Modeling innovation efficiency, its micro-level drivers, and its impact on stock returns

Ebenezer Fiifi Emire Atta Mills, Kailin Zeng, Liu Fangbiao and Li Fangyan

Chaos, Solitons & Fractals, 2021, vol. 152, issue C

Abstract: Motivated by the COVID-19 pandemic and ensuing challenges to human and economic welfare, this research seeks to evaluate innovation efficiency, its micro-level drivers, and its impact on stock returns. This study considers innovation-related activities during two growth phases experienced by 138 listed pharmaceutical manufacturing companies in China: research and development (R&D) and marketing. A dynamic two-phase network data envelopment analysis (DEA) model measured R&D efficiency, marketing efficiency, and dynamic integrated innovation efficiency. A projection difference analysis was presented to offer a feasible solution to address inefficient companies. Then, panel regression methods were adopted to examine micro-level drivers of innovation efficiency. Additionally, a portfolio formation test was used to investigate innovation efficiency as a characteristic impacting stock returns. The results indicate that the listed companies are generally innovation inefficient. Only two are DEA innovation efficient. Low marketing and R&D efficiencies are impeding innovation efficiency amelioration, with the most losses attributed to the marketing phase. Most companies lack good conditions for R&D and the commercialization of scientific and technological breakthroughs. Institutional investors and financial analysts' coverage have a positive and significant impact on innovation efficiency. Foreign institutional ownership and stock market overvaluation impact listed companies with high innovation efficiency positively. There is a negative and significant effect of size on innovation efficiency. However, growth in pharmaceutical manufacturing company size positively influences innovation efficiency at higher levels. The portfolio formation test results reveal that investors consider companies with high innovation efficiency as high-profile targets that provide high stock returns. The findings of this study offer stakeholders avenues to shape the initiation, process, and outcome of innovation.

Keywords: Innovation efficiency; Data envelopment analysis; Dynamic network DEA model; Panel regression; Pharmaceutical manufacturing companies (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:152:y:2021:i:c:s0960077921006573

DOI: 10.1016/j.chaos.2021.111303

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