Sustainable products and their marketing have played a crucial role in developing more sustainable consumption patterns and solutions for socio-ecological problems. They have been demonstrated to significantly decrease social consumption problems. Neuromarketing has recently gained considerable popularity and helped companies generate deeper insights into consumer behavior. It has provided new ways of conceptualizing consumer behavior and decision making. Thus, this research aims to investigate the factors influencing managers’ decisions to adopt neuromarketing techniques in sustainable product marketing using the fuzzy analytic hierarchy process (AHP) approach. Symmetric triangular fuzzy numbers were used to indicate the relative strength of the elements in the hierarchy. Data were collected from the marketing managers of several companies who have experience with sustainable product marketing through online shopping platforms. The results revealed that the accuracy and bias of neuromarketing techniques have been the main critical factors for managers to select neuromarketing in their business for advertising and branding purposes. This research provides important results on the use of neuromarketing techniques for sustainable product marketing, as well as their limitations and implications, and it also presents useful information on the factors impacting business managers’ decision making in adopting neuroscience techniques for sustainable product development and marketing.

Women’s involvement in tourism has exerted an immense impact on the nation’s economy. The low involvement of women in some countries has sparked the interest of scholars in investigating the factors that influence their involvement. The main purpose of this study was to examine the influence of self-efficacy (SE), perceptions of women’s work (PWW), empowering leadership (EL), and psychological empowerment (PE) on tourism involvement. Consequently, the study determined the impact of tourism involvement in sustainable tourism development (STD). The population of the study was women entrepreneurs from the Saudi Arabia tourism industry. A total of 201 women participated in the study from the selected sample of 300, obtained through convenient sampling. For the data analysis, SPSS and PLS-SEM were employed. The results revealed that PWW, SE, EL, and PE significantly and positively influenced women entrepreneurs’ involvement in tourism (WEIT). Further, WEIT significantly and positively impacted STD. This research is a pioneering work in which a research model was developed to incorporate PWW, SE, EL, PE, tourism involvement, and STD in light of social exchange theory and social identity theory. The findings provide implications for policymakers and academia and contribute to the existing body of knowledge on women’s entrepreneurship, empowerment, tourism, and business management.

The current study takes its philosophical roots from organizational behavior and psychology domains to investigate the impact of sleep deprivation on the job performance of mothers working in primary, secondary, and higher education institutions (HEIs) of Pakistan. It also examines the mediating role of workplace deviance in the relationship between sleep deprivation and the job performance of working mothers. The authors followed the non-probability convenience sampling technique to study the relationship between sleep deprivation, workplace deviance, and job performance. The structural analyses indicated that sleep deprivation has a significant negative impact on the job performance of working mothers and sleep-deprived individuals often tend to perform poorly at the workplace. Such workers are also more likely to engage in workplace deviant behaviors. Moreover, workplace deviance is also found to act as a mediating variable in the relationship between sleep deprivation and job performance. The present research bridges the literature gap on the rarely investigated factors, namely sleep deprivation and workplace deviance, and provide a detailed understanding of how these factors can influence the performance of working mothers, specifically in Pakistan.

Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries’ sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries’ sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries’ sustainability performance.

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

The hybrid laser powder bed fusion/high-pressure torsion (LPBF/HPT) approach has emerged as an effective method to enhance the mechanical properties of metals/alloys via the ultrafine and/or nanoscale grain refinement route. However, most studies on this approach have largely focused on the investigation of microstructure and mechanical properties, while studies on tribological performance are significantly limited despite its importance in many engineering applications. Thus, in this study, the tribological behavior of a nanostructured AlSi10Mg alloy (AlSi10Mg being a critical material for key automotive components) fabricated by the hybrid LPBF/HPT approach is investigated for the first time. The results of dry sliding wear tests conducted in reciprocating linear pin-on-disk mode demonstrate enhancement in the tribological performance after HPT processing, as indicated by the comprehensive reductions in coefficient of friction (COF), mass loss, m, and specific wear rate, k values with increasing number of HPT revolutions, amounting to improvements of ~ 25, ~ 64, and ~ 71%, respectively, after 10 revolutions relative to the as-received condition. The overall enhanced tribological performance can be ascribed to the significantly high hardness attained due to the extreme grain refinement resulting from the HPT-imposed torsional strains.