Intellectual capital is an overarching concept that includes the intangible, human-related factors that are relevant to the innovation process, such as human capital and social capital. In the present study, intellectual capital was assessed by indicators measuring different aspects of human and social capital. Factor analysis demonstrated the existence of three underlying factors, with all variables of the model having important contributions to them. A linear regression analysis indicated that 8 out of the 12 variables of intellectual capital used have a statistically significant impact on the measure of innovation output. These findings were discussed and their implications for policy were considered. The paper provides research evidence on the importance of intellectual capital for innovation output and discusses potential ways to achieve smart, sustainable and inclusive growth in the context of the next generation of sustainable smart specialisation strategies.

The main aim of this paper is to demonstrate that clusters can support the sustainable development of rural areas through the creation of shared value. This is done via the close exam-ination of six different cases of rural clusters in Greece, Italy, Germany, Poland, Denmark, and Sweden. Qualitative as well as quantitative data were taken from the clusters, which demonstrated that their main business approaches naturally coincided with the creation of economic, social, and environmental benefits for the local communities in which they operated. The case clusters were created in a top-down manner, aimed at boosting regional R&D activities and making the local economy more competitive and more sustainable. However, private initiative took over and al-lowed these clusters to flourish because meeting the regions’ economic, social, and environmental needs successfully coincided with the target of the clusters’ own development and profitability. The results show that clusters, with their potential for shared value creation, can constitute a powerful engine for the revitalisation and development of rural areas, addressing the significant challenges which they are currently facing.

Innovation systems consist of different organisations from the quadruple helix, as well as the interactions and linkages between them. Smart technologies and ICT play a key role in the efficiency of systems. At the same time, regional scale is considered crucial for studying innovation in systems. However, the lack of many important data at the regional level compounds the efforts to study them. The paper proposes a novel methodological approach to the regionalisation of national-level indicators in order to address this issue. This is based on the model fit approach, using regressions to “regionalise” national-level indicators based on similar indicators that are available. The approach is tested on the data for Greek NUTS 2 regions and produces regional-level estimates for four innovation indicators, based on four available indicators that are found to be strongly correlated to them. However, the same approach can be used for any EU country or the whole of the EU. The results, their prospects for future research, and potential applications are considered. Overall, the availability of regional-level indicators is considered crucial for the formulation of impactful development policies.

The significance of Regional Innovation Systems (RIS) as a strategic tool for enhancing a region’s competitiveness has been increasingly recognized. This paper presents a model of RIS that was developed using the system dynamics (SD) methodology. The goal of this model is to amalgamate the systemic approach with computer modeling and simulation disciplines into a comprehensive dynamic framework for analyzing RIS. Within this framework, the paper explores the impact of smart technologies on regional development through the RIS. Specifically, the SD model serves as an ‘experimental tool’ for conducting extensive what-if scenario analyses concerning smart technologies. The efficacy of these technologies is examined in terms of their dynamic influence on regional development, with insights derived from simulation outcomes. Data from two Greek regions provides a strategic analysis over a designated time horizon.