Livieratos, Antonios D., Tsekouras, George, Vanhaverbeke, Wim and Tsiliki, Georgia (2026) Unlocking SME innovation success through sequenced collaboration. Small Business Economics. ISSN 0921-898X (Print), 1573-0913 (Online) (doi:10.1007/s11187-025-01156-6)

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Abstract

This study examines how small and medium-sized enterprises (SMEs) sequence their open innovation (OI) activities over the course of individual innovation projects. Moving beyond static firm-level analyses, we explore the dynamics of partner engagement and value capture in 106 European SMEs. Using a dataset of 500 OI activities—termed “OI moves”—we identify patterns that combine partner type and exploitation mode. By comparing more and less successful SMEs, we reveal that sequencing plays a critical role in innovation outcomes. Successful SMEs tend to engage R&D service provid-ers early, prioritize internal exploitation initially, and later transition to co- and external exploitation. In con-trast, less successful SMEs rely prematurely on external exploitation and fail to retain value from their innovation efforts. Our dynamic, journey-based approach advances the predominantly static treatment of OI in prior research by operationalizing OI as sequences of linked activities rather than isolated collaboration choices. This enables us to identify a limited set of recurrent pathways associated with successful outcomes, as well as distinct pathways that consistently lead to unsuccessful outcomes. We also highlight the underexplored role of exploitation modes in OI: not just which partners SMEs engage, but when and under which value-capture logic. The sequencing of inter-nal, joint, and external exploitation emerges as a key dif-ferentiator between successful and less successful SME innovation strategies. For theory, the study contributes a dynamic process perspective to OI research, demonstrat-ing that value capture is path-dependent and shaped by the temporal ordering of OI moves. For practice, the find-ings provide actionable guidance of steps for SMEs to fol-low in order to be successful with OI.

Item Type:	Article
Additional Information:	Open access funding provided by HEAL-Link Greece. This research is based on data collected by the INSPIRE project (H2020 project under grant agreement No 691440) funded by the European Commission. The authors would like to thank the following.
Uncontrolled Keywords:	Open Innovation, SMEs, machine learning, OI moves, innovation pathways, innovation journeys, innovation sequencing, innovation strategy
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HB Economic Theory H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Faculty / School / Research Centre / Research Group:	Greenwich Business School Greenwich Business School > School of Business, Operations and Strategy
Last Modified:	12 Jan 2026 09:43
URI:	https://gala.gre.ac.uk/id/eprint/52268

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