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Chatbots and team-based working dynamics: management decision implications

Antonio Cimino Orcid Logo, Paul Jones Orcid Logo, Francesco Longo, Vittorio Solina Orcid Logo, Ciro Troise Orcid Logo

Management Decision, Pages: 1 - 29

Swansea University Author: Paul Jones Orcid Logo

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DOI (Published version): 10.1108/md-07-2025-2149

Abstract

Purpose: This study investigates the relationship between artificial intelligence (AI)-related system characteristics and two interpersonal states commonly associated with effective teamwork, namely employee well-being and mutual trust. While generative AI has shown potential to improve organization...

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Published in: Management Decision
ISSN: 0025-1747 1758-6070
Published: Emerald Publishing Limited 2026
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa71424
first_indexed 2026-02-16T12:27:14Z
last_indexed 2026-04-25T06:49:04Z
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spelling 2026-04-24T15:22:24.5603774 v2 71424 2026-02-16 Chatbots and team-based working dynamics: management decision implications 21e2660aaa102fe36fc981880dd9e082 0000-0003-0417-9143 Paul Jones Paul Jones true false 2026-02-16 CBAE Purpose: This study investigates the relationship between artificial intelligence (AI)-related system characteristics and two interpersonal states commonly associated with effective teamwork, namely employee well-being and mutual trust. While generative AI has shown potential to improve organizational performance, its specific effects on internal team-based working relationships remain underexplored. Design/methodology/approach: A theoretical model is developed to explore the influence of three antecedent variables, quality of information, system quality and generative AI use, on collaboration within teams. Collaboration is operationalized using two key constructs: employee well-being and mutual trust. The model is empirically tested using data from a large-scale survey of 208 professionals working in team-based environments. Data analysis is conducted using partial least squares structural equation modeling (PLS-SEM). Findings: The results confirm that all three antecedent variables positively influence team-based collaboration dynamics. Specifically, the use of generative AI chatbots, such as ChatGPT, is shown to enhance employee well-being and foster mutual trust within teams, both of which act as interpersonal enablers of team collaboration. These outcomes suggest that the integration of high-quality AI tools can meaningfully support collaborative processes in professional settings. Originality/value: This study contributes to the emerging field of generative AI research by shifting the focus from performance outcomes to collaboration mechanisms within teams. It offers practical implications for managers seeking to optimize teamwork in AI-enabled environments, including investing in system quality, redesigning workflows to integrate AI effectively and promoting a culture of trust and transparency around AI adoption. Journal Article Management Decision 0 1 29 Emerald Publishing Limited 0025-1747 1758-6070 Artificial intelligence, Collaboration, Generative AI chatbots, Structural equation modeling, Working dynamics 17 2 2026 2026-02-17 10.1108/md-07-2025-2149 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Not Required 2026-04-24T15:22:24.5603774 2026-02-16T12:24:08.9917732 Faculty of Humanities and Social Sciences School of Management - Business Management Antonio Cimino 0000-0002-8230-1098 1 Paul Jones 0000-0003-0417-9143 2 Francesco Longo 3 Vittorio Solina 0000-0002-5585-3444 4 Ciro Troise 0000-0002-8899-8949 5 71424__36592__90538a2ed8e645599f838e8ca16821c4.pdf 71424.AAM.pdf 2026-04-24T15:13:59.6441861 Output 595273 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/
title Chatbots and team-based working dynamics: management decision implications
spellingShingle Chatbots and team-based working dynamics: management decision implications
Paul Jones
title_short Chatbots and team-based working dynamics: management decision implications
title_full Chatbots and team-based working dynamics: management decision implications
title_fullStr Chatbots and team-based working dynamics: management decision implications
title_full_unstemmed Chatbots and team-based working dynamics: management decision implications
title_sort Chatbots and team-based working dynamics: management decision implications
author_id_str_mv 21e2660aaa102fe36fc981880dd9e082
author_id_fullname_str_mv 21e2660aaa102fe36fc981880dd9e082_***_Paul Jones
author Paul Jones
author2 Antonio Cimino
Paul Jones
Francesco Longo
Vittorio Solina
Ciro Troise
format Journal article
container_title Management Decision
container_volume 0
container_start_page 1
publishDate 2026
institution Swansea University
issn 0025-1747
1758-6070
doi_str_mv 10.1108/md-07-2025-2149
publisher Emerald Publishing Limited
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description Purpose: This study investigates the relationship between artificial intelligence (AI)-related system characteristics and two interpersonal states commonly associated with effective teamwork, namely employee well-being and mutual trust. While generative AI has shown potential to improve organizational performance, its specific effects on internal team-based working relationships remain underexplored. Design/methodology/approach: A theoretical model is developed to explore the influence of three antecedent variables, quality of information, system quality and generative AI use, on collaboration within teams. Collaboration is operationalized using two key constructs: employee well-being and mutual trust. The model is empirically tested using data from a large-scale survey of 208 professionals working in team-based environments. Data analysis is conducted using partial least squares structural equation modeling (PLS-SEM). Findings: The results confirm that all three antecedent variables positively influence team-based collaboration dynamics. Specifically, the use of generative AI chatbots, such as ChatGPT, is shown to enhance employee well-being and foster mutual trust within teams, both of which act as interpersonal enablers of team collaboration. These outcomes suggest that the integration of high-quality AI tools can meaningfully support collaborative processes in professional settings. Originality/value: This study contributes to the emerging field of generative AI research by shifting the focus from performance outcomes to collaboration mechanisms within teams. It offers practical implications for managers seeking to optimize teamwork in AI-enabled environments, including investing in system quality, redesigning workflows to integrate AI effectively and promoting a culture of trust and transparency around AI adoption.
published_date 2026-02-17T08:22:27Z
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score 11.104242

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