Evidence map›Paper›PMID 42073883›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

Customer Mistreatment and Venting to Conversational AI: Emotional Exhaustion as Mediator and Trust in Conversational AI as Moderator.

Jialin Cheng, Jingxuan Jiang

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jialin ChengSchool of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Jingxuan JiangCollege of Business, Shanghai University of Finance and Economics, Shanghai 200433, China.

Funding

National Natural Science Foundation of China 72402215Philosophy and Social Science Foundation of Zhejiang Province of China 26NDJC037YBMS
6 · The paper itself

Abstract

Artificial intelligence (AI) technologies, such as service robots, substantially influence frontline employees in the hospitality sector. This study highlights that conversational AI (CAI) may function as a viable outlet for hospitality workers to vent negative work-related issues. This function is particularly relevant because employees in this industry frequently experience customer mistreatment. Grounded in conservation of resources theory, we conceptualize venting to CAI as a resource-replenishing coping strategy triggered by customer mistreatment. Further, we theorize that this relationship is mediated by emotional exhaustion and moderated by trust in CAI, thereby strengthening the indirect effect. We collected and analyzed two-wave data from 394 frontline employees with CAI experience in the hospitality industry. The results indicate that customer mistreatment indirectly impacted frontline employees' venting behaviors towards CAI, with emotional exhaustion functioning as the mediating mechanism. This indirect effect is particularly pronounced when employees exhibit high levels of trust in CAI. These findings offer practical insights for hospitality organizations aiming to leverage CAI as an accessible, low-risk tool for supporting employee emotional well-being and mitigating the negative consequences of customer mistreatment.

Indexed as

customer mistreatmentemotional exhaustiontrust in CAIventing to conversational artificial intelligence (CAI)

Identifiers

PMID42073883
PMCPMC13113624

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.