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Privacy Calculus in the Sharing Economy

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presentation given at EMAC 2018 marketing conference on the effect of the privacy calculus on customer loyalty.
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Privacy Calculus in the Sharing Economy

  1. 1. Privacy Calculus in the Sharing Economy Julien Cloarec Lars Meyer-Waarden Andreas Munzel Toulouse Capitole University – Toulouse School of Management TSM Research (UMR 5303 CNRS)
  2. 2. Introduction Sharing Economy Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 2 What Is the Sharing Economy? Market formed through an intermediating technology platform that facilitates exchange activities among a network of equivalently positioned economic actors (Perren and Kozinets, 2018)
  3. 3. Introduction Sharing Economy and Privacy Issues Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 3 What Do We Know? Privacy concerns might threaten its growth (Lutz et al., 2017) Privacy calculus affects renting out frequency (Teubner and Flath, 2016; Lutz et al., 2017) What Is Unknown? Trade-off between personalization and privacy risks? (Kannan and Li, 2017) How C2C platforms can ensure a thick market to be sustainable (Evans and Schmalensee, 2016) cost-benefit analysis performed by consumers when it comes to privacy-related decisions (Dinev and Hart, 2006) “the degree to which an Internet user is concerned about website practices related to the collection and use of his or her personal information” (Hong & Thong, 2013, p. 276)
  4. 4. Conceptual Framework Research Model Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 4 Risk Beliefs Satisfaction with Personalization Information Collection Concerns Sharing Frequency Trust Beliefs Internet Literacy Privacy Calculus Control Variables on all DV: Gender, Age, Education, Daily Internet Use, Collaboration Frequency H1(-) H2a(+) H2b(-) H3a(-) H3b(+) H7(+) H8a(-) H8b(-) H4(-) Mediation
  5. 5. Conceptual Framework Personalization-Privacy Issues Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 5 Risk Beliefs Satisfaction with Personalization Information Collection Concerns Privacy Calculus H1(-) H2a(+) H2b(-) Personalization-privacy issues are critical for marketing (Aguirre et al., 2015) “the degree to which a person is concerned about the amount of individual- specific data possessed by websites” (Hong and Thong, 2013, p. 278) Privacy benefit (White, 2004) (e.g., satisfaction with targeted emails, tailored services or individualized offers) Expectation that a high potential for loss is associated with the release of personal information to the firm (Dowling and Staelin, 1994) H1, H2a and H2b: Privacy calculus theory (e.g., Smith et al., 2011)
  6. 6. Conceptual Framework Personalization-Privacy Issues Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 6 Risk Beliefs Satisfaction with Personalization Information Collection Concerns Sharing Frequency Privacy Calculus H1(-) H2a(+) H2b(-) H3a(-) H3b(+) H3a: Privacy calculus theory (Smith et al., 2011) H3b: Sharing economy relies on the platform economy Matchmaking is essential (Evans & Schmalensee, 2016) => Personalization Satisfaction leads to loyalty (Anderson and Srinivasan, 2003) H4(-) Mediation (Lutz et al., 2017) declarative proxy of behavioral loyalty
  7. 7. Conceptual Framework Trust Beliefs Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 7 Risk Beliefs Satisfaction with Personalization Information Collection Concerns Sharing Frequency Trust Beliefs Privacy Calculus H1(-) H2a(+) H2b(-) H3a(-) H3b(+) H7(+) H4(-) Mediation degree to which people believe a firm is dependable regarding the protection of their information (Gefen et al., 2003) H7: Trust strengthens the positive satisfaction-loyalty relationship (Anderson and Srinivasan, 2003)
  8. 8. Conceptual Framework Internet Literacy Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 8 Risk Beliefs Satisfaction with Personalization Information Collection Concerns Sharing Frequency Trust Beliefs Internet Literacy Privacy Calculus H1(-) H2a(+) H2b(-) H3a(-) H3b(+) H7(+) H8a(-) H8b(-) Internet literacy is a critical individual characteristics for the sharing economy (Benoit et al., 2017) High Internet-literate individuals are more able to develop strategies to protect their privacy (Dinev and Hart, 2005) H4(-) Mediation “the ability to use an Internet-connected computer and Internet applications to accomplish practical tasks” (Dinev and Hart, 2005, p. 9) They rely more on their own capabilities which reduces the value and, hence, the satisfaction with personalized recommendation H3a and H3b: Their behaviors should be less affected by a privacy calculus
  9. 9. Methodology Measurement, Sample and Model Assessment Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 9 Sample Representative sample of the French population (n=637) provided by a large consumer panel provider (2014) Measurement Model Assessment All the psychometric properties of the measures were satisfying (Fornell and Larcker, 1981; Hair et al., 2014) c² = 382.559, df = 108, GFI = .935, AGFI = .908, RMSEA = .063; TLI = .963; CFI = .971 Measurement Constructs Items Adapted from Information Collection Concerns “I am concerned that websites are collecting too much personal information about me” Hong and Thong (2013) Risk Beliefs “Providing websites with my personal information would involve many unexpected problems” Hong and Thong (2013) Trust Beliefs “Websites would keep my best interests in mind when dealing with my personal information” Hong and Thong (2013) Satisfaction with Personalization “I am satisfied when firms use my personal information to tailor their offers” Self-developped Internet Literacy “I can search information online” Faurie and Leemput (2007) Sharing “How often do you use the Internet on computers, laptops, smartphones or tablets to share products or services?”
  10. 10. Results Structural Model Testing Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 10 Risk Beliefs Satisfaction with Personalization Information Collection Concerns Sharing Frequency Trust Beliefs Internet Literacy Privacy Calculus –.09ns .58*** –.18*** –.07ns .14** .18*** –.21** .10*** .01ns –.07* R² = .35 ***p < .001, **p < .01, *p < .05, ns: not significant Research model implemented with the PROCESS macro (Hayes, 2017) Percentile corrected method and 5,000 bootstrap samples (Zhao, Lynch, and Chen, 2010) Control Variables on all DV: Gender, Age, Education, Daily Internet Use, Collaboration Frequency
  11. 11. Results Moderation Analyses Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 11 Satisfaction with Personalization Sharing Frequency Trust Beliefs Internet Literacy Interaction effect = .10*** Interaction effect = –.07* ***p < .001, **p < .01, *p < .05, ns: not significant SwP→SF SwP→SF SwP→SF
  12. 12. Results Moderated Mediation Analyses Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 12 Satisfaction with Personalization Information Collection Concerns Sharing Frequency Trust Beliefs Internet Literacy Index of partial moderated mediation = -.02* 95% CI = [-.04, -.01] Index of partial moderated mediation = .01* 95% CI = [.00, .04] ***p < .001, **p < .01, *p < .05, ns: not significant ICC→SF ICC→SF ICC→SF
  13. 13. Discussion Theoretical Contributions Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 13 How C2C platforms can ensure a thick market to be sustainable (Evans and Schmalensee, 2016) Better understanding of personalization-risk issues (Kannan and Li, 2017) • Satisfaction with personalization is a strong driver of sharing frequency • Risk beliefs have no significant effect Moderating effects of two important characteristics (Benoit et al., 2017) • The positive effect of satisfaction with personalization on sharing frequency is strengthened by trust beliefs and is weakened by Internet literacy
  14. 14. Discussion Limitations and Future Research Cloarec, Meyer-Waarden & Munzel | EMAC | Glasgow | May-June 2018 14 Did not distinguish the customers and the peer services providers “Satisfaction with Personalization” may involve an artefact Sharing frequency is only a declarative proxy of behavior Two types of frequency – customers and peer services providers Manipulate personalization in an experimental design Examine longitudinal behavior of sharing Limitations Future Research
  15. 15. Thank You For Your Attention! Questions? Julien Cloarec Lars Meyer-Waarden Andreas Munzel Toulouse Capitole University – Toulouse School of Management TSM Research (UMR 5303 CNRS)
  16. 16. References Cloarec, Meyer-Waarden & Munzel | AFM | Strasbourg | May 2018 16 Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness. Journal of Retailing, 91(1), 34–49. http://doi.org/10.1016/j.jretai.2014.09.005 Anderson, R. E., & Srinivasan, S. S. (2003). E-Satisfaction and E-Loyalty: A Contingency Framework. Psychology and Marketing, 20(2), 123–138. http://doi.org/10.1002/mar.10063 Benoit, S., Baker, T. L., Bolton, R. N., Gruber, T., & Kandampully, J. (2017). A Triadic Framework for Collaborative Consumption (CC): Motives, Activities and Resources and Capabilities of Actors. Journal of Business Research, 79, 219–227. http://doi.org/10.1016/j.jbusres.2017.05.004 Dinev, T., & Hart, P. (2005). Internet Privacy Concerns and Social Awareness as Determinants of Intention to Transact. International Journal of Electronic Commerce, 10(2), 7–29. http://doi.org/10.2753/JEC1086-4415100201 Dinev, T., & Hart, P. (2006). An Extended Privacy Calculus Model for E-Commerce Transactions. Information Systems Research, 17(1), 61–80. http://doi.org/10.1287/isre.1060.0080 Dowling, G. R., & Staelin, R. (1994). A Model of Perceived Risk and Intended Risk-Handling Activity. Journal of Consumer Research, 21(1), 119. http://doi.org/10.1086/209386 Evans, D. S., & Schmalensee, R. (2016). Matchmakers: The New Economics of Multisided Platforms. Boston: Harvard Business Review Press. Faurie, I., & Leemput, C. van de. (2007). Influence du Sentiment d’Efficacité Informatique sur les Usages d’Internet des Étudiants. L’Orientation Scolaire et Professionnelle, 36(4), 533–552. http://doi.org/10.4000/osp.1549 Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27(1), 51–90. Hayes, A. F. (2017). Introduction to Mediation, Moderation, and Conditional Process Analysis. 2nd Edition. New-York: Guilford Press. Hong, W., & Thong, J. Y. (2013). Internet Privacy Concerns: An Integrated Conceptualization and Four Empirical Studies. MIS Quarterly, 37(1), 275–298. Kannan, P. K., & Li, H. “Alice.” (2017). Digital Marketing: A Framework, Review and Research Agenda. International Journal of Research in Marketing, 34(1), 22–45. http://doi.org/10.1016/j.ijresmar.2016.11.006 Lutz, C., Hoffmann, C. P., Bucher, E., & Fieseler, C. (2017). The Role of Privacy Concerns in the Sharing Economy. Information, Communication & Society. http://doi.org/10.1080/1369118X.2017.1339726 Milanova, V., & Maas, P. (2017). Sharing Intangibles: Uncovering Individual Motives for Engagement in a Sharing Service Setting. Journal of Business Research, 75, 159–171. http://doi.org/10.1016/j.jbusres.2017.02.002 Pearson, B. (2017). 5 Principles For Long-Term Loyalty In The Sharing Economy. Retrieved November 13, 2017, from https://www.forbes.com/sites/bryanpearson/2017/11/06/5-principles-for-long-term-loyalty- in-the-sharing-economy/ Smith, H. J., Dinev, T., & Xu, H. (2011). Information Privacy Research: An Interdisciplinary Review. MIS Quarterly, 35(4), 989–1016. Teubner, T., & Flath, C. M. (2016). Privacy in the Sharing Economy. Working paper. White, T. B. (2004). Consumer Disclosure and Disclosure Avoidance: A Motivational Framework. Journal of Consumer Psychology, 14(1–2), 41–51. http://doi.org/10.1207/s15327663jcp1401&2_6 Xu, H., Luo, X. (Robert), Carroll, J. M., & Rosson, M. B. (2011). The Personalization Privacy Paradox: An Exploratory Study of Decision Making Process for Location-Aware Marketing. Decision Support Systems, 51(1), 42–52. http://doi.org/10.1016/j.dss.2010.11.017

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