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The benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.

This study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.

The I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.

The maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

The purpose of this paper is to investigate the complex interdependence of the factors in driving or hindering construction productivity at the industry, project and activity levels in a systemic manner.

A mixed-methods design, which combines a critical literature review, an interview-based survey with 32 industry experts and five focus group meetings participated in by 109 representatives of a wide range of industry stakeholder groups, was employed to identify the drivers for and constraints on construction productivity enhancement in Hong Kong and explore the interrelated insights into the drivers and constraints.

The study conceptualised and validated a systemic framework for examining construction industry productivity, and developed three causal loop diagrams (CLDs) for illustrating the dynamic structures that underpin the complex systems of the drivers and constraints.

Although the scope of the study was limited to Hong Kong, the results could be interpreted for critical learning in other urban contexts.

The systemic perspective of construction productivity and the CLDs of the drivers and constraints support the systems thinking of industry stakeholders in the formulation of holistic strategies for long-term construction industry productivity enhancement.

The study conceptualises construction productivity from a systemic perspective and provides empirically supported CLDs to facilitate future investigations into the complex system of construction productivity.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

Improvements in the facilitation of foreign direct investment (FDI) through institutional quality play a significant role in the establishment of an open economy. However, the impact of institutional quality on the facilitation of FDI along the Belt and Road countries is not well explored. This study aims to empirically investigate the influence of institutional quality on the degree of FDI facilitation and explore the impact mechanism using national-level panel data from countries along the Belt and Road.

For the data set, all variables have been normalized, and principal component analysis has been used. For the empirical models, robust standard errors and dynamic GMM method have been used to alleviate heteroscedasticity and endogeneity.

The empirical results indicate that institutional quality has a significantly positive effect on the degree of FDI facilitation. Furthermore, the impact mechanism involves the mediating mechanism. In other words, the effect of institutional quality that promotes FDI facilitation is influenced by factors such as laws and regulations. In addition, the implementation of the Belt and Road Initiative (BRI) has significantly enhanced the promotional effect of institutional quality on the facilitation of FDI.

Policymakers should focus on improving the institutional quality and the influence of mediating mechanisms, such as policies and regulations, in the institutional environment.

This study contributes to extant literature on the impact of institutional quality on FDI facilitation of significance to China, the BRI countries, and other countries to facilitate openness in international investment. This study also contributes to the extant literature on the influence of the BRI on the development of BRI countries. This will inform policy makers, investment institutions and enterprises about the development of effective policies to aid the development of BRI countries and improve the efficiency and the returns on FDI.

This paper explores the vocational training of construction workers in Guangdong Province of China and identifies its position in the global political-economic spectrum of skill formation.

The paper reviews construction vocational education and training (VET) of major political economies to develop a theoretical framework that guides an in-depth case study of Guangdong. Document analysis, field trip observations, meetings and semi-structured interviews were combined to explore the political-economic environment, political stakeholders and quality assurance mechanisms of industrial training in Guangdong's construction sector. The findings were compared with construction VET of other economies reported in the literature.

Construction training in Guangdong is deeply rooted in the local history and culture, under strong dominance of the state, while continually evolves to respond to the fluid market and therefore can be conceptualised as “market-in-state”. The political stakeholders are embedded within the state to ensure that skills policies are implemented in-line with industry policies. The differences between the training of Guangdong and its foreign counterparts are attributed to their divergent political-economic models.

As the case study was undertaken only with Guangdong, the generalisability of its findings can be improved through future research within a broader context of multiple provinces of China through both qualitative and quantitative research approaches.

Plausible foreign VET approaches are likely adaptable to the Chinese context only when conducive political-economic environment could be enabled. The findings are useful for developing countries to learn from the VET experience of industrialised economies. Construction workers' training in Guangdong can be improved by strengthening labour regulation at lower subcontracting levels and ensuring the presence of industrial associations and unions for collective training supervision.

The paper contributes to the field of construction engineering and management with a theoretical framework that guides empirical studies on the influence of the political-economic environment upon the ways political stakeholders develop and participate in construction VET. The exploration based on this framework revealed the position of the vocational training of construction workers in Guangdong in the global political-economic spectrum of skill formation.

While the investment in construction projects has increased over the past few decades, low construction project productivity (CPP) appeared to be persistent, thereby reflecting an “investment-in-failure” paradox between the investment and CPP. Hence, this paper aims to develop a systematic and holistic CPP evaluation framework to explain the apparent paradox in the construction industry.

The paper first reviews the literature about the theories of system, production, principal–agent and project success evaluation to re-conceptualise the CPP and proposes a two-stage CPP evaluation framework. The framework is subsequently explored through a sequential qualitative mixed-methods design within the context of the Hong Kong construction industry by combining 32 semi-structured interviews with senior industry experts and exploratory case studies, with three real-life construction projects.

The paper identifies three system boundaries for CPP evaluation, that is, parameter, timeframe and stakeholder, and develops a two-stage CPP evaluation framework to indicate site efficiency and utilisation effectiveness, thereby accessing the productivity of both the construction and post-construction stages. The “investment-in-failure” paradox associated with current CPP evaluation approaches is primarily attributed to the narrowly defined CPP boundaries.

The qualitative exploration of the evaluation framework only focusses on the Hong Kong construction industry. Further case studies within other urban contexts could be used to improve the generalisability of the findings. Quantitative research is also necessary to advance theoretical development of the two-stage CPP evaluation.

The systemic CPP conceptualisation and the two-stage CPP evaluation framework support the systems thinking of industry stakeholders and enable them to formulate holistic strategies for long-term CPP enhancement.

The research demonstrates the needs to expand the system boundaries of CPP to reflect its systemic value and to shift the paradigm of CPP evaluation from being output-orientated and quantity-focussed to being outcome-orientated and value-focussed.

This study aims to investigate the effect of post-treatment on anti-corrosion performance of Al coating on the surface of Ti-6Al-4V (TC4) fastener.

The Al coatings with different layer structures were prepared on TC4 by middle-frequency and direct-current combined magnetron sputtering. The cross-sectional morphology and surface roughness of coatings were characterized by scanning electron microscope and atomic force microscope. The corrosion resistance was evaluated by electrochemical method. The monolayer coating was post-treated by Alodine chemical conversion, Ar⁺ bombardment and a combination of two methods above.

The results show that the interfaces in bilayer and trilayer coatings reduce the defects. Ar⁺ bombardment reduces the corrosion current density, and Alodine chemical conversion leads to a higher pitting corrosion potential. The combined post-treatment has the highest polarization resistance.

The corrosion resistance of the Al coating is enhanced as the layer quantity increases. The combination of two post-treatments, Ar⁺ bombardment and Alodine chemical conversion, could achieve an overall improvement in corrosion resistance of Al coating.

The return behavior of customers has a great impact on the e-retail industry and has resulted in the emergence of return-freight insurance (RI). Additionally, customer loss aversion arising from returns affects e-retailers' decisions and manufacturers' profits. Therefore, the main purpose of the authors' study is to determine how e-retailers and manufacturers choose their RI strategy and pricing according to customers' loss aversion.

The authors propose three scenarios: no RI, customer purchase RI and free e-retail RI (FRI). Meanwhile, the authors also model a Stackelberg game between e-retailers and manufacturers for analysis. Then, according to customer return behavior and loss aversion, the authors study the optimal pricing decision and RI premium allocation scheme for e-retailers and manufacturers under different scenarios.

It was found that the loss sensitivity reduces customers' willingness to buy RI, which is not conducive to the development of e-retailers and manufacturers. Additionally, with higher loss sensitivity, e-retailers and manufacturers offer FRI to gain higher profits, which supports the implementation of the FRI strategy.

The authors introduce customers' loss aversion into RI to analyze the optimal pricing decisions and profits of e-retailers and manufacturers, enriching the application of loss aversion theory. In addition, this study analyzes the two-way cost-sharing mechanism between manufacturers and e-retailers to provide FRI, which provides a theoretical basis for RI premium sharing.

Building on the theory of brand psychological ownership, this paper aims to explore the mediating role of brand psychological ownership in the relationship between brand personality (innocence/coolness) and consumers’ preferences, as well as identify the boundary conditions of this relationship.

To test the hypotheses, a series of four experiments were conducted in Wuhan, a city in southern China, using questionnaires administered at two universities and two supermarkets. Hypotheses were tested using PLS-SEM in SmartPLS 4.

The results indicate that brand personality, specifically the dimensions of innocence and coolness, has a significant impact on consumers’ brand preferences. Brands with a cool personality are preferred over those with an innocent personality. Moreover, the relationship between brand personality and consumers’ brand preferences is moderated by power motivation and identity centrality.

This study contributes to the literature by differentiating between brand personality of innocence and coolness as two separate constructs and proposing brand psychological ownership as a mechanism through which brand personality affects brand preferences. The study’s samples were drawn from universities and supermarkets in southern China, providing evidence for the significant moderating effects of power motivation and identity centrality on consumers’ brand preferences.