Socially desirable responding within the context of privacy-related research : a personality perspective
van der Schyff, Karl and Flowerday, Stephen and Renaud, Karen (2022) Socially desirable responding within the context of privacy-related research : a personality perspective. South African Journal of Information Management. ISSN 2078-1865 (In Press)
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Abstract
Background: Socially desirable responding within the context of self-reported surveys is a well-known and persistent problem that plagues quantitative studies. Such forms of responding are particularly problematic within the context of personality-based studies that investigate privacy-related decision-making. In such instances, certain respondents may feel pressured to provide socially desirable responses, which reduces the overall quality of the collected data. Objectives: The objective of our study was to evaluate the extent to which the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) elicit socially desirable responses within the context of privacy-related decision-making. Method: To evaluate our hypotheses, we empirically situate our study within the context of respondents’ intended use of Facebook privacy settings. To this end, we analyzed 576 survey responses using partial least squares structural equation modelling (PLS-SEM). Results: We found that some personality traits were indeed significantly related to socially desirable responding – albeit not always as expected. For example, we found highly agreeable individuals to be unlikely to provide socially desirable responses: choosing honest responses. Neuroticism, on the other hand, had the opposite effect. Conclusion: Based on our results, we conclude that neurotic individuals seem predisposed towards responding in a socially desirable manner within the context of privacy-related surveys. We therefore advise researchers within the field of privacy-based personality studies to take care when analysing their results.
ORCID iDs
van der Schyff, Karl, Flowerday, Stephen and Renaud, Karen ORCID: https://orcid.org/0000-0002-7187-6531;-
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Item type: Article ID code: 79746 Dates: DateEvent22 February 2022Published22 February 2022AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 25 Feb 2022 15:33 Last modified: 11 Nov 2024 13:24 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/79746