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In recent years, especially after the COVID-19 pandemic, home textile production orders decreased significantly. This sudden drop in production has increased industry competition, making customer satisfaction more challenging. As a result, it has become imperative for the industry to deftly navigate such ongoing challenges.

This study examines textile production efficiency methodically. Customer requirements like quality, on-time delivery, better working conditions, cost-effectiveness and facility safety audits are understood first. Quality function deployment (QFD) turns client requirements into technical requirements. Prioritise and analyse risks using Monte Carlo simulation and Pareto charts. Consequently, experts and literature propose corrective measures, which are tested in a pilot run to see how they affect production.

QFD, define, measure, analyse, improve and control (DMAIC) and Monte Carlo simulation were used to reduce high-priority risks and meet client requirements in this study. The house of quality helped relate customers’ requirements and technical requirements. Monte Carlo simulation has also improved risk prioritisation by providing a flexible mathematical structure for identifying and managing the most important risks.

This study is novel in the way it applies this integrated approach to the understudied home textile sector. Unlike traditional DMAIC, this study introduces a novel matrix encompassing all defects. This study offers a data-driven approach to improve product quality, meet customer expectations and reduce prioritised risks in home textile manufacturing.

This study examines the impact of debt maturity structure on stock price crash risk (SPCR) in Asian economies and the moderating effect of firm age on this relationship.

The study utilized annual data from 432 nonfinancial firms publicly listed in six Asian countries: China, Hong Kong, Japan, Singapore, Pakistan and India. The observation period covers 14 years, from 2007 to 2020. The sample was categorized into three groups: the entire sample and one group each for developing and developed Asian economies. A generalized least squares panel regression method was employed to test the research hypotheses.

The results suggest that long-term debt has a significant negative influence on SPCR in Asian economies, indicating that firms with high long-term debt experience lower future SPCR. Moreover, firm age negatively moderates this relationship, implying that older firms may experience a more pronounced reduction in SPCR due to high long-term debt. Finally, firms in developed Asian economies with high long-term debt are more effective in mitigating the risk of a significant drop in their stock prices than firms in developing Asian economies.

This study contributes to the literature in several ways. To the best of the researcher’s knowledge, this is the first of such efforts to investigate the relationship between debt maturity structure and crash risk in Asia. Additionally, it reveals that long-term debt influences SPCR directly and indirectly in Asia through the moderating role of firm age. Lastly, it is likely one of the first studies by a research team in Asia to compare the nonfinancial markets of developed and developing Asian countries.

This study examines the influence of investor protection on stock price crash risk (SPCR) in Asian economies.

This study used yearly data from 432 nonfinancial companies publicly listed firms in six countries (i.e., China, India, Pakistan, Hong Kong, Japan and Singapore) from 2007 to 2020 to investigate the relationship between investor protection and the risk of stock price crashes. The hypothesis was tested using a generalized least square panel regression.

The results suggest that investor protection significantly affects SPCR in Asian economies. Furthermore, the findings show that the stocks of firms whose investors received the best protection were less prone to crash in developed Asian economies. However, in developing Asian economies, the stocks of firms whose investors received the best protection were more prone to crashes.

It provides awareness and understanding of how the level of investor protection affects SPCR, which could be useful for decision-makers and professionals across a spectrum of financial and non-financial institutions, such as portfolio managers and traders in commercial banks, investment banks and mutual funds. This knowledge enables informed decision-making and the formulation of effective policies to manage stock market volatility.

This study appears to be the first of its kind to focus on the link between investor protection and SPCR within the specific context of developed and developing Asian economies.

The purpose of this study is to investigate the relationship between sustainable leadership and sustainable project performance. Specifically, the study aims to examine the mediating role of knowledge integration, examining how knowledge integration within an organization influences project outcomes. In addition, the study seeks to explore the moderating role of top management knowledge values, examining how the values and beliefs of top management influence the relationship between sustainable leadership and project performance.

A three-wave survey of 392 employees working in construction sector projects in Pakistan used both hierarchical regression analysis and Hayes’ PROCESS macro method to evaluate the hypotheses.

The study results show that sustainable leadership positively relates to sustainable project performance and knowledge integration mediating this relationship. In addition, the top management knowledge value moderates the indirect effect of sustainable leadership on sustainable project performance via knowledge integration.

Although the model was tested using three-wave data, it is important to note that the data were obtained from a single source. Therefore, it is possible that common method bias may have influenced the results, and this cannot be disregarded.

Organizations seek to prioritize sustainability and integrate sustainability considerations into their project management processes. Organizations can achieve improved sustainable project performance by investing in sustainable leadership development, fostering a culture of knowledge sharing and learning, prioritizing top management support for sustainable performance and integrating sustainable considerations into project management processes.

The study’s grounding on organizational learning theory adds an original and valuable perspective to the relationship between sustainable leadership and sustainable project performance. This investigation is original, as it combines sustainable leadership, knowledge integration and the moderating role of top management knowledge value to understand their impact on sustainable project performance. This unique approach contributes to the literature by providing new insights into these relationships and mechanisms in the construction industry.

Research in the field of intrapreneurial behaviour (IB) is developing in terms of the personality results of employees in various occupations. An important developing field is the study of how personality traits (PTs) influence occupational results. While previous theories like situationism focus on the impact of situations, the role of situations as a catalyst for personality in the context of IB has not been explored. Theories like person-job fit have also established the personality-job resemblance for performance measures. This paper aims to conceptualize the activation of PTs for IB responses from employees using trait activation theory (TAT).

The TAT explains the personality-triggering effect in diverse situations for performance measures. Plenty of research studies have established IB of employees as a performance measure at the individual level that further rejuvenates organisational performance measures. The current study aims to conceptualise how personality and situational factors interact to affect IB outcomes in firms using the TAT. Employees in different occupations utilise personality coherent conflict handling styles (CHS) to manage conflict situations, which might serve as situational cues to activate personality effects in employees engaged in IB.

A theoretical framework for understanding how situational cues activate PTs for IB development has been proposed in this study.

The findings of the study have repercussions for pedagogy and the practices that are currently used in human resource management.

The study concludes with a series of propositions by arguing and supporting literature on existing conceptions of the trait activation process. Attempting to advance the conceptualisation of TAT, the study also offered implications for theory, pedagogy and practice regarding the relationship between PTs, CHS and IB.

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.

The breakout of the COVID-19 pandemic has forced governments all over the globe to bring radical changes to all walks of life. Strict lockdowns are not only adversely affecting the social, economic, and psychological wellbeing of individuals but also questioning the sustainability of most businesses. In wake of the current scenario, this study is aimed at exploring how the COVID-19 pandemic is influencing the sustainability of entrepreneurship particularly from a female perspective and further providing insights into the role of Islamic financial institutions in the sustainability of businesses during COVID-19.

This is a qualitative study that takes social constructivism approach to study the underlying phenomenon. Semi-structured interviews are conducted to collect primary data. Secondary data are also utilized in this study to theoretically define various concepts relating to entrepreneurial sustainability. The application of thematic analysis revealed various risks associated with sustainability. The interviews reveal the ground realities and tell us about the hardships being faced by the entrepreneurs due to ongoing crises. The participants of the study also shed light on the role of Islamic financial institutions during the pandemic.

The study results revealed that it may look impossible for women entrepreneurs to halt or avoid the adverse consequences of the pandemic; however, a few female entrepreneurs strived to guard their existing portfolios with the help of Islamic microfinance institutions. Whereas, several women, especially those running home-based businesses, lost their income streams. Despite these rapid challenges, most female entrepreneurs are working on inventive online systems to sustain their business activities during the crisis. Finally, guidelines are suggested which can help achieve sustainability of the entrepreneurial startups.

The outcomes of this study are expedient for funding agencies, government authorities and Islamic financial institutions as well as for non-government institutions to establish sustainable and broader policies for women to become successful entrepreneurs during severe disasters like COVID-19. Moreover, the study is a helpful tool for women entrepreneurs to avert the worst impact of the pandemic with the help of Islamic microfinance institutions. The themes of this study help generate realistic information to appraise the strategies to create facilitating business environments that drive the women to carry out the entrepreneurial activity during any crisis like the COVID-19.

The results of this study provide evidence that crisis can be anticipated up to some extent if entrepreneurs become able to take proactive decisions in case of expected or identifiable threats. The study may also help the women entrepreneurs to comprehend the serious consequences of the pandemic by shifting their mode of financing to Islamic finance. Although this pandemic is a cause of physical discomfort instead this research may encourage the female entrepreneurs not to lose heart, just find the potential opportunities for their home-based and small businesses and manage funding from the Islamic microfinance institutions.

The study adds to the existing literature on entrepreneurial sustainability with a particular focus on the role of Islamic microfinance institutions for women entrepreneurs' sustainability in Pakistan. Secondly, the study employs the entrepreneurial sustainability model (ESM) that, according to the best of our knowledge, has not been used by the researchers earlier to study the given research phenomenon. Thirdly, the study findings are expedient for funding agencies, government authorities and financial institutions as well as for non-government institutions to establish sustainable and broader policies for women to become successful entrepreneurs during disasters like COVID-19.

This study aims to explore entropy evaluation in the bi-directional flow of Casson hybrid nanofluids within a stagnated domain, a topic of significant importance for optimizing thermal systems. The aim is to investigate the behavior of unsteady, magnetized and laminar flow using a parametric model based on the thermo-physical properties of alumina and copper nanoparticles.

The research uses boundary layer approximations and the Keller-box method to solve the derived ordinary differential equations, ensuring numerical accuracy through convergence and stability analysis. A comparison benchmark has been used to authenticate the accuracy of the numerical outcomes.

Results indicate that increasing the Casson fluid parameter (ranging from 0.1 to 1.0) reduces velocity, the Bejan number decreases with higher bidirectional flow parameter (ranging from 0.1 to 0.9) and the Nusselt number increases with higher nanoparticle concentrations (ranging from 1% to 4%).

This study has limitations, including the assumption of laminar flow and the neglect of possible turbulent effects, which could be significant in practical applications.

The findings offer insights for optimizing thermal management systems, particularly in industries where precise control of heat transfer is crucial. The Keller-box simulation method proves to be effective in accurately predicting the behavior of such complex systems, and the entropy evaluation aids in assessing thermodynamic irreversibilities, which can enhance the efficiency of engineering designs.

These findings provide valuable insights into the thermal management of hybrid nanofluid systems, marking a novel contribution to the field.

In today’s world, the demand for energy to power industrial and domestic activities is increasing. To meet this need and enhance thermal transport, solar energy conservation can be tapped into via solar collector coating for thermal productivity. Hybrid nanofluids (HNFs), which combine nanoparticles with conventional heat transfer fluids, offer promising opportunities for improving the efficiency and sustainability of renewable energy systems. Thus, this paper explores fluid modeling application techniques to analyze and optimize heat transfer enhancement using HNFs. A model comprising solar energy radiation with nanoparticles of copper (Cu) and alumina oxide (Al2O3) suspended in water (H2O) over an extending material device is developed.

The model is formulated using conservation laws to build relevant equations, which are then solved using the Galerkin numerical technique simulated via Maple software. The computational results are displayed in various graphs and tables to showcase the heat transfer mechanism in the system.

The results reveal the thermal-radiation-boost heat transfer phenomenon in the system. The simulations of the theoretical fluid models can help researchers understand how HNFs facilitate heat transfer in renewable energy systems.

The originality of this study is in exploring the heat transfer properties within renewable energy systems using HNFs under the influence of nonlinear thermal radiation.