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Aluminum alloy is considered an ideal material in aerospace, automobile and other fields because of its lightweight, high specific strength and easy processing. However, low hardness and strength of the surface of aluminum alloys are the main factors that limit their applications. The purpose of this study is to obtain a composite coating with high hardness and lubricating properties by applying GO–PVA over MAO coating.

A pulsed bipolar power supply was used as power supply to prepare the micro-arc oxidation (MAO) coating on 6061 aluminum sample. Then a graphene oxide-polyvinyl alcohol (GO–PVA) composite coating was prepared on MAO coating for subsequent experiments. Samples were characterized by Fourier infrared spectroscopy, X-ray diffraction, Raman spectroscopy and thermogravimetric analysis. The friction test is carried out by the relative movement of the copper ball and the aluminum disk on the friction tester.

Results showed that the friction coefficient of MAO samples was reduced by 80% after treated with GO–PVA composite film.

This research has made a certain contribution to the surface hardness and tribological issues involved in the lightweight design of aluminum alloys.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0427/

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

This study empirically investigates the impact of directors' and officers' liability insurance on corporate environmental investment.

This paper takes A-share listed firms in the most polluting industries from 2013 to 2019 as the research sample. The authors perform multiple regression analysis to examine the research question, and other approaches such as PSM and Heckman two-stage model are applied to test the robustness of the main results.

The authors find that D&O insurance insured firms significantly decrease the level of corporate environmental investment. The results keep consistent after alleviating potential endogenous concerns. Further analysis shows that the negative association between D&O insurance and environmental investment is more pronounced in firms facing greater environmental pressure and stronger market supervision, and firms located in regions with a rich legal environment.

This research extends the literature on the antecedents of corporate environmental investment and the consequences of D&O insurance.

The study may deepen people's understanding of D&O insurance and inform them of its negative effects. This research sheds light on the potential factor resulting in a relatively low level of corporate environmental investment in China, which has an important policy implication for government to carry out some regulations to make a difference.

Against the backdrop that more importance has been attached to environmental protection globally, this paper is the first study to examine the impact of D&O insurance on corporate environmental investment in the context of the transitional and emerging market-China.

Limited use of Internet of Things (IoT) technology on construction sites has restricted its value in the construction industry. To propel its widespread application, this paper explores the influencing factors and action paths of construction companies' IoT technology adoption behavior.

First, literature research, technology adoption theories, and semi-structured expert interviews were employed to build the adoption model. Second, a questionnaire survey was conducted among Chinese construction contractors to collect empirical data. Third, the structural equation model method and regression analysis were used to test the adoption model. Finally, the findings were further validated with interviews, case studies, and field observations.

External environmental pressure (EEP), perceived benefit (PB), top management support (TMS), company resource readiness (CRR), adoption intention (AI), and perceived compatibility (PCA) have a direct positive impact on adoption behavior (AB). In contrast, perceived cost (PC) and perceived complexity (PCL) exert a direct negative impact on AB. The EEP, PB, and PC are critical factors affecting AB, whereas AI is strongly affected by CRR and TMS. Besides, AI plays a part mediating role in the relationship between seven factors and AB. Company size and nature positively moderate AI's positive effect on AB.

This paper contributes to the knowledge of IoT technology adoption behavior in the construction sector by applying the technology adoption theories. Exploring the implementation barriers and drivers of IoT technology in construction sites from the perspective of organizational technology adoption behavior and introducing moderating variables to explain adoption behavior are innovations of this paper. The findings can help professionals better understand the IoT technology adoption barriers and enhance construction companies' adoption awareness, demand, and ability. This work also provides a reference for understanding the impact mechanism of the adoption behavior of other innovative technologies in construction.

Based on the resource orchestration perspective, this study aims to explore whether and how strategic supply management (SSM) affects firms’ operational performance (OP) and innovation performance (IP).

Survey data comprising 404 valid responses are collected from traditional manufacturing firms in China. Confirmatory factor analysis confirms the reliability and validity of the measures. Structural equation modeling and bootstrapping are used to test all hypotheses.

SSM improves firms’ OP and IP. Furthermore, supply base resource mobilization (SBRM) and supply market resource mobilization (SMRM) have partial mediating effects on the relationships. SBRM has a greater effect on OP, while SMRM has a greater effect on IP. In addition, these two types of resource mobilization form different mediating paths between SSM and firm performance, and environmental uncertainty positively moderates this relationship.

With the development of national innovation strategies such as the “Made in China 2025” plan, the Chinese manufacturing industry aims to move from low-cost manufacturing to innovative and high-quality manufacturing. The study’s findings further emphasize the role of purchasing and supply management in external resource management. In addition to demonstrating the differential effects of heterogeneous resource mobilization on OP and IP, different mediation pathways through external resources mobilization are identified in the relationship between SSM and firm performance.

This paper examines the role of purchasing in facilitating early supplier involvement in new product development (NPD) in contexts of technological uncertainty (TU). Taking a purchasing perspective, it develops a moderate model to explain the effects of supplier involvement on NPD performance and whether and how knowledge orchestration capability (KOC) and TU affect these relationships. Additionally, KOC drivers are defined.

A total of 317 usable questionnaires from Chinese high-technology firms were collected. Moderated multiple regression (MMR) was used to test all hypotheses. Resource orchestration theory (ROT) was the adopted theoretical lens.

Two forms of supplier involvement (as knowledge source and co-creator) were found to distinctly affect NPD performance and have potential substitutive relationships. Purchasing KOC positively moderates the relationships between forms of supplier involvement on NPD performance. TU strengthens the moderating role of purchasing KOC. Furthermore, purchasing status and supply complexity are important antecedents for purchasing KOC.

These findings serve as a blueprint for involving purchasing in technologically uncertain NPD projects and improve supplier NPD integration. Additionally, management should recognize the purchasing function's role and empower it to identify ideas, knowledge and solutions within supply networks.

This research contributes to the ROT by examining the role of purchasing KOC on supplier involvement in NPD performance, especially under TU. Moreover, it demonstrates significant and positive relations between purchasing department status and external supply complexity on its KOC.

Although supply chain collaboration (SCC) theoretically boosts the adoption of supply chain finance (SCF) through information sharing and cost savings, many companies with good supply chain partnerships still hesitate to engage in SCF. To disentangle this puzzle, this study aims to explore how two dimensions of information transparency (i.e. information quantity and information quality) and two types of transaction dependence (i.e. dependence on suppliers and dependence on customers) influence the relationship between SCC and SCF adoption.

This study uses secondary survey data from a Chinese bank, including 464 Chinese companies that have adopted SCF to varying degrees. This study then performs the logistic regression analysis to test the hypotheses.

This study empirically confirms that SCC shows a positive relationship with SCF adoption. More interestingly, information quantity negatively moderates this positive relationship, whereas information quality positively moderates this positive relationship. Most surprisingly, dependence on customers rather than dependence on suppliers strengthens this positive relationship.

This study makes theoretical contributions to the SCF literature by demonstrating the distinct moderating mechanisms regarding the relationship between SCC and SCF adoption. The findings also help companies reexamine their interactions with supply chain members.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

This study aims to investigate the role of regional environmental transparency on corporate environmental disclosure.

This study uses the introduction of a nationwide automated air pollution monitoring network in China as a quasi-natural experiment and employs regression analysis. Robustness checks, including parallel trend test and placebo test, are performed to test the robustness of the results.

Sharing air pollution data with the public can improve corporate environmental disclosure. Firms with poorer environmental, social and governance (ESG) performance prefer to disclose less informative information after the automated network is implemented compared with firms with better ESG performance. The relationship between information sharing and corporate environmental transparency is more pronounced when local air pollution is severer, firms face stronger investor scrutiny and firms are from heavily polluting industries. The mechanism tests suggest the automated system can draw public environmental attention and improve governments’ aspiration for environmental governance. Finally, corporate environmental disclosure can reduce stock price crash risk and cost of equity.

Real-time pollution data reporting is an important solution to raising public environmental awareness and then enhancing the effectiveness of pollution control.

This study has implications for policy-making regarding environmental governance and environmental disclosure.

This study confirms that pollution information transparency can motivate firms to increase environmental disclosure.

To further promote application of BIM technology in construction of prefabricated buildings, influencing factors and evolution laws of willingness to apply BIM technology are explored from the perspective of willingness of participants.

In this paper, a tripartite game model involving the design firm, component manufacturer and construction firm is constructed and a system dynamics method is used to explore the influencing factors and game evolution path of three parties' application of BIM technology, from three perspectives, cost, benefit and risk.

The government should formulate measures for promoting the application of BIM according to different BIM application willingness of the parties. When pursuing deeper BIM application, the design firm should pay attention to reducing the speculative benefits of the component manufacturer and the construction firm. The design firm and the component manufacturer should pay attention to balancing the cost and benefit of the design firm while enhancing collaborative efforts. When the component manufacturer and the construction firm cooperate closely, it is necessary to pay attention to balanced distribution of interests of both parties and lower the risk of BIM application.

This study fills a research gap by comprehensively investigating the influencing factors and game evolution paths of willingness of the three parties to apply BIM technology to prefabricated buildings. The research helps to effectively improve the building quality and construction efficiency, and is expected to contribute to the sustainability of built environment in the context of circular economy in China.