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Using trait activation theory (TAT), this paper aims to empirically assess that work drive (WD) personality characteristics of employees will express better towards intrapreneurial behaviour (IB) outcomes in higher levels of job autonomy (JA) at the workplace.

Using cross-sectional design, a total of 258 engineering employees from the automotive industry of Pakistan were surveyed. The partial least square structural equation modelling (PLS-SEM) statistical method was used to evaluate the hypothesised theoretical model.

Statistical results revealed a significant and positive relationship between WD and IB. WD personality was also found to be activated with its interplay with JA for personality expression towards IB.

The present study offers a deeper insight into the interplay of organisational and individual factors as determinants of IB of employees. The study's findings suggested the importance of the rightful application of organisational situational cues in person–situation interaction for positive workplace behaviours by employees. Personality assessment of employees and their interplay with situational cues, as per conceptualisation of TAT, can assist the corporations towards enhanced levels of employees' behavioural tendencies towards intrapreneurship.

The application of TAT from the present study's perspective is novel theoretically as the literature on the activation of personality traits towards IB outcomes is non-existent. Also, the assessment of situational cues in JA will open avenues for organisational behaviour researchers to seek more organisational situational moderators for their assessment of various personality–outcome relationships in diverse contexts to activate personality traits.

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.

From 1979 to 2023, the takaful structure has been adopted in many jurisdictions, making the documenting of its early days of establishment relatively difficult and somewhat unreliable. This is unlike conventional insurance, where the history and legislation are well documented and archived in various research (Hellwege, 2016; Marano and Siri, 2017). The purpose of this paper is to provide a chronology for the establishment and development of takaful via the takaful establishment in each jurisdiction, documenting its first takaful operator and first takaful regulation.

This paper has used a qualitative method in the form of reviewing literature and available data such as journals, books and official resources. The data is thoroughly analysed in order to build the chronology for takaful. It adopted an exploratory research design, which is deemed suitable in situations where few works of literature have examined the subject (Neuman, 2014). The paper explores the establishment and non-establishment of takaful in 57 countries. The paper categorises the countries into seven regions starting with the GCC, Levant, Asia, Central Asia, Africa, Europe and Others.

The takaful chronology presented in this paper shows that takaful operations exist in 47 jurisdictions, starting from Sudan and the UAE in 1979, with the most recent adopters being Morocco and Iran in December 2021. It is found that 22 jurisdictions do not have takaful regulations, and the Takaful Act 1984, issued in Malaysia, is considered the first takaful regulation that sets the basis for other regulations that follow.

The paper contributes to the literature by providing a comprehensive chronology of takaful, especially as the few existing timelines have been found to be incomplete and consist of contradictory information.

This research was initiated by motivation from a real business problem that delves into lean management practices in dairy farm operations. It investigates how lean management practices can be applied as an improvement strategy in the dairy business to evaluate its impact on performance, where profitability is a decisive factor.

Based on the qualitative design, a 5-phase action research methodology was used in this study, where multiple data collection sources were used, including focus group discussions, on-site observation or Gemba walks and process mapping. The impact is evaluated by comparing the key performance measures with the same period before and after research.

The research revealed that lean management practices can significantly improve dairy business performance. It explained vital aspects of lean management practices and their sequence with examples of first-hand applications. It explained, how lean management practices were applied in dairy farm operations. Furthermore, the research resulted in significant benefits, in terms of quality, cost and profitability.

This research was conducted in a real business setting in the field environment, to improve dairy business performance. It was a distinctive application of lean management practices to solve a national problem. This could be used as a road map to bring continuous improvement at the national level to improve the performance of food value chains.

This research is unique because it addresses the methodological, population and empirical gaps in dairy farm operations. It adds value to the existing knowledge base by sharing best practices, developed and implemented for the first time to the best of our knowledge, like high-level process mapping and performance measures at different levels. Furthermore, the solutions can be simulated in related farm operations to bring breakthrough improvements in dairy business performance.

As a highly knowledge-intensive activity, digitalization is changing the construction industry landscape and is encouraging construction firms to explore the transformation. This study establishes a new theoretical model aimed at examining the impact of three types of intellectual capital (IC) on digitalization through the lens of knowledge-based view and explores how IC and digitalization influence sustainability performance from the triple bottom line principles.

A questionnaire survey was conducted to collect data from Chinese construction firms using convenience sampling. A total of 181 valid responses were obtained. Then, a partial least squares structural equation modelling (PLS-SEM) technique was executed through Smart PLS 3.0 software. The measurement model was assessed to ensure reliability and validity, and the structural model was analysed to test the proposed hypotheses.

The empirical results confirm the positive impact of IC on digitalization and digitalization on sustainability performance. Moreover, digitalization plays a significant mediating role in the relationship between IC and sustainability performance.

The results provide empirical evidence supporting the different roles of IC and digitalization in improving sustainability. The findings contribute to enhancing the understanding of digitalization practices from the perspective of IC and provide theoretical and managerial implications for sustainability issues in the context of the construction industry.

The purpose of this study is to comprehensively explore the impact of digitalization on healthcare supply chains (HcSCs). It seeks to understand how digital technologies enhance efficiency, transparency and responsiveness within these complex logistical systems. The study aims to provide a holistic view of the transformative potential of digitalization in the healthcare sector, with a particular focus on improving patient care and streamlining operational processes.

This research employs a systematic review methodology, carefully curating a selection of 45 relevant articles from 66 articles rigorously screened using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to provide a holistic view. It follows established systematic review protocols, incorporating a meticulous search strategy and precise keyword selection. The chosen research design enables a comprehensive examination of the existing body of knowledge concerning digital platforms, real-time tracking technologies, transparency and responsiveness in the context of HcSCs.

The findings of this study emphasize the pivotal role of digital technologies in reshaping HcSCs. Digital platforms, real-time tracking systems and technological integrations substantially enhance efficiency, transparency and responsiveness. Data-driven decision-making, improved communication and agile responses to dynamic demands are key aspects. These findings underscore the transformative impact of digitalization on healthcare logistics, emphasizing the potential for streamlined operations, enhanced patient care and more efficient resource allocation.

Despite the systematic methodology, this study is subject to certain limitations. It relies on existing literature, which may not cover the most recent developments in the rapidly evolving field of digital HcSCs. Furthermore, the study may be influenced by publication bias. The implications suggest the need for continued research to explore emerging digital technologies and their effects on healthcare logistics, ensuring that supply chains remain agile and responsive.

The practical implications of this research are significant for HcSC managers with insights into digital technologies to enhance transparency and collaboration and improve resource visibility. The integration of data analytics can lead to more effective inventory management and demand forecasting. Blockchain (BC) technology can ensure transparent and secure transactions, fostering trust among stakeholders. For practitioners, this research offers actionable guidance for navigating the digital age, promoting operational efficiency and ensuring a consistent supply of essential medical products. Researchers can use these insights as a foundation for further exploration into the potential of digitalization in HcSCs.

The social implications of digitalization in HcSCs are far-reaching. They encompass improved patient care, as digital technologies enhance the efficiency, transparency and responsiveness of supply chains. This translates to better access to critical medical supplies, potentially reducing healthcare disparities and benefiting underserved populations. Enhanced patient safety is a significant social outcome, as transparent and secure transactions enabled by technologies like BC mitigate the risks associated with counterfeit medications. Furthermore, digitalization builds trust among stakeholders, promotes accountability and fosters resilient healthcare systems, which are capable of responding effectively to crises. It also has the potential to make healthcare more affordable, contributing to increased healthcare access and transparency in decision-making.

The originality and value of this study lie in its comprehensive synthesis of diverse findings related to digitalization in HcSCs. While prior studies have examined isolated facets of digital technology adoption, this research provides a comprehensive overview. It contributes to a deeper understanding of the transformative potential of digitalization within the healthcare sector, offering practical approaches to enhance patient care and streamline operations.

This study aims to focus on key approaches to education for sustainability (EfS) leadership development in the context of Malaysian and Japanese universities. The authors identify key indicators of effective EfS leadership development approaches using both descriptive and inferential analyses, identify and compare the preferred leadership learning methods of academics and examine the impact of marital status, country of residence and administrative position on the three EfS leadership development approaches.

The study is quantitative in approach and survey in design. Data were collected from 664 academics and analysed using the efficient partial least squares (PLSe2) methodology. To provide higher education researchers with more analytical insights, the authors re-estimated the models based on the maximum likelihood methodology and compared the results across the two methods.

The inferential results underscored the significance of four EfS leadership learning methods, namely, “Involvement in professional leadership groups or associations, including those concerned with EfS”, “Being involved in a formal mentoring/coaching program”, “Completing formal leadership programs provided by my institution” and “Participating in higher education leadership seminars”. Additionally, the authors noted a significant impact of country of residence on the three approaches to EfS leadership development. Furthermore, although marital status emerged as a predictor for self-managed learning and formal leadership development (with little practical relevance), administrative position did not exhibit any influence on the three approaches.

In addition to the theoretical and methodological implications drawn from the findings, the authors emphasize a number of practical implications, namely, exploring the applicability of the results to other East Asian countries, the adaptation of current higher education leadership development programmes focused on the key challenges faced by successful leaders in similar roles, and the consideration of a range of independent variables including marital status, administrative position and country of residence in the formulation of policies related to EfS leadership development.

This study represents an inaugural international comparative analysis that specifically examines EfS leadership learning methods. The investigation uses the research approach and conceptual framework used in the international Turnaround Leadership for Sustainability in Higher Education initiative and uses the PLSe2 methodology to inferentially pinpoint key learning methods and test the formulated hypotheses.