Predicting Small- and Medium-Sized Enterprise Competitive Firm Performance From Big Data Analytics Capabilities

Abstract

The objective of this research was to determine if, and to what extent, the tangible resources, human skills, and intangible resources components of big data analytics capability, collectively and individually, significantly predict competitive firm performance among small- and medium-sized enterprises (SMEs) in the United States. This research applied the resource-based theory (RBT) posited by Barney (1991). A quantitative correlational-predictive survey was executed among smalland medium-sized enterprises in the United States. The results of the multiple regression model confirmed that the components of BDAC collectively and individually influence the competitive firm performance of SMEs. Additionally, the research also contributed to affirming the relevance of the resource-based theory as a foundational framework. The primary insight was that tangible resources should be regarded as a foundational priority. Secondly, the non-technological resources, human skills and intangible resources together should be the next priority. Thirdly, within the nontechnological factors, the creation of a culture of data-driven decision-making and organizational learning is relatively more consequential than human skills. There were few, if any, quantitative correlational-predictive research focused on SMEs in recent years in the United States that related BDAC to competitive firm performance (CFP) as guidance for SME leadership. This research filled that gap.