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Building Capacity for Digital Transformation: Strategic Priorities and Gaps in the Pharmaceutical Ecosystem

Authors: Christina Rettig; Wim Vanhaverbeke;

Building Capacity for Digital Transformation: Strategic Priorities and Gaps in the Pharmaceutical Ecosystem

Abstract

This study explores how actors in ecosystems shaped by non-digital incumbents with legacy business models build the capacities required for digital transformation, using the pharmaceutical industry as a case in point. As the pharmaceutical business model is tightly interwoven and rooted in a pre-digital era, transformation efforts depend on the coordinated development of capacities that extend beyond their traditional core. While previous research has largely focused on individual organizations, it has often neglected the ecosystem perspective – which is particularly relevant for digital transformation, where a wide range of actors must coordinate across boundaries. To address this gap, we identify five capacity dimensions in the literature and contextualize how they are prioritized, interrelated, and shared across the ecosystem over time. Our findings reveal that although pharma is a well-established ecosystem with collaborative structures, digital transformation remains largely an actor-level effort: Technical and operational aspects are addressed early on but mostly lack cross-actor integration, regulatory and financial capacity follow selectively, and organizational capacity is addressed last, if at all. These patterns reflect the absence of structured collaboration at the ecosystem level, with partnerships often transactional and driven by short-term needs. The study contributes by identifying a boundary to ecosystem theory, showing that collaborative capacity building for digital transformation does not naturally emerge in historically non-digital environments. Practical implications include entry points for coordination and integration through internal roles, intermediary platforms, shared benchmarks, and holistic planning approaches.

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Keywords

Computer. Automation, Engineering sciences. Technology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
gold