Detecting Social Desirability Bias with Human-Computer Interaction: A Mouse-Tracking Study

Paul A. Weisgarber, Joseph S. Valacich, Jeffrey L. Jenkins, David Kim, Manasvi Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social desirability bias undermines self-report accuracy, necessitating novel approaches to detect and mitigate its impact. This study aimed to investigate the influence of social desirability on questionnaire responses by analyzing mouse cursor movements and answering behaviors. Respondents (n=238) completed a health and wellness questionnaire while their mouse cursor data was recorded. The results revealed that individuals under a higher social desirability treatment exhibited significantly longer response times and slower mouse cursor speeds, supporting the hypothesis that they may engage in more cautious and deliberate responding. However, no significant differences were found in terms of mouse cursor deviations or answer switches between the two groups. These findings suggest that analyzing mouse cursor movements can provide valuable insights into the influence of social desirability bias on questionnaire responses, offering a potentially scalable method for detection and future intervention.

Original languageEnglish (US)
Title of host publicationProceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages4673-4682
Number of pages10
ISBN (Electronic)9780998133171
StatePublished - 2024
Event57th Annual Hawaii International Conference on System Sciences, HICSS 2024 - Honolulu, United States
Duration: Jan 3 2024Jan 6 2024

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Conference

Conference57th Annual Hawaii International Conference on System Sciences, HICSS 2024
Country/TerritoryUnited States
CityHonolulu
Period1/3/241/6/24

Keywords

  • HCI dynamics
  • mouse cursor movements
  • online survey research
  • self-report data
  • social desirability response bias

ASJC Scopus subject areas

  • General Engineering

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