Why is your company not robotic? The technology and human capital needed by firms to become robotic

Maria Teresa Ballestar de las Heras, Aida Garcia-Lazaro, Jorge Sainz, Ismael Sanz

Research output: Contribution to conferencePaperpeer-review

Abstract

The impact of companies’ adoption of robotics is increasingly interesting. This study aims to elucidate how the adoption of these technologies will affect companies and society. Companies that use these technologies expect to gain a competitive advantage, but robotization implies risks that must be managed by companies and governments. This research focuses on one of the most sensitive elements of this transformation process—the workforce.

First, we analyze the characteristics of the workforce and the degree of adoption of robotics using a sample of 4,640 firms with 26 years of observation. We develop a predictive model using a supervised artificial neural network multilayer perceptron (ANN-MLP) to evaluate a company’s readiness to make this transformation according to its workforce’s characteristics.

Second, we focus on the characterization and segmentation of the companies for which the ANN-MLP is unable to correctly predict the degree of adoption of robotics. This classification failure means that there are unidentified factors that determine why a company has a workforce composition and structure that do not correspond to its expected degree of robotization. For this analysis, we investigate the main business indicators of these companies and cluster them using an unsupervised artificial neural network, specifically the Kohonen self-organizing map.

Our findings will enable companies to understand the importance of transforming to robotics at the right moment, considering factors such as the optimum structure and composition of the workforce. The combination of technology and human capital is the key to boosting the efficiency of the transformation process toward robotics. At this point, a recommendation model to determine whether the company has sufficient maturity to make the transition is crucial for decision makers.

Original languageEnglish
Pages328-343
Number of pages16
DOIs
Publication statusPublished - 31 Mar 2022

Keywords

  • Artificial neural networks
  • Machine learning
  • Robotics
  • Unemployment

ASJC Scopus subject areas

  • Marketing

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