Blending minds and machines in service operations: enhancing managerial responses to online reviews through human-generative AI collaboration

Lin Jia; Xin Liu; Tong Dora Chen; Yichuan Wang; Kenichi Takahashi; Yuan Wang

doi:10.1108/ITP-06-2025-0773

Blending minds and machines in service operations: enhancing managerial responses to online reviews through human-generative AI collaboration

Lin Jia
, Xin Liu
, Tong Dora Chen
, Yichuan Wang^*
, Kenichi Takahashi
, Yuan Wang

^*此作品的通讯作者

管理学院

Beijing Institute of Technology
University of Sheffield
Tsinghua University
Shanghai Jiao Tong University

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

Purpose – Generative artificial intelligence (GAI) is increasingly embedded in service operations, particularly in crafting managerial responses (MRs) to online negative and positive reviews. Yet, little is known about how firms should optimally allocate responsibilities between human staff and GAI across distinct response tasks, nor how such allocations shape consumer perceptions and downstream behavioral intentions. This study investigates how customers evaluate MRs produced by different agents including humans, GAI and multiple forms of human and GAI collaboration across specific response tasks. Design/methodology/approach – We conducted a randomized online experiment. Main effects were examined using ANOVA with data from 879 participants, and the underlying mechanisms were further analyzed using the PROCESS macro with a parallel mediation model. Findings – Our findings show that to improve customer satisfaction and increase booking intentions, negative reviews are most effectively addressed through an augmented human approach in which GAI creates an initial draft and experienced professional staff refine it. In contrast, positive reviews are best handled solely by experienced professional staff. Originality/value – This study advances knowledge on how to combine GAI with human efforts in service operations, identifies key psychological mechanisms that mediate the effects of different collaboration modes on consumer perceptions and behavioral intentions and offers actionable guidance for service operations managers. The results suggest that GAI should be deployed strategically to augment, rather than replace, human intelligence.

源语言	英语
页（从-至）	1-26
页数	26
期刊	Information Technology and People
DOI	https://doi.org/10.1108/ITP-06-2025-0773
出版状态	已接受/待刊 - 2026

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title = "Blending minds and machines in service operations: enhancing managerial responses to online reviews through human-generative AI collaboration",

abstract = "Purpose – Generative artificial intelligence (GAI) is increasingly embedded in service operations, particularly in crafting managerial responses (MRs) to online negative and positive reviews. Yet, little is known about how firms should optimally allocate responsibilities between human staff and GAI across distinct response tasks, nor how such allocations shape consumer perceptions and downstream behavioral intentions. This study investigates how customers evaluate MRs produced by different agents including humans, GAI and multiple forms of human and GAI collaboration across specific response tasks. Design/methodology/approach – We conducted a randomized online experiment. Main effects were examined using ANOVA with data from 879 participants, and the underlying mechanisms were further analyzed using the PROCESS macro with a parallel mediation model. Findings – Our findings show that to improve customer satisfaction and increase booking intentions, negative reviews are most effectively addressed through an augmented human approach in which GAI creates an initial draft and experienced professional staff refine it. In contrast, positive reviews are best handled solely by experienced professional staff. Originality/value – This study advances knowledge on how to combine GAI with human efforts in service operations, identifies key psychological mechanisms that mediate the effects of different collaboration modes on consumer perceptions and behavioral intentions and offers actionable guidance for service operations managers. The results suggest that GAI should be deployed strategically to augment, rather than replace, human intelligence.",

keywords = "Booking intention, Consumer satisfaction, Generative artificial intelligence, Human-AI collaboration, Managerial response, Service operations",

author = "Lin Jia and Xin Liu and Chen, \{Tong Dora\} and Yichuan Wang and Kenichi Takahashi and Yuan Wang",

note = "Publisher Copyright: {\textcopyright} 2025 Emerald Publishing Limited",

year = "2026",

doi = "10.1108/ITP-06-2025-0773",

language = "English",

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T2 - enhancing managerial responses to online reviews through human-generative AI collaboration

AU - Jia, Lin

AU - Liu, Xin

AU - Chen, Tong Dora

AU - Wang, Yichuan

AU - Takahashi, Kenichi

AU - Wang, Yuan

PY - 2026

Y1 - 2026

N2 - Purpose – Generative artificial intelligence (GAI) is increasingly embedded in service operations, particularly in crafting managerial responses (MRs) to online negative and positive reviews. Yet, little is known about how firms should optimally allocate responsibilities between human staff and GAI across distinct response tasks, nor how such allocations shape consumer perceptions and downstream behavioral intentions. This study investigates how customers evaluate MRs produced by different agents including humans, GAI and multiple forms of human and GAI collaboration across specific response tasks. Design/methodology/approach – We conducted a randomized online experiment. Main effects were examined using ANOVA with data from 879 participants, and the underlying mechanisms were further analyzed using the PROCESS macro with a parallel mediation model. Findings – Our findings show that to improve customer satisfaction and increase booking intentions, negative reviews are most effectively addressed through an augmented human approach in which GAI creates an initial draft and experienced professional staff refine it. In contrast, positive reviews are best handled solely by experienced professional staff. Originality/value – This study advances knowledge on how to combine GAI with human efforts in service operations, identifies key psychological mechanisms that mediate the effects of different collaboration modes on consumer perceptions and behavioral intentions and offers actionable guidance for service operations managers. The results suggest that GAI should be deployed strategically to augment, rather than replace, human intelligence.

AB - Purpose – Generative artificial intelligence (GAI) is increasingly embedded in service operations, particularly in crafting managerial responses (MRs) to online negative and positive reviews. Yet, little is known about how firms should optimally allocate responsibilities between human staff and GAI across distinct response tasks, nor how such allocations shape consumer perceptions and downstream behavioral intentions. This study investigates how customers evaluate MRs produced by different agents including humans, GAI and multiple forms of human and GAI collaboration across specific response tasks. Design/methodology/approach – We conducted a randomized online experiment. Main effects were examined using ANOVA with data from 879 participants, and the underlying mechanisms were further analyzed using the PROCESS macro with a parallel mediation model. Findings – Our findings show that to improve customer satisfaction and increase booking intentions, negative reviews are most effectively addressed through an augmented human approach in which GAI creates an initial draft and experienced professional staff refine it. In contrast, positive reviews are best handled solely by experienced professional staff. Originality/value – This study advances knowledge on how to combine GAI with human efforts in service operations, identifies key psychological mechanisms that mediate the effects of different collaboration modes on consumer perceptions and behavioral intentions and offers actionable guidance for service operations managers. The results suggest that GAI should be deployed strategically to augment, rather than replace, human intelligence.

KW - Booking intention

KW - Consumer satisfaction

KW - Generative artificial intelligence

KW - Human-AI collaboration

KW - Managerial response

KW - Service operations

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DO - 10.1108/ITP-06-2025-0773

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SN - 0959-3845

SP - 1

EP - 26

JO - Information Technology and People

JF - Information Technology and People

ER -

Blending minds and machines in service operations: enhancing managerial responses to online reviews through human-generative AI collaboration

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