Artificial Intelligence, CEO Turnover, and Directional Change in Firm Innovation

2023 | International Conference on Information Systems | Citations: 0

Authors: Lou, Bowen; Ma, Xinyu; Wu, Lynn

Abstract: We examine the role of artificial intelligence (AI) in facilitating a change in ... Expand

Abstract: We examine the role of artificial intelligence (AI) in facilitating a change in innovation directions after a leadership change. Using patent data for firms that have gone through a CEO turnover, we find that firms with greater AI investment are more successful in changing their innovation directions. The effect of AI is driven principally by the continued development of innovation in areas that are modestly different from the past. Further analyses show that this effect is likely due to firms with AI investment that can enable strategic change in cultivating culture of exploring frontiers in innovation and managing R&D. A new CEO can direct more resources to the acquisition of employees with greater technological capabilities such as AI skills to facilitate the innovation change. Overall, our study sheds light on the value of AI in fostering the change in innovation directions during uncertain and turbulent times. Collapse

Topics: artificial intelligence innovation management research and development online labor market IT skill

Methods: machine learning instrumental variables estimation scale validation Compustat experimental group

AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms

2021 | Management Information Systems Quarterly | Citations: 0

Authors: Lou, Bowen; Wu, Lynn

Abstract: Advances in artificial intelligence (AI) could potentially reduce the complexit ... Expand

Abstract: Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm's ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm's discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental "followon" drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts during both the development and the operational stages of the tool. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development.Lynn Wu is an associate professor of operations, information and decisions at the Wharton School of Business, University of Pennsylvania. She researches and teaches how emerging information technologies, such as artificial intelligence and data analytics, affect innovation, business strategy, and productivity. She has won several best paper awards from top journals and flagship conferences in information systems and received two early career awards in information systems. Collapse

Topics: artificial intelligence innovation management online labor market database system research and development

Methods: machine learning instrumental variables estimation experiment propensity score matching scale validation

Theories: resource based view of the firm

Business Models in the Education Technology Industry: What Makes Them Successful?

2017 | International Conference on Information Systems | Citations: 0

Authors: Wendler, Wiebke S.; Stumpf-Wollersheim, Jutta; Welpe, Isabell M.

Abstract: This study focuses on business models of education technology firms (i.e., techn ... Expand

Abstract: This study focuses on business models of education technology firms (i.e., technology providers that enable service innovation in higher education). The purpose of this research is to analyze which choices in business models (concerning customer identification, value delivery, financial aspects and value proposition) foster firm performance—financially and in web and social media. We conduct a complete analysis of education technology firms targeting the higher education market (N = 407). We use databases (e.g., Crunchbase) and firms’ websites to retrieve relevant information in a standardized procedure. The empirical investigations show that most factors (e.g., support) responsible for business model success in the education technology industry are related to the firms’ services. This study is the first to systematically evaluate the business models of education technology firms and among the first studies to address the influence of business models on firm performance. Collapse

Topics: business model social media value creation systems development system support

Methods: nonparametric test qualitative coding design methodology Kruskal Wallis test Mann Whitney U test