Information and communication technology (ICT) companies strive for ceaseless innovation to remain competitive while facing the challenge of maximizing firm value (FV) with limited resources, and increasing the interests of shareholders. However, capital structures have a considerable effect on FV, and the literature still disagrees with the optimum structure in specific industries and countries. Therefore, this study evaluates the FV of ICT companies in terms of profitability efficiency using data envelopment analysis. In addition, this study applies a Tobit regression and Kruskal-Wallis one-way ANOVA to identify the impact of leverage, liquidity, and firm size, which are major capital structure factors influencing FV. The analysis yields three main results. First, in the ICT industry, small and medium companies tend to have better profitability efficiency than companies of other sizes. Second, only small and medium ICT manufacturing companies’ profitability efficiency is positively impacted by the current ratio. Third, only mid-sized service companies’ profitability efficiency is positively impacted by the debt-equity ratio. The results have policy and practical implications for improving the FV of ICT companies.

Due to safety issues in the construction industry, interest in research on occupational safety and health (OSH) regulations remains high. Previous studies indicated that OSH regulations not only affect performance in and of themselves, but also indirectly by increasing awareness of such regulations. Studies also demonstrated that OSH regulation can affect innovation and corporate safety. However, the effect of OSH regulation on innovation remains unclear, as the relationship between the perception of OSH regulation and innovation is not fully understood. This study measures the innovation efficiency of companies in the Korean construction industry using data envelopment analysis (DEA), and investigates the relationship between innovation efficiency and companies’ perceptions of OSH regulations. Results indicate that companies that positively recognize OSH regulations tend to be more innovative than those that do not. This study also validates differences in innovation efficiency depending on the perception of OSH regulations by bootstrap DEA. The results of this study suggest appropriate strategies to promote innovation in the construction industry from the perspectives of both government and practitioners in firms.

The literature has two different perspectives on which innovation types should be implemented to achieve innovation performance; some argue that they should pursue process-oriented innovation, while others maintain that both product- and process-oriented innovation should be performed. Though innovation efficiency should be measured, which takes both input and output variables into account, the research so far has been measured only with the performance of the innovation. Accordingly, this study identifies which innovation type is the most advantageous in terms of innovation efficiency for the chemical firms. We use data of 64 Korean chemical companies from a 2016 Korean innovation survey and perform data envelopment analysis to calculate innovation efficiency. Kruskal–Wallis one-way ANOVA and bootstrap DEA were also conducted to compare the difference of innovation efficiency among groups, depending on which innovation types are oriented. The result shows that focusing on process innovation rather than both product and process innovation is more appropriate to achieve higher innovation efficiency, and pursuing process innovation rather than product innovation tends to achieving a higher level of innovation efficiency in the Korean chemical industry.

Although government regulations have a significant impact on innovation, research on the impact of government regulations on innovation efficiency has yet to be sufficiently uncovered. In this study, the effects of government regulations on innovation efficiency in ICT service industry are verified through tobit regression analysis after calculating innovation efficiency by DEA. Government policies are divided into 11 categories, and two different innovation efficiencies are calculated as the innovation process is divided into two stages: R&D efficiency and commercialization efficiency. The results prove that “regulations for small and medium-sized business” and “financial market regulation and separation of banking and commerce” have negatively effects on R&D efficiency.

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