Entrepreneurial intentions, considered to be the best predictor of entrepreneurial behaviour, have attracted extensive attention among academics, practitioners, and policymakers. This study examines the mediating role of the theory of planned behaviour between university students’ proactive personality, entrepreneurship education, entrepreneurial opportunities, and entrepreneurial intentions. The results of this study showed that both attitudes toward entrepreneurship and perceived behavioural control mediated these relationships, except that perceived behavioural control did not mediate the effect of entrepreneurship education on entrepreneurial intentions, and subject norm did not mediate any relationship. Lastly, this study guides universities, policymakers and practitioners to fully focus on developing attitude entrepreneurship and perceived behaviour control through education and training among graduates and employees. Suppose there is a presence of good entrepreneurial opportunities. In that case, they will form stronger intentions to start new businesses and expand their businesses to drive socio-economic growth, innovation and job creation among graduates.

The ability to take advantage of new digital solutions and technology will give companies a competitive edge, and operational optimization remains a major concern. A significant area of risk is cyber security because software-based technologies are integral to ship operations. Particular emphasis has been placed on the vulnerabilities of the Global Navigation Satellite System (GNSS), since it is an essential part of many maritime facilities and hence a target for hackers. Presently, research has shown that increased integration of new enabling technologies, like the Internet of Things (IoT) and big data, is driving the dramatic proliferation of cybercrimes. However, most of the attacks are related to ransomware attacks and/or with direct attack to the information technology (IT) and infrastructure. Nevertheless, there is a strong trend toward increased systems integration, which will produce substantial business value by making it easier to operate autonomous vessels, utilizing smart ports more, reducing the need for labour, and improving economic stability and service efficiency. Cybersecurity is becoming more and more important as a result of the quick digital transformation of the offshore and maritime sectors, which has also brought new dangers and laws. The marine sector has started to take cybersecurity seriously in light of the multiple documented instances of cyberattacks that have exposed business or personal data, caused large financial losses, and caused other problems. However, the body of existing research on emerging threats in maritime cyberspace is either inadequate or ignores important variables. Based on the most recent developments in the maritime sector, the article presents a classification of the most serious cyberthreats as well as the risks to cybersecurity in maritime operations and possible mitigation strategies from an educational research perspective.

Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.

This study evaluates the sustainability and ethical practices of Kerry Logistics Network Limited (KLN), a prominent logistics service provider headquartered in Hong Kong. Using normative ethical theories, stakeholder analysis, and the Circle of Sustainability framework, this research examines KLN’s alignment with global sustainability standards, particularly the United Nations Sustainable Development Goals (SDGs). The findings reveal that KLN has achieved significant milestones in environmental management, such as reducing greenhouse gas emissions by 11% from 2021 to 2022 through the deployment of electric trucks and incorporating renewable energy in warehouse operations. KLN has also enhanced social responsibility and governance practices by implementing fair labor policies and establishing a rigorous code of conduct, ensuring compliance with ethical guidelines across its supply chain. However, the study identifies areas for improvement, including biodiversity actions, battery recycling processes, and transparency in stakeholder engagement. Emphasizing the importance of third-party validation, this paper underscores KLN’s leadership in the logistics industry and provides insights for other companies aiming to improve sustainability performance through comprehensive, verifiable practices.

The existence of residential well-being of the locals in the sense of equilibrium-state is a competitive advantage for tourism in a given destination. The rise of overtourism could jeopardize this equilibrium and ultimately the effectiveness of tourism in a vulnerable destination. The research question of the study aimed to answer: what are the spiral dynamics of the multifactorial characteristics of the sense of place that can be mapped under the influence of overtourism. Answering the question draws attention to the sense of place—which can be interpreted as a synonym for local character—of the issues of overtourism and residential well-being. Mapping the mechanism of action of the multifactorial characteristic of locality can help to identify non-supportive functions, to pinpoint the balance point for moving towards a supportive quality, and to answer the “how yes” questions at individual, local and collective levels. The answer to the research question is the result of concluding three district-specific sub-questions. The assessment of the results was based on the content analysis of 251 posts (2017–2021) in the local public Facebook group (supplemented by a questionnaire survey of local residents (2022), 30 in-depth interviews with experts and residents (2022) conducted as part of the cross-sectional research, and 10 additional in-depth interviews with residents (2024) conducted for the last sub-question. The flowchart showing the current state of the district along a negative spiral dynamic, the possibility to turn it in a positive direction, and the mind-map-like summary of local, individual and collective mitigation and solution alternatives supporting the change of direction can be considered as a novel scientific result.

This financial modelling case study describes the development of the 3-statement financial model for a large-scale transportation infrastructure business dealing with truck (and some rail) modalities. The financial modelling challenges in this area, especially for large-scale transport infrastructure operators, lie in automatically linking the operating activity volumes with the investment volumes. The aim of the paper is to address these challenges: The proposed model has an innovative retirement/reinvestment schedule that automates the estimation of the investment needs for the Business based on the designated age-cohort matrix analysis and controlling for the maximum service ceiling for trucks as well as the possibility of truck retirements due to the reduced scope of tracking operations in the future. The investment schedule thus automated has a few calibrating parameters that help match it to the current stock of trucks/rolling stock in the fleet, making it to be a flexible tool in financial modelling for diverse transport infrastructure enterprises employing truck, bus and/or rail fleets for the carriage of bulk cargo quantifiable by weight (or fare-paying passengers) on a network of set, but modifiable, routes.