Joining forces for online feedback management: policy recommendations for human–AI collaboration

Abstract Online customer feedback management (CFM) is becoming increasingly important for businesses. Providing timely and effective responses to guest reviews can be challenging, especially as the volume of reviews grows. This paper explores the response process and the potential for artificial intelligence (AI) augmentation in response formulation. We propose an orchestration concept for human–AI collaboration in co-writing within the hospitality industry, supported by a novel NLP-based solution that combines the strengths of both human and AI. Although complete automation of the response process remains out of reach, our findings offer practical implications for improving response speed and quality through human–AI collaboration. Additionally, we formulate policy recommendations for businesses and regulators in CFM. Our study provides transferable design knowledge for developing future CFM products.

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Institutions

Publication Details

Journal
Data & Policy
Published
2025-01-01
DOI
https://doi.org/10.1017/dap.2025.13
Citations
3
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
12.60

Funders

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article

Joining forces for online feedback management: policy recommendations for human–AI collaboration

Dzmitry Katsiuba, Mateusz Dolata, Gerhard Schwabe
3 citations
Data & Policy
Digital Marketing and Social Media
12.60
article

Joining forces for online feedback management: policy recommendations for human–AI collaboration

Dzmitry Katsiuba, Mateusz Dolata, Gerhard Schwabe
article en
3 citations

Abstract

Abstract Online customer feedback management (CFM) is becoming increasingly important for businesses. Providing timely and effective responses to guest reviews can be challenging, especially as the volume of reviews grows. This paper explores the response process and the potential for artificial intelligence (AI) augmentation in response formulation. We propose an orchestration concept for human–AI collaboration in co-writing within the hospitality industry, supported by a novel NLP-based solution that combines the strengths of both human and AI. Although complete automation of the response process remains out of reach, our findings offer practical implications for improving response speed and quality through human–AI collaboration. Additionally, we formulate policy recommendations for businesses and regulators in CFM. Our study provides transferable design knowledge for developing future CFM products.

Data & PolicyVol. 7
University of Zurich (CH)
Universität Zürich, Innosuisse - Schweizerische Agentur für Innovationsförderung
Openalex Percentile: Top 2%
Digital Marketing and Social Media
12.60
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Joining forces for online feedback management: policy recommendations for human–AI collaboration — Dzmitry Katsiuba, Mateusz Dolata, et al. · Data & Policy (2025) | TGRS Research Map | TGRS