The document examines the intersection of privacy concerns and the sharing economy, highlighting how privacy calculus can impact user behavior and trust on C2C platforms. It discusses the balance between personalization and privacy risks, and presents a conceptual framework including factors like risk beliefs, satisfaction with personalization, and internet literacy. The research findings suggest that satisfaction with personalization drives sharing frequency, moderated by trust and internet literacy.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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