Date of Graduation


Document Type


Degree Name

Doctor of Philosophy in Business Administration (PhD)

Degree Level



Information Systems


Rajiv Sabherwal

Committee Member

Mary Lacity

Second Committee Member

Arun Rai

Third Committee Member

Zach Steelman


Artificial Intelligence, BHAR, Ethics, Layoffs, Recommender Systems, Societal Implications


"If we do it right, we might be able to evolve a form of work that taps into our uniquely human capabilities and restores our humanity. The ultimate paradox is that this technology may become a powerful catalyst that we need to reclaim our humanity." - John Hagel

Artificial intelligence (AI) is viewed as a disruptive technology that some executives believe will take over a lot of jobs. However, others believe that AI will bolster growth, improve business processes, and create new business opportunities. This dissertation focuses on the tension arising from such contrasting expected impacts of AI. Extant research has investigated the algorithm development eye of AI and neglected the implications eye of AI on the users of AI and organizations. This dissertation seeks to address this gap by examining the impacts at individual and firm levels. It uses data on publicly-traded US companies, interviews, and surveys, to address this broad question through three essays on the impacts of AI on individuals, firms, and society. The essays use multiple data collection methods, including crawling social media (essay 1), archival data (essays 1 and 2), interviews (essay 3), and a mixed-method study based on interviews and longitudinal surveys involving three rounds of data collection (essay 3). More specifically, using signaling and automation-augmentation theories as foundations, Essay 1 examines the impact of the nature of AI investments and investors’ concerns related to layoffs and ethics, and optimism about hiring arising from the nature of AI investments on a firm’s long-term abnormal returns. We find that investors positively react to AI investment for both automation and augmentation. Moreover, the positive effects of automation AI investment are amplified by optimism that they would lead to hiring and attenuated by concerns that they would lead to layoffs or ethical issues. By contrast, the positive effects of augmentation AI investment are amplified by concerns that they would lead to layoffs. Using dynamic capabilities and exploration-exploitation strategic theories, Essay 2 examines the interplay among strategic AI orientation, overall IT strategy of the firm, and industry environment on firm performance. We find that a firm’s strategic AI orientation has one-year positive lagged effect on its performance with the effect being stronger when the firm’s strategic AI orientation aligns with the firm’s overall IT strategy – revenue-focused and cost-focused. Such an effect becomes more pronounced in dynamic environment especially for firms focusing on revenue generation and pursuing exploration AI strategy. Drawing upon needs-affordances-features (NAF) theory and the IS success model, Essay 3 examines the adoption of a specific AI product, namely a recommender system (RS), and underscores the importance of alignment between action opportunities enabled through the features of RS and the user’s psychological needs. It adds to the methodological rigor by providing a novel measure of alignment. We find that the alignment between RS affordances and users’ psychological needs significantly impacts the use of recommendations. Moreover, users of RS seem information hungry about generated recommendations and feel positive about RSs that consider their preferences. Our findings suggest that current RSs might lack good design features related to engagement with users as our sample of RS users perceive their interactive features negatively. To the best of our knowledge, this dissertation is the first to investigate AI's implications on different performance metrics at the firm and the individual level. Each essay offers theoretical and practical insights, and directions for future research.

Available for download on Monday, October 14, 2024