Unraveling the Digital Transformation Paths of Industrial Districts: The Role of Relational Learning in the Management of Contrasting Imprints

Purpose: Understa nding digital transformation of SMEs belonging to industrial district is fundamental. Yet, there are disagreeing views regarding how these firms select specific technological trajectories over other available ones. Building on Imprinting Theory, the research explores how Relational Learning is a key capability to balance diverse contrasting imprints affecting digital transformation trajectories. Design/Methodology/Approach: 6 purposefully selected case study concerning SMEs operating within the Prato Textile District were considered. Data were collected through 24 interviews with 18 key informants. A Grounded Theory based inductive approach was selected to interpret the encoded data and draw managerial implications. Findings: Relational Learning capability allows SMEs to manage imprints, such as the ones coming from MNEs and the ones from the environment. This organizational capability allows managers to prioritize the right imprint during a susceptibility period. SMEs embedded of Relational Learning are more prone to digitally transform by embracing disruptive innovations and are also more disposed to become imprinters. Originality/Value: Literature about Digital Transformation in Industrial Districts lacks a holistic investigation observing how technologies diffuse in these contexts. Specifically, why a firm selects a specific digital transformation path need to be underpinned.