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Innovation ecosystems (IEs) have attracted the attention of policymakers and researchers because of their potential to positively affect territories, creating shared value. However, due to the fragmentation of IEs, how this happens in different IEs has been explored only partially. This research aims to bridge this gap, aiming to support policymakers in understanding how to foster shared value in diverse IEs.

The paper identifies, based on the literature, two “drivers of aggregation” of IE's actors as key dimensions characterizing shared value in IEs, namely physical proximity and dominant issue. If these are combined, three archetypes emerge: Hub- and Chain-Driven, Place-Driven, Competence- and Issue-Driven IEs.Then, elements useful for understanding shared value creation in these archetypes are framed and studied in real cases.

Results reveal that aggregation drivers affect shared value creation, which differ among archetypes: in Competence- and Issue-Driven IEs alignment is challenged by the low physical proximity, which in Place-Driven IEs is high, but not enough to grant shared value; in Hub- and Chain-Driven IEs, the hub is the orchestrator, representing both a driver and a risk.

Differences in shared value creation processes relate to the set-up of the IE, which has relevant implications for policy definition. In Competence- and Issue-Driven IEs, policies at diverse levels align in funding and promoting the IE; in Place-Driven IEs, policies support anchors' development on-site; in Hub- and Chain-Driven IEs, policies, sometimes absent, should foster partnerships for projects for the territory, IE's enlargement and resilience.

This study provides theoretical and empirical insights into how firms can enhance their performance strategically and financially by integrating learning and transformational capabilities into research and development (R&D) activities based on the dynamic capabilities approach. To achieve this, the authors propose that transformational capabilities in R&D mediate the relationship between learning capabilities in R&D and competitive advantage.

Using a purposive sampling technique and standardized questionnaires, data were collected from 118 R&D and senior managers. To analyze the data, multiple regression analysis, along with SPSS PROCESS macro (Model 4), was used.

The results support the theoretical assumption that there are direct and indirect positive and significant effects between learning capabilities in R&D and competitive advantage as mediated by transformational R&D capabilities.

This study explores R&D from a dynamic capabilities perspective and suggests that learning capabilities should come first to enhance businesses’ competitive advantage. Furthermore, transformational R&D capabilities can explain the relationship between learning capabilities in R&D and competitive advantage. The authors recommend that researchers should investigate the contributions of R&D to promote competitive advantage.