Increasing awareness of digital transformation across Asia Pacific is putting a strong spotlight on how enterprises think of leadership and related key practices. The pace of transition to the new digital organisation creates even larger leadership gaps as most organisations have not moved rapidly enough to develop relevant leadership practices. With the support of literature, an analysis of related theories of transformational leadership, ambidexterity and dynamic capabilities are examined and the most suitable leadership attributes and practices for a digital enterprise is proposed. We test the proposed research model via a sample of leaders and senior managers from the Asia Pacific region. The results highlight the significant roles of leadership attributes, strategic priorities, organisational focus areas for exploration and digital governance practices for exploitation, in influencing effective digital leadership. The article reports valuable insights and relevant implications for leaders, enterprises, and researchers.

This study aims at examining the key factors influencing the adoption intention of artificial intelligence (AI)-enabled mobile application for public engagement. Digital technologies such as AI provide the opportunity for public agencies to be inclusive and invite citizens to participate in shaping and reshaping the future of public policies and methods of governance. The authors test the proposed research model and results highlight the significant roles of collaboration, hedonic motivation, reliability, and degree of app savviness in the adoption intention of AI application for public engagement. The article reports valuable insights and relevant implications for public agencies, service providers and researchers.

Purpose: 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.Design/methodology/approach: 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.Findings: 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.Practical implications: 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.Originality/value: 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.

The rapid adoption of smart technologies is increasingly evident in both personal and business contexts. The ‘post-pandemic’ economic recovery of 2022 and 2023 coincided with a global energy supply shortage driven by heightened energy demand and supply chain disruptions stemming from the ongoing Russian-Ukrainian conflict. The implementation of smart metering systems is a central component of European policies aimed at enhancing the competitiveness and environmental sustainability of energy markets. However, limited research exists on the acceptance of Smart Meter Technology (SMT) in general, specifically in Norway, as compared to other nations. SMT devices offer the potential for real-time energy consumption monitoring, enabling users to track and modify their usage patterns for optimized consumption. This study employs a mixed-methods research design to gather insights from both SMT consumers and vendors. Findings underscore the pivotal roles of familiarity, cost, social influence, and perceived usefulness in shaping consumer adoption of SMT. This article provides critical insights and implications for researchers, network operators, electricity companies, and government agencies.