CoChat
Sign Up
Cited by 2,013Privacy, Security, and Data ProtectionPrivacy-Preserving Technologies in DataInformation and Cyber Security

Information Privacy Research: an Interdisciplinary Review1

Smith, H. Jeff, Dinev, Tamara, Xu, Heng·2011·MIS Quarterly
DOI 10.2307/41409970
Open this paper in CoChat — free account

Abstract

To date, many important threads of information privacy research have developed, but these threads have not been woven together into a cohesive fabric. This paper provides an interdisciplinary review of privacy-related research in order to enable a more cohesive treatment. With a sample of 320 privacy articles and 128 books and book sections, we classify previous literature in two ways: (1) using an ethics-based nomenclature of normative, purely descriptive, and empirically descriptive, and (2) based on their level of analysis: individual, group, organizational, and societal. Based upon our analyses via these two classification approaches, we identify three major areas in which previous research contributions reside: the conceptualization of information privacy, the relationship between information privacy and other constructs, and the contextual nature of these relationships. As we consider these major areas, we draw three overarching conclusions. First, there are many theoretical developments in the body of normative and purely descriptive studies that have not been addressed in empirical research on privacy. Rigorous studies that either trace processes associated with, or test implied assertions from, these value-laden arguments could add great value. Second, some of the levels of analysis have received less attention in certain contexts than have others in the research to date. Future empirical studies—both positivist and interpretive—could profitably be targeted to these under-researched levels of analysis. Third, positivist empirical studies will add the greatest value if they focus on antecedents to privacy concerns and on actual outcomes. In that light, we recommend that researchers be alert to an overarching macro model that we term APCO (Antecedents → Privacy Concerns → Outcomes).

Integrity signals

Registry

Crossref · 10.2307/41409970

Cites

22 of 22 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Some blockchain solutions such as Uport or Sovrin deal with private key loss by providing recovery and revocation mechanisms for the private keys.

    “Some blockchain solutions such as Uport [20] or Sovrin [82] deal with this issue by providing recovery and revocation mechanisms for the private keys.”

    From Privacy-Preserving Solutions for Blockchain: Review and Challenges (2019) · cites this paper as [20]

  • Current blockchain implementations such as uPort or Sovrin are starting or planning to adopt these standards.

    “current blockchain implementations such as uPort [20] or Sovrin [82] are starting or planning to adopt these standards.”

    From Privacy-Preserving Solutions for Blockchain: Review and Challenges (2019) · cites this paper as [20]

  • uPort supports a certain degree of unlinkability, as users can create many unlinkable uPortIDs.

    “uPort support certain degree of unlikability, as user can create many unlinkable uPortIDs.”

    From Privacy-Preserving Solutions for Blockchain: Review and Challenges (2019) · cites this paper as [20]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Smith, H. J., Dinev, T., & Xu, H.
Affiliations
  • Department of Decision Sciences and Management Information Systems, Farmer School of Business, Miami University, Oxford, OH 45056 U.S.A.
  • Florida Atlantic University (US)
  • Pennsylvania State University (US)
Venue
MIS Quarterly
Publisher
MIS Quarterly
Published
2011-12-01
Volume
35
Issue
4
Pages
989-A27
ISSN
  • 0276-7783 (ISSN-L)
  • 2162-9730
DOI
10.2307/41409970
OpenAlex
W2096178490
Cited by
2,013
References
22
Citations by year
2026: 157 · 2025: 130 · 2024: 140 · 2023: 182 · 2022: 214 · 2021: 169 · 2020: 170 · 2019: 146 · 2018: 146 · 2017: 123 · 2016: 124 · 2015: 121 · 2014: 95 · 2013: 55 · 2012: 32
FWCI
204.64
Type
journal article
Language
en
Open access
closed
Locations
MIS Quarterly (publishedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
Keywords
data privacy, privacy research, levels of analysis, privacy concerns

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review