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Purpose: This article investigates innovation efficiency in European Union (EU) countries and addresses methodological inconsistencies in previous research. It evaluates how efficiently national innovation systems (NISs) convert innovation-related inputs into measurable outputs, with the aim of improving the reliability and interpretability of efficiency assessments.Design/Methodology/Approach: To identify a parsimonious and statistically relevant set of indicators, the study employs Multi-Cluster Feature Selection (MCFS), a hybrid method that combines unsupervised clustering with the supervised Least Absolute Shrinkage and Selection Operator (LASSO). The technique is applied to longitudinal data derived from the European Innovation Scoreboard (EIS), resulting in a consistent subset of thirteen indicators encompassing key stages of the innovation process. Following indicator selection, a two-stage Data Envelopment Analysis (DEA) model is applied to assess efficiency at both the technological/ knowledge-production stage and the commercialisation stage. This approach supports differentiation between countries that are efficient in generating knowledge outputs and those that are effective in converting these outputs into economic results.Findings: The findings indicate substantial variation in innovation efficiency across EU countries. Few countries achieve high efficiency at both stages, highlighting the difficulty of sustaining performance across the entire innovation value chain. The analysis reveals persistent inefficiencies, particularly at the commercialisation stage, consistent with previousresearch emphasising structural barriers to translating research and development outputs into economic gains. The results also demonstrate that differences in country rankings reported in the literature are often attributable to differences in indicator selection and DEA model specification. Even among studies using similar DEA frameworks, variation in indicator inclusion leads to different classifications of country performance.Practical Implications: The methodological choices improve comparability across countries and over time, while also reducing complexity. The two-stage DEA structure provides policymakers with further insight into the internal functioning of national innovation systems and the sources of inefficiency, particularly at the commercialisation stage. This enables more targeted policy interventions that distinguish between weaknesses in knowledge production and weaknesses in the economic exploitation of innovation outputs.Originality/Value: This study contributes to the literature by introducing MCFS into the field of innovation efficiency assessment and by offering a streamlined, empirically justified set of indicators suitable for DEA applications. By combining a data-driven feature selection approach with a two-stage DEA model, the article addresses methodological fragmentation in previous research and provides a more transparent and replicable framework for evaluating national innovation systems. Namen: članek preučuje inovacijsko učinkovitost držav članic Evropske unije (EU) in obravnava metodološke nedoslednosti v dosedanjih raziskavah. Ovrednoti, kako učinkovito nacionalni inovacijski sistemi (NIS) pretvarjajo vložke, povezane z inovacijami, v merljive rezultate, s ciljem izboljšati zanesljivost in interpretabilnost ocen učinkovitosti.Zasnova/metodologija/pristop: za določitev statistično relevantnega nabora kazalnikov študija uporablja metodo MCFS (Multi-Cluster Feature Selection – MCFS), ki je hibridna metoda, ki združuje nenadzorovano razvrščanje v skupine in nadzorovano regresijo (Least Absolute Shrinkage and Selection Operator – LASSO). Tehnika je uporabljena na longitudinalnih podatkih iz Evropskega inovacijskega pregleda (European Innovation Scoreboard – EIS), kar je privedlo do konsistentnega nabora trinajstihkazalnikov, ki zajemajo ključne stopnje inovacijskega procesa. Po izbiri kazalnikov je uporabljena dvostopenjska metoda podatkovne ovojnice (Data Envelopment Analysis – DEA) za oceno učinkovitosti tako na stopnji tehnološke proizvodnje oziroma proizvodnje znanja kot tudi na stopnji komercializacije. Ta pristop omogoča razlikovanje med državami, ki so učinkovite pri ustvarjanju rezultatov znanja, in tistimi, ki so uspešne pripretvarjanju teh rezultatov v gospodarske učinke. Ugotovitve kažejo na znatne razlike v inovacijski učinkovitosti med državami članicami EU. Malo držav dosega visoko učinkovitost na obeh stopnjah, kar poudarja težavnost ohranjanja uspešnosti vzdolž celotne inovacijske vrednostne verige. Analiza razkriva trajne neučinkovitosti, zlasti na stopnji komercializacije, kar je skladno z dosedanjimi raziskavami, ki izpostavljajo strukturne ovire pri pretvarjanju rezultatov raziskav in razvoja v gospodarske koristi. Rezultati prav tako dokazujejo, da so razlike v razvrstitvah držav, ki jih opažamo v literaturi, pogosto posledica različnihizborov kazalnikov in specifikacij metode DEA. Tudi med študijami, ki uporabljajo podobne okvire DEA, razlike pri vključitvi kazalnikov vodijo do različnih razvrstitev uspešnosti držav.Praktične implikacije: metodološke odločitve omogočajo izboljšano primerljivost med državami in skozi čas, hkrati pa zmanjšujejo kompleksnost. Dvostopenjska struktura metode DEA oblikovalcem politik nudi globlji vpogled v notranje delovanje nacionalnih inovacijskih sistemov in vire neučinkovitosti, zlasti na stopnji komercializacije. To omogoča boljciljno usmerjene politične intervencije, ki razlikujejo med šibkostmi pri proizvodnji znanja in šibkostmi pri gospodarskem izkoriščanju inovacijskih rezultatov.Izvirnost/vrednost: študija prispeva k literaturi z uvedbo metode MCFS na področje ocenjevanja inovacijske učinkovitosti in ponuja poenostavljen,empirično utemeljen nabor kazalnikov, primeren za uporabo v okviru metode DEA. S kombinacijo podatkovno zasnovanega pristopa k izbiri spremenljivk in dvostopenjske metode DEA članek obravnava metodološko razdrobljenost v dosedanjih raziskavah ter zagotavlja preglednejši in ponovljiv okvir za vrednotenje nacionalnih inovacijskih sistemov.

Purpose The purpose of this study is to measure and analyze the national innovation efficiency of organisation for economic co-operation and development (OECD) countries. This is to determine to what extent OECD countries efficiently use the elements that enable innovation activities possible in generating innovation outputs. Design/methodology/approach An input–output model was constructed to measure efficiency. The inputs and outputs in the research model are the input and output sub-indices of the Global Innovation Index. Data envelopment analysis was used to measure the national innovation efficiency levels of OECD countries. Findings The results show that national innovation efficiency is generally high in OECD countries. However, some countries lag behind in innovation efficiency. OECD countries’ ability to create and provide the elements that enable innovation activities is higher than their ability to create innovation outputs. OECD countries have a good innovation environment and a high level of resources, but they should focus on how to create more innovation outputs. Originality/value This study presents a measurement of national innovation efficiency of OECD countries which contributes “Innovation Strategy” agenda. The results empirically show that overall innovation indices cannot be the only indicator of the performance of national innovation systems. In this study, an innovation efficiency/performance matrix is constructed to present the relative positions of the countries to help in examining countries’ strengths, weaknesses and potentials based on innovation efficiency and innovation performance simultaneously. This study contributes to the literature by presenting a broader perspective and measurement of national innovation efficiency by taking an extensive number of indicators into account.

Green innovation plays an important role in coordinating the relationship between ecological environment and economic development and has become a new driving force for the development of a resource-saving and environment-friendly economy. To explore the effects and logic of innovation efficiency and environmental protection, using the inter-provincial (city) panel data of the Yangtze River Economic Belt from 2007 to 2017 in China, the green innovation efficiency and environmental protection level of the upper, middle, and lower reaches of the Yangtze River Economic Belt were analyzed. Results show that the overall environmental protection level of the Yangtze River Economic Belt is on the rise. From a regional perspective, the environmental protection level in the upper reaches is the highest, which is greater than the overall level of the Yangtze River Economic Belt, followed by the lower and middle reaches, which are less than the overall level of the Yangtze River Economic Belt. The efficiency of green innovation has promoted the overall environmental protection level of the Yangtze River Economic Belt, inhibited the environmental protection level in the downstream areas, promoted the environmental protection level in the upstream areas, and has no obvious impact on the environmental protection level in the middle reaches. Further mechanism analysis shows that the possible transmission path of green innovation efficiency to environmental protection is as follows: green innovation efficiency promotes the environmental protection level by improving the ecological environmental efficiency. The robustness of the above conclusion is tested, and it has good robustness. The research conclusions of this study provide reliable empirical evidence and policy enlightenment for the development and optimization of green innovation efficiency and the realization of green innovation efficiency driving environmental protection.

PurposeAlthough the impact of environmental, social and governance (ESG) on firms' innovation has attracted attention, the existing research findings diverge. The authors believe that failure to consider both innovation input and output is an important reason for the divergence of conclusions in the extant literature when discussing the impact of ESG and firm innovation. Thus, based on signaling theory, this study aims to reconcile these divergent findings by examining the impact of ESG performance on firms' innovation efficiency.Design/methodology/approachTo seek empirical evidence to support the authors’ theoretical view, the authors conduct an empirical test based on the Tobit model using 8 years of data from Chinese listed companies.FindingsAlthough ESG performance effectively improves firms' innovation efficiency, the institutional-level signaling environment (including state-owned firms and regional market development) weakens the positive effect of ESG performance on firms' innovation efficiency. Further tests suggest that financing constraints partially mediate the relationship between ESG performance and firms' innovation efficiency.Originality/valueBy systematically revealing whether, how and under what circumstances ESG performance improves firms' innovation advantages, this study bridges the gap in the existing literature and highlights important implications to suggest how firms can better capture the value associated with ESG.

To develop innovation, firms make several decisions on the allocation of resources to specific innovation activities. Important innovation decisions include among others the decision to collaborate with other partners for innovation activities and the decision to engage in complex R&D projects such as projects with environmental benefits. Although there are very few empirical works that examine these two decisions together, while supporting that R&D collaborations are more important for the development of environmental innovations than for conventional innovations, an empirical work that examines the joint impact of these two decisions on corporate innovation efficiency is still lacking. This study aims to fulfill this gap by making one of the first attempts to employ a new dataset based on the Greek Community Innovation Survey (CIS), conducted for the years of 2012–2014 analyzing 2456 companies. Econometric results indicate that firm’s decision to eco-innovate exerts a positive influence on firms’ innovation efficiency directly. On the contrary, regarding the decision to engage in R&D collaborations, econometric results indicate that there is not a direct or an indirect, via eco-innovation, impact on innovation efficiency.

This article measures renewable energy firm-level pure innovation efficiency, green productivity, technical efficiency, scale efficiency and total investment efficiency from micro input–output factors using Banker, Charnes and Cooper’s (BCC) data envelopment analysis (DEA) approach. Its main novelty is that it clearly explores the effective impacts of government subsidies and tax rebate policies on renewable energy firms’ investment efficiency using China’s renewable energy firm-level panel data. Our observational findings indicate that between 2001 and 2018, the aggregate degree of total investment performance from renewable energy firms rose steadily before declining. Renewable energy firms had larger ranges of total investment efficiency and size efficiency, and their levels of pure technological efficiency were both greater than 0.457%. At the 16% trust mark, current government subsidies and taxation rebates had dramatically positive effects on pure technological efficiency and total investment efficiency; additionally, government subsidies have a stronger positive impact on total investment efficiency and pure technical efficiency than taxation rebates. Furthermore, the ownership concentrations of renewable energy companies greatly encourage pure technological efficiency, size efficiency and total investment efficiency, and asset returns will significantly increase their average degree of total investment efficiency and pure technical efficiency.

High innovation efficiency is regarded as an important starting point for enhancing the competitive advantage of enterprises and encouraging them to achieve sustainable development. However, to date, how digitalization affects firms’ innovation efficiency is still unclear. We integrate absorptive capacity and stakeholder theory to argue that in the short term, digitalization undermines firms’ absorptive capacity, thereby reducing their innovation efficiency. Second, we propose that corporate social responsibility (CSR) (including internal and external CSR) weakens the negative impact of digitalization on firms’ innovation efficiency by mitigating its destructive effect on their absorptive capacity. In contrast, corporate social irresponsibility (CSiR) (including internal and external CSiR) enhances the negative impact of digitalization on firms’ innovation efficiency by increasing the destructive effect of digitalization on their absorptive capacity. Finally, we argue that internal CSR plays a stronger buffering role than does external CSR. In contrast, internal CSiR plays a stronger enhancing role than does external CSiR. Based on the fixed effects model, we obtain empirical evidence to support most of our theoretical views from our empirical tests of 212 Chinese pharmaceutical listed companies. This research brings new insights into a more nuanced understanding of the impact of digitalization on firms’ innovation.

Based on the theory of social capital and strategic revolution, this paper selects 3006 family enterprises in China from 2015 to 2020, establishes a fixed effect panel model, and discusses the impact of the digital economy on the green innovation efficiency of family enterprises from the perspective of rationalization and upgrading of industrial structure. It is found that the relationship between digital economy and green innovation efficiency of family enterprises is inverted U-shaped. The digital economy affects the green innovation efficiency of family enterprises by promoting the rationalization and upgrading of industrial structure. The inverted U-shaped relationship between the two is more significant in the eastern region, the first and the second echelon provinces. Therefore, this paper improves green innovation efficiency of family enterprises from three aspects: digital economy, rationalization and upgrading of industrial structure, so as to finally realize the evergreen and sustainable development of family enterprises. It provides important policy implications for China to realize the green economic transformation and the goals of \"carbon peak\" & \"carbon neutrality\" under the global digital tide.

Green innovation has become an important combination of high-quality economic growth and ecological sustainability. In this paper, the super-efficiency network SBM model was used to measure the two-stage green innovation efficiency of the industrial technology research and development (R&D) stage and achievement transformation stage in China (30 provinces and cities) from 2009 to 2019. The results show the following points. Firstly, in terms of temporal series, the efficiency of technology R&D and achievement transformation has experienced three stages of “upward-declining-revitalized period”. Secondly, in terms of spatial trend, the industrial green innovation efficiency gradually increases from northwest to southeast. The high-efficiency areas are still concentrated in the eastern coastal region, with a clear trend towards balanced development in the central and western regions. Finally, openness, industrial structure, government technical expenditures, enterprise scale, and environmental regulation all have different degrees of impact on the efficiency of green innovation in the two stages. Based on the above, this paper is helpful for the government to formulate laws and regulations and coordinate the level of regional economic development and clarify the spatio-temporal characteristics and influencing factors of the efficiency of green innovation.

To ameliorate the efficiency of urban green innovation is the key to realizing green economic transition. This paper constructs a Super-NSBM model with green patents as the intermediate output, uses this model to assess and decompose the green innovation efficiency of 284 Chinese cities, and finally analyzes the spatiotemporal characteristics and influential factors. The research result showed the gap of urban green innovation total efficiency among various regions in China is narrowing, while the spatial differentiation of decomposition efficiency is deepening. This means that a spatial collaborative innovation division pattern of “Eastern Region R&D + Southwest and Northeast Region Transformation” has gradually formed. In the meantime, this paper also found that the spillover effects of the urban green innovation total efficiency and phased efficiency all can form a significant demonstration effect on the surrounding areas. Finally, financial agglomeration, industrial structure, knowledge sharing, economic activity, higher education, opening, and environmental regulations may affect urban green innovation total efficiency and phased efficiency, and this effect has regional heterogeneity.