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Cited by 57AI in Service InteractionsCustomer Service Quality and LoyaltySocial Robot Interaction and HRI

Seeking empathy or suggesting a solution? Effects of chatbot messages on service failure recovery

Open access
Haupt, Martin, Rozumowski, Anna, Freidank, Jan, Haas, Alexander·2023·Electronic Markets
DOI 10.1007/s12525-023-00673-0
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Abstract

Chatbots as prominent form of conversational agents are increasingly implemented as a user interface for digital customer-firm interactions on digital platforms and electronic markets, but they often fail to deliver suitable responses to user requests. In turn, individuals are left dissatisfied and turn away from chatbots, which harms successful chatbot implementation and ultimately firm’s service performance. Based on the stereotype content model, this paper explores the impact of two universally usable failure recovery messages as a strategy to preserve users’ post-recovery satisfaction and chatbot re-use intentions. Results of three experiments show that chatbot recovery messages have a positive effect on recovery responses, mediated by different elicited social cognitions. In particular, a solution-oriented message elicits stronger competence evaluations, whereas an empathy-seeking message leads to stronger warmth evaluations. The preference for one of these message types over the other depends on failure attribution and failure frequency. This study provides meaningful insights for chatbot technology developers and marketers seeking to understand and improve customer experience with digital conversational agents in a cost-effective way.

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  • The selection of the e-commerce domain for the experiment was driven by the industry's rapid transition toward replacing or supplementing human customer support agents with CAs.

    “The selection of this domain for the experiment was driven by the industry's rapid transition toward replacing or supplementing human customer support agents with CAs (Han et al., 2023;Haupt et al., 2023).”

    From Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies (2026) · cites this paper as Haupt et al., 2023

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1 of the 81 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

  1. Brendel, A. B., Greve, M., Diederich, S., Bührke, J., & Kolbe, L. M. (2020). ‘You are an Idiot!’ – How Conversational Agent Communication Patterns Influence Frustration and Harassment. AMCIS 2020 Proceedings. 13.
  2. McKee, K. R., Bai, X., & Fiske, S. T. (2022). Warmth and competence in human-agent cooperation. AAMAS Proceedings.898–907
  3. Mozafari, N., Weiger, W. H., & Hammerschmidt, M. (2021). That’s so embarrassing! When not to design for social presence in human–chatbot. ICIS 2021 Proceedings, 5. https://aisel.aisnet.org/icis2021/hci_robot/hci_robot/5
  4. Seeger, A.‑M., & Heinzl, A. (2021). Chatbots often fail! Can anthropomorphic design mitigate trust loss in conversational agents for customer service? ECIS Proceedings, 12.

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Authors
Haupt, M., Rozumowski, A., Freidank, J., & Haas, A.
Affiliations
  • Justus-Liebig-Universität Gießen (DE)
  • FHNW University of Applied Sciences and Arts Northwestern Switzerland (CH)
  • Technische Hochschule Mittelhessen (DE)
Corresponding author
  • Haupt, Martin
  • Rozumowski, Anna
  • Freidank, Jan
  • Haas, Alexander
ORCID
Venue
Electronic Markets
Publisher
Springer Science and Business Media LLC
Published
2023-11-04
Volume
33
Issue
1
ISSN
  • 1019-6781 (ISSN-L)
  • 1422-8890
DOI
10.1007/s12525-023-00673-0
OpenAlex
W4388329942
Semantic Scholar
d22dd7c2e25a5a77dbb03ddf9950a215ecb1830b
Cited by
57
References
81
Citations by year
2026: 29 · 2025: 21 · 2024: 7
FWCI
5.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Electronic Markets (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Econstor (Econstor) (submittedVersion, cc-by)
Funders
Justus-Liebig-Universität Gießen
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Keywords
chatbots, service recovery, empathy, stereotype content model, competency assessment, failure attribution, failure frequency, customer satisfaction, customer experience

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