In this study, we suggest a new perspective on the linkage between alliance portfolio diversity and innovation performance based on a contingency approach. Using a longitudinal data set on alliance portfolios and patents of 182 firms in the U.S. manufacturing industries, we examined that alliance portfolio diversity has a U-shaped relationship with firm-level innovation. Internal value creation capabilities in terms of routine and ability are found to moderate the relationship between alliance portfolio diversity and innovation performance: organizational search routine strengthens the relationship of alliance portfolio diversity and innovation performance while technological capabilities weaken and flip the relationship.
This study examines how characteristics of chief technology officer (CTO) drive radical innovation of the firm. Using data of 140 firms in US manufacturing industries, this study finds that the characteristics of the CTO explains a significant proportion of a firm’s relative radicalness even after controlling for CEO and top management team (TMT) characteristics, organizational capabilities, and other firm characteristics. Specifically, a CTO’s diverse functional experience increases a firm’s radicalness. In terms of individual prior functions, marketing experience was significantly related to radical innovation. In addition, the CTO’s diverse industrial experience increases the firm’s radicalness. In contrast to the existing theories, the CTO’s education level, tenure and age showed insignificant effects. However, the number of degrees in engineering and science showed significant effect on the performance of radical innovation. These results of the analysis on firm-level radicalness provide implications for both academics and practitioners.
Artificial intelligence-based investment services (robo-advisors) are becoming increasingly commercialized. Robo-advisors are expected to expand further due to the enhancement of accessibility to investment for general investors through customized portfolio selection and automated transactions established upon the artificial intelligence-based algorithm. This study comprehensively investigates factors that influence acceptance intention of and resistance to robo-advisors using a combined model of technology acceptance model and innovation resistance model. The model was examined through conducting a choice-based conjoint analysis of 158 users with investment experience and age ranging from 20s to 60s. The independent variables of the research for robo-advisors are transparency, customization, social presence, and user control. The effects of the independent variables on acceptance intention and innovation resistance are analyzed, respectively, through mediator variables of perceived usefulness, perceived complexity, and perceived safety. This study indicates the fundamental factors for the promotion of the domestic robo-advisor market based on the analysis of further advanced overseas robo-advisor markets. The significance of this study derives from providing implications on the direction of development for companies or financial institutions in the sphere of robo-advisors.
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