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Construction workers’ safety compliance is attracting considerable critical attention as it plays a decisive role in improving safety on construction sites. This study applied the concept of differentiating safety compliance into deep compliance (DC) and surface compliance (SC) and relied on trait activation theory to investigate the effects of situational awareness (SA) and emotional intelligence (EI) on safety compliance.

Cross-sectional survey data were collected from 239 construction workers in Australia, and these responses were statistically analyzed using the partial least squares structural equation modeling (PLS-SEM) to validate the proposed model.

Results revealed that both EI and SA positively impacted DC and negatively impacted SC. Moreover, SA partially mediated the link between EI and two types of safety compliance (DC and SC). The outcomes showed that construction workers’ ability in regulating their emotions could influence their perception of environmental cues and the effectiveness of safety compliance behavior.

This study sheds light on investigating the antecedents of DC and SC from the perspective of trait activation theory. The findings also have practical implications, stating that construction site managers or safety professionals should consider providing training on construction workers’ EI and SA to enhance their willingness to expend conscious efforts in complying with safety rules and procedures, which can lead to improved safety outcomes.

This study aims to explore the relationship between network position and innovation under major environmental turbulence.

The authors use a difference-in-differences identification approach using the 2009 Industry Revitalization Plan in response to the global financial crisis as a natural experiment with a sample of Chinese listed firms from 2001 to 2017.

The findings show that a major environmental turbulence can facilitate firm innovation, and firms that occupy central positions in the interlock network show worse innovation performance while firms with high brokerage show better innovation performance.

The literature on environmental implication has largely focused on the threats and overlooked the potential opportunities. Moreover, social network literature has elaborated on the benefits and constraints of network positions from a static perspective but largely overlooked their implications facing environmental change. By exploring the bright side of major environmental turbulence and including this factor as a key contingency in exploring the effects of centrality and brokerage, this study integrates external environmental context with social network research and provides empirical evidence responding to the call for more attention to network dynamics and extends our understanding of the context-contingent network effects on firm innovation.

Green innovation, particularly in manufacturing firms, is one of the most advocated methods to curb the effects of climate change. This study aims to investigate the impact of the integration of green customers and suppliers on the green innovation performance of food manufacturing firms in Pakistan. The institutional and resource-based view theories determine the moderating role of regulatory pressure and the mediating role of green knowledge integration capability (GKIC).

Data was collected from 511 middle management-level employees of food manufacturing firms in Pakistan. The questionnaire was tested for reliability and validity. Hierarchical regression is used to test the proposed hypothesis.

A marginal improvement in integration with green customers can increase the green innovation performance (GIP) of a firm by 23.6%. Green supplier integration can improve the GIP by 14.2%, whereas the GKIC mediates the relationship between Green Customers Integration (GCI) and GIP but not for green suppliers integration (GSI). The moderating effect of regulatory pressure was significant for the relationship between GCI and GIP but insignificant for GSI.

Food manufacturing accounts for approximately 16% of global green house gases (GHG) emissions. Sustainable development goals (SDGs) cannot be achieved without a significant decrease in GHG emissions by food manufacturing companies. Therefore, it is crucial to investigate firms' green innovation performance in this sector. The findings of this study can help policymakers develop policies for achieving SDGs.

Building on social exchange theory (SET), the main aim of this paper is to empirically study the impact of high-commitment work practices (HCWPs) systems on radical innovation. Additionally, the paper examines the mediating roles of employee innovative work behaviour (IWB) and knowledge sharing (KS) in the relationship between HCWPs and radical innovation.

Using a survey questionnaire, data were collected from employees working in pharmaceutical, manufacturing and technological industries in Jordan. A total of 408 employees participated in the study. Structural equation modelling (SEM) using AMOS v28 was employed to test the research hypotheses.

This research found that HCWPs in the form of a bundle of human resource management (HRM) practices are significant for employee IWB and KS. However, similar to previous studies, this paper failed to find a direct significant impact for HCWPs on radical innovation. Rather, the impact was mediated by employee IWB. Additionally, this paper found that HCWPs are significant for KS and that KS is significant for employee IWB.

Distinctively, this paper considered the mediating effect of employee IWB on radical innovation. Extant research treated IWB as a consequence of organisational arrangements such as HRM practices; this paper considered IWB as a foundation and source for other significant organisational outcomes, namely radical innovation. Additionally, the paper considered employees' perspectives in studying the relationship between HRM, KS, IWB and radical innovation.

Data-driven models are increasingly being used to predict the fatigue life of many engineering components exposed to multiaxial loading. However, owing to their high data requirements, they are cost-prohibitive and underperforming for application scenarios with limited data. Therefore, it is essential to develop an advanced model with good applicability to small-sample problems for multiaxial fatigue life assessment.

Drawing inspiration from the modeling strategy of empirical multiaxial fatigue models, a modular neural network-based model is proposed with assembly of three sub-networks in series: the first two sub-networks undergo pretraining using uniaxial fatigue data and are then connected to a third sub-network trained on a few multiaxial fatigue data. Moreover, general material properties and necessary loading parameters are used as inputs in place of explicit damage parameters, ensuring the universality of the proposed model.

Based on extensive experimental evaluations, it is demonstrated that the proposed model outperforms empirical models and conventional data-driven models in terms of prediction accuracy and data demand. It also holds good transferability across various multiaxial loading cases.

The proposed model explores a new avenue to incorporate uniaxial fatigue data into the data-driven modeling of multiaxial fatigue life, which can reduce the data requirement under the promise of maintaining good prediction accuracy.

Although trust is generally taken as a fundamental factor in influencing relational behavior in contractor–subcontractor collaboration, the determination of an optimal level of trust is still lacking. Trust with an optimal tipping point that matches dependence best is considered the optimal trust to improve relational behavior between general contractors and subcontractors. To fill the knowledge gap, this study explores how combinations of trust and dependence trigger relational behavior between general contractors and subcontractors through a configurational approach.

Questionnaires were administered to 228 middle management and technical staff members of the general contractor. The data were analyzed using fuzzy-set qualitative comparative analysis (fsQCA), and the inductive analytic method allowed researchers to explore configurations of different dimensions and levels of dependence and trust.

Necessity analysis results indicated that neither dependence nor trust was a necessary condition for facilitating relational behavior. Through sufficiency analysis, four configurations of optimal trust matched with dependence were identified in contractor–subcontractor collaboration. Even if contractors rely only on subcontractors for resources, the optimal trust between contractors and subcontractors should include both institution- and cognition-based trust. In the event that contractor–subcontractor collaboration involves relational dependence, both affect- and cognition-based trust are necessary for the optimal trust.

This study enhances existing research by delving deeper into a nuanced understanding of optimal trust in dependence scenarios, and enriches project governance theory by uncovering the internal transmission of relational governance. Practically, this study offers general contractors guidance on how to establish optimal trust strategies based on the dual dependence level with subcontractors, which can facilitate subcontractors' relational behavior, and ultimately improve contractor–subcontractor collaboration performance.

The purpose of this study is to explore the causal relationship between smart transportation technology innovation and green transportation efficiency.

A comprehensive framework is used in this paper to assess the level of green transportation efficiency in China based on the instrumental variable – generalized method of moments model, followed by an examination of the impact of innovation in smart transportation technology on green transportation efficiency. Additionally, their non-linear relationship is explored, as are their important moderating and mediating effects.

The findings indicate that, first, the efficiency of green transportation is significantly enhanced by innovation in smart transportation technology, which means that investing in such technologies contributes to improving green transportation efficiency. Second, in areas where green transportation efficiency is initially low, smart transportation technology innovation exerts a particularly potent influence in driving green transportation efficiency, which underscores the pivotal role of such innovation in bolstering efficiency when it is lacking. Third, the relationship between smart transportation technology innovation and green transportation efficiency is moderated by information and communication technology, and the influence of smart transportation technology innovation on green transportation efficiency is realized through an increase in energy efficiency and carbon emissions efficiency.

Advancing green transportation is essential in establishing a low-carbon trajectory within the transportation sector.

Network embeddedness has been widely considered in enterprise innovation as an effective means of overcoming resource dilemmas. However, while focussing on acquiring external innovation resources, the existing research often ignores the vital role of internal routine updates. Therefore, this study explores the mechanism by which network embeddedness affects innovation performance of enterprises from the perspective of organisational routine updating.

This paper proposes a theoretical model based on social network theory and organisational routines–immune response theory. A total of 328 pieces of research data on high-tech enterprises in China were collected, and the hypotheses were verified using hierarchical regression analysis.

The results show that the two forms of network embeddedness – structural embeddedness and relational embeddedness, have a positive effect on enterprise innovation performance and a significant positive effect on organisational routine revision and organisational routine creation. Both organisational routine revision and organisational routine creation positively affect enterprise innovation performance and partially mediate the relationship between network embeddedness and enterprise innovation performance.

This conclusion provides a new perspective on the impact of network embeddedness on enterprise innovation performance and expands the related research on organisational routine updating. This study provides a theoretical reference for high-tech enterprises to improve their competitiveness and innovation performance through network embeddedness and organisational routine updating.

Technology 4.0 comes with a challenge to understand the degree of users’ willingness to adopt a digital transformation. Metaverse, being a digital transformation, enables real-world activities in the virtual environment, which attracts organizations to adopt the new fascinating technology. This paper thus explores the uses and gratification factors affecting user adoption and recommendation of metaverse from the management perspective.

The study adopts a mixed approach where structural topic modeling is used to analyze tweets about the metaverse, and the themes uncovered from structural topic modeling were further analyzed through data collection using structural equation modeling.

The analyses revealed that social interaction, escapism, convenient navigability, and telepresence significantly affect adoption intent and recommendation to use metaverse, while the trendiness showed insignificance. In the metaverse, users can embody avatars or digital representations, users can express themselves, communicate nonverbally, and interact with others in a more natural and intuitive manner.

This paper contributes to research as it is the first of its kind to explore the factors affecting adoption intent and recommendation to use metaverse using Uses and Gratification theory in a mixed approach. Moreover, the authors performed a two-step study involving both qualitative and quantitative techniques, giving a new perspective to the metaverse-related study.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.