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Article
Peer-Review Record

The Impact of AI’s Response Method on Service Recovery Satisfaction in the Context of Service Failure

Sustainability 2023, 15(4), 3294; https://doi.org/10.3390/su15043294
by Zengmao Yang *, Jinlai Zhou and Hongjun Yang
Sustainability 2023, 15(4), 3294; https://doi.org/10.3390/su15043294
Submission received: 19 October 2022 / Revised: 19 November 2022 / Accepted: 9 February 2023 / Published: 10 February 2023
(This article belongs to the Section Health, Well-Being and Sustainability)

Round 1

Reviewer 1 Report

I think that this is a topical issue, therefore the study carried out is perfectly justified.

I think it is a well-structured work, it brings novelty, it is well documented and the conclusions are coherent

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report


Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

This paper investigates the possible benefits of scripted AI responses to failures which communicate sincere apology through "self-deprecating humor". Authors fail to ground this research in the extensive literature on sincere apology. Instead, there are multiple definitions of self-deprecating humor offered, which are repetitive, and which consist of tautologous definitions such as 'self-deprecating humor consists of two parts, self deprecation and humor'. Cited resources recruited to support the introduction of the thesis, that AI can support service industry efforts through self-deprecating humor when making mistakes, are not relevant to the context of this discussion, e.g. one reference is to a resource focusing on business to business transactions when the focus of the statement that this reference is supposed to support is on "The service industry", and another reference is to a resource focusing on machines making moral decisions when the focus of the statement is errors caused by "non-habitual environments". Why is this reference to a discussion of people rejecting machine-made moral decisions? Where is the evidence that AI “is more accurate and efficient at work tasks”? Which tasks? All? Some? The problem here is that this use of references signals poor scholarship, which causes the reader to feel suspicious about any claims made in this paper. It is important that references refer to sources which directly and clearly support the assertions made in the paper. Another problem is that the paper neglects extensive research that might support the main idea, namely on sincere apology and cooperation, with the self-deprecating humor a means for expressing sincerity of regret for mistakes that demand apologies. Overall, the paper is receptive, shallow, incoherent, and poorly researched. Because there is no reason to recommend that this paper is read, there is no reason to recommend its publication. Moreover, this paper has NOTHING to do with Sustainability. Nothing. It does not belong in this journal, as it does not focus on the main focus of this journal. CONCLUSION: The advise here is to REJECT this paper.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

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