Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness - Nripendra P. Rana (14047252) (2021)
The data-centric revolution generally celebrates the proliferation of business analytics and AI in exploiting firm’s potential and success. However, there is a lack of research on how the unintended consequences of AI integrated business analytics (AI-BA) influence a firm’s overall competitive advantage. In this backdrop, this study aims to identify how factors, such as AI-BA opacity, suboptimal business decisions and perceived risk are responsible for a firm’s operational inefficiency and competitive disadvantage. Drawing on the resource-based view, dynamic capability view, and contingency theory, the proposed research model captures the components and effects of an AI-BA opacity on a firm’s risk environment and negative performance. The data were gathered from 355 operational, mid-level and senior managers from various service sectors across all different size organisations in India. The results indicated that lack of governance, poor data quality, and inefficient training of key employees led to an AI-BA opacity. It then triggers suboptimal business decisions and higher perceived risk resulting in operational inefficiency. The findings show that operational inefficiency significantly contributes to negative sales growth and employees’ dissatisfaction, which result in a competitive disadvantage for a firm. The findings also highlight the significant moderating effect of contingency plan in the nomological chain. Other Information Published in: European Journal of Information Systems License: http://creativecommons.org/licenses/by/4.0/ See article on publisher's website: https://dx.doi.org/10.1080/0960085x.2021.1955628