Analysis of Multilevel Factors Mobilizing the Spectrum of Interorganizational Knowledge Sharing for Facilitating Digital Transformation at Scale: Qualitative Study

TL;DR

This study explores how different levels of factors influence knowledge sharing between organizations during digital transformation, offering a model to guide effective collaboration.

Contribution

The study introduces an integrative model explaining multilevel factors that shape interorganizational knowledge-sharing ecosystems during digital transformation.

Findings

Macro-level factors like policy and technology strategies influence the initiation of interorganizational partnerships.

Interorganizational mechanisms such as governance and coordination practices sustain knowledge-sharing relationships.

Individual motivations and organizational absorptive capacity affect how knowledge is shared and utilized.

Abstract

Interorganizational knowledge sharing is vital for scaling digital transformation efforts that span multiple organizations and system-wide change. However, existing frameworks provide limited insights into the cross-level dynamics that shape how learning ecosystems emerge, evolve, and operate across multiple organizations. This gap leaves practitioners without clear guidance on how multilevel contextual conditions and mechanisms interact to influence the development and sustainability of formal and informal knowledge-sharing relationships.

This study aimed to examine how knowledge is orchestrated across organizations in the digital transformation of health care, identifying key factors that foster an evolving interorganizational learning ecosystem. We developed an integrative model that explains how these influences give rise to diverse modes of collaboration and partnership.

We adopted a qualitative approach using a multilevel perspective to examine visions and experiences across individual, organizational, interorganizational, and sectoral levels. Drawing on a formative evaluation (2018‐2023) of England’s Global Digital Exemplar (GDE) program, we used multiple case studies and conducted interviews with experts both within and beyond organizational settings for data collection and adopted a grounded theory approach to analyze the data.

The study identified a set of interconnected factors operating at the macroenvironmental, interorganizational, organizational, and individual levels that influence how interorganizational relationships and partnerships are initiated, structured, and sustained. Macro-level influences included policy developments, program mandates, technology supplier strategies, and intermediary actions. Interorganizational mechanisms involved relational recognition, collective identity, governance structures, proximity, and coordination practices. Organizational factors included external search strategies, absorptive capacity, past collaboration experience, and internal knowledge routines. Individual-level mechanisms encompassed intrinsic and extrinsic motivations as well as personal inhibitors. Synthesizing these findings, we have proposed an integrative model that positions relationship type along a 2D spectrum (formal-informal, internal-external origins) and illustrates how different factors trigger, mandate, control, and enable the evolution of an interorganizational learning ecosystem.

This study advances the theoretical understanding of learning ecosystems by explaining how multilevel contextual conditions activate mechanisms that give rise to diverse and evolving forms of interorganizational collaboration. Practically, we offer diagnostic and reflective tools that support policymakers and practitioners in assessing contextual conditions, selecting appropriate knowledge-sharing mechanisms, and monitoring how learning ecosystems develop over time. Our findings provide actionable guidance for designing and sustaining interorganizational learning systems capable of supporting digital transformation at scale.

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