Abstract

Understanding user privacy expectations is important and challenging. General Data Protection Regulation (GDPR) for instance requires companies to assess user privacy expectations. Existing privacy literature has largely considered privacy expectation as a single-level construct. We show that it is a multi-level construct and people have distinct types of privacy expectations. Furthermore, the types represent distinct levels of user privacy, and, hence, there can be an ordering among the types. Inspired by expectations-related theory in non-privacy literature, we propose a conceptual model of privacy expectation with four distinct types – Desired, Predicted, Deserved and Minimum. We validate our proposed model using an empirical within-subjects study that examines the effect of privacy expectation types on participant ratings of privacy expectation in a scenario involving collection of health-related browsing activity by a bank. Results from a stratified random sample (N = 1,249), representative of United States online population (±2.8%), confirm that people have distinct types of privacy expectations. About one third of the population rates the Predicted and Minimum expectation types differently, and differences are more pronounced between younger (18–29 years) and older (60+ years) population. Therefore, studies measuring privacy expectations must explicitly account for different types of privacy expectations.

Highlights

  • Internet, mobile applications and Internet-of-Things technologies have enabled collection and use of unprecedented amount of user data

  • Inspired by the work in Consumer Satisfaction/Dissatisfaction and service quality domains, in this work, we propose a conceptual model for privacy expectation as a multi-level construct

  • We proposed a conceptual model of privacy expectation with Desired, Deserved, Predicted, and Minimum types, and ordering among the types

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Summary

INTRODUCTION

Mobile applications and Internet-of-Things technologies have enabled collection and use of unprecedented amount of user data. Share and combine large amount of user data including sensitive data related to personal health, income and religion (Rao et al, 2014) Such data practices often violate users’ privacy expectations regarding products and services (Lin et al, 2012; Martin and Shilton, 2016b; Rao et al, 2016). Empirical studies that measure privacy expectations (Lin et al, 2012; Martin and Shilton, 2016a,b) have largely considered privacy expectation as a single-level construct. Inspired by the work in Consumer Satisfaction/Dissatisfaction and service quality domains, in this work, we propose a conceptual model for privacy expectation as a multi-level construct.

SCOPE OF THE WORK
BACKGROUND
Expectation-Related Theory in Privacy Literature
Expectation-Related Theory in Non-privacy Literature
Theoretical Work on Privacy Expectation
Empirical Studies on Privacy Expectation
CONCEPTUAL MODEL OF PRIVACY EXPECTATION
Desired Type
Predicted Type
Deserved Type
Minimum Type
Ordering of Privacy Expectation Types
EMPIRICAL STUDY
Sample and Procedure
Variables
Study Scenario
Questionnaire Design
RESULTS
Multiple Privacy Expectation Types Exist
Privacy Expectation Types Can Be Ordered
Knowledge Impacts the Predicted Type
Investment Impacts the Desired Type
Privacy Expectation Types Vary by Groups
Age The study categorized participants into four age ranges
Construct Validity
Ecological Validity
Internal Validity
External Validity
IMPLICATIONS AND CONCLUSIONS
Studies Must Explicitly Consider the Type of Privacy Expectation
Privacy Research Must Focus on All Privacy Expectation Types
Studying Privacy Expectation Types Can Benefit Regulators
ETHICS STATEMENT

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