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Using Traditional Text Analysis and Large Language Models in Service Failure and Recovery

Francisco Villarroel Ordenes et al.

Journal of Service Research2025https://doi.org/10.1177/10946705241307678article
AJG 4ABDC A*
Weight
0.48

Abstract

Service failure and recovery (SFR) typically involves one or more people (or machines) talking or writing to each other in a goal-directed conversation. While SFR represents a prime context to understand how language reflects and shapes the service experience, this subfield has only begun to apply text analysis methods and language theories to this context. This tutorial offers a methodological guide for traditional text analysis methods and large language models and suggests some future research paths in SFR. We also provide user-friendly workflow repositories, in Python and KNIME Analytics, that researchers with (and without) coding experience can use. In doing so, we hope to encourage the next wave of text analysis in SFR research.

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https://doi.org/https://doi.org/10.1177/10946705241307678

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@article{francisco2025,
  title        = {{Using Traditional Text Analysis and Large Language Models in Service Failure and Recovery}},
  author       = {Francisco Villarroel Ordenes et al.},
  journal      = {Journal of Service Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/10946705241307678},
}

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Using Traditional Text Analysis and Large Language Models in Service Failure and Recovery

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Evidence weight

0.48

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.41 × 0.4 = 0.16
M · momentum0.63 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.