Revealing the Black Box of Privacy Concern: Understanding How Self-disclosure Affects Privacy Concern in the Context of On-Demand Services Through Two Competing Models

Chenwei Li*, Patrick Y.K. Chau

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


As a prevalent economic paradigm, on-demand services match service providers and consumers with respective needs through the on-demand service platform. Consumers have to express their needs through self-disclosure, which inevitably raises privacy concern. However, how consumers’ self-disclosure influences their privacy concern has not been well studied and remains as a black box. In this study, we would like to investigate how consumers’ prior self-disclosure affects their privacy concern through two competing models derived from two theories in the literature: prominence interpretation theory and information processing theory. Based on prominence interpretation theory, the first model explains how the amount of consumers’ prior self-disclosure in the past use affects the prominence and interpretation of requests for self-disclosure, thus finally influences consumers’ privacy concern about their information. Based on information processing theory, the second model proposes a two-step approach that the amount of consumers’ prior self-disclosure in the past use affects consumers’ beliefs in the first step, and in the second step consumers’ beliefs impact their evaluation of the on-demand service platform, thus finally influence their privacy concern. The models will be tested based on survey data collected from on-demand service consumers. The potential theoretical contributions and practical implications for consumers, service providers, and platforms are discussed.

Original languageEnglish
Title of host publicationThe Ecosystem of e-Business
Subtitle of host publicationTechnologies, Stakeholders, and Connections - 17th Workshop on e-Business, WeB 2018, Revised Selected Papers
EditorsJennifer J. Xu, Bin Zhu, Xiao Liu, Michael J. Shaw, Han Zhang, Ming Fan
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783030227838
Publication statusPublished - 2019
Externally publishedYes
Event17th Annual Workshop on e-Business, WeB 2018 - Santa Clara, United States
Duration: 12 Dec 201812 Dec 2018

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356


Conference17th Annual Workshop on e-Business, WeB 2018
Country/TerritoryUnited States
CitySanta Clara


  • Information processing theory
  • On-demand services
  • Privacy concern
  • Prominence interpretation theory
  • Self-disclosure

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