This paper analyses theoretical and empirical scientific literature about the impact of technological innovations on unemployment, considering the former as a key driver of long-term productivity and economic growth. Using panel data from 25 European countries for the period of 2000–2012, we aim to examine whether technological innovations affect unemployment. We used triadic patent families per million inhabitants as our main proxy for technological innovations, as well as other unemployment controls, in our model, which were estimated using System Generalized Method of Moments (SGMM). Finding no significant relationship between technological innovations and unemployment in our base estimation, we re-estimated it testing the impact with a time lag as well as using alternative proxies for technological innovations. Overall, the research estimations do not suggest that technological innovations have an effect on unemployment.

Energy taxes are one of the main market-based tools directed toward mitigating climate change in the European Union (EU). Therefore, the aim of this article was to analyze whether energy taxes really contribute to the reduction of greenhouse gas (GHG) emissions and the successful implementation of climate change policy. Applying the Granger causality test on time series and using panel data analysis, the direct and indirect (via the reduction of fossil energy consumption (FEC) and energy intensity (EI), as well as the increase of renewable energy consumption (REN)) impacts of energy taxes on GHG emissions in EU countries were analyzed in the present study. The results showed that energy taxes did not Granger-cause fossil energy consumption, energy intensity, renewable energy consumption, and GHG emissions in almost all EU countries. Regarding the panel data analysis, the results showed that energy taxes did not, directly and indirectly, influence GHG emissions. Therefore, this paper shows that generally, energy tax policy in EU countries is ineffective. Thus, tax policy should be reformed and matched with an emissions trading system in seeking climate change mitigation.

Purpose: This paper aims to model organizational resilience structure. Based on the central insights of the scientific literature, organizational resilience is modelled as the result of an organizational capacity to bounce-back and bounce-forward.Design/methodology/approach: The paper is based on a quantitative empirical study to support the structural perspective of organizational resilience and investigate the relationships among the dimensions to test the above hypothesis by applying confirmatory factor analysis (CFA) and structural equation modelling (SEM) methods.Findings: The results confirmed three models that could be adopted to assess organizational resilience. The first model endorsed endogenous positive interrelationship among all three dimensions. The second model indicated that bounce-back dimensions, i.e. network and leadership and culture, have endogenous effects. Only the leadership and culture dimension is positively associated with a bounce-forward, i.e. change-ready and learning dimension. And the third model demonstrated that the network dimension is linked to leadership and culture, which is linked to the change ready and learning dimension.Originality/value: This study attempts to provide empirical evidence identifying the links between the bounce-back and bounce-forward stages of organizational resilience. These results contribute to the development of organizational resilience theory, confirming the conceptual statements that resilience is the ability to return to the routine and to adapt to the changing environment by overcoming dynamic events, stressing the idea of the importance of enhanced learning capacity, which allows for growth by constantly learning from oneself by gaining unique experiences.