Generating innovation with environmental impact is crucial for firms to achieve sustainable eco-innovative performance. In the reference literature on open innovation, gaps still persist at the level of scarce and limited knowledge on the use of knowledge sources and flows, for the purpose of strengthening the eco-innovative performance of the bioeconomy sector. To address these caveats, this study analyses the effects of open innovation on eco-innovation, based on inbound and outbound support practices. Specifically, it aims to analyse the effects of these practices on the eco-innovative performance of bioeconomy and non-bioeconomy firms, using secondary data gathered from the Community Innovation Survey—CIS 2010 for a sample of moderately innovative countries, namely Slovakia, Spain, Hungary, Italy, Portugal and the Czech Republic. The conceptual model proposed is tested using multivariate tobit regression models, in order to ensure the accuracy and reliability required to validate empirical tests. Overall, the empirical evidence allows the conclusion that inbound and outbound practices and public policies have a positive and significant influence on the eco-innovative performance of the firms studied. The contribution provided is two-fold: (i) in theoretical terms, an operational model of open innovation inbound and outbound practices is extended, crossing financial flows and innovation directions; and (ii) in empirical terms, new light is shed on the still limited knowledge about the positive and significant effects of open innovation outbound practices on the eco-innovative performance of companies belonging to a global strategic sector—that is, the bioeconomy sector, which has renewed strategic importance in the face of global climate change.

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This study analyzes the determinant factors of eco-innovation, considering business units with different levels of technological intensity (high technology versus low technology). It aims, in the first instance, to complement the approach on the determinants of eco-innovation in the existent literature by incorporating the novelty related to the analysis of the effects arising from the adoption of the lean management principles. Specifically, it aims to analyze the effects of the previously referred to determinant factors both on the economic performance and on the innovative performance of Portuguese industrial and service companies with different levels of technological intensity (high-tech versus low-tech). The conceptual model presented is of an innovative nature, since it includes four groups of determinant factors present in the literature, namely technology, market, public policies, and cooperation relationships, and adds a fifth group of determinant factors still to be explored empirically concerning the adoption of lean management principles. In the empirical approach, five research hypotheses arising from the literature review are tested, using secondary data collected from the Community Innovation Survey (CIS)—CIS 2010 for a total sample of 334 companies, made up of 95 high-tech companies and 239 low-tech companies. The conceptual model is tested using a logistic regression method, which indicated a suitable accuracy and reliability for the purposes of empirical tests. The empirical evidence confirms that most of the groups of determinants previously identified in the literature have a significant influence on eco-innovation. In addition, the empirical evidence obtained here indicates a positive and significant effect of lean management principles on eco-innovation.

This study analyzes the productive structure of Portugal in the period 2013–2017, using indicators of localization and specialization applied to 308 Portuguese local authorities. From an empirical approach using a threshold model, the following indicators are used: (i) localization quotient; (ii) specialization coefficient; (iii) Theil entropy index; (iv) rate of industrialization; and (v) the density of establishments by business size. The selected period 2013–2017 is due to the available data concerning firms located per local authority, and the choice of threshold model is justified through the possibility of assessing the non-linear effects of specialization and diversification on productivity, considering, in simultaneous terms, different regimes per business size. Estimation of the threshold model identified a positive, statistically significant relation between industrialization and productivity. Similarly, the terms of interaction between exports and diversification, and between the former and higher education institutions, shows a catalyzing effect of productivity. In addition, the most specialized micro-firms affect productivity significantly and positively, while the least specialized have the opposite effect. Small, less specialized companies have a significant and negative effect on productivity, contrasting with less specialized, medium-sized companies, which affect productivity positively. For large firms, the impact on productivity is negative for both high and low levels of specialization, reinforcing the need to fill existing gaps in strategic diversification, as well as the vertical and horizontal integration of the activities of production chains with high value added.