Big data analytics capabilities and firm performance: An MCDM approach

The digital revolution in the 21st century accentuated the importance of well established IS/IT departments and decision-making based on insights obtained through big data. The research on big data analytics provides the thematic basis of inquiry for business practitioners and scholars. In this regard, big data analytics capabilities are more likely to provide performance advantages to business firms. Through careful review of the existing literature, it was found that empirical research in big data is at a rudimentary stage. The paucity of empirical studies leaves business professionals into uncharted waters when it comes to implementation and capacity building. It was also observed that the internal mechanisms to devise strategies based on big data analytics are not fully explored. Furthermore, there is a dearth of literature regarding big data analytics capabilities of the firms through dynamic capability view of competitive advantage. This study explored the interdependence of big data analytics capabilities such as infrastructure, human resource, and management capabilities and the impact of these capabilities on firm performance. This study applied both qualitative and quantitative data analysis on the data collected from 8 Chief information technology officers of 8 different firms. The hypothesis of this study was tested through IFDEMATEL then ANP and SAW (MCDM methods). Results show that big data analytics capabilities are interdependent and infrastructure capabilities are the highest ranked capability and related to firm performance the most followed by human resource and management capabilities. Keywords: Analytical network process (ANP), Big data Analytics Capabilities, Dynamic Capabilities, Firm Performance, Multi-criteria decision-making methods (MCDM), Technique for Order-preference by similarity to Ideal Solution (TOPSIS)