Near is more:learning efficiency in research and development innovation among interlocking firms

Research question/issue:This study examines whether geographic proximity produces a proximity preference as interlocking firms observe each other and learn innovative behaviors through information transmission among interlocking directors.Research findings/insights:We study the performance of A-share-listed companies in China from 2007 to 2017 on the basis of resource dependence theory,agglomeration effect theory,and Porter’s competitive theory.When target firms learn about research and development–related innovation behaviors from interlocking firms closer to them,they experience more efficient learning effects and have improved convergent traits.Moreover,this proximity advantage increases the willingness of the target firm to communicate with and learn from interlocking firms closer to them.Highly developed areas and research and development–intensive industries positively affect the learning efficiency of interlocking firms.Theoretical/academic implications:Our conclusion is consistent with resource dependence theory;target firms in highly developed areas are more willing to imitate and study nearby interlocking firms to maintain their peer relations,innovation potential,and competitiveness.Our conclusion is also consistent with competition theory,which states that the exchange of information between target firms in highly research and development–intensive industries and distant interlocking firms increases innovation differentiation,innovation potential,and competitiveness,even when such exchange has a high cost.Practitioner/policy implications:The results support resource dependence theory and peers’effects.The information obtained by interlocking directorates through external social relations guides firm decision-making,and closer distances reveal more obvious effects.

Do investments in green energy,energy efficiency,and nuclear energy R&D improve the load capacity factor?An augmented ARDL approach

Renewable energy,energy efficiency,and nuclear energy research and development(RER,EER,and NER)budgets are immensely important to fulfill sustainable development goals 7,9,and 13,by accelerating energy innovation,energy transition,and climate control.The literature on the drivers of the load capacity factor(LCF),a recently developed ecological quality measure,is mounting;however,the roles of energy investments in the LCF are largely unknown.Accordingly,this study assesses the impacts of RER,EER,NER,and financial globalization(FIG)on the LCF using data from 1974 to 2018 for Germany.Advanced and reliable time series tests(Augmented ARDL,DOLS,and Fourier causality)are adopted to analyze cointegration,long-run impacts,and causal connections.The outcomes unveil that both green energy and energy efficiency R&D promote the LCF by enhancing ecological quality.However,the positive impact of NER on the LCF is found to be weaker than the impacts of RER and EER.FIG curbs ecological degradation by expanding the LCF.Additionally,the U-shaped connection between economic growth(ECG)and the LCF confirms the load capacity curve.Therefore,policymakers should focus on RER and EER to preserve the environment and promote sustainable growth.

Evaluating R&D efficiency of China's listed lithium battery enterprises

Promoting the growth of the lithium battery sector has been a critical aspect of China's energy policy in terms of achieving carbon neutrality.However,despite significant support on research and development(R&D)investments that have resulted in increasing size,the sector seems to be falling behind in technological areas.To guide future policies and understand proper ways of promoting R&D efficiency,we looked into the lithium battery industry of China.Specifically,data envelopment analysis(DEA)was used as the primary approach based on evidence from 22 listed lithium battery enterprises.The performance of the five leading players was compared with that of the industry as a whole.Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019.However,during this period,a significant increase in R&D expenditure was witnessed.This finding was supported,as the results showed that the average technical efficiency of the 22 enterprises was 0.442,whereas the average pure technical efficiency was at 0.503,thus suggesting that they were suffering from decreasing returns to scale(DRS).In contrast,the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry's average.Moreover,they were experiencing increasing scale efficiency(IRS).We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale;rather,it should also encompass assisting businesses in developing efficient managerial processes for R&D.