BIG DATA AS A SERVICE IN THE DIGITAL ECONOMY:  A STRUCTURAL MODEL OF ADOPTION, CAPABILITIES, AND PERFORMANCE

Abdulrasaq MUSTAPHA

Abdulrasaq MUSTAPHA Walter Sisulu University, Mthatha, South Africa

Keywords: BDaaS adoption, Big data analytics, Technology–Organization–Environment (TOE), Socio-Technical Systems (STS), Resource-Based View (RBV), Organizational performance

Abstract

This study explores the determinants and performance implications of Big Data as a Service (BDaaS) adoption through an integrated theoretical lens, combining the Technology–Organization–Environment (TOE) framework, Diffusion of Innovation (DOI), Socio-Technical Systems (STS), and the Resource-Based View (RBV). Drawing on a theory-driven model, the research tests a set of hypotheses using structural equation modeling on firm-level data. The findings indicate that technological readiness, organizational capacity, and environmental pressures significantly influence BDaaS adoption. Moreover, the perceived relative advantage and compatibility of BDaaS are strong predictors of adoption decisions. Implementation success is shown to enhance advanced analytics capabilities, which in turn mediate the effect on organizational performance. Importantly, privacy and compliance concerns moderate the relationship between implementation and success, underscoring the relevance of data governance in digital transformation. This study contributes to both academic and practical understanding of how firms can leverage BDaaS for sustained competitive advantage in data-intensive environments.

Author Biography

Abdulrasaq MUSTAPHA, Walter Sisulu University, Mthatha, South Africa

This study explores the determinants and performance implications of Big Data as a Service (BDaaS) adoption through an integrated theoretical lens, combining the Technology–Organization–Environment (TOE) framework, Diffusion of Innovation (DOI), Socio-Technical Systems (STS), and the Resource-Based View (RBV). Drawing on a theory-driven model, the research tests a set of hypotheses using structural equation modeling on firm-level data. The findings indicate that technological readiness, organizational capacity, and environmental pressures significantly influence BDaaS adoption. Moreover, the perceived relative advantage and compatibility of BDaaS are strong predictors of adoption decisions. Implementation success is shown to enhance advanced analytics capabilities, which in turn mediate the effect on organizational performance. Importantly, privacy and compliance concerns moderate the relationship between implementation and success, underscoring the relevance of data governance in digital transformation. This study contributes to both academic and practical understanding of how firms can leverage BDaaS for sustained competitive advantage in data-intensive environments.