Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness

2022 | European Journal of Information Systems | Citations: 0

Authors: Rana, Nripendra P.; Chatterjee, Sheshadri; Dwivedi, Yogesh K.; Akter, Shahriar

Abstract: The data-centric revolution generally celebrates the proliferation of business a ... Expand

Abstract: 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. Collapse

Topics: artificial intelligence data quality analytical information system competitive advantage explainable artificial intelligence

Methods: partial least squares path modeling survey partial least squares regression conceptual modelling theoretical contribution

Theories: contingency theory dynamic capabilities theory resource based view of the firm

Pursuing weak paths: A Proposal for performance evaluation of imputation in PLS

2018 | International Conference on Information Systems | Citations: 0

Authors: Reitz, Andreas; Beimborn, Daniel

Abstract: The ongoing discussion about the use of Partial least squares structural equatio ... Expand

Abstract: The ongoing discussion about the use of Partial least squares structural equation modeling highlights a much bigger problem: the simplicity of handling makes it easy, to undervalue the required academic rigor (Petter 2018). As PLS obtains results for virtually any data, the problem is not restricted to structural and measurement models but also extends to the quality of the data. One important facet of data quality is completeness of data. Missing data occurs in all scientific areas. Yet, there seems to be a gap in methodical treatment in the IS and management literature. But, ignoring or mishandling missing data may reduce efficiency and power as well as bias the results and lead to inaccurate variances and standard deviations. Especially when it comes to PLS-SEM, most papers seem to not report the portion of missing data or how the researchers handled this issue. We therefore propose an evaluation of the most recent and advanced techniques in combination with PLS-SEM, in particular, multiple imputation by fully conditional specification (FCS MI). Collapse

Topics: missing data customer satisfaction mobile phone

Methods: data imputation partial least squares regression experiment decision tree classification structural equation modeling