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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.

The purpose of this study is to scrutinize the influence of individual learning styles on the effectiveness of augmented reality (AR)-based learning in structural engineering. There has been a lack of research examining the correlation between learning efficiency and learning style, particularly in the context of quantitatively assessing the efficacy of AR in structural engineering education.

Using Kolb’s experiential learning theory (ELT), a model that emphasizes learning through experience, students from the construction management department are assigned four learning styles (converging, assimilating, diverging and accommodating). Performance data were gathered, appraised, and compared through the three dimensions from the Knowledge, Attitude and Practices (KAP) survey model across four categories of Kolb’s learning styles in both text-graph (TG)-based and AR-based learning settings.

The findings indicate that AR-based materials positively impact structural engineering education by enhancing overall learning performance more than TG-based materials. It is also found that the learning style has a profound influence on learning effectiveness, with AR technology markedly improving the information retrieval processes, particularly for converging and assimilating learners, then diverging learners, with a less significant impact on accommodating learners.

These results corroborate prior research analyzing learners' outcomes with hypermedia and informational learning systems. It was found that learners with an “abstract” approach (convergers and assimilators) outperform those with a “concrete” approach (divergers and accommodators). This research emphasizes the importance of considering learning styles before integrating technologies into civil engineering education, thereby assisting software developers and educational institutions in creating more effective teaching materials tailored to specific learning styles.

There has been a dramatic rise in the use of online food delivery apps (FDAs) services since the COVID-19 pandemic. Though online FDAs have contributed significantly to the rise in demand for products from the gourmet industry, little is known regarding the factors that inspire customers to order from online FDAs, subsequently influencing customers’ satisfaction. Considering the knowledge gap, this study utilizes the stimulus-organism-response (S-O-R) model to conceptualize the factors: stimuli (eWOM, online reviews and online deals as external stimuli, and late-night craving and convenience as internal stimuli) that determine the organism level (i.e. customers’ inspiration) to subsequently generate the response (i.e. customers’ satisfaction).

We collected the data from 388 users and analyzed it via partial least squares – structural equation modeling (PLS-SEM).

The results reveal that online reviews, deals, late-night food cravings and convenience positively determine customers’ inspiration and satisfaction. In contrast, eWOM fails to impact customers’ inspiration directly and indirectly, affecting customers’ satisfaction through inspiration. Besides, customers’ inspiration positively mediates the relationship between stimuli (e.g. online reviews, online deals, late-night cravings and convenience) and customers’ satisfaction.

This study is novel in that it explores the impact of internal (late-night craving and convenience) and external (eWOM, online reviews and online deals) stimuli on customer inspiration and subsequently predicts customer satisfaction. We also expand prior studies on food delivery apps by studying customer inspiration as a mediating mechanism between internal and external stimuli and customer satisfaction.

We expect to provide a complete theoretical framework and large sample evidence on the impact of corporate social responsibility (CSR) on the efficiency of labor investment. We also hope to provide micro-evidence based on labor investment behavior for the two-sided impact of corporate CSR behavior.

This paper measures labor investment efficiency by estimating the difference between actual and expected net hiring of enterprises. CSR is measured on the basis of the CSR score of Chinese listed companies published by Hexun.com. A regression model is constructed to analyze the relationship between CSR and labor investment efficiency. Possible endogeneity problems are controlled by lagging independent variables, propensity score matching method and difference-in-difference method.

Results show that CSR can improve labor investment efficiency by reducing over-hiring and under-hiring in emerging markets. The existence of the mediating effect of agency cost, information disclosure quality and employment fluctuation confirms that CSR improves labor investment efficiency through two mechanisms of corporate governance and labor market friction. The improvement effect of CSR on labor investment efficiency is more significant in non-state-owned, high CEO shareholding ratio and high-average urban wage enterprises.

In conclusion, our study is an important supplement to the existing research on the factors affecting labor investment efficiency. Our research conclusions will be helpful for enterprises in developing countries or enterprises in labor-intensive industries to improve labor investment inefficiency. The conclusion of the mechanism analysis in this paper provides more complete and reliable microscopic evidence for accurately identifying the specific path of CSR's impact on labor investment efficiency. This paper verifies the positive impact of CSR from the perspective of labor investment efficiency in the context of a developing country, which provides evidence for the theoretical conflicts related to CSR based on the effectiveness of enterprise labor investment decisions.

Prefabricated building has emerged as a hallmark of modern construction industrialization and a pivotal driver of industrial upgrading. In this new building type, the supply of high-quality prefabricated components plays a crucial role in ensuring project quality, cost-effectiveness and on-time completion. Consequently, selecting the optimum suppliers for these components is vital. This study provides valuable insights for construction enterprises, guiding them in the optimal selection of prefabricated component suppliers and thereby contributing to the sustainable development of the construction industry.

The entropy weight method is used to integrate and rank 19 commonly used evaluation indices, forming a supplier evaluation system from the enterprises perspective. Subsequently, the VIKOR multi-attribute decision model, combined with a comprehensive evaluation method based on cloud modeling, is applied to identify the most suitable suppliers through case study.

The findings emphasized that product quality, particularly the component compliance rate, is paramount in supplier selection. Additionally, companies should prioritize cost management and fundamental supplier capabilities, such as transportation efficiency and operational flexibility, while fostering strong partnerships with high-quality suppliers. Furthermore, all stakeholders need to enhance the supply chain’s responsiveness and adaptability, ensuring these improvements are achieved without strict cost controls.

This study minimizes the influence of subjective biases from decision-makers’ by integrating quantitative and qualitative analysis methods, thereby enhancing the comprehensiveness and accuracy of evaluations. By effectively addressing the fuzziness and uncertainty inherent in evaluation data, it establishes a robust system for selecting prefabricated building suppliers. This approach offers reliable and practical decision support, providing theoretical backing for enterprises in choosing prefabricated component suppliers and promoting the sustainable development of the prefabricated construction industry.

We examine the relationship between board demographic diversity, board structural diversity, board capital diversity and corporate carbon performance (CCP). Additionally, we investigate how corporate sustainable resource use mediates these relationships.

We utilize unbalanced panel data from Refinitiv Eikon covering 9,960 global firms from 2002 to 2022. We conduct a panel regression analysis to examine the relationship between board demographic diversity, board structural diversity, board capital diversity and CCP. In addition, we estimate entropy balancing estimation and two-step system GMM to address endogeneity issues.

The results indicate that board demographic diversity (including tenure, gender, and cultural diversity), structural diversity (such as board independence, board size, CEO-chairman duality, board meetings, and board compensation), and capital diversity (comprising board member affiliation and specific skills) all have a positive and significant association with corporate carbon performance. Additionally, our findings reveal that corporate sustainable resource use fully mediates the relationship between board demographic diversity and CCP and partially mediates the relationship between board structural diversity, board capital diversity, and CCP.

Our study findings are based on a diverse range of global firms, ensuring that the results address the global challenges of firm-level climate change response and governance issues.

Our group diversity constructs offer new insights into the literature and further advance research on board group diversity. Additionally, for the first time, we explore the mediating role of sustainable resource use through the resource-based view (RBV) between-group diversity attributes and corporate carbon performance.

This study aims to examine the factors influencing Filipinos’ intention to adopt mobile wallets to pay social health insurance premiums.

The authors used an integrative model framework using the key indicators from the lenses of the technology acceptance model, the unified theory of acceptance and usage of technology model 2 and the theory of planned behavior with trust serving as a mediator. The sample size was calculated using an inverse square root ratio composed of 624 survey participants purposively identified across selected cities in the Philippines. The formulated hypotheses were examined using partial least squares structural equation modeling and deep learning–based artificial neural networks.

The results substantiate this study’s integrative model explaining the positive influence and relative importance of perceived usefulness, habit and subjective norms in developing trust in mobile wallet applications. Moreover, health insurance literacy, subjective norms and trust positively and significantly drive individuals’ intentions to adopt mobile wallets as a payment platform for social health insurance premiums. The mediation analysis also exemplified that trust positively mediates the influences of technology acceptance factors such as perceived usefulness, habit and subjective norms in the intention of individuals to adopt mobile wallet applications in social health insurance payment of premiums.

This study is a pioneering study in the Philippine context that used an integrative model to predict and explain the relative importance of predictors of Filipino intentions to adopt mobile wallets as a payment platform for social health insurance premiums.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.