavior, 29(5), 2037–2046.Google Scholar
  74. 74.
    Riekkinen, J. (2016). Dissonance and neutralization of subscription streaming era digital music piracy: An initial exploration. In PACIS 2016, proceedings of the 20 th Pacific Asia conference on information systems, Chiayi, Taiwan, June 27–July 1 2016, paper 251. Available at: https://dblp.org/db/conf/pacis/pacis2016.html. Last accessed October 31, 2018.
  75. 75.
    Rubin, R. B., & McHugh, M. P. (1987). Development of parasocial interaction relationships. Journal of Broadcasting & Electronic Media, 31(3), 279–292.Google Scholar
  76. 76.
    Schultz, D. E. (2006). Media synergy: The next frontier in a multimedia marketplace. Journal of Direct, Data and Digital Marketing Practice, 8(1), 13–29.Google Scholar
  77. 77.
    Shih, Y. Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet Research, 14(3), 213–223.Google Scholar
  78. 78.
    Shim, S., Eastlick, M. A., Lotz, S. L., & Warrington, P. (2001). An online prepurchase intentions model: The role of intention to research. Journal of Retailing, 77(3), 397–416.Google Scholar
  79. 79.
    Shoham, A., & Ruvio, A. D. M. (2008). (Un)ethical consumer behavior: Robin hoods or plain hoods. Journal of Consumer Marketing, 25(4), 200–210.Google Scholar
  80. 80.
    Shoham, A., & Ruvio, A. (2008). Opinion leaders and followers: A replication and extension. Psychology & Marketing, 25(3), 280–297.Google Scholar
  81. 81.
    Simpson, P. M., Banerjee, D., & Simpson, C. L., Jr. (1994). Softlifting: A model of motivating factors. Journal of Business Ethics, 13(6), 431–438.Google Scholar
  82. 82.
    Sinclair, G., & Green, T. (2016). Download or stream? Steal or buy? Developing a typology of today’s music consumer. Journal of Consumer Behaviour, 15(1), 3–14.Google Scholar
  83. 83.
    Steiger, J. H., & Lind, J. C. (1980). Statistically based tests for the number of common factors. Annual Meeting of the Psychometric Society Iowa City, IA, 758, 424–453.Google Scholar
  84. 84.
    Stevens, J. (1996). Applied multivariate statistics for the behavioral sciences. Mahwah, NJ: Erlbaum.Google Scholar
  85. 85.
    Tan, B. (2002). Understanding consumer ethical decision making with respect to purchase of pirated software. Journal of Consumer Marketing, 19(2), 96–111.Google Scholar
  86. 86.
    Taylor, S. A., Ishida, C., & Wallace, D. W. (2009). Intention to engage in digital piracy a conceptual model and empirical test. Journal of Service Research, 11(3), 246–262.Google Scholar
  87. 87.
    Venkatraman, M. P. (1989). Opinion leaders, adopters, and communicative adopters: A role analysis. Psychology & Marketing, 6(1), 51–68.Google Scholar
  88. 88.
    Wang, X., & McClung, S. R. (2011). Toward a detailed understanding of illegal digital downloading intentions: An extended theory of planned behavior approach. New Media & Society, 13(4), 663–677.Google Scholar
  89. 89.
    Wu, S. I. (2006). A comparison of the behavior of different customer clusters towards Internet bookstores. Information & Management, 43(8), 986–1001.Google Scholar
  90. 90.
    Yoon, C. (2011). Ethical decision-making in the Internet context: Development and test of an initial model based on moral philosophy. Computers in Human Behavior, 27(6), 2401–2409.Google Scholar
  91. 91.
    Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology, 30(1), 165–200.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Economics and StatisticsUniversity of SalernoFiscianoItaly
  2. 2.Department of Political, Social and Communication StudiesUniversity of SalernoFiscianoItaly
  3. 3.Department of SociologyCity University of LondonLondonUK

Personalised recommendations