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Organizations' technological management capabilities (TMC) have emerged as a powerful tool to enable manufacturing firms to deal with environmental issues. This empirical investigation aims to introduce and validate a novel conceptual framework that seeks to uncover the latent relationships among the selected constructs of this study. Organizational TMC could enhance green production (GP) and reinforce the green competitive advantage (GCA) among manufacturing firms. Therefore, this research investigates the role of TMC of firms such as artificial intelligence capability (AIC), big data analytics capability (BDAC) and Internet of things capability (IOTC) in reshaping green innovation (RGI), employee development (ED), GP and GCA.

The Partial Least Squares-Structural Equation Modeling was proposed to test and validate this research’s conceptual model using 463 valid responses from manufacturing under the China–Pakistan Economic Corridor (CPEC) umbrella.

Our statistical findings confirmed that TMCs such as AIC, BDAC and IOTC supported the GP and CGA. ED and RGI positively correlated to GP. The hypotheses testing results also confirmed the mediating role of ED, RGI and GP and the moderating role of green firm innovativeness capability (GFIC) in the underdeveloped context of the manufacturing industry under the CPEC.

Moreover, the statistical findings of this study extend the existing literature by validating the possible direct, indirect/mediation and indirect/moderation relationship between TMC and GCA.

Taking job demand-resource (JD-R) and self-determination perspective, the current study focused to see how basic need satisfaction (BNS) – as a personal demand – impacts work engagement directly and indirectly through personal resource (i.e. self-efficacy). Moreover, the aim was to test the dimension-wise impact of BNS, i.e. the need for autonomy, need for belongingness and need for competence in the aforementioned relationship.

This research is a time-lagged survey in which three-wave data of 398 white-collar employees were collected from the service and manufacturing sector of Pakistan through convenience sampling. Each wave of data collection was two months apart. The matched responses yielded an overall response rate of 66.33%. The collected responses were duly analysed using partial least squares structural equation modeling (PLS-SEM).

Results of the study confirmed all direct and indirect hypotheses encompassing the impact of the combined BNS construct on work engagement via self-efficacy. Nonetheless, in the dimension-wise analysis, the indirect impact of the need for job autonomy on work engagement was not validated. This depicted that the need for competence and relatedness are more important predictors of work engagement through the self-efficacy path.

It has been observed that prior research on work engagement was mainly focused on the role of job demands (JDs) and personal resources; however, the role of personal demands along with personal resources has little been discussed. The authors tested the total as well as the specific impact of each component of basic need on work engagement making it possible to examine the total predicting role of basic need satisfaction and the specific contribution of satisfaction of each need on work engagement.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

In recent years, the use of mobile devices for academic persistence has grown to be an indispensable element of students’ learning, highlighting the broad acceptance and adaptability of mobile technology in learning environments. The current study examines how college students in rural areas use mobile devices and how self-efficacious they are when seeking online information. Additionally, the study investigated the connection between mobile devices usage (MDU), mobile devices self-efficacy (MDSE) and online information seeking behavior (OISB) on the basis of demographic differences.

A quantitative research design was used by deploying a five-point Likert scale for measurement, Statistical Package for Social Sciences (SPSS) v.26 was used for data analysis. A variety of statistical methodologies, including t-tests, ANOVA and correlation coefficients, were conducted to inspect and assess MDU, MDSE and OISB across gender and age groups. Data from 331 students at the public sector college in a rural region was gathered using a questionnaire. A total of 315 legitimate replies were received.

The study's conclusions showed that the respondents used their mobile devices for educational purposes less frequently. Nonetheless, the respondents' degrees of MDSE and OISB appear to be high. Furthermore, a strong link was demonstrated among the MDU, MDSE and OISB. On the contrary, there was a negative correlation link between MDU and both MDSE & OISB, while a positive correlation between MDSE and OISB was found. The results also showed substantial variance in all research components based on age and gender, indicating that male and younger respondents performed more efficiently than female and adult respondents.

These results indicate that information literacy guidelines and a variety of educational initiatives should be put together by the government, educational policymakers, librarians and educators, with a focus on how to use mobile devices for learning and information seeking. This will make it possible for students to more efficiently find the information using their portable devices.

The disruptive pandemic has badly affected supply chain operations across the globe and implementing green supply chain strategies is challenging for manufacturing firms, especially in emerging countries. Therefore, this study aims to identify the significant challenges hindering the green supply chain as a pathway towards sustainability in the post-COVID-19 era.

Fuzzy Delphi Methodology (FDM), Interpretive structural modeling (ISM) and MICMAC were applied. FDM was applied to select the most relevant challenges and later ISM and Matrices d'Impacts cross-multiplication appliqúe a classmate MICMAC were used for modeling and classifying critical challenges.

Lack of trust between firms and supply chain partners, and difficulty in transforming positive environmental attitudes into action are the significant challenges to implementing green supply chain management. Lack of communication between government and Chinese firms is the least important factor which shows that the government is trying to support firms and reduce the negative effects after the drastic impacts of COVID-19. However, COVID-19 left a draconian effect on organization’s green supply chain and it’s not easy to overcome.

None of the previous studies applied mixed methodologies of FDM, ISM and MICMAC to evaluate Green supply chain as a pathway to sustainable operations in the post-COVID-19 era. Challenging factors of green supply chain operations in COVID-19 are different from earlier studies and contribute to the literature of emerging countries.

This study investigates the mediating role of working capital management (WCM) efficiency between board diversity (based on gender and financial knowledge) and firm performance. The study further examines which WCM approach (conservative, moderate, and aggressive) they employ to increase (decrease) firm performance.

The study employs listed energy firms of Pakistan over the period 2010 to 2019. The system generalized method of moments estimator and logit model are utilized to estimate the underlying relationships.

The results show that WCM efficiency partially mediates the relationship between board financial expertise (BFE) and firm performance. Nonetheless, the presence of female directors is merely symbolic until they reach a certain level as only the quadratic term of board gender diversity (BGD) has a significant effect on firm performance. Female directors do not influence WCM efficiency. The results also demonstrate that BGD encourages a conservative WCM approach, while BFE encourages a moderate WCM approach. Furthermore, both conservative and moderate WCM approaches are significantly associated with firm performance.

The findings hold implications for increasing the representation of women and financial experts on board to improve the capital structure decisions of the energy firms in Pakistan.

This study is the first attempt to explore the mediating role of WCM efficiency between board diversity and firm performance. To the best of the authors' knowledge, no previous study has investigated the effect of BGD and BFE on different WCM approaches distinctly.

This paper aims to present a study on stability analysis of Jeffrey fluids in the presence of emergent chemical gradients within microbial systems of anisotropic porous media.

This study uses an effective method that combines non-dimensionalization, normal mode analysis and linear stability analysis to examine the stability of Jeffrey fluids in the presence of emergent chemical gradients inside microbial systems in anisotropic porous media. The study focuses on determining critical values and understanding how temperature gradients, concentration gradients and chemical reactions influence the onset of bioconvection patterns. Mathematical transformations and analytical approaches are used to investigate the system’s complicated dynamics and the interaction of numerous characteristics that influence stability.

The analysis is performed using the Jeffrey-Darcy type and Boussinesq estimation. The process involves using non-dimensionalization, using the normal mode approach and conducting linear stability analysis to convert the field equations into ordinary differential equations. The conventional thermal Rayleigh Darcy number RDa,c is derived as a comprehensive function of various parameters, and it remains unaffected by the bio convection Lewis number Łe. Indeed, elevating the values of ζ and γ′ in the interval of 0 to 1 has been noted to expedite the formation of bioconvection patterns while concurrently expanding the dimensions of convective cells. The purpose of this investigation is to learn how the temperature gradient affects the concentration gradient and, in turn, the stability and initiation of bioconvection by taking the Soret effect into the equation. The results provide insightful understandings of the intricate dynamics of fluid systems affected by chemical and biological elements, providing possibilities for possible industrial and biological process applications. The findings illustrate that augmenting both microbe concentration and the bioconvection Péclet number results in an unstable system. In this study, the experimental Rayleigh number RDa,c was determined to be 4π2at the critical wave number ( δcˇ) of π.

The study’s novelty originated from its investigation of a novel and complicated system incorporating Jeffrey fluids, emergent chemical gradients and anisotropic porous media, as well as the use of mathematical and analytical approaches to explore the system’s stability and dynamics.

This study explores value-added food products in smallholder dairy farming in developing countries by analyzing external pressures, supply chain learning, farmer innovation, education level, and food safety compliance.

We employed a quantitative approach by surveying 418 smallholder dairy farmers in three districts of Pakistan using interviewer-administered questionnaires. Data analysis involved confirmatory factor analysis and structural equation modeling.

The results indicate that external pressure significantly affects value-added smallholder dairy farms. This relationship is mediated by supply chain learning and farmers' innovative behavior, and moderated by farmers' education level and compliance with food safety standards.

Further research is required to explore the drivers of value addition at the supply chain level.

This study contributes to the understanding of smallholder dairy farming dynamics and provides practical implications for improving value addition by managing the interplay between antecedents and promoting best practices in the industry.

This study aims to optimize heat transfer efficiency and minimize friction factor and entropy generation in hybrid nanofluid flows through porous media. By incorporating factors such as melting effect, buoyancy, viscous dissipation and no-slip velocity on a stretchable surface, the aim is to enhance overall performance. Additionally, sensitivity analysis using response surface methodology is used to evaluate the influence of key parameters on response functions.

After deriving suitable Lie-group transformations, the modeled equations are solved numerically using the “spectral local linearization method.” This approach is validated through rigorous numerical comparisons and error estimations, demonstrating strong alignment with prior studies.

The findings reveal that higher Darcy numbers and melting parameters are associated with decreased entropy (35.86% and 35.93%, respectively) and shear stress, increased heat transmission (16.4% and 30.41%, respectively) in hybrid nanofluids. Moreover, response surface methodology uses key factors, concerning the Nusselt number and shear stress as response variables in a quadratic model. Notably, the model exhibits exceptional accuracy with $R^2$ values of 99.99% for the Nusselt number and 100.00% for skin friction. Additionally, optimization results demonstrate a notable sensitivity to the key parameters.

Lubrication is a vital method to minimize friction and wear in the automobile sector, contributing significantly to energy efficiency, environmental conservation and carbon reduction. The incorporation of nickel and manganese zinc ferrites into SAE 20 W-40 motor oil lubricants, as defined by the Society of Automotive Engineers, significantly improves their performance, particularly in terms of tribological attributes.

This work stands out for its focus on applications such as hybrid electromagnetic fuel cells and nano-magnetic material processing. While these applications are gaining interest, there is still a research gap regarding the effects of melting on heat transfer in a NiZnFe_2O_4-MnZnFe_2O_4/20W40 motor oil hybrid nanofluid over a stretchable surface, necessitating a thorough investigation that includes both numerical simulations and statistical analysis.

The COVID-19 pandemic has caused widespread disruptions to global industries, with the textile sector in South Asia being particularly hard hit. While previous studies have focused on the performance of textile sectors in individual countries, there is a gap in the literature on the comparative impact of the pandemic on the textile industry in South Asian nations. This study aims to fill this gap by investigating the performance of the textile sector in South Asian countries and identifying best practices for overcoming the pandemic’s adverse effects.

Using a comparative approach, this study analyzes the impact of COVID-19 on the performance of the textile sector in Pakistan, India and Bangladesh.

Our findings reveal that COVID-19 significantly negatively impacts the textile industry in Pakistan and India. However, Bangladesh has shown effective practices to support the textile industry and mitigate the pandemic’s adverse effects.

The findings of this study hold considerable implications for legislators, leaders, investors and supply chain management professionals operating within the South Asian textile sector. This research has the potential to inform policymakers in formulating strategies to facilitate the textile sector’s resilience during emergencies like the COVID-19 pandemic.

This paper provides significant theoretical additions to the current body of literature regarding the impact of COVID-19 on the textile sector in South Asia. The research uses the global value chain (GVC) theory as a theoretical framework to enhance understanding of the impact of global supply chains and interdependencies on the textile sector in the region.