Paper Number
2365
Paper Type
Completed
Description
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.
Recommended Citation
Katsiuba, Dzmitry; Kew, Tannon; Dolata, Mateusz; Gurica, Matej; and Schwabe, Gerhard, "Artificially Human: Examining the Potential of Text-Generating Technologies in Online Customer Feedback Management" (2023). ICIS 2023 Proceedings. 12.
https://aisel.aisnet.org/icis2023/socmedia_digcollab/socmedia_digcollab/12
Artificially Human: Examining the Potential of Text-Generating Technologies in Online Customer Feedback Management
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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