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This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries.

The TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model.

This research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention.

This paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers.

This research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders.

Organizations deal with digital technologies to achieve their strategic goals. The shift toward digitization is a major challenge because it requires companies to create a digital outlook that influences organizational design. As a result, investigation of institutional theory and entrepreneurial orientation theory in the European food industry has become the focus of research in recent times.

To this end, data were collected from 83 companies related to the food industry in the European context. By applying a hybrid phase of the partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods, this study captured the causal–non-linear relationships among the study constructs.

The findings revealed that the variables of institutional theory and entrepreneurial orientation theory affect the adoption of the digital strategy. There is also a dual interaction role for e-business capabilities and digital transformation. The results of non-linear relationships confirmed that digital strategy adoption is highly influenced by digital transformation, followed by risk-taking, digital leadership, e-business capabilities, organizational agility, proactiveness and innovativeness.

The authors provided significant implications for practitioners and academics about the most influential determinants of digital strategy – businesses must move swiftly toward digitization across its various units to achieve their objectives. An organization’s leadership must realize that equipping the employees with necessary skills is the first step toward digitalization.

The current study underscores the digital strategy, which is usually an overlooked area of investigation, in the food industry. The study identifies some important predictors of digital strategy adoption with the interaction’s role of digital transformation and e-business capabilities. Such relationships have been rarely discussed. In addition, the adoption of a hybrid SEM-AAN approach makes the study an original one.