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This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

Embodied intelligent robots are the iconic productivity of the Industry 4.0 era, and their potential to bring about a productivity surge mainly comes from the driving force of robots on innovation rather than efficiency. However, the dynamic impact of robots on the innovation capability of enterprises has not been empirically tested.

This study integrates panel vector autoregression and threshold effects to investigate this dynamic relationship by a multi-level analysis based on data of Chinese A-share manufacturing listed enterprises.

(1) The short-term momentum of industrial robot applications (IRA) on exploitative innovation (EII) is significant and the long-term momentum on exploratory innovation (ERI) is stronger. (2) EII affected by IRA is the main source of short-term total factor productivity (TFP) growth, while ERI is the driving factor for long-term TFP growth. (3) The impact of IRA on TFP exhibits a double-threshold effect based on ERI and follows a “stepped” incremental pattern. The promoting effect of IRA on TFP will significantly increase only when ERI surpasses certain thresholds.

Industrial robots accelerate the potential productivity growth in the long term, mainly coming from the augmented contribution of ERI, providing reference and inspiration for enterprises to fully utilize the endogenous growth potential of robots and implement innovation strategies. It also provides forward-looking guidance for organisations to undertake adaptive changes for the forthcoming AI economic revolution.

On the premise of verifying whether the platformization organization of DEEs is born, this work aims to explore the evolutionary process of the organizational structure of digital entrepreneurial enterprises (DEEs) and to further reveal the drivers of organizational structure evolution from the perspective of data resources.

The authors use a longitudinal two-case approach to analyze rich archival and interview data from two DEEs in China.

The findings reveal that the organizational structure of DEEs evolves from hierarchy, network and flatlization to platformization, that the drivers of evolution include building data flow channels, removing barriers of data flow and forming data rules. Meanwhile, the coordination devices in this process have gradually evolved from hierarchy to standard operating procedures, shared culture, norms, etc. to achieve a balance between commercial and creative success.

This work develops a framework for the evolution of organizational structure of DEEs from organization design theory lens and provide some management insights into the development of DEEs.

This paper uses the Technology Acceptance Model (TAM) to assess the readiness of the HR function within the UK Civil Service (CSHR) to implement AI to support performance. Academic literature in relation to AI acceptance in HR functions is currently limited, so this paper aims to establish a better understanding of the current landscape and level of ambition in this area.

A quantitative research approach was adopted to determine likely behavioral intentions of workers in the human resource (HR)function if AI were implemented, by investigating key aspects of the TAM (the perceived usefulness of AI and the transparency of the CSHR in adopting AI).

While the results suggest that the CSHR is not ready to harness AI opportunities, employees were personally ready, despite perceiving a lack of sufficient knowledge in this area. The paper identifies that more time needs to be spent on raising awareness and upskilling the HR function before the CS can be considered fully ready to harness these opportunities.

The penetration of artificial intelligence (AI) technologies into the global workforce brings transformative potential to the governance structures and use of digital platforms in public sector organizations. AI is likely to play a role in the operation of HR functions and influence how they might operate in the near future.

The research explores how artificial intelligence-driven natural language processing can be integrated into digital game-based learning to enhance science education worldwide. It conducted a thorough literature review focusing on electronic bibliographic databases, identifying nine key themes. The chapter discusses the potential of digital games to improve science education, including theoretical frameworks, integration techniques and problem-solving methods. It also examines the impact of gaming on critical thinking and natural language processing. The findings suggest that integrating natural language processing into digital game-based learning shows promise for improving inquiry-based teaching and student performance in science education. Digital platforms can offer personalized feedback, support natural language interactions and customize content for each student. However, addressing pedagogical concerns and technological limitations is crucial for fully realizing these benefits. Practically, the research offers guidance for educators, curriculum developers and policymakers. It emphasizes the importance of educating educators on natural language processing, developing inquiry-driven digital learning platforms and establishing supportive policies. This signifies a move toward student-centered learning and the integration of innovative technologies into education. In terms of originality and value, the study contributes to the existing knowledge by discussing challenges and potential strategies for integrating digital game-based learning with natural language processing globally. It suggests that future research and practical efforts in this area can benefit from a comprehensive understanding of the opportunities and implications of this educational approach, informed by extensive research across various themes.

This paper aims to investigate the impact of perceived usefulness of online reviews and crowd cues on restaurant selection. In addition, the authors also examine the moderating role of perceived crowding and gender in this process.

The proposed hypotheses were tested with survey data (N = 200) collected from customers visiting full-service restaurants in Malaysia. The data were analyzed using SEM through Smart PLS.

The findings supported that the perceived usefulness of online reviews and perceived crowding have a positive effect on a consumer's purchase intentions, i.e. their decision of restaurant selection. Besides, a higher level of perceived crowding strengthens the relationship between the perceived usefulness of online reviews and their purchase intentions. This finding delineates that consumers prefer to dine in a crowded place with useful online reviews in an unfamiliar place. Finally, the results show that the effect of the usefulness of online reviews on purchase intentions does not vary with respect to gender (no significant contingent effect). However, the effect of perceived crowding varies with respect to gender – male customers have higher intentions to join crowded restaurants as compared to females.

Limitation of this study is its cross-sectional research design; data were collected in a single time frame. Longitudinal research design can be used to get in-depth knowledge of this phenomenon. Secondly, a non-probability sampling technique was used in this study, future research can used probability sampling technique to enhance generalizability of the study. Moreover, this study focused on the human crowding aspect, future studies can cover both aspects of crowding (human crowding and spatial crowding) in retailing or other service sectors (Blut and Iyer, 2020).

This study has multiple practical implications.

This study extends the current research on usefulness of online reviews and perceived crowding by investigating its direct and conditional effects. Specifically, the authors contribute in extant research by explaining its differential effects for male and female customers, when they select which restaurants to dine.

This study examines the impact of inter-organisational justice (i.e. distributive, procedural and interactional) in the buyer–supplier relationship on supply risk and, in turn, on a firm’s marketing and financial performance.

A structured survey was administered both online and in-person to Jordan-based manufacturing companies. The 137 responses received were analysed using partial least structural equation modelling.

The study found that while establishing both procedural and interactional justice in the relationship has a negative impact on supply risk, promoting distributive justice, surprisingly, has no impact. Moreover, supply risk was found to be detrimental to the firm’s marketing and financial performance.

This study considers only the direct role of inter-organisational justice in reducing supply risk. Future research could enhance our understanding of this role by exploring the underlying mechanisms and conditions that could govern it.

Managers can alleviate supply risk by ensuring procedural and interactional justice in the relationship through involving suppliers in the decision-making processes, consistently adhering to established procedures and communicating transparent and ample information.

Addressing supply risk can help in maintaining community resilience and economic stability.

The study highlights inter-organisational justice as a new approach to mitigating supply risk. Moreover, by examining how supply risk can affect a firm’s marketing performance, it also highlights a new implication of supply risk. Furthermore, by exclusively examining the impact of supply risk on a firm’s financial performance, the study provides a more nuanced interpretation of the effect of supply risk and how it can be reduced.

As the world advances towards a sustainable and more environmentally friendly future, the need for protecting the environment becomes more and more apparent in every area. The tourist business has seen remarkable expansion, making a considerable contribution to the worldwide Gross Domestic Product (GDP). Nevertheless, this expansion has coincided with an increase in the release of greenhouse gases and the deterioration of natural ecosystems. In order to tackle these difficulties, there is an increasing acknowledgment of the need for a fundamental change in approach towards implementing sustainable tourism practices. This chapter analyses the present state of tourism and its contribution to the increase of carbon footprints. It also investigates possible remedies, including the implementation of environmentally friendly hotels, the use of cleaner energy sources, and the adoption of ideas from the circular economy. In addition, by using bibliometric analysis, the chapter offers valuable information on current research patterns in sustainable tourism, such as prominent authors, nations, keywords, and topics. This chapter provides a comprehensive analysis of the current state and future potential of sustainable tourism. It is a vital reference for policymakers, academics, and industry professionals, offering guidance on how to develop a more sustainable and resilient tourist business.