PERSON-TECHNOLOGY FIT IN CONTEMPORARY WORK SETTINGS: A LITERATURE REVIEW (1990–2025)
DOI:
https://doi.org/10.32782/3041-2021/2025-3-42Keywords:
person-technology fit; task-technology fit; digital transformation; technostress; digital well- being; person-environment fitAbstract
This paper presents a scoping review of studies published between 1990 and 2025 that examine the phenomenon of person-technology fit within workplace contexts. The review aims to trace the evolution of conceptual approaches, measurement methods, and empirical findings, and to identify gaps for future inquiry. A purposive corpus of 41 peer-reviewed publications was compiled through searches in Scopus, Web of Science, and Google Scholar and augmented via backward- and forward-snowballing.The analysis reveals four dominant conceptualisations of person-technology fit: (1) a functional- instrumental perspective rooted in task-technology fit; (2) a psychological-attributive perspective grounded in the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology; (3) a multidimensional person-environment perspective that emphasises needs-supplies, demands-abilities, and value congruence; and (4) a dynamic “too-little / too-much” perspective distinguishing undersupply from oversupply of technological functionality. Measurement is still dominated by short self-report scales, whereas algorithmic indices based on usage data and natural-language processing remain rare and methodologically isolated.Synthesis of antecedents shows that digital self-efficacy, interface transparency and flexibility, and organisational support strengthen perceived fit, whereas technostress and excessive system complexity weaken it. High person-technology fit is consistently linked to higher performance, job satisfaction, and digital well-being, while misfit is associated with emotional exhaustion and turnover intentions.The review is limited by its purposive selection, English-language focus, and reliance on cross-sectional surveys. Future research should standardise terminology and instruments, employ longitudinal and multi- source designs, explore emerging technological contexts (e.g., generative AI, the metaverse), and include under-represented populations.
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