Firm characteristics and the adoption of data analytics in performance management: a critical analysis of EU enterprises

Abstract

PurposeThis study aims to investigate the factors influencing the adoption of data analytics in performance management. By examining the role of organizational and environmental contexts, this study contributes to the existing literature by proposing a novel and detailed technology-organization-environment (TOE) model for the complex interplay between firm characteristics and the adoption of data analytics. The results offer valuable insights and practical implications for organizations seeking to leverage data analytics for effective performance management.Design/methodology/approachThe research draws upon a data set encompassing over 21,869 companies operating across all European Union member states. A multilevel logistic regression model was developed to evaluate the influence of organizational and environmental factors on the likelihood of adopting performance analytics in organizations.FindingsThe findings indicate that the lack of awareness of the benefits of data analytics and its practical application to address specific business challenges is a significant barrier to its adoption. Organizational contexts, such as variable-pay systems, employee training, hierarchical structures and frequency of monetary rewards, also influence the adoption of data analytics.Research limitations/implicationsThe study informs managers about the strategic role of data analytics capabilities in performance management for improved business intelligence and driving data culture.Practical implicationsThe study helps managers understand the strategic role of data analytics capabilities in performance management, leading to improved business intelligence and fostering a data-driven culture in five key areas: structural alignment, strategic decision-making, resource allocation, performance improvement and change management.Originality/valueThe study advances the TOE theory, making it a more detailed and complete framework, particularly applicable to the adoption of performance analytics. It identifies the main factors of adoption that play a crucial role in this process.