privacy paradox mydata2017

11
Privacy : from rebalancing privacy paradox to taking account ignorance an lazyness Sarah Medjek (Fing - University Paris Nanterre) Christophe Benavent (University Paris Nanterre)

Upload: christophe-benavent

Post on 21-Jan-2018

181 views

Category:

Marketing


0 download

TRANSCRIPT

Privacy : from rebalancing privacy paradox to taking account ignorance an lazyness

Sarah Medjek (Fing - University Paris Nanterre) 

Christophe Benavent (University Paris Nanterre)

Ligne 1 Ligne 2 Ligne 3 Ligne 40

2

4

6

8

10

12

1 colonne

2 colonne

3 colonne

Personal information disclosure

Attitude variables

Conativevariables

• Trust• Privacy Concerns• Privacy Self Efficacy• Benefits• Social Influence• Personal characteristics• Information control• Potential consequences • Privacy protection• Risks • Reputation• Perceived usefulness• Perceived ease of use• Perceived value• Perceived relevance

1. Behavioral intentions2. Internet usage/interest3. Enjoyment4. General willingness5. Commitment

State of art : a quick synthesis

One paradox and  three theories1) Privacy calculus as an actualisation :

immediate benefit more valued than discounted future risks

2) Theory of Construal Levels ( trope and Liberman) Judgment is contingent to degree of distance, abstraction and/or temporal proximity

Liberman N, Trope Y. The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. Journal of Personality and Social Psychology. 1998;75:5–18

3) Learned helplessness (Seligman) Resignation : abscence of control drive to depression and acceptance of worse.

Peterson, C.; Maier, S. F.; Seligman, M. E. P. (1995). Learned Helplessness: A Theory for the Age of Personal Control. New York: Oxford University Press.

VariablesUsedfor« MesInfos»

Perceived Risk

Social Influence

PerceivedBenefit

Self Efficacy Trust

Innovativeness

0.297

0.236

Privacy Concern

-0.176

0.1290.158

0.222

0.223

0.136 0.353

Model Path Analysis ( With Laavan)

Estimator ML Minimum Function Test Statistic 13.211 Degrees of freedom 5 P-value (Chi-square) 0.021 RMSEA 0.068 90 Percent Confidence Interval 0.025 0.112 P-value RMSEA <= 0.05 0.208

0.356

- The path of trust come through self-trust : culture, self representation are the key point that facilitate data disclosure. Toward an experiential model where challenges and capabilities are key elements.

- Social Influence play an ambiguous effet as it could be as negative than positive depending the level of positive/negative opinions and reflect a limitated, imitative process of consumer knowledge.

- Privacy concern is unefficient. High or low, there is simply no impact. What we think about don't have any influence on behavior.

Complementary results. Optimum transparency : an experiment

Three experimental groups that are trained to use Amazon recommandation engine with different level of transparency ( zero information,simplified, complete)

More details : effect controlled by degre of experience with the site, and purposes

Conclusions and questions

- Pims are usual applications : usefull, usable, fun and appropriable

- Fear is not the way

- Social and mimetic influence would be the strong diffusion vector

- Competition..... also, because it's also a comparative judgment

-

AwarenessBenefitsData collectionData controlData protectionEducationInnovativenessPrivacy concernsPrivacy self efficacyRisksSocial influenceTrust (companies)Usage Trust (Cozy)Risks (cozy)Ease of useEnjoyment/pleasureIntention to re-useSatisfactionRelative advantage/usefulness

Benefits (cozy)Value added/perceived valueData interestSupriseSympathyBrand assesmentPrivacy involvementPrevious privacy experienceCustomer empowermentValueAnxietyExperimentation feedbackCustomer commitmentNon-Adoption- Lack on interestDoesn’t satisfy needsDislike use of technologyResistance to changeRestrictionsTechnophobiaIdiological 

Variables testées lors de l’expérimentation MesInfos 2014