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Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

This paper aims to explore augmented reality (AR) applications in construction safety academic literature and propose possible improvements for future scholarly works. The paper explicitly focuses on AR integration with Construction 4.0 technologies as an effective solution to safety concerns in the construction industry.

This study applied a systematic review approach. In total, 387 potentially relevant articles from databases were identified. Once filtering criteria were applied, 29 eligible papers where selected. The inclusion criteria were being directly associated with construction safety focused on an AR application and AR interactions associated with the Construction 4.0 technologies.

This study investigated the structure of AR applications in construction safety. To this end, the authors studied the safety purposes of AR applications in construction safety: pre-event (intelligent operation, training, safety inspection and hazard alerting), during-event (pinpointing hazard) and post-event (safety estimation) applications. Then, the integration of AR with Construction 4.0 technologies was elaborated. The systematic review also revealed that the AR integration has contributed to developing several technical aspects of AR technology: display, tracking and human–computer interaction. The study results indicate that AR integration with construction is effective in mitigating safety concerns; however, further research studies are required to support this statement.

This study contributes to exploring applications and integrations of AR into construction safety in order to facilitate the leverage of this technology. This review can help encourage practitioners and researchers to conduct further academic investigations into AR application in construction safety.

This study aims to identify causes of (un)ethical behaviour in research and how they influence adherence to research ethics.

The authors developed and tested a conceptual model that includes mediation and helps to understand the mechanism of adherence to ethical standards of research based on the “social judgment theory” (SJT). In Study 1, the authors conducted an exploratory study using the exploratory factor analysis technique to identify factors responsible for adherence to research ethics. In Study 2, the authors used SJT to provide support for establishing a relationship between key variables.

Two factors, “Proclivity to Egoism” and “Proclivity to Emotivism”, were identified based on the personal beliefs of researchers. These factors were found to play an important role in determining the tendency towards adherence to standards of research ethics (Belmont Report and COPE). SJT successfully explains the mechanism of adoption of ethical standards. Adherence to Belmont principles was seen to mediate relationship between factors identified and tendency to adhere to COPE.

Majorly, this study is unique as it establishes and guides to incorporate researchers’ point of view in formulating ethical standards and guidelines, apart, from various other important theoretical and societal implications.

Drawing on a dynamic capability view, this study develops a decision support model that determines the most suitable configuration of strategies and challenges to adopt additive manufacturing (AM) to expedite digital transformation and performance improvement of the surgical and medical device (SMD) supply chain.

To investigate the research objective, a multi-method and multi-study research design was deployed using quality function deployment and fuzzy set qualitative comparative analysis.

The study finds that only resilience strategies or negation (i.e. minimisation) of challenges are not enough; instead, a configuration of resilience strategies and negation of challenges is highly significant in enhancing performance.

SMD supply chain decision-makers will find the decision support model presented in this study as beneficial to be resilient against various challenges in the digital transformation of service delivery process.

This study builds new knowledge of the adoption of AM technology in the SMD supply chain. The decision support model developed in this study is unique and highly effective for fostering digital transformation and enhancing SMD supply chain performance.

This study aimed to understand coopetition in the context of Brazilian specialty coffee grower Small and medium enterprises (SMEs), based on the need to differentiate the beans in and outside the farm level, taking into account the stakeholders’ influence.

In this study twenty semistructured interviews were carried out with coffee growers and managers of cooperatives, associations and supporting institutions involving two Brazilian coffee geographical indications. Data were analyzed using a mixed grid composed of qualitative, semantic and categorical factors.

Strategic moves undertaken by coffee growers and stakeholders have shaped the pathway of coopetition among coffee growers, as determinants to frame it as a deliberate or emergent pattern (intentional or unplanned, respectively). Our findings provide evidence that coopetition development among firms is deliberate when influenced by firms’ or stakeholders’ cooperative moves and emergent when influenced by firms’ or stakeholders’ competitive moves.

Although the firm/stakeholder relationship is often approached as a joint wealth creation effort, stakes are not always fairly distributed, so one of the parties may be negatively affected, with consequences for the development of coopetition. Underpinned by a stakeholder-oriented resource-based theoretical lens, this investigation of the development patterns of coopetition linked to the strategic actions undertaken by firms and stakeholders has resonance on competitive advantages.

This study aims to investigate the favourable conditions that influence transgenerational value transmission (TVT), value acceptance and value similarity between generations in indigenous African business-owning families.

This study adopts a multiple case study design and draws on semi-structured face-to-face interviews to collect data from participants in seven indigenous Black business-owning families located in South Africa. The software ATLAS.ti was utilised to manage the data and reflexive thematic analysis was undertaken.

The analysis reveal four themes describing how transmission factors facilitate favourable conditions for successful TVT in IBSA business-owning families, namely, authoritarian parenting, a loving and connected family relational climate, the continuous reinforcement of autonomy during childhood development and family authenticity in the face of societies dominant values climate. Furthermore, value similarity is perceived to exist among the different family generations in the business-owning families.

This study is among the first to adopt the value acquisition model to empirically examine successful TVT and examine the extent of value similarity or dissimilarity, using the business-owning family as the unit of analysis. Novel contributions to family business literature and practices are proposing a model for TVT in an African context and studying relationships from a business-owning family perspective. The model for TVT could be used to socialise the NextGen members into value sets and behaviours that help business-owning families preserve their entrepreneurial legacy and family business longevity.

This study aims to explore the gendered nature of housing insecurity by investigating how gender affects women’s experience moving from transitional to market housing. By describing women’s pathways out of supportive or transitional housing support, the authors show how patriarchal forces in housing policies and practices affect women’s efforts to find secure housing. The authors argue that gender-neutral approaches to housing will fail to meet women’s needs.

This study explores the narratives from women accessing support services in Halifax, Canada. The first author conducted deep narrative interviews with women seeking to move from transition to market housing.

This research sheds light on the effects of gendered barriers to safe, suitable and affordable housing; how women’s experiences and expectations are shaped by these barriers; and, how housing-based supports must address the uniquely gendered experiences women face as they access market housing. In addition, this research reveals the importance of gender-responsive services that empower women facing a sexist housing market.

Little research has explored questions related to gender and housing among those seeking to move from transitional to marker housing, and existing research focuses on women’s housing insecurity as it relates to domestic violence. The sample of women included those having housing insecurity for a variety of reasons, including substance use and young motherhood.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

The paper aims to provide an efficient dispatching schedule for ready-mix concrete (RMC) trucks and create a balance between batch plants and construction sites.

The paper focused on developing a new metaheuristic swarm intelligence algorithm using Java code. The paper used statistical criterion: mean, standard deviation, running time to verify the effectiveness of the proposed optimization method and compared its derivatives with other algorithms, such as genetic algorithm (GA), Tabu search (TS), bee colony optimization (BCO), ant lion optimizer (ALO), grey wolf optimizer (GWO), dragonfly algorithm (DA) and particle swarm optimization (PSO).

The paper proved that integrating GWO and DA yields better results than independent algorithms and some selected algorithms in the literature. It also suggests that multi-independent batch plants could effectively cooperate in a system to deliver RMC to various construction sites.

The paper provides a compelling new hybrid swarm intelligence algorithm and a model allowing multi-independent batch plants to work in a system to deliver RMC. It fulfills an identified need to study how batch plant managers can expand their dispatching network, increase their competitiveness and improve their supply chain operations.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.