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In an increasingly competitive landscape, both researchers and businesses are showing growing interest in promoting employee's innovative work behavior (EIWB). Although earlier studies have highlighted the significance of transformational leadership (TL) in cultivating innovation among employees, there needs to be more understanding regarding the precise mechanisms and processes by which leaders exert their influence over the IWB of their employees. This study is based on the social exchange theory (SET) and upper echelon theory (UET) to investigate how the relationship between TL and employees' IWB is mediated by the employees' intellectual agility (EIA) and the employee's voice (EV). To the best of researchers' knowledge, this study represents the pioneering effort to examine the mediating mechanisms of EIA and EV between TL and EIWB within the specific context of small and medium Enterprises (SMEs) in a developing country. An online self-administered questionnaire was utilized to collect data from 430 SMEs in Pakistan. The proposed hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The study findings revealed a significant influence of TL on EIWB mediated by both their EIA and EV. These findings empower leaders to recognize their pivotal roles in nurturing innovation within their enterprises and crafting an optimal culture and climate conducive to innovative endeavors. Furthermore, this insight enables leaders to establish innovative environments that promote employees' confident sharing of ideas and concepts. The study also includes a comprehensive finding and their implications, limitations, and suggestions for future research directions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to investigate the synergistic impact of workplace spirituality (WPS), responsible leadership (RL), and pro-environmental behavior (PEB) within the context of Pakistan's Small and Medium Enterprises (SMEs) along with the mediating mechanism of Affective commitment (AC).Methods: Our study utilizes structural equation modeling (SEM) to analyze survey data collected from 390 employees within Pakistani SMEs. We develop a single unified framework to test the hypothesized relationships between RL, PEB, and AC as a mediator.Results: Our results unveil significant positive relationships between WPS, RL, and PEB. Our findings endorse both the direct connections between WPS, RL, and PEB, as well as the mediating mechanisms through AC. This study offers novel insights into the influence of PEB in the context of Pakistan's SMEs.Conclusion: The research significantly contributes to the existing literature by examining WPS, RL, PEB, and AC in the context of Pakistan's SMEs. The study's conclusions emphasize the potential benefits of integrating WPS and RL into business practices to encourage PEB. Additionally, we discuss the limitations of our research and suggest future avenues for further exploration in this vital area of sustainable business management. [ABSTRACT FROM AUTHOR]

Employees' pro-environmental behaviors contribute to environmental sustainability. Drawing on social exchange theory, this research extends corporate social responsibility (CSR) literature by investigating the positive effect of perceived CSR on employees' pro-environmental behaviors and exploring the mediating mechanisms of environmental consciousness and environmental commitment between perceived CSR and employees' pro-environmental behaviors. Survey data were collected from 301 employees working in the textile manufacturing industry of Pakistan. The findings of this study demonstrate a positive direct impact of perceived CSR on pro-environmental behaviors. Moreover, environmental consciousness and environmental commitment mediated this relationship. Taken together, this study indicates that perceived CSR has three paths to impact employee pro-environmental behavior: direct effect, indirect effect via environmental consciousness, and indirect effect via environmental commitment. The contributions of this study include supporting social exchange theory and presenting pioneering evidence of environmental consciousness and environmental commitment mediating processes between perceived CSR and pro-environmental behaviors. This paper provides directions for future research and suggests practical implications for organizations. [ABSTRACT FROM AUTHOR]

Corporate social responsibility (CSR) is ever-evolving concept and gaining significance in the business world. This research proposes a research model that elucidates the mechanism by which Corporate social responsibility (CSR), ethical leadership, and moral reflectiveness promote pro-environmental behavior among employees of Small and Medium Enterprises (SMEs). In today's ever-changing environment, small and medium companies (SMEs) are critical for any economy to thrive and prosper. SMEs account for more than 30% of the GDP in Pakistan. However, little is known about the processes by which type of leadership influence the pro-environmental behavior, or the circumstances under which such impacts are strengthened or mitigated. The current study attempted to fill the gap by investigating a dual process model in which ethical leadership and a moral reflectiveness acted as two explanatory mechanisms in the impact of Corporate social responsibility (CSR) on proenvironmental behavior among employees working in small and medium enterprises (SMEs). The structural equation modeling technique was used to test the research model's hypothesized relationships. A survey was used to collect data from 390 employees. Results showed that perceived CSR directly impacted moral reflectiveness and ethical leadership. Moreover, the moral reflectiveness and ethical leadership mediated the relationship between the perceived CSR and pro-environmental behavior. These findings contributed significantly to perceived CSR, ethical leadership, moral reflectiveness, and pro-environmental behavior among employees by exploring and integrating the holistic research work into one framework to add to the body of knowledge. Practical implications and future research directions are also highlighted. [ABSTRACT FROM AUTHOR]

Macroeconomic indicators are the key to success in the development of any country and are very much important for the overall economy of any country in the world. In the past, researchers used the traditional methods of regression for estimating macroeconomic variables. However, the advent of efficient machine learning (ML) methods has led to the improvement of intelligent mechanisms for solving time series forecasting problems of various economies around the globe. This study focuses on forecasting the data of the inflation rate and the exchange rate of Pakistan from January 1989 to December 2020. In this study, we used different ML algorithms like k-nearest neighbor (KNN), polynomial regression, artificial neural networks (ANNs), and support vector machine (SVM). The data set was split into two sets: the training set consisted of data from January 1989 to December 2018 for the training of machine algorithms, and the remaining data from January 2019 to December 2020 were used as a test set for ML testing. To find the accuracy of the algorithms used in the study, we used root mean square error (RMSE) and mean absolute error (MAE). The experimental results showed that ANNs archives the least RMSE and MAE compared to all the other algorithms used in the study. While using the ML method for analyzing and forecasting inflation rates based on error prediction, the test set showed that the polynomial regression (degree 1) and ANN methods outperformed SVM and KNN. However, on the other hand, forecasting the exchange rate, SVM RBF outperformed KNN, polynomial regression, and ANNs. [ABSTRACT FROM AUTHOR]

The purpose of the study is to observe the impact of policy intervention on financial sustainability using the structural vector autoregression (SVAR) analysis. The population of the study is the manufacturing sector of Pakistan, which is an emerging economy. Data for 249 firms operating in the manufacturing sector are taken, collected from Datastream from 2005 to 2019, with total observations of 2,400. To conduct the analysis, R software is used for its better visualization. Results show that firm performance, corporate governance, and sectoral policies have a positive and long-term impact on financial sustainability, whereas earning management and financialization not only have a negative impact, but this impact affects the operations of the corporate for a longer period. This study would be helpful for policymakers as it gives a framework for financial sustainability based on the policies and strategies developed by the sector. [ABSTRACT FROM AUTHOR]

Financial distress is one of the leading reasons for corporate default, foreclosure, and subsequent bankruptcy. Traditionally accounting information-based models have been used to identify the financially distressed firms. Inaccurate identification results in type-I and type-II errors. These errors are attributable to the elements of conservatism and prudence within the accounting information. Enclosure of market information into these models may reduce the quantum of errors and improve the existing ability of these models to accurately identify the financially distressed firms. This study has infused the Corporate Social Responsibility information into the Altman Z-Score. The enclosure has been made using additive index methodology, resulting in formation of a Composite Default Index. 161 non-financial firms listed on the Pakistan Stock Exchange have been taken as the sample over the study period of 2010-2016. The accuracy of Composite default Index has been found significantly better as compared to the ZScore Default Index as the type-I and type-II errors are reduced by 41% and 132% respectively. This study has implication for the corporate regulators, practitioners, stakeholders and researchers in form of providing a better mechanism to identify the financially distressed firms, future studies may consider other forms of market information and the accounting information-based models to improve upon the same, further. [ABSTRACT FROM AUTHOR]

AI (artificial intelligence) is a significant technological advancement that has everyone buzzing about its incredible potential. The current research study evaluates the influence of supervised artificial intelligence techniques, i.e., machine learning techniques on the nonfinancial firms of Pakistan and focuses on the practical application of AI techniques for the accurate prediction of corporate risks which in turn will lead to the automation of corporate risk management. So, in this study, we used financial ratios for accurate risk assessment and for the automation of corporate risk management by developing machine learning algorithms using techniques, namely, random forest, decision tree, naïve Bayes, and KNN. A secondary data collection technique will be used. For this purpose, we collected annual data of nonfinancial companies in Pakistan for the period ranging from 2006 to 2020, and the data are analyzed and tested through Python software. Our results prove that AI techniques can accurately predict risk with minimum error values, and among all the techniques used, the random forest technique outperforms as compared to the rest of the techniques. [ABSTRACT FROM AUTHOR]