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Considering the high impacts caused by manufacturers on sustainability, this research aims to analyse how Brazilian manufacturing companies deal with sustainability issues. To do this, sustainability parameters are analysed to verify possible improvement opportunities.

This research uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and grey relational analysis (GRA) to analyse data from a survey with Brazilian professionals regarding aspects of sustainability in Brazilian manufacturers. The average score levels and the ranking of these aspects are evaluated.

Through the analysis performed, it was possible to verify that manufacturers in Brazil still have a long path to travel in the search for sustainability. Comparatively, it was observed that practices related to local communities received the lowest scores, on average. In contrast, on average, practices related to productivity and efficiency, occupational accidents and diseases, and compliance with environmental legislation received the highest scores.

The results presented in this paper show that there are several improvement opportunities to be sought by Brazilian manufacturing companies regarding sustainability aspects. Particular attention should be given to local community practices. Besides companies, policymakers can also use this analysis to guide their future actions, encouraging manufacturing companies to better support the local community. Researchers can use the instrument of analysis (TOPSIS and GRA) to analyse other realities and compare them with the findings presented.

The analysis of Brazilian manufacturing companies’ reality regarding sustainability practices and considering a model based on Global Reporting Initiative (GRI) and Brazilian Institute of Corporate Governance (IBGC) is novel in the literature. The use of TOPSIS and GRA, as well as comparing their findings, generated interesting insights for companies, policymakers and researchers. The analysis presented shows the need for more significant concern for local communities and can be used to support further debates and action plans to minimise this gap.

The current estimation practice in construction projects greatly needs upgrading, as there has been no improvement in the cost overrun issue over the past 70 years. The purpose of this research was to develop a new multiple regression analysis (MRA)-based model to forecast the final cost of road projects at the pre-design stage using data from 43 projects in New Zealand (NZ).

The research used the case study of 43 completed road projects in NZ. Document analysis was conducted to collect data, and statistical tests were used for model development and analysis.

Eight models were developed, and all models achieved the required F statistics and met the regression assumptions. The models’ mean absolute percentage error (MAPE) was between 21.25% and 22.77%. The model with the lowest MAPE comprised the road length and width, number of bridges, pavement area, cut and fill area, preliminary cost and cost indices change.

The model is based on road projects in NZ. However, it was designed to be able to adapt to other contexts. The findings suggest that the model can be used to improve traditional conceptual estimating methods. Past project data is often stored by the project team but rarely used for analysing and forecasting purposes. This research emphasises that past data can be effectively used to predict the project cost at the pre-design stage with limited information.

No research was conducted to adopt cost modelling techniques into the conceptual estimation practice in the NZ construction industry.

This paper highlights the effects of supervisor bullying (SBL) on work–family conflict (WFC), employee voice behavior (EVB), working compulsively (WCO) and working excessively (WEX), as well as the effects of WFC, EVB and WEX on employees' sleeping problems.

Data were gathered from 473 five-star hotel employees, and their responses were analyzed using AMOS v.23.

SBL significantly lowers EVB while significantly increasing WFC. SBL increases WEX and WCO levels, which may be considered a short-term positive outcome of SBL.

This paper will help improve understanding of employee reactions to an emotionally charged workplace occurrence.

This research seeks to reveal the mediating role of work engagement and affective commitment as individual aspects that have the potential to bridge the effect of empowering leadership on the task performance of Correctional Service counselors in Indonesia, especially due to the limited literature on these two aspects in the context of public organizations.

Quantitative research was conducted on 350 counselors throughout Indonesia. The data was collected by distributing questionnaires online. The collected data were then analyzed using Structural Equation Modeling to test the seven research hypotheses.

Empowering leadership significantly strengthens task performance, work engagement and affective commitment. For indirect effects, this study found that affective commitment partially mediates the effect of empowering leadership on task performance. Meanwhile, work engagement failed to act as a mediator because it did not significantly impact strengthening task performance.

Notably, the unexpected result of work engagement's inability to significantly boost task performance deviates from the prevailing trends observed in previous empirical research, thereby adding a novel dimension to the findings of this study.

This study aimed to identify a specific taxonomy of entry modes for international construction contractors and to develop a decision-making mechanism based on case-based reasoning (CBR) to facilitate the selection of the most suitable entry modes.

According to the experience orientation of the construction industry, a CBR entry mode decision model was established, and based on successful historical cases, a two-step refinement process was carried out to identify similar situations. Then the validity of the model is proved by case analysis.

This study identified an entry mode taxonomy for international construction contractors (ICCs) and explored their decision-making mechanisms. First, a two-dimension model of entry mode for ICCs was constructed from ownership and value chain dimensions; seven common ICC entry modes were identified and ranked according to market commitment. Secondly, this study reveals the impact mechanism of the ICC entry mode from two aspects: the external environment and enterprise characteristics. Accordingly, an entry mode decision model is established.

Firstly, sorting out the categories of entry mode in the construction field, which provide an entry mode list for ICCs to select. Secondly, revealing the impact mechanism of ICC entry mode, which proposes a systematic decision-making system for the selection of ICC entry mode. Thirdly, constructing a CBR entry mode decision-making model from an empirical perspective, which offers tool support and reduces transaction costs in the decision-making process.

The study on entry modes for ICCs is still in the preliminary exploratory stage. The authors investigate the entry mode categories and decision-making mechanisms for ICCs based on Uppsala internationalization process theory. It widens the applied scope of Uppsala and promotes cross-disciplinary integration. In addition, the authors creatively propose a two-stage retrieval mechanism in the CBR model, which considers the order of decision variables. It refines the influence path of the decision variables on ICCs' entry mode.

While the significance of public cooperation for police effectiveness is widely acknowledged, less is known about factors associated with cooperation in hate crime cases. The current study aims to explore how individuals’ perspectives on police legitimacy, contact experience with police and race/ethnicity shape their willingness to cooperate with police in hate crime incidents.

This study used a sample of 693 college students and was conducted at a public university in the south-central region of the southern United States of America. Ordinary least squares (OLS) regression models were used to examine factors related to willingness to cooperate with police.

Findings show that those who have a high level of positive perceptions of police legitimacy and those who have a low level of negative personal experience with police reported more willingness to cooperate. Asian respondents were less likely to report that they would cooperate with police compared to white respondents.

This study, emphasizing the relationships between perceived police legitimacy and positive personal experiences with a willingness to cooperate in hate crime cases, has practical implications. The identification of racial/ethnic differences in cooperation attitudes, particularly the lower likelihood of cooperation among Asian respondents, contributed to the current literature and underscores the importance of considering diverse perspectives and outreach efforts.

This study aims to determine the influence of store attributes on customer experience, brand love and brand loyalty at Hijup stores.

This research uses quantitative methods. The technique of determining the sample used is purposive sampling. The sample criteria in this study were consumers who had visited and bought products directly at the Hijup store with a minimum age of 17 years. The amount of data collected is 224 samples. Data was collected by distributing online questionnaires. The data analysis technique used the structural equation modeling operated through the IBM AMOS 26.0 program.

The results of the study reveal that customer experience is influenced by all dimensions of the store attribute variable including merchandise, communication with staff, store atmosphere and transaction convenience. In addition, this study shows that customer experience also positively affects brand love and brand loyalty. Finally, the analysis shows that brand love positively affects brand loyalty.

The theoretical contribution of this research is the testing of four variables (store attribute, customer experience, brand love and brand loyalty) in the same model in the context of halal fashion, thus helping to broaden insight and understanding of the influence of store attributes on customer experience, brand love and brand loyalty in halal fashion. This research can be a reference for academics to develop further research following this research topic.

This study provides practical implications for managers to increase their efforts in creating good store attributes, to create a positive customer experience that can build customer brand love and brand loyalty.

The long-term effect of the company’s success in developing brand love and brand loyalty is that it makes it easier for customers to trust, be satisfied and recommend the brand to others.

In the context of the halal concept, several studies among Muslims in Asia and western countries have yielded important information about consumer behavior toward halal products such as food and tourism. Departing from previous research, this research is to fill the gaps of previous research and get better insights into the customer experience visiting halal fashion stores. Therefore, this study tries to define and validate consumer profiles about halal fashion and identify customer experience, brand loyalty and brand love in the context of halal fashion.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

The purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control.

This study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN.

This study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.

Once the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM.

To the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.

Drawing on Shore and colleagues' model of inclusive workplaces (2018) and the perceptions of social context framework (Borgogni et al., 2010), this study aims to develop and provide a preliminary validation of the Social Drivers of Inclusive Workplaces (SDIW) scale.

Using inductive and deductive approaches, items were developed. The resulting pool of 28 items was administrated to 1,244 employees using an anonymous online survey. The factor structure of the SDIW scale was tested through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Reliabilities were estimated. Alternative models were tested through CFAs. Nomological validity and measurement invariance across gender were explored.

The EFA revealed a three-factor structure, including inclusive colleagues, supervisors and top management. This solution was confirmed by the CFA and outperformed all alternative models, showing good reliabilities. Measurement invariance across gender was confirmed. Correlations indicated that the SDIW total score and each dimension were positively associated with belongingness needs satisfaction and affective commitment, while negatively related to interpersonal strain, negative acts and turnover intention.

This study provides practitioners with a reliable tool to map social drivers of inclusion within workplaces in order to design tailored interventions.

This study contributes to the inclusion literature, as it is the first to provide a scale that simultaneously measures employees' perceptions of inclusive behaviours enacted by the three main social actors within the workplace.