This study examines the impact of gender at three different positions in a firm’s hierarchy on innovative activities, looking at over 6474 firms in 30 emerging countries. We create a dummy variable for each of the six survey questions on product innovation, process innovation, organizational innovation, marketing innovation, and R&D (Research & Development) spending. Each dummy acts as a dependent variable in a separate logit regression, and the sum of the dummies acts as the dependent variable in another ordered logit regression. We use the female ownership percentage, female top management, and female majority in the workforce as test variables. We use the Heckman two-stage model to address endogeneity concerns with gender. We find that the female ownership percentage is generally positively related to individual innovation measures as well as the composite measure, while female top management is positively associated with marketing innovation only, and a female majority in the workforce is not significantly related to any measure. The results suggest that promoting innovation in emerging countries would involve governments encouraging further market participation by women and supporting female CEOs (Chief Executive Officers) to innovate, and firms fostering innovation among female workers.

The pharmaceutical industry, where research and development (R&D) efficiency is central to company survival, has recently faced significant challenges. To increase efficiency, companies must implement strategies such as open innovation (OI), wherein they sell their intellectual property, maximize their use of external resources, adjust their structures, and implement new business models. In this study, we divided 701 U.S. pharmaceutical companies according to their OI strategies to measure and compare their R&D efficiencies between 2001 and 2016. We analyzed the deal data of companies by first dividing them into four groups (inside-out, outside-in, coupled, and closed) to calculate R&D efficiency using stochastic and meta-frontier analyses. In the first group analysis, the coupled group shows high technical efficiency, but in an overall comparison, the inside-out group achieves the highest efficiency values. These values increased between 2005 and 2010, when the R&D crisis in the industry was great at its highest. We thus identified the characteristics of each group based on our results, and presented extensive analyses using a time-series comparison and enterprise-level analysis. We claim that pharmaceutical companies can still cope with the current R&D crisis by implementing different OI strategies.

Past studies examining survival factors of biotechnology firms have focused on pioneer countries, such as the USA, the UK and Germany. However, as the biotechnology industry in Asia is reaching the take-off stage and showing a high growth rate, the research on survival factors in the context of Asian latecomers is needed. The present research investigates internal and external factors affecting the survival of SMEs (Small and Medium-sized Enterprises) in the biotechnology industry in South Korea. The Cox hazard model was employed to perform a robust estimation in survival analysis. The analysis of internal factors showed that the origin of a firm (i.e., having prior experience or spin-offs) and the business sub-sector (i.e., platform-based) affect the hazard rates of biotechnology firms. In terms of external factors, unlike strategic alliances, government R&D funding lowered hazard rates for the firm’s survival. Additionally, considering that the reasons of firm exit can be divided into bankruptcy and M&A (Mergers and Acquisitions), the different effects of origins from other firms and strategic alliance for firm survival are confirmed. The results suggest that prior experience, platform-based and constant government R&D funding contribute to the sustainable development of SMEs in the biotechnology industry.

The financial valuation of a drug that is still under development is required for various purposes. The risk-adjusted net present value (r-NPV) method, which recently emerged in the biotech industry, uses the development attrition rate as a discount factor to reflect risk during each development phase. The r-NPV method was developed to overcome the disadvantages of the prevailing discounted cash flow and real options methods and considers drug type, as well as the stage of development in its approach. Using this method, the current study examines technology values in the biopharmaceutical industry and matches the clinical development periods and success rates of these new drugs by analyzing datasets from ClinicalTrials.gov and MedTrack DB. It thus provides support for an empirical valuation model for experts in the field. Notably, there is limited research on the attrition rate and development period of new substance drugs and the research results are not consistently presented. In addition to new substance drugs, further research is necessary to deepen understanding of the attrition rate and development period of biologically-based drugs because of their inherent physical and developmental differences. Similarly, research on performance specifics within drug class models would enable refinement of the model.

Government R&D investments are steadily increasing with the perception that R&D plays an important role in technological innovation and sustainable economic growth. In particular, because biotechnology is recognized as one of the next growth engines, the Korean government has recently increased their investment in biotechnology R&D. However, careful analysis of the efficiency of government-sponsored R&D projects is still lacking. In this paper, we measured the technical efficiency and technology gap ratio to investigate the efficiency of Korean government-sponsored R&D projects of 16 sub-biotechnologies from 2007 to 2013 using a metafrontier Data Envelopment Analysis approach. There was no improvement in overall efficiency between 2007 and 2013. Biochip development technology has been the most efficient sub-biotechnology field and the least efficient fields have been biotechnology product safety and efficacy assessment technology. Medical science and engineering is the closest to optimal production technology among sub-biotechnologies. The efficiency of universities and government-funded research institutes is high, while the efficiency of companies is relatively low. The results suggest that the government should improve the R&D planning process and establish a customized R&D investment strategy that considers the characteristics of technologies and the seven organization types of R&D conductors to increase R&D efficiency.

R&D project valuation is important for effective R&D portfolio management through decision making, related to the firm’s R&D productivity, sustainable management. In particular, scholars have emphasized the necessities of capturing option value in R&D and developed methods of real option valuation. However, despite suggesting various real option models, there are few studies on simultaneously employing mean-reverting stochastic process and Markov regime switching to describe the evolution of cash flow and to reflect time-varying parameters resulting from changes of economic environment. Therefore, we suggest a mean-reverting binomial lattice model under Markov regime switching and apply it to evaluate clinical development with project cases of the pharmaceutical industry. This study finds that decision making can be different according to the regime condition, thus the suggested model can capture risks caused by the uncertainty of the economic environment, represented by regime switching. Further, this study simulates the model according to rate parameter from 0.00 to 1.00 and risk-free interest rates for regimes 1 and 2 from ( $r_1$ = 4%, $r_2$ = 2%) to ( $r_1$ = 7%, $r_2$ = 5%), and confirms the rigidity of the model. Therefore, in practice, the mean-reverting binomial lattice model under Markov regime switching proposed in this study for R&D project valuation contributes to assisting company R&D project managers make effective decision making considering current economic environment and future changes.