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The fundamental purpose of this study is to analyse the determinants of higher education quality in Afghanistan based on insights from student perceptions. Understanding this part holds paramount importance in enunciating sound policies for the smooth functioning of the higher education sector of Afghanistan.

This study aims to classify students' background and demographic data, distinguishing their perception of higher education quality using a deductive research approach. A sample of 418 students from five top private universities in Afghanistan was chosen to assess their perceptions of higher education dimensions by employing a multinomial regression analysis.

The findings indicate that extracurricular activities, students' scholarship status, parents' education, age, previous academic results and the university they attend significantly impact their perception of the quality of higher education.

This research is essential for education policymakers and university administrators. These findings can be replicated to develop regulations and target specific groups of students to ensure a favourable academic environment and boost the brand image of their universities. This would ensure long-term quality improvement and assurance outcomes, allowing higher education institutions to compete with regional and international institutions.

This study contributes to identifying the determinants of higher education quality based on the perceptions of the students in Afghanistan.

Because the success of m-payment services depends on the enablers and barriers that affect user satisfaction, the present research explores the effects of perceived value and sacrifices on users' satisfaction with m-payment services. The predicted relationships among perceived value, perceived sacrifices, users' satisfaction, continuance intention, word-of-mouth (WOM), shopping effectiveness, quality of life (QOL) and stickiness were established based on the mobile technology acceptance model (MTAM) and the value-based adoption model (VAM).

A representative data sample of 430 Egyptian banking clients was analyzed to test the hypotheses using partial least squares-structural equation modeling (PLS-SEM).

The findings revealed that all perceived value constructs significantly positively affect users' satisfaction. Moreover, all perceived sacrifice constructs significantly negatively affect users' satisfaction. Users' satisfaction, in turn, has a significant positive effect on continuance intention, WOM, shopping effectiveness, QOL and stickiness with m-payment services.

This is the first study to examine several levels of m-payment outcomes, including m-payment, consumer and bank outcomes, based on the integration of MTAM and VAM models.

The study aims to investigate the mediation effect of the Audit Committee’s (AC) effectiveness on the relationship between knowledge intensity and firm performance (FP) by considering the disparate effect of each AC characteristic on its effectiveness.

The study uses the partial least squares-structural equation model (PLS-SEM) to weigh the AC characteristics for its effectiveness and analyzes the relationships between the variables included in the models. Data was collected from authentic sources for 133 National Stock Exchange (NSE)-listed companies in six industries covering the period 2016 to 2020.

The results indicate that eight out of eleven AC characteristics, namely, nonexecutive directors, independence, expertise, AC-charter, multiple directorships, frequency of AC meetings, attendance of AC meetings and board meetings by AC directors, significantly influence the AC effectiveness while mediating the relationship between knowledge intensity and FP. Further, each characteristic of AC has a disparate effect on AC effectiveness depending on the measurement context.

Apart from guiding the policymakers, management and stakeholders to effectively use AC characteristics in enhancing FP, this study further contributes to the literature by providing a new way to weight AC characteristics based on their individual contributions; and exploring new path models to analyze the multidimensional effect of various AC characteristics.

To the best of the authors’ knowledge, the study is the first to examine the mediation role of AC effectiveness on the relationship between the knowledge intensity of the firms and their performance. It demonstrates improvisation in measuring AC effectiveness using the disparate weights for each AC characteristic, computed based on their relative contribution to AC effectiveness.

To develop a fuzzy causal model of enterprise flexibility dimensions in a case study of Indian pharmaceutical industry.

The eight dimensions of enterprise flexibility were identified based on literature review. Fermatean fuzzy decision-making trail and evaluation laboratory (FF-DEMATEL) technique is applied to develop the cause-and-effect interrelationship model among various enterprise flexibility dimensions.

The information technology flexibility, supply chain flexibility, technical flexibility and marketing flexibility are found to be causing/influencing other flexibilities and contributing to overall enterprise flexibilities. Therefore, more attention needs to be paid to develop and sustain them for competitive advantage.

Fermatean fuzzy sets offer more flexibility and more accurate handling complex uncertain group decision making. FF-DEMATEL is a more accurate method to develop inter-dependencies and causal model than ISM, TISM. Ratings from the limited number of decision experts (DEs) from few pharmaceutical firms were done. Future study should take bigger sample of firms and more number of DEs to generalize the findings.

The model will help managers in pharmaceutical industry to prioritize the dimensions of enterprise flexibility to achieve agility, responsiveness, resilience and competitive advantage.

To the best knowledge of the authors, causal modeling enterprise flexibility dimensions using FF-DEMATEL has been studied for the first time in a developing economy context.

The purpose of this paper is to review studies on mathematical optimization of the sustainable gasoline supply chain to help decision-makers understand the current situation, the exact dimensions of the problem and the models provided in the literature. So, a more realistic mathematical optimization model can be achieved by fully covering all dimensions of the supply chain of this product.

To evaluate and comprehend the mathematical optimization of the sustainable gasoline supply chain research area, a systematic literature review is undertaken that covers material collection, descriptive analysis, content analysis and material evaluation steps. Finally, based on this process, 69 related articles were carefully investigated.

The results of the systematic literature review show the main areas of the published papers on mathematical optimization of sustainable gasoline supply chain problems and the gaps for future research in this field presented based on them.

This approach is subject to limitations because the protocol of the systematic review of the research literature only included searching for the considered combination of keywords in the Scopus and ProQuest databases. Furthermore, the protocol used in this paper restricts documents to English.

The results have significant implications for both academicians and practitioners in this field. It can be useful for academics to comprehend the gaps and future trends in this field. Also, for practitioners, it can be useful to identify and understand the parts of the mathematical optimization model, which can help them model this problem effectively and efficiently.

No systematic literature review has been done in this field by considering gasoline to the best of the authors’ knowledge and delivers new facts for the future development of this field.

The purpose of this study is to determine the enablers of the quality of work life (QWL) of employees working in the Garment industries.

The study was carried out in a fivefold step. In the first step, the enablers of QWL were identified through an exhaustive literature survey, in the second step identified vital few components through Pareto analysis. Then the third step was followed by exploratory factor analysis (EFA) to further, to identify the precise components and validate the same using confirmatory factor analysis in fourth step. The final step included interpretive structural modeling and Cross-Impact Matrix Multiplication Applied to Classification analysis to model the validated components and determine the interrelationships and linkages.

Predominant QWL enablers of employees working in the garment industries are training and development, satisfaction in job, compensation and rewards, relation and co-operation, grievance handling, work environment, job nature, job security and facilities.

In this study, the interpretive structural model is designed based on the opinion of the experts who are working in the garment industry considering the responses from employees in garment sectors. The framework can be extended further to the other sectors.

In future, the researchers in QWL may develop a model to quantify the level of employees’ QWL who are working in different sectors. Enablers of QWL are essential, and based on this further statistical analysis can be carried out. This study will provide limelight to the researchers in choosing the valid and reliable set of enablers for the empirical studies. Organizations can get benefit by implementing the outcome of this research for the enhancement of the QWL of employees.

The study was carried out in 133 garment industries where 851 workers constituted the final valid responses that were considered for analysis. The outcomes from the study help administrators, policy and decision-takers in taking decisions to enhance QWL.