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This study examines the impact of five dimensions of decent work on the faculty engagement in Pakistan’s higher education institutions. Furthermore, it examines the moderating influence of intrinsic religiosity on the associations above.

This research employed a cross-sectional approach to collect data from 542 faculty members working with higher education institutions through electronic and in-person questionnaire administration.

The findings indicate that safe interpersonal working conditions, opportunities for free time and rest, adequate compensation, and the availability of healthcare services significantly positively impact the level of work engagement among faculty members. Additionally, the research revealed that intrinsic religiosity reinforces the previously established significant associations.

The research acknowledges specific constraints that could impact the applicability of its findings, including the utilization of a cross-sectional methodology, the dependence on self-reported information, and the possibility of sample biases. Subsequent investigations may rectify these constraints to provide a more all-encompassing comprehension of the subject matter.

The findings possess practical significance for the management of HEIs in cultivating an ideal working atmosphere for faculty members. Ensuring adequate compensation, secure working environments and healthcare accessibility is underscored to enhance faculty engagement. Furthermore, acknowledging the significance of intrinsic religiosity can improve faculty engagement.

This research contributes to the current body of literature by investigating the complex relationship among decent work, faculty engagement, and intrinsic religiosity in the specific context of higher education institutions in Pakistan.

Prior research has investigated the development of Islamic communities. The authors contribute to this line of research by analyzing the effects of Islamic values and Islamic religious practices on entrepreneurship intentions in Islamic communities. Using theory of planned behavior as a theoretical lens, they also take into account that the relationship between religion and entrepreneurial intentions can be mediated by individual’s attitude toward entrepreneurship.

The authors analyze primary data obtained from a sample of 1,895 Pakistani university students. They also use structural equation modeling to perform a nuanced assessment of the relationship between Islamic values and practices and entrepreneurship intentions and to account for mediating effects.

The results show that both Islamic values and Islamic practices positively influence entrepreneurship intentions. Both effects are mediated by the attitude toward entrepreneurship.

The authors contribute to prior research on entrepreneurship in Islamic communities by applying a more fine-grained approach to capture the link between religion and entrepreneurship. Further, they contribute to the literature on entrepreneurship intentions by showing that the influence of religion on entrepreneurship intentions is mainly due to religious values and practices, which shape the attitude toward entrepreneurship and thereby influence entrepreneurship intentions in religious communities.

The present study aims to investigate the impact of corporate governance proxies by ownership structure and firm-specific characteristics, i.e. firm size, leverage, growth opportunities, previous year dividend, firm risk, profitability, and liquidity on dividend behavior of the Pakistan Stock Exchange (PSX) listed firms.

Final sample of the study consists of 140 PSX-listed firms. The study covers a period of six years, starting from 2015 to 2020. Dividend payout dummy, dividend payout ratio, and dividend yield were used to assess the dividend behavior of the sample firms. The appropriate regression procedures (logistic, probit, ordinary least square (OLS), and fixed effect regression) are used to test the study hypothesis. To check the robustness of the result, a system GMM estimation technique is also used in the present study.

The study reveals that institutional ownership, foreign ownership, and individual ownership have a significant positive whereas managerial ownership has a significant negative impact on the dividend decision of sample firms. Among firm-specific characteristics, it was found that liquidity, profitability, and the previous year's dividend were significantly positive, while growth opportunities were significantly inversely associated with dividend payout decisions of PSX-listed firms.

This study sheds light on the relationship between dividend policy, ownership structure, and firm-specific factors in the context of an emerging market like Pakistan. The study's findings have important implications for managers, minority shareholders, lawmakers, and investors looking for guidance on the dividend policy of publicly-traded non-financial firms.

The literature lacks studies that together analyze the ownership characteristics and firm-specific variables on dividend decisions, particularly in the context of developing economies. The current study aims to fill this gap.

This study aims to consolidate existing research on digital technology in business education, focusing on adoption determinants, theoretical frameworks and implementation challenges.

A hybrid literature review was conducted. The first phase involved a keyword co-occurrence analysis of 254 Association of Business Schools-ranked journal articles from the Web of Science database to identify key research themes. To deepen the analysis, the second phase included a content analysis of the 24 most globally cited articles, each with a minimum of 65 total global citations.

The review reveals that studies primarily focus on factors influencing digital technology adoption, including institutional support, perceived usefulness and ease of use. Researchers frequently cite theoretical models such as the technology acceptance model and the unified theory of acceptance and use of technology. Identified challenges include concerns over technology effectiveness, resistance to change and the influence of cultural and generational differences on technology use.

The findings guide educators and policymakers in formulating strategies for effective technology adoption, emphasizing alignment with educational goals and addressing cultural barriers.

Understanding generational and cultural impacts enables the creation of inclusive digital education strategies that cater to diverse student needs.

This study synthesizes fragmented literature, offering new insights into the integration of digital technologies in business education.

The role of insurance is a backbone for consumers to secure their future. It is important to know where to invest and what are the benefits. Therefore, for the Muslim segment, Islamic insurance system provides Riba (interest)-free environment where consumers invest their money and recover their losses according to sharia. This paper aims to examine the determinants that influence purchase intention of consumers toward Islamic insurance (Takaful) adoption in Pakistan with the help of the modified theory of planned behavior (TPB).

The authors added four specific variables related to Islamic sharia compliance in the conventional form of the TPB. The relationship among the variables is assessed by using partial least squares structural equation modeling, while the data are collected from 305 respondents.

The results suggest that attitude, subjective norm and perceived behavioral control are strong predictors of an Islamic insurance adoption in Pakistan. Moreover, factors such as compatibility, relative advantage and awareness have positive and significant impacts on takaful participation. A negative but insignificant relation is found between perceived risk and intention.

This paper provides insight for understanding the factors that lead to consumers' purchase intention of Islamic insurance.

This paper makes a unique contribution to the literature with reference to Pakistan, being a pioneering attempt to investigate the factors of Islamic insurance adoption by modifying the TPB and applying more rigorous statistical techniques like confirmatory factor analysis (CFA) and partial least square structural equation modeling.

Depleting reserves of crude oils and their adverse environmental effects have shifted focus toward environment friendly and biobased lubricant base oils. Natural oils and fats act as good lubricants but they have low oxidation and thermal stability which makes them unsuitable for modern day uses. This paper aims to produce trimethylolpropane ester biolubricant from cottonseed oil and study the effects of its use in spark ignition (SI) engines.

In this work, cottonseed oil is converted to TMP lubricant by a two-step based catalyzed esterification. The lubricants thermophysical properties are then analyzed and a 20% blend with synthetic poly-alpha olefin is used in an spark ignition engine.

The produced lubricant has viscosity @100^oC of 4.91 cSt, a viscosity index of 230 and a flash point of 202^oC. When used as a 20% blend in a petrol engine, the rate of oil deterioration was reduced by 18%, however, the overall wear increased by 6.7%. However, this increase is offset by its improved environmental impacts.

In its current state, such a biolubricant can be used as an additive to most commercially available lubricants to improve oil deterioration characteristics and environmental impact. However, further work on improving biolubricant’s wear characteristics is needed for the complete replacement of mineral oil-based lubricants.

This study aims to analyze the trends manifested in literature from the area of knowledge management and performance, with emphasis on bibliometric analysis.

To explore the studies focused on the area under investigation, the authors performed a search in ISI Web of Science and Scopus using the combination of keywords such as “Knowledge management” AND “Performance.” Generally, this study covered a period of 33 years, from 1988 to 2021 because the first study was published in 1970 and the databases have not covered all the journals and studies which date back to the early 1970s or 1980s. The final data set comprised 1,583 publications with 40 articles removed during the screening and eligibility process.

The results of the bibliometric analysis indicate that the interest in the area of knowledge management and performance has significantly increased, especially from 2000 to 2021. The application of bibliometric analysis on the relationship between knowledge management and performance uncovered various themes, productive authors and widely cited documents. The study highlighted how the knowledge management–performance relationship has evolved over the years and how the interplay between knowledge management and performance may help the firms in gaining the sustainable competitive advantage.

To the best of the author’s knowledge, this study is the first of its kind to conduct the bibliometric analysis on knowledge management and performance. This study can be a starting point for scholars interested in understanding how knowledge management is related to performance.

This research explains the critical role of technological readiness and green dynamic capabilities in enhancing the sustainability performance of manufacturing firms, which is pivotal for achieving the United Nations’ Sustainable Development Goals. The theoretical framework is grounded in the dynamic capability theory, positing that technological readiness enhances a firm’s green dynamic capabilities, and employee green behavior moderates the effect on the sustainability performance of manufacturing firms.

Quantitative data from 1,660 managerial employees of a diverse sample of manufacturing firms was aggregated at the firm level using interclass correlation and interrater agreement, ensuring robustness using at least two responses per firm. With the final dataset of 418 firms, structural equation modeling was conducted using AMOS26.

The findings reveal that technological readiness positively affects sustainability performance and enhances it through green dynamic capabilities. Furthermore, the study highlights the positive moderating role of employees’ green behavior, amplifying the impact of green dynamic capabilities on sustainability performance.

This research makes a novel contribution to the body of knowledge by integrating dynamic capability theory with empirical evidence on sustainability performance. It represents a significant step toward promoting a more sustainable and responsible future for organizations and society and provides comprehensive insights into the complex interplay of these variables. These insights are crucial for academia, industry practitioners and policymakers striving to foster sustainable practices within the manufacturing sector.

The study examines the project success factors for leadership behavioural practices and communication impacting project success, providing empirical evidence to address the challenges in the digitalized environment in the Australian construction sector.

A quantitative approach was employed to collect survey data from 109 project managers and followers (project engineers, supervisors, team members, and senior managers) with diverse project management experience in the construction sector. An exploratory factors analysis/multivariate regression/relative importance index/t-test was used to identify the key project success factors and validate the study's results.

Data analysis identified four key project success factors: (1) Relationship Management, (2) Leading by Example, (3) Self-Management, and (4) Effective Communication, along with seventeen “behavioural practices attributes” impacting project success positively and significantly and emphasizing inclusiveness, relationship building, self-feedback objectivity, sharing information, collaboratively resolving disputes, and controlling emotions that significantly impact project success.

The study's results will address the industry's challenges in the complex digitalized environment and specific issues experienced in the construction industry: delays and inefficiencies, supply chain management, communication barriers with multicultural workforce and safety protocols implementation, regulatory and safety compliance, infrastructure demands, skills shortages, sustainability, and new technology adoption to achieve project success.

The quantification of research findings, employing an innovative approach, underscores the distinctive nature of this study. The key success factors will help formulate innovative practices using stakeholder analysis, communication plans, conflict resolution strategies, promoting collaboration, safety leadership, providing cultural awareness, and enhancing the decision-making process to face challenges in the construction sector.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.