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Industry 4.0 or I4.0 has transformed the manufacturing landscape by integrating social and technical factors by means of the sociotechnical framework. However, the sociotechnical aspects of digitalization of total quality management (TQM 4.0), especially in small and medium enterprises (SMEs) remain largely unexplored. This groundbreaking research endeavors to delve into the pivotal role played by social (soft) and technical (hard) TQM 4.0 in driving I4.0 readiness among SMEs.

A research framework has been developed by harnessing the principles of Socio-technical systems (STS) theory. Data collection from a sample of 310 randomly selected SMEs manufacturing in Malaysia through an online survey approach. The collected data is then subjected to analysis using Partial Least Square-Structural Equation Modeling (PLS-SEM) through SmartPLS.

The study findings indicate that both hard and soft TQM 4.0 factors are vital to promoting I4.0 readiness (R² = 0.677) and actual implementation (R² = 0.216). Surprisingly, the findings highlight that customer-related construct has no impact on hard TQM 4.0 attributes. Furthermore, hard TQM 4.0 factors have played a partial mediating role on the relationship of soft TQM 4.0 and I4.0 attributes (20% = VAF = 80%).

This is a novel research as it explores the underexplored domain of sociotechnical aspects of TQM 4.0 within SMEs amid I4.0 transformation. The study distinctive contributes include revealing the pivotal role of both soft and hard TQM 4.0 factors in driving I4.0 readiness, emphasizing the primacy of people-related dimensions for successful implementation in manufacturing SMEs.

The Metaverse holds vast amounts of user data, making it essential to address threats to its confidentiality, integrity and availability. These threats are not purely technological, as user actions and perceptions, shaped by psychological factors, can influence cybersecurity challenges. Thus, a holistic approach incorporating technological and psychological dimensions is crucial for safeguarding data security and privacy. This research explores users’ cybersecurity behavior in the Metaverse by integrating the technology threat avoidance theory (TTAT) and the theory of planned behavior (TPB).

The model was assessed using data collected from 746 Metaverse users. The empirical data were analyzed using a dual structural equation modeling-artificial neural network (SEM-ANN) approach.

The main PLS-SEM findings indicated that cybersecurity behavior is significantly affected by attitude, perceived behavioral control, subjective norms, perceived threat and avoidance motivation. The ANN results showed that perceived threat with a normalized importance of 100% is the most significant factor influencing cybersecurity behavior. The ANN results also showed that perceived severity with a normalized importance of 98.79% significantly impacts perceived threat.

The novelty of this research stems from developing a unified model grounded in TTAT and TPB to understand cybersecurity behaviors in the Metaverse. Unlike previous Metaverse studies that solely focused on measuring behavioral intentions or user behaviors, this study takes a step further by evaluating users’ cybersecurity behaviors. Alongside its theoretical insights, the study offers practical recommendations for software developers, decision-makers and service providers.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.