This study examines the effects of commercially-oriented research and development (R&D) competence and technology commercialisation (TC) policies in public research organisations (PROs) on R&D efficiency of companies when they receive technology transfer from PROs. This study especially uses three-stage data envelopment analysis in order to control the companies’ absorptive capacity and innovation hurdles that affect the efficiency. The results show that companies that receive technology transfer from PROs that have good alignment between commercially-oriented R&D competence and TC policies have higher R&D efficiency than companies that do not. On the other hand, the misalignment between PROs’ commercially-oriented R&D competence and TC policies can have negative effects on R&D efficiency of companies that receive technology transfer. Consequently, this study discloses that the alignment between technology transferor's commercially-oriented R&D competence and TC policies is critical for improving the R&D efficiency of companies when the companies acquire technologies from external organisations.

Strategic resource distribution and efficient use of limited resources are important in business operations. Cost behaviour is an indicator of these investment strategies. Previous studies have attributed the asymmetry of cost behaviour to managerial expectations. When managers expect future sales revenue to increase, they incur surplus expenses to avoid adjustment costs. In this research, we analyzed whether the cost behaviour stemming from such choices is related to future R&D efficiency. We divided KOSPI and KOSDAQ samples showing sales increases into ‘stickiness’ and ‘anti-stickiness’ groups and compared R&D efficiency using data from successive years. In the case of the KOSPI market, the stickiness group had higher future R&D efficiency than the anti-stickiness group. Conversely, in the case of the KOSDAQ market, the anti-stickiness group had higher future R&D efficiency than the stickiness group. The results elucidate the relationship between cost behaviour and R&D efficiency and show that investment strategy patterns depend on the specific characteristics of stock markets. This research enhances the related literature and the understanding of cost behaviours that reflect distinct investment strategies.

The national resources that can be used in the national innovation system are limited. Therefore, interest in R&D efficiency is increasing in terms of how well-limited resources are utilised to achieve innovation. This study analyzed the effects of external risk factors on a country's R&D efficiency using the DEA-Tobit regression model. Air pollution was set as an environmental risk, and job insecurity was selected as a social risk. The results showed a positive correlation between R&D efficiency and both risk factors. In the case of environmental risks, long-term R&D investments or technology development due to strong regulations have affected R&D efficiency. Social risks have served as motivation for high performance. This study provides implications that policymakers should consider the impact of risk on performance and manage it through organisation and monitoring.

As global demand for critical minerals intensifies with the expansion of energy transition technologies and advanced manufacturing, developing sustainable and efficient exploration strategies has become a national priority. In this shift, AI-driven exploration technologies are emerging as a transformative force, reshaping how mineral resources are discovered, assessed, and managed. This study analyzes the global research landscape in critical mineral exploration by examining patent and scientific publication data to evaluate both research efficiency and knowledge spillover capacity. Using data envelopment analysis and super-efficiency modeling, we compare national R&D performance, identify leading countries, and quantify diffusion dynamics. The results reveal significant disparities: countries such as the United States, South Korea, and Canada demonstrate high research efficiency and strong spillover effects, supported by active innovation ecosystems and technological adoption. In contrast, resource-rich nations including China, Australia, and Russia show limited diffusion due to weaker AI-based innovation incentives and insufficient industry–academia collaboration. Italy stands out as an effective model of policy-driven R&D utilization and technological diffusion. These findings highlight the strategic importance of combining AI-enabled exploration, qualitative research impact, and international cooperation. The study offers policy implications for countries seeking to strengthen resource security and enhance competitiveness through sustainable and innovation-driven mineral exploration.