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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.

This paper investigates the mediating role of organizational prosocial behavior (OPB) in the relationship between spiritual leadership (SL) and knowledge-sharing (KS) from the intrinsic motivation perspective.

A survey was used to gather data from middle and executive management employees at industrial firms in Sahab Industrial City in Jordan. The study applied quantitative exploratory methods. The study used a self-reported questionnaire to gather data, with 268 valid responses being used to conduct the analysis. The analysis of data proceeded with the aid of SEM-PLS using SmartPLS 4.

The results advocate the positive link between SL and KS routing through the mediator (OPB). The mediating role of OPB was found to be partial.

This study offers practical implications for organizations that wish to optimize KS among employees. It emphasizes the crucial role of SL in determining employee OPB and proposes that managers strive to engender organization-wide transcendental values.

This study furthers the understanding of KS by testing the relationship between SL and KS using OPB as a mediator, which has not been investigated theoretically or empirically.

The case study was developed based on secondary data from the publicly available initial accident report of PIA flight PK8303. The facts presented in the report were then analyzed in the light of the Human Factor Analysis and Classification System (HFACS).

The case revolves around the terrible aviation mishap that occurred on May 22, 2020, when Pakistan International Airlines (PIA) Flight 8303 crashed in a Karachi residential area. A total of 97 people lost their lives in this tragedy, and it was Pakistan’s 18th major aviation disaster. The case study explores the human errors and failures of ground handling agencies, air traffic controllers, regulatory agencies, airline employees and cockpit crew by using the HFACS. The focus remains on mistakes made by people, which revolve around inefficient and ineffective communication, and contempt of safety regulations at various stages of flight PK8303.

The case study is designed for the students of aviation management at undergraduate and graduate levels.

Drawing on resource-orchestration theory (ROT), this study investigates the influence of market intelligence and entrepreneurial orientation (EO) on international performance of born global (BG) small and medium enterprises (SMEs) in emerging markets with the mediating role of global technological competence. Quality focus is used as a moderator between global technological competence and international performance.

Data were gathered through a survey, and PLS-SEM was employed for hypotheses testing with a sample of 256 BG SMEs.

The results showed that market intelligence, EO, global technological competence and quality focus positively relate to international performance. Moreover, market intelligence and EO are positively associated with global technological competence. Besides, global technological competence significantly mediates the relationship between market intelligence, EO and international performance. Finally, quality focus strengthens the positive relationship between global technological competence and international performance.

Our research demonstrates that if management utilizes or invests on market intelligence, EO, global technological competence and quality focus, then the BG SMEs will increase their international performance.

The paper contribution lies in its focus on exogenous constructs (i.e. market intelligence, EO, global technological competence and quality focus) to determine the international performance of born global SMEs in emerging markets.