Big Data Analytics Adoption and Its Impact on SME Market and Financial Performance: An Analysis Using the Technology–Organisation–Environment (TOE) Framework

Abstract

Big data analytics has revolutionised business competition by uncovering hidden patterns in raw data, enabling better decision-making, increased productivity, knowledge generation and enhanced innovation. With the rapid expansion of big data, it is essential to seek and implement solutions to manage these datasets and extract valuable insights and knowledge from them. Although prior research has attempted to describe the role of traditional database management systems, there is still a scarcity of empirical evidence on the role of big data analytics adoption by small businesses in an emerging market such as India. The authors contribute to the existing body of knowledge by analysing the factors influencing big data analytics adoption in SMEs and predicting the potential impact on their market and financial performance. The ‘partial least square-structural equation modelling (PLS-SEM)’ technique was utilised to analyse data. This research provides a strong research model for big data analytics applications in which one of the two proposed technological factors (‘perceived usefulness’), one organisational factor (‘top management support’) and two environmental factors (‘competitive pressure and government rules and regulations’) significantly influence big data analytics adoption. Additionally, the results of this article may serve as a catalyst, encouraging SMEs to embrace big data analytics more willingly and recognising its potential benefits in enhancing business processes, decision-making, and overall competitive strength in the market. Grounded in the ‘Technological-Organisational-Environmental (TOE) framework, resource base view (RBV) and technology acceptance model (TAM)’ theories, this article aimed to measure the causal association between the drivers and outcomes.