Data-driven strategies in operation management: mining user-generated content in Twitter

Jose Ramon Saura (), Domingo Ribeiro-Soriano () and Daniel Palacios-Marqués ()
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Jose Ramon Saura: Rey Juan Carlos University
Domingo Ribeiro-Soriano: Universitat de Valencia
Daniel Palacios-Marqués: Universitat Politècnica de València

Annals of Operations Research, 2024, vol. 333, issue 2, No 14, 849-869

Abstract: Abstract In recent years, the business ecosystem has focused on understanding new ways of automating, collecting, and analyzing data in order to improve products and business models. These actions allow operations management to improve prediction, value creation, optimization, and automatization. In this study, we develop a novel methodology based on data-mining techniques and apply it to identify insights regarding the characteristics of new business models in operations management. The data analyzed in the present study are user-generated content from Twitter. The results are validated using the methods based on Computer-Aided Text Analysis. Specifically, a sentimental analysis with TextBlob on which experiments are performed using vector classifier, multinomial naïve Bayes, logistic regression, and random forest classifier is used. Then, a Latent Dirichlet Allocation is applied to separate the sample into topics based on sentiments to calculate keyness and p-value. Finally, these results are analyzed with a textual analysis developed in Python. Based on the results, we identify 8 topics, of which 5 are positive (Automation, Data, Forecasting, Mobile accessibility and Employee experiences), 1 topic is negative (Intelligence Security), and 2 topics are neutral (Operational CRM, Digital teams). The paper concludes with a discussion of the main characteristics of the business models in the OM sector that use DDI. In addition, we formulate 26 research questions to be explored in future studies.

Keywords: Data-driven strategies; Operation Management; User-generated content; Twitter (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-04776-3

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