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The purpose of this study is to present an analysis of the Islamic good governance concept and means known as Al-Siyasah Al-Syar’iyyah as a principle and approach for combating corruption. This literature review aims to synthesize extant literature that discusses the determinants of integrity and how to prevent and combat corruption based on the Al-Siyasah Al-Syar’iyyah perspective.

A systematic search was conducted on a literature review based on Scopus and referred journals from Google Scholar databases. A manual search on Google Scholar was performed to identify additional relevant studies. Studies were selected based on the predetermined criteria. They were thematically examined using content analysis.

The study found that most of the 45 works of the literature, (41 studies and four chapters) suggested that corruption should be considered a sin and that education of Al-Siyasah Al-Syar’iyyah’s perspective against corruption, emphasizing the principle of piety, the institutionalization of justice and accountability, good governance performance with an emphasis on its belief in self-accountability and justice, is the means to combat corruption.

This study is unique in that it focuses on locating material on battling corruption from the standpoint of Al-Siyasah Al-Syar’iyyah. Based on the al-Quran, the Sunnah and the best practices of Muslim rulership, this notion provides an epistemological, ethical and ontological stance in Islam.

In recent years, instant messaging platforms like WhatsApp have gained substantial popularity in both academic and practical domains. However, despite this growth, there is a lack of a comprehensive overview of the literature in this field. The primary purpose of this study is to bridge this gap by analyzing a substantial dataset of 12,947 articles retrieved from the Dimensions.ai, database spanning from 2011 to March 2023.

To achieve the authors' objective, the authors employ bibliometric analysis techniques. The authors delve into various bibliometric networks, including citation networks, co-citation networks, collaboration networks, keywords and bibliographic couplings. These methods allow for the uncovering of the social and conceptual structures within the academic discourse surrounding WhatsApp.

The authors' analysis reveals several significant findings. Firstly, the authors observe a remarkable and continuous growth in the number of academic studies dedicated to WhatsApp over time. Notably, two prevalent themes emerge: the impact of coronavirus disease 2019 (COVID-19) and the role of WhatsApp in the realm of social media. Furthermore, the authors' study highlights diverse applications of WhatsApp, including its utilization in education and learning, as a communication tool, in medical education, cyberpsychology, security, psychology and behavioral learning.

This paper contributes to the field by offering a comprehensive overview of the scholarly research landscape related to WhatsApp. The findings not only illuminate the burgeoning interest in WhatsApp among researchers but also provide insights into the diverse domains where WhatsApp is making an impact. The analysis of bibliometric networks offers a unique perspective on the social and conceptual structures within this field, shedding light on emerging trends and influential research. This study thus serves as a valuable resource for scholars, practitioners and policymakers seeking to navigate the evolving landscape of WhatsApp research. The study will also be useful for researchers interested in conducting bibliometric analysis using Dimensions.ai, a free database.

The global construction industry faces both challenges and opportunities from electronic waste (e-waste). This study aims to present a bibliometric analysis and comprehensive literature assessment on e-waste in concrete construction materials.

This study studies 4,122 Scopus documents to examine garbage generation in different countries and inventive ways to integrate e-waste into construction as a sustainable strategy. This study lists famous researchers and their cooperation networks, demonstrating a robust and dynamic area with a surge in research output, notably from 2018 to 2022. Data is visually represented using VOS Viewer to show trends, patterns and study interests throughout time.

The findings imply that e-waste can improve construction materials’ mechanical characteristics and sustainability. The results are inconsistent and suggest further optimization. e-Waste into construction has garnered scientific interest for its environmental, life cycle, and economic impacts. This field has great potential for improving e-waste material use, developing sophisticated prediction models, studying environmental implications, economic analysis, policy formulation, novel construction methods, global cooperation and public awareness. This study shows that e-waste can be used in sustainable building. It stresses this area’s need for research and innovation. This lays the groundwork for using electronic trash in buildings, which promotes a circular economy and environmental sustainability.

The findings underscore the critical role of ongoing research and innovation in leveraging e-waste for sustainable building practices. This study lays the groundwork for integrating e-waste into construction, contributing to the advancement of a circular economy and environmental sustainability.

The social implications of integrating e-waste into construction are significant. Using e-waste not only addresses environmental concerns but also promotes social sustainability by creating new job opportunities in the recycling and construction sectors. It fosters community awareness and responsibility towards sustainable practices and waste management. Additionally, this approach can reduce construction costs, making building projects more accessible and potentially lowering housing prices.

This research contributes to the field by offering a bibliometric analysis and comprehensive assessment of e-waste in concrete construction materials, highlighting its global significance.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

This study aims to investigate the effect of organizational ambidexterity (OA) and organizational green culture (OGC) on corporate sustainability (CS) while incorporating the mediating role of green innovation (GI) to provide a detailed insight into CS. The study also presents a research framework based on the Organizational Ambidexterity theory and Natural Resource-based view to explain the factors contributing to CS.

Using stratified sampling, the study collected data through survey-based empirical research from 307 textile companies registered with the Securities and Exchange Commission of Pakistan (SECP) or the All-Pakistan Textile Mills Association (APTMA). The collected data were analysed using path analysis, mediation analysis and moderation analysis through smart PLS-SEM version 4.0 to assess the composition and causal association of factors.

The study found a significant relationship between OA and OGC with CS. Furthermore, the study revealed that green innovation partially mediates the relationship between OGC and CS. The proposed research framework can be valuable for promoting and recommending actions to enhance CS.

The study on CS in the textile sector of Pakistan has limitations such as a narrow focus, cross-sectional design and reliance on self-reported data. Future research should explore additional factors, conduct longitudinal research, investigate contextual factors, scrutinize specific green innovation practices and broaden the scope of the study to include SMEs and other textile organizations.

The research framework can help senior executives to foster CS by promoting OGC, OA and GI. Practitioners and academicians can also utilize or further investigate the proposed framework for validation and to foster CS.

This study fills gaps in the existing literature by investigating the mediating effect of GI between OGC and CS. The proposed research framework provides a comprehensive understanding of the factors contributing to CS based on the Organizational Ambidexterity theory and Natural Resource-based view.

The purpose of this paper is to integrate job demands–resources theory with core self-evaluation theory to examine how and when sustainable project leadership stimulates sustainable project performance. Psychological empowerment is used as a mechanism between sustainable project leadership and sustainable project performance, whereas core self-evaluation acts as a moderator between sustainable project leadership and psychological empowerment.

This study adopts a time-lagged two-phase approach to collecting data from 257 software engineers working in medium-sized and large companies in the Polish financial sector.

This study reveals the direct and indirect (through psychological empowerment) effects of sustainable project leadership on sustainable project performance. It also confirms that the “sustainable project leadership–psychological empowerment” relationship strengthens with increasing level of project team members’ core self-evaluation.

This article explores a unique research framework. It significantly enriches the literature on sustainable project management, increasing the understanding of effective leadership practices that address the needs of software engineering teams to support sustainable project performance.