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Artificially Human: Examining the Potential of Text-Generating Technologies in Online Customer Feedback Management


Katsiuba, Dzmitry; Kew, Tannon; Dolata, Mateusz; Gurica, Matej; Schwabe, Gerhard (2023). Artificially Human: Examining the Potential of Text-Generating Technologies in Online Customer Feedback Management. In: International Conference on Information Systems, ICIS 2023, Hyderabad, India, 9 December 2023 - 13 December 2023. Association for Information Systems, 12.

Abstract

Online customer feedback management plays an increasingly important role for businesses. Yet providing customers with good responses to their reviews can be challenging, especially as the number of reviews grows. This paper explores the potential of using generative AI to formulate responses to customer reviews. Using advanced NLP techniques, we generated responses to reviews in different authoring configurations. To compare the communicative effectiveness of AI-generated and human-written responses, we conducted an online experiment with 502 participants. The results show that a Large Language Model performed remarkably well in this context. By providing concrete evidence of the quality of AI-generated responses, we contribute to the growing body of knowledge in this area. Our findings may have implications for businesses seeking to improve their customer feedback management strategies, and for researchers interested in the intersection of AI and customer feedback. This opens opportunities for practical applications of NLP and for further IS research.

Abstract

Online customer feedback management plays an increasingly important role for businesses. Yet providing customers with good responses to their reviews can be challenging, especially as the number of reviews grows. This paper explores the potential of using generative AI to formulate responses to customer reviews. Using advanced NLP techniques, we generated responses to reviews in different authoring configurations. To compare the communicative effectiveness of AI-generated and human-written responses, we conducted an online experiment with 502 participants. The results show that a Large Language Model performed remarkably well in this context. By providing concrete evidence of the quality of AI-generated responses, we contribute to the growing body of knowledge in this area. Our findings may have implications for businesses seeking to improve their customer feedback management strategies, and for researchers interested in the intersection of AI and customer feedback. This opens opportunities for practical applications of NLP and for further IS research.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:Electronic Word-of-Mouth, Online feedback management, Managerial response, Artificial intelligence, Generative AI
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:13 December 2023
Deposited On:01 Feb 2024 10:03
Last Modified:07 May 2024 15:10
Publisher:Association for Information Systems
Series Name:International Conference on Information Systems
Number:2365
OA Status:Closed
Official URL:https://aisel.aisnet.org/icis2023/socmedia_digcollab/socmedia_digcollab/12/
Other Identification Number:merlin-id:24318