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I adapt the Integrated Model of Workplace Safety (Christian et al., 2009) to information security and highlight the need to understand additional factors that influence security compliance and additional security outcomes that need to be studied (i.e. security participation).

This model argues that distal factors in four major categories (employee characteristics, job characteristics, workgroup characteristics and organizational characteristics) influence two proximal factors (security motivation and security knowledge) and the security event itself, which together influence two important outcomes (security compliance and security participation).

Safety is a systems design issue, not an employee compliance issue. When employees make poor safety decisions, it is not the employee who is at fault; instead, the system is at fault because it induced the employee to make a poor decision and enabled the decision to have negative consequences.

Security compliance is as much a workgroup issue as an individual issue.

I believe that by reframing information security from a compliance issue to a systems design issue, we can dramatically improve security.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

This study explores the interplay between levels of cultures and aspects of quality management (QM), aiming to develop a conceptual framework and introduce propositions regarding managing quality in a multinational company (MNC).

A conceptual framework delineating the relationship between the levels of cultures in MNCs and various aspects of QM is proposed. Thereafter, based on a theory elaboration approach, a case study in Swedish facilities of MNCs is used to further illustrate the link between constructs of the framework, contributing to the identification of challenges and possibilities in managing quality in MNCs.

The research identifies key propositions regarding the intricate relationship between levels of cultures and their influences on aspects of QM in MNC. Proposition 1 emphasises the impact of national cultural differences on perceptions of QM principles. Proposition 2 reveals that diverse QM perceptions affect global consistency in QM practices. However, proposition 3 suggests that emphasising technical aspects in common QM practices fosters shared perceptions and a cohesive organisational culture, leading to Proposition 4, that a QM-centric organisational culture mediates national cultural differences, facilitating the management of quality globally.

This research relies on a case study from a Swedish perspective. There is a need for quantitative or mixed method approaches to validate the proposed framework.

This research yields practical insights into cross-cultural QM challenges and possibilities in MNCs.

By integrating national and organisational culture into the QM framework, this research offers a conceptual model and propositions as a foundation for future cross-cultural QM research in MNCs.

Organisations are confronted with the challenge of navigating various pressures arising from activities that shape environmental and social impacts, which stakeholders find significant. This research endeavours to ascertain a process facilitating the analysis and seamless integration of sustainability into corporate strategy. The goal is to establish an “integrated” ESG governance framework adept at effectively managing institutional pressures.

This research employs an action research approach, focusing on a leading company within the sugar industry. The investigation delves into the relationship dynamics associated with business issues through a process that engages, either directly or indirectly, board members, top managers, as well as industrial and commercial customers, along with final consumers.

The formulation of a sustainability strategy serves as a guiding framework for the Board of Directors in effectively navigating tensions arising from environmental, social and economic pressures.

The research contributes to bridging the realms of business governance and institutional theory (viewed under a paradoxical lens). On a managerial level, the study introduces a structured process aimed at seamlessly integrating sustainability objectives into governance, aligning with international ESG guidelines (OECD, 2023; WEF, 2020).

The originality of this research lies in crafting a sustainability strategy by the BoD that takes into account the impact of governance and responds to the demands of strategic stakeholders.

Although previous research has acknowledged the significance of comprehending the initial acceptance and adoption of ChatGPT in educational contexts, there has been relatively little focus on the user’s intention to continue using ChatGPT or its continued usage. Therefore, the current study aims to investigate the students’ continuance intentions to use ChatGPT for learning by adopting the stimulus–organism–response (SOR) model.

This study has employed the SOR model to investigate how UTAUT factors (such as performance expectancy, facilitating conditions, effort expectancy and social influence) influence the cognitive responses of students (e.g. trust in ChatGPT and attitude towards ChatGPT), subsequently shaping their behavioral outcomes (e.g. the intention to continue using ChatGPT for study). A sample of 392 higher students in Vietnam and the PLS-SEM method was employed to investigate students’ continuance intention to use ChatGPT for learning.

This study reveals that students’ continuance intention to use ChatGPT for learning was directly affected by their attitude toward ChatGPT and trust in ChatGPT. Meanwhile, their attitude toward ChatGPT was built on effort expectancy, social influence, and facilitating conditions and trust in ChatGPT was developed from effort expectancy and social influence.

By extending the analysis beyond initial acceptance, this research provides valuable insights into the factors that influence the sustained utilization of ChatGPT in an educational environment.

Preservice teacher (PST) professional development programs are crucial for cultivating high-quality STEAM teachers of the future, significantly impacting the quality of regional STEAM education. The Guangdong-Hong Kong-Macao Greater Bay Area, as a region of cross-border cooperation, integrates the resources and advantages of Guangdong, Hong Kong, and Macao, possessing rich cultural heritage and innovative capabilities. Transdisciplinary Education for Cultural Inheritance (C-STEAM) is an effective approach to promoting educational collaboration within the Greater Bay Area, facilitating the integration of both technological and humanities education. This study aims to develop a Technology-Enabled University-School-Enterprise (T-USE) collaborative education model and implement it in the Greater Bay Area, to explore its role as a support mechanism in professional development and its impact on C-STEAM PSTs' professional capital.

Adopting a qualitative methodology, the study interviewed PSTs who participated in a C-STEAM teacher education course under the T-USE model. Thematic coding is used to analyze their knowledge acquisition, interaction benefits with community members, and autonomous thinking and decision-making in theoretical learning and teaching practice.

The findings show that the T-USE model significantly enhanced the PSTs' human capital, including teaching beliefs, knowledge, and skills. In terms of social capital, PSTs benefited from collaboration with PST groups, university teaching teams, in-service teachers, and enterprises, though challenges such as varying levels of expertise among in-service teachers and occasional technical instability emerged. For decisional capital, the T-USE model provided opportunities for autonomous thinking and promoted teaching judgment skills through real teaching challenges and scenarios. Reflective practice activities also supported PSTs' professional growth.

This study reveals the effectiveness and internal mechanism of the T-USE model in C-STEAM PST training, offering significant theoretical and practical references for future PST education.

Given the growing significance of contemporary socio-economic and infrastructural conversations of Public-Private Partnerships (PPP), this research seeks to provide a general overview of the academic landscape concerning PPP.

To offer a nuanced perspective, the study adopts the Latent Dirichlet Allocation (LDA) methodology to meticulously analyse 3,057 journal articles, mapping out the thematic contours within the PPP domain.

The analysis highlights PPP's pivotal role in harmonising public policy goals with private sector agility, notably in areas like disaster-ready sustainable infrastructure and addressing rapid urbanisation challenges. The emphasis within the literature on financial, risk, and performance aspects accentuates the complexities inherent in financing PPP and the critical need for practical evaluation tools. An emerging focus on healthcare within PPP indicates potential for more insightful research, especially amid ongoing global health crises.

This study pioneers the application of LDA for an all-encompassing examination of PPP-related academic works, presenting unique theoretical and practical insights into the diverse facets of PPP.

This paper endeavors to create a predictive model for the energy consumption associated with the multi-material fused deposition modeling (FDM) printing process.

An online measurement system for monitoring power and temperature has been integrated into the dual-extruder FDM printer. This system enables a comprehensive study of energy consumption during the dual-material FDM printing process, achieved by breaking down the entire dual-material printing procedure into distinct operational modes. Concurrently, the analysis of the G-code related to the dual-material FDM printing process is carried out.

This work involves an investigation of the execution instructions that delineate the tooling plan for FDM. We measure and simulate the nozzle temperature distributions with varying filament materials. In our work, we capture intricate details of energy consumption accurately, enabling us to predict fluctuations in power demand across different operational phases of multi-material FDM 3D printing processes.

This work establishes a model for quantifying the energy consumption of the dual-material FDM printing process. This model carries significant implications for enhancing the design of 3D printers and advancing their sustainability in mobile manufacturing endeavors.

One of the issues currently being discussed around the globe, and especially in the tourism industry, is revisit intention. This study uses a bibliometric analysis strategy based on the Web of Science (WOS) database to examine the literature on revisit intention.

In this study, a sample of 482 articles was analyzed. The R programming language was used to process the data and graph the results.

The results found the occurrence of publications by year, publication source information and authors, journals, countries, institutions, thematic maps, current trends of topics in hospitality and tourism toward revisiting intention, and the most cited papers in revisit intention. This study highlights the importance of revisiting intention in the hospitality and tourism industry. The bibliometric analysis helps to set the research agenda on revisit intention.

To the best of the authors’ knowledge, this is the first study of its kind to present an empirical evaluation of revisit intention using inclusive mapping.

This study aims to develop and validate a scale to measure Teacher Entrepreneurial Behavior (TEB), which encapsulates the behaviors teachers employ to identify and amplify innovation in schools. TEB are catalysts for innovation, navigating their peers through risks and building trust, which empowers the collective to transcend structural constraints and pioneer new educational initiatives. Despite the importance of TEB, there is a notable absence of a well-validated measurement instrument.

Drawing on existing empirical TEB studies, this study conducts four interconnected studies following scale-development procedures. The content validity, construct validity, internal consistency, and external validity of the proposed scale were assessed using exploratory factor analysis, confirmatory factor analysis, invariance analysis, and regression analysis.

The result is a multidimensional TEB model featuring 15 items with a good model fit. The TEB scale comprises four factors: Advocating Innovation, Seeking Resources, Cultivating Cohesiveness, and Mitigating Risk.

This study represents a rigorous attempt to develop and validate a reliable instrument for measuring TEB. It provides a validated tool for future research aimed at understanding the nature of TEB as an independent construct and associated dynamics. Accurate measurement is important for the robustness and replicability of research. Furthermore, the insights gained on TEB scale can significantly inform both the preparation and evaluation of teacher leaders by emphasizing the importance of entrepreneurial behaviors in promoting teachers’ collaboration and actualizing innovative initiative.