Exploring Stakeholder Acceptance of Workplace Personalization

ABSTRACT Personalization in the workplace may be used to support workers by simplifying tasks or reducing workload, but requires the collection of personal data, and this raises concerns over privacy and possible discrimination if applied indiscriminately. The study explored the factors associated with acceptance of three types of workplace personalization system (Suggester, Swappers and Controller, within‐subjects ) and two personal data types (Heart Rate, Performance, between‐subjects ) by presenting vignettes using an online experimental platform (Prolific.com) and capturing respondents' ( n = 204) attitudes using recognized acceptance questionnaires (e.g., the Advanced Transport Telematics Acceptance Assessment, [ATTAA]). Results show acceptance is influenced by the type of personalization system, particularly when physiological (heart rate) data is used, with “Swapper” systems receiving the highest ratings for “Usefulness” and “Satisfying” (interpreted as higher acceptance) compared to suggesters and controllers. Acceptance ratings were not significantly different between personalization types when performance data were used. The Affinity for Technology (AFT) and Need for Cognition (NFC) scales were used to categorize participant characteristics, but only revealed significant differences associated with NFC and usefulness—most notably when using performance data. Overall, the results support the need to consider the type of intervention and the type/amount of personal data required when designing and implementing workplace personalization systems and highlight a particular need for caution when physiological data is required.

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Publication Details

Journal
Human Factors and Ergonomics in Manufacturing & Service Industries
Published
2026-02-01
DOI
https://doi.org/10.1002/hfm.70030
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
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article

Exploring Stakeholder Acceptance of Workplace Personalization

Joshua Duvnjak, Ayse Kucukyilmaz, Robert J. Houghton, David R. Large
Human Factors and Ergonomics in Manufacturing & Service Industries
Human-Automation Interaction and Safety
article

Exploring Stakeholder Acceptance of Workplace Personalization

Joshua Duvnjak, Ayse Kucukyilmaz, Robert J. Houghton, David R. Large
article en

Abstract

ABSTRACT Personalization in the workplace may be used to support workers by simplifying tasks or reducing workload, but requires the collection of personal data, and this raises concerns over privacy and possible discrimination if applied indiscriminately. The study explored the factors associated with acceptance of three types of workplace personalization system (Suggester, Swappers and Controller, within‐subjects ) and two personal data types (Heart Rate, Performance, between‐subjects ) by presenting vignettes using an online experimental platform (Prolific.com) and capturing respondents' ( n = 204) attitudes using recognized acceptance questionnaires (e.g., the Advanced Transport Telematics Acceptance Assessment, [ATTAA]). Results show acceptance is influenced by the type of personalization system, particularly when physiological (heart rate) data is used, with “Swapper” systems receiving the highest ratings for “Usefulness” and “Satisfying” (interpreted as higher acceptance) compared to suggesters and controllers. Acceptance ratings were not significantly different between personalization types when performance data were used. The Affinity for Technology (AFT) and Need for Cognition (NFC) scales were used to categorize participant characteristics, but only revealed significant differences associated with NFC and usefulness—most notably when using performance data. Overall, the results support the need to consider the type of intervention and the type/amount of personal data required when designing and implementing workplace personalization systems and highlight a particular need for caution when physiological data is required.

Human Factors and Ergonomics in Manufacturing & Service IndustriesVol. 36(2)
University of Nottingham (GB), Heriot-Watt University (GB)
Reduced inequalities
Openalex Percentile: Top 75%
Human-Automation Interaction and Safety
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