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Digital technologies are used in various local government activities. Adopting suitable digital technology strategies could enhance service efficiency, effectiveness, and accountability. The challenges of technology adoption among local governments, however, are also evident. One of the major challenges is capacity, including the lack of knowledge or awareness of how to balance the local government’s resources and the strategies that need to be implemented. This challenge also forms a research gap. The study aims to consolidate the understanding of local government digital technology adoption strategies via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It analyses the adoption opportunities, challenges, and strategies through the lens of people, processes, and technology frameworks. The results show that: (a) Strategies concerning the people aspects include building a platform for public participation, employees’ skills, and decision-makers' positive mindset development. (b) Strategies concerning the process aspects include recognizing the players’ roles, having a clear aim and procedure, proper regulation, and receiving user input. (c) Strategies considering the technology aspects include understanding the effect of the technology, technological preparedness, and convenience adoption. The findings inform local government policymakers in digital technology adoption and transformation endeavors.

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.

The present study explores the effect of supply chain integration on the operational performance of an internet-based online business based in China. The study is based on primary data collected through a questionnaire distributed to supply chain professionals of the stated companies. After distributing the questionnaire and collecting the responses, an exploratory factor analysis is conducted to validate the instrument. Afterward, the confirmatory factor analysis (CFA) analysis is conducted, and finally, the structural equation modeling (SEM) technique is used to determine the validity of the research hypotheses. The results indicate that integrating different aspects of the supply chain positively impacts the operating performance, improving the financial performance of the companies involved in the integration process.

The discussion on employee burnout (BOT) has existed in academic literature for a long time. While BOT was identified as a personal issue, there is a lack of a system approach solution. In this regard, a milestone was achieved in 2019 when the World Health Organization (WHO) introduced a new definition of BOT, recognising it as an occupational phenomenon, thereby placing a greater responsibility on organisational leadership to manage it. Since then, different leadership models have been proposed by scholars to reduce the effect of BOT in an organisation. Humble leadership (HL), as a people-first approach, has recently gained importance in organisational management literature. Studies have indicated that HL significantly influences different employee outcomes. However, research on the significance of HL to mitigate BOT of employees was scarce—similarly, on the underlying mechanisms of how and why. While HL has the potential to reduce BOT in an organisation, little or no research has studied it. Acknowledging these knowledge gaps, the basic aim of this study is to enrich the existing body of knowledge by proposing HL as an effective organisational management strategy to reduce the effect of BOT in the healthcare sector. The study introduces two mediators, work engagement (WREN) and subjective wellbeing (SUBW), to explain the underlying mechanism between HL and BOT. The conditional indirect role of altruism (ALM) was also tested. The data were obtained from hospital employees by employing a survey method (questionnaire, n = 303). Structural equation model (SEM) was considered for testing the hypothesised model to study the interrelationships between variables. The results confirmed that the manifestation of HL in an organisation reduces BOT significantly, and WREN and SUBW mediate this relationship. The study also demonstrates the buffering effect of ALM in the above-proposed relationships. The empirical findings offer multiple contributions in theory and practice, among which the most important one was to realise the profound importance of HL in reducing the effect of BOT in healthcare management.