Relief through digital modalities: evaluating text-based chatbots and voicebots in service failures

Purpose This study aims to examine the comparative effect that two widespread technologies, text-based chatbots and voicebots, have on perceived relief and subsequent customer outcomes in service failure settings. The moderating effect of service failure severity on this mediation relationship is also evaluated. Design/methodology/approach This research adopts an experiential design, consisting of an initial exploratory field study (Study 1), to examine how AI bot modality influences real-world customer evaluations in a naturalistic service failure setting. This was followed by two between-subjects experimental studies (Studies 2 and 3), which sought to validate the underlying mechanism (i.e. perceived relief) and test the moderating role of service failure severity under controlled conditions. Findings Study 1 provided initial empirical evidence that, in a real-world service failure context, customers who interacted with a chatbot (vs a voicebot) gave significantly higher satisfaction ratings. Study 2 provided evidence of the positive indirect effect of chatbots on customer reviews via perceived relief. Study 3 supported this mediation relationship while also demonstrating that such a relationship is moderated by the severity of the service failure. Research limitations/implications Based on the findings of this study, it would be a stretch to conclude that chatbots are invariably better. The dynamic nature of customer preferences and technological advancements necessitates continuous evaluation and adaptation of customer service strategies. Managers should regularly review the performance of both chatbots and voicebots, incorporating customer feedback and technological improvements to optimize their service delivery approaches. By understanding and applying these insights, managers can better navigate service failures, ultimately leading to improved customer outcomes. Practical implications This study used an overall measure of satisfaction as the dependent variable. Future research might examine similar effects with other dependent variables, such as customer loyalty, trust or perceived value. Investigating these additional dimensions could provide a more comprehensive understanding of how chatbots and voicebots influence various aspects of customer experience and behavior. Originality/value This study represents one of the first to empirically examine the differential effects of text-based chatbots and voicebots (bot modality) on salient customer outcomes. To the best of the authors’ knowledge, it is the first study to specifically examine their comparative effects in response to service failure settings where these AI-driven bots are predominantly used.

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Publication Details

Journal
European Journal of Marketing
Published
2026-03-12
DOI
https://doi.org/10.1108/ejm-08-2024-0693
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Relief through digital modalities: evaluating text-based chatbots and voicebots in service failures

Nicole Hartley, Felix Septianto, Luis Arango, Yunen Zhang et al.
European Journal of Marketing
AI in Service Interactions
article

Relief through digital modalities: evaluating text-based chatbots and voicebots in service failures

Nicole Hartley, Felix Septianto, Luis Arango, Yunen Zhang, Mai Nguyen
article en

Abstract

Purpose This study aims to examine the comparative effect that two widespread technologies, text-based chatbots and voicebots, have on perceived relief and subsequent customer outcomes in service failure settings. The moderating effect of service failure severity on this mediation relationship is also evaluated. Design/methodology/approach This research adopts an experiential design, consisting of an initial exploratory field study (Study 1), to examine how AI bot modality influences real-world customer evaluations in a naturalistic service failure setting. This was followed by two between-subjects experimental studies (Studies 2 and 3), which sought to validate the underlying mechanism (i.e. perceived relief) and test the moderating role of service failure severity under controlled conditions. Findings Study 1 provided initial empirical evidence that, in a real-world service failure context, customers who interacted with a chatbot (vs a voicebot) gave significantly higher satisfaction ratings. Study 2 provided evidence of the positive indirect effect of chatbots on customer reviews via perceived relief. Study 3 supported this mediation relationship while also demonstrating that such a relationship is moderated by the severity of the service failure. Research limitations/implications Based on the findings of this study, it would be a stretch to conclude that chatbots are invariably better. The dynamic nature of customer preferences and technological advancements necessitates continuous evaluation and adaptation of customer service strategies. Managers should regularly review the performance of both chatbots and voicebots, incorporating customer feedback and technological improvements to optimize their service delivery approaches. By understanding and applying these insights, managers can better navigate service failures, ultimately leading to improved customer outcomes. Practical implications This study used an overall measure of satisfaction as the dependent variable. Future research might examine similar effects with other dependent variables, such as customer loyalty, trust or perceived value. Investigating these additional dimensions could provide a more comprehensive understanding of how chatbots and voicebots influence various aspects of customer experience and behavior. Originality/value This study represents one of the first to empirically examine the differential effects of text-based chatbots and voicebots (bot modality) on salient customer outcomes. To the best of the authors’ knowledge, it is the first study to specifically examine their comparative effects in response to service failure settings where these AI-driven bots are predominantly used.

European Journal of MarketingVol. 60(13)
Griffith University (AU), University of Auckland (NZ), The University of Queensland (AU), Jiangxi University of Finance and Economics (CN)
Openalex Percentile: Top 21%
AI in Service Interactions
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