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

Jose Ramon Saura1, Domingo Ribeiro-Soriano2, Daniel Palacios-Marqués3

  • 1Rey Juan Carlos University, Madrid, Spain.

Annals of Operations Research
|June 15, 2022
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Summary
This summary is machine-generated.

This study uses data mining on Twitter data to reveal characteristics of new business models in operations management. Key findings identify positive, negative, and neutral topics related to data-driven innovation.

Keywords:
Data-driven strategiesOperation ManagementTwitterUser-generated content

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Area of Science:

  • Operations Management
  • Data Science
  • Business Analytics

Background:

  • The business ecosystem increasingly focuses on data automation, collection, and analysis for product and business model improvement.
  • Operations management benefits from enhanced prediction, value creation, optimization, and automation through data-driven insights.

Purpose of the Study:

  • To develop and apply a novel data-mining methodology to identify characteristics of new business models in operations management.
  • To analyze user-generated content from Twitter to understand trends in business models.

Main Methods:

  • Data mining techniques applied to Twitter data.
  • Sentiment analysis using TextBlob with various classifiers (vector, multinomial naïve Bayes, logistic regression, random forest).
  • Latent Dirichlet Allocation for topic separation and keyness/p-value calculation.
  • Computer-Aided Text Analysis and textual analysis in Python for validation.

Main Results:

  • Identification of 8 distinct topics from Twitter data.
  • Positive topics include Automation, Data, Forecasting, Mobile accessibility, and Employee experiences.
  • Negative topics include Intelligence Security, while neutral topics are Operational CRM and Digital teams.

Conclusions:

  • The study characterizes business models in the Operations Management (OM) sector leveraging Data-Driven Innovation (DDI).
  • Discussion highlights key features of DDI in OM.
  • 26 research questions are proposed for future exploration in this domain.