The objective of this research was to enhance the role of relational learning in promoting radical changes within traditional industrial districts. Considering digital transformation with new 4.0 technologies as a radical innovation, the study sought to understand new possible trajectories capable of promoting these radical changes in contexts permeated by strong inertia. Three articles form the core of this research. The first paper conducts a bibliometric analysis to understand the expansion of the academic discourse on districts and innovation. The second article explores the dynamics of radical digitization processes within industrial districts, emphasizing the role of multinational firms and relational learning in shaping technology adoption decisions. The third article links digital transformation and relational learning to green supply chains, emphasizing the need for collaborative efforts to achieve sustainability in textile supply chains.

Tech Upgrading in Industrial Districts: Crafting Relational Bridges for the Future

MARRUCCI, ANNA
2024

Abstract

The objective of this research was to enhance the role of relational learning in promoting radical changes within traditional industrial districts. Considering digital transformation with new 4.0 technologies as a radical innovation, the study sought to understand new possible trajectories capable of promoting these radical changes in contexts permeated by strong inertia. Three articles form the core of this research. The first paper conducts a bibliometric analysis to understand the expansion of the academic discourse on districts and innovation. The second article explores the dynamics of radical digitization processes within industrial districts, emphasizing the role of multinational firms and relational learning in shaping technology adoption decisions. The third article links digital transformation and relational learning to green supply chains, emphasizing the need for collaborative efforts to achieve sustainability in textile supply chains.
29-mar-2024
Italiano
digital transformation
industrial district
relational learning
Ciappei, Cristiano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/215939
Il codice NBN di questa tesi è URN:NBN:IT:UNIPI-215939