With the diffusion of digitalization technologies that allow to analyze high volumes of operational data in real time, data-driven decision-making can now entail benefits on organizational performance not only at the strategic, but also at the operational level. However, little is known about the individual and organizational preconditions that make this approach possible on the shop-floor. Through a quantitative survey issued to 101 Italian auto supplier firms and regression models, this article investigates the antecedents of datadriven decision making at the micro- and meso level, by analyzing the effect of involving production workers in continuous improvement, and moderating it with the skill gaps of Team Leaders and Supervisors. The results show that production workers’ involvement has a positive impact on the adoption of data-driven decision-making at the plant level; notwithstanding, when Team Leaders suffer skill gaps, the effect becomes negative. Therefore, the involvement of production workers for the sense-making of operational data might be ineffective because of skill gaps in those who are in charge of recombining their context-dependent knowledge with data analysis to create knowledge and make operational decisions. The same results were not confirmed for Supervisors, suggesting a possible delayering in favour of a central role of Team Leaders, yet to be tested with further qualitative empirical research. This study contributes to work organization and knowledge management literatures using the concept of organizational knowing cycle for operational decision-making, disentangling the roles of different organizational levels (production workers and first-line managers) in sense making of, and knowledge creation from, the analysis of operational data. As such, this study positions the digital transformation as a “skill-biased technological and organizational change”. In this vein, the present study has relevant implications for practitioners, too, as it highlights that specific operational employees’ capabilities can limit the benefits of digitalization, hindering the diffusion of data-driven decision-making. Recommendations are offered to HR managers and education policymakers, who are called to foster the diffusion of high-involvement managerial practices and promote an urgent upskilling of first-line managerial roles through ad-hoc education and training paths.

Are Team Leaders’ Skill Gaps Hindering the Diffusion of Data-Driven Decision-Making in Manufacturing? A quantitative micro and meso-level analysis / Colombari, Ruggero; Neirotti, Paolo. - ELETTRONICO. - (2021), pp. 1160-1179. ((Intervento presentato al convegno IFKAD 2021 - Managing Knowledge in Uncertain Times tenutosi a Roma (Italia) nel 01/09/2021-03/09/2021.

Are Team Leaders’ Skill Gaps Hindering the Diffusion of Data-Driven Decision-Making in Manufacturing? A quantitative micro and meso-level analysis

Colombari, Ruggero;Neirotti, Paolo
2021

Abstract

With the diffusion of digitalization technologies that allow to analyze high volumes of operational data in real time, data-driven decision-making can now entail benefits on organizational performance not only at the strategic, but also at the operational level. However, little is known about the individual and organizational preconditions that make this approach possible on the shop-floor. Through a quantitative survey issued to 101 Italian auto supplier firms and regression models, this article investigates the antecedents of datadriven decision making at the micro- and meso level, by analyzing the effect of involving production workers in continuous improvement, and moderating it with the skill gaps of Team Leaders and Supervisors. The results show that production workers’ involvement has a positive impact on the adoption of data-driven decision-making at the plant level; notwithstanding, when Team Leaders suffer skill gaps, the effect becomes negative. Therefore, the involvement of production workers for the sense-making of operational data might be ineffective because of skill gaps in those who are in charge of recombining their context-dependent knowledge with data analysis to create knowledge and make operational decisions. The same results were not confirmed for Supervisors, suggesting a possible delayering in favour of a central role of Team Leaders, yet to be tested with further qualitative empirical research. This study contributes to work organization and knowledge management literatures using the concept of organizational knowing cycle for operational decision-making, disentangling the roles of different organizational levels (production workers and first-line managers) in sense making of, and knowledge creation from, the analysis of operational data. As such, this study positions the digital transformation as a “skill-biased technological and organizational change”. In this vein, the present study has relevant implications for practitioners, too, as it highlights that specific operational employees’ capabilities can limit the benefits of digitalization, hindering the diffusion of data-driven decision-making. Recommendations are offered to HR managers and education policymakers, who are called to foster the diffusion of high-involvement managerial practices and promote an urgent upskilling of first-line managerial roles through ad-hoc education and training paths.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2965714