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

Conditionally Accepted at Journal of Service Research

17 Pages Posted: 14 Apr 2025

See all articles by Francisco Villarroel Ordenes

Francisco Villarroel Ordenes

Alma Mater Studiorum University of Bologna

Grant M. Packard

York University - Schulich School of Business

Jochen Hartmann

TUM School of Management,Technical University of Munich

Davide Proserpio

Marshall School of Business - University of Southern California

Date Written: December 16, 2024

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 sub-field 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 (LLMs), 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. 

Keywords: Service Failure and Recovery, Text Mining, NLP, Language Theory, Large Language Models (LLMs), Machine Learning

Suggested Citation

Villarroel Ordenes, Francisco and Packard, Grant M. and Hartmann, Jochen and Proserpio, Davide, Using Traditional Text Analysis and Large Language Models in Service Failure and Recovery (December 16, 2024). Conditionally Accepted at Journal of Service Research, Available at SSRN: https://ssrn.com/abstract=5139918 or http://dx.doi.org/10.2139/ssrn.5139918

Francisco Villarroel Ordenes (Contact Author)

Alma Mater Studiorum University of Bologna ( email )

Bologna
Italy

Grant M. Packard

York University - Schulich School of Business ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada
416-736-2100 x77199 (Phone)

HOME PAGE: http://https://www.grantpackard.com

Jochen Hartmann

TUM School of Management,Technical University of Munich ( email )

Arcisstrasse 21
Munchen, 80333
Germany

Davide Proserpio

Marshall School of Business - University of Southern California ( email )

701 Exposition Blvd
Los Angeles, CA 90089
United States

HOME PAGE: http://dadepro.github.io/

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