Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance

Abstract

Despite the influx of data analytics (DA) practices among firms, their impact on operational performance remains ambiguous. This study examined the pull and push factors affecting the data analytics adoption (DAA) from the theoretical perspectives of the technology–organization–environment (TOE) model, theory of perceived risk (TPR), and resource-based view (RBV). The study analyzed data from 169 firms on the basis of the positivist paradigm and employed the partial least square to run the reflective–formative two-stage analysis. Accordingly, the results indicated that the three TOE model aspects exhibited a positive direct impact on DAA and indirectly impacted operational performance through DAA. However, the perceived risk did not display a similar effect in both situations. This study further revealed that the environment push factor had more explanatory power than the perceived risk pull factor, suggesting that a conducive TOE environment would motivate DAA, subsequently enhancing operational performance. The study provided valuable empirical evidence on the determinants of DAA and its subsequent effect on firms’ operational performance. Uniquely, the study also contributed to the literature from the perspective of higher-order-construct analysis in examining the determinants of DAA and its effect on operational performance. Furthermore, the mediation analysis covered the interaction of indirect-path coefficients, minimizing errors in interpreting the mediation effect.