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The purpose of this study is twofold. The first objective is to identify the factors that affect Gen Z tourists' M-payment behaviour. Next, this study investigates the inter-relationship between Gen Z tourist’s perception of M-payment benefits, adoption behaviour, usage risk and future usage intention.

The sample comprised Malaysian Gen Z individuals (n = 326) who had experience using M-payment methods while travelling outbound. Given the complex nature of the model and the goal to predict and explain relationships within Gen Z's M-payment usage, partial-least square-structural equation modelling was used to assess the study framework and test the proposed relationships.

This study reveals significant influences on Gen Z tourists' behavioural intentions towards M-payment usage. Perceived benefits, performance expectancy, social influence and perceived trust positively impact behavioural intentions, while effort expectancy exhibits no significant effect. Furthermore, perceived trust is strongly influenced by perceived security, which also positively influences behavioural intentions. A mediated relationship is evident as trust mediating the effect of perceived security on behavioural intentions.

This study’s findings contribute to understanding the intricate relationships influencing Gen Z's M-payment behaviour and underscore trust's pivotal role in mediating the security–behavioural intention relationship.

This study is among the first to consider Mental Accounting Theory and the Unified Theory of Acceptance and Use of Technology as crucial underpinning theories in comprehending the intricate relationships that influence Gen Z travellers' perceptions and behaviours concerning M-payment systems.

Computer vision (CV) offers a promising approach to transforming the conventional in-person inspection practices prevalent within the construction industry. However, the reliance on centralized systems in current CV-based inspections introduces a vulnerability to potential data manipulation. Unreliable inspection records make it challenging for safety managers to make timely decisions to ensure safety compliance. To address this issue, this paper proposes a blockchain (BC) and CV-based framework to enhance safety inspections at construction sites.

This study adopted a BC-enhanced CV approach. By leveraging CV and BC, safety conditions are automatically identified from site images and can be reliably recorded as safety inspection data through the BC network. Additionally, by using this data, smart contracts coordinate inspection tasks, assign responsibilities and verify safety performance, managing the entire safety inspection process remotely.

A case study confirms the framework’s applicability and efficacy in facilitating remote and reliable safety inspections. The proposed framework is envisaged to greatly improve current safety inspection practices and, in doing so, contribute to reduced accidents and injuries in the construction industry.

This study provides novel and practical guidance for integrating CV and BC in construction safety inspection. It fulfills an identified need to study how to leverage CV-based inspection results for remotely managing the safety inspection process using BC. This work not only takes a significant step towards data-driven decision-making in the safety inspection process, but also paves the way for future studies aiming to develop tamper-proof data management systems for industrial inspections and audits.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

The purpose of this study is to examine the vital link between manufacturing firms and the environment, delving into the intricate connections among factors affecting these firms. Specifically, it investigates how the environmental performance of manufacturing firms is shaped by their adoption of environmental management practices and the regulatory environment in which they operate.

Data are currently being collected through a structured questionnaire from employees working in manufacturing firms in Pakistan. Random sampling was used to select the participants. The hypotheses were tested using PLS-SEM analysis.

The study reveals a positive correlation between green manufacturing practices and superior environmental performance. Effective environmental management systems further help firms reduce their environmental footprint. External environmental regulations play a significant role as moderators, influencing the strength and direction of the relationship between green manufacturing, environmental management and environmental performance.

The practical implications offer valuable insights and guidance for manufacturing companies seeking to improve their environmental responsibility and performance. Additionally, policymakers gain insights into how regulatory frameworks can be designed or modified to better support sustainability efforts within the manufacturing sector.

This study offers timely insights for sustainable business practices, aligning with corporate responsibility efforts. It contributes to both academic knowledge and provides actionable guidance for fostering environmentally responsible practices in the manufacturing sector.