Personality-based pair programming: toward intrinsic motivation alignment in very small entities

This study explores whether personality-based role assignments (Pilot, Navigator, Solo) can raise intrinsic motivation in pair programming, focusing on designing a framework and process extension for the resource-constrained environment of very small entities (VSEs). We employed a mixed-methods desi...

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Vydané v:PeerJ. Computer science Ročník 11; s. e2774
Hlavní autori: Valovy, Marcel, Buchalcevova, Alena
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States PeerJ. Ltd 01.04.2025
PeerJ Inc
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ISSN:2376-5992, 2376-5992
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Shrnutí:This study explores whether personality-based role assignments (Pilot, Navigator, Solo) can raise intrinsic motivation in pair programming, focusing on designing a framework and process extension for the resource-constrained environment of very small entities (VSEs). We employed a mixed-methods design across three quasi-experimental datasets ( = 73 participants), applying linear mixed-effects (LME) modeling to assess motivational outcomes and thematically analyzing ( = 25) interviews for socio-psychological insights. Openness strongly correlates with Pilot roles; Extraversion & Agreeableness favor Navigator roles; and Neuroticism aligns more comfortably with Solo roles-each yielding substantial boosts in intrinsic motivation (up to 60-65%). Twelve qualitative themes underscore the influence of mentorship, pairing constellations, and flow disruptions on developer experiences. Building on these results, we propose the role-optimization motivation alignment (ROMA) framework, mapped to the ISO/IEC 29110 Software Basic Profile and Agile Guidelines, with practical tasks (T1-T7) to facilitate systematic role-trait alignments in small agile teams. Although our data primarily involve Gen-Z undergraduates, the recurring patterns suggest broader applicability, further supported by a separately published application for ongoing generalizability. Personality-driven role optimization may significantly enhance collaboration and developer satisfaction in VSEs, though further studies in professional settings and investigations into AI-assisted or distributed pair programming are warranted.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.2774