Muhammad Irfan

The journal retracts the article titled “Analyzing the Impact of Enterprise Social Media on Employees’ Competency through the Mediating Role of Knowledge Sharing”

This study develops and validates an integrated soil–land–climate (SLC) framework to predict farm household income in South Sindh and South Punjab, Pakistan. Using a Multinomial Endogenous Switching Regression (MESR) model, we assess how soil fertility, water scarcity, and climate stress impact agricultural productivity and income. Results show that a one‐unit increase in soil fertility index raises farm income by 1.39 units ( p < 0.01), while water availability increases income by 2.43 units ( p < 0.05). Climate change perception demonstrates the strongest effect, boosting income by 7.39 units ( p < 0.01). CSA adoption reduces income risk by 49% ( p < 0.01) and revenue skewness by 38% ( p < 0.05). The SLC framework revealed feedback loops in which water scarcity accelerated soil salinization ( r = 0.62, p < 0.01). The joint adoption of CSA practices results in a 45.9% increase in income and a 49.0% reduction in downside risk. The validity of the MESR model was confirmed with robust statistical results. Soil fertility (coefficient = 0.23) and farm size (coefficient = 0.18) are key factors influencing farm income, while rainfall variability (coefficient = −0.21) shows a climate variability impact. Policy simulations indicate that improving soil health raises annual income, while drip irrigation subsidies targeting farms > 8 km from markets yield 3:1 benefit–cost ratios. This study provides evidence for climate adaptation policies in Pakistan by recommending targeted subsidies for drip irrigation, soil amendments, and strengthening of FBOs.

This study investigates the specific pathways through which the behavioral bias of greed influences investors' financial behavior (IFB) in the stock market, with a focus on the mediating roles of calendar, fundamental, and technical anomalies. A quantitative, cross sectional design was employed, collecting data from a sample of 405 investors in Pakistan's major financial centers using a structured questionnaire. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results provide strong evidence for a direct, highly significant relationship, showing that Greed has strong positive effect on IFB (beta = 0.993, p < 0.001), suggesting that the drive for wealth accumulation is a primary determinant of the financial behaviors studied. Furthermore, the analysis confirmed significant positive indirect effects, supporting the mediation hypotheses. Calendar Anomalies served as the mediating mechanism (beta = 0.245, p = 0.002), followed by Fundamental Anomalies (beta = 0.149, p = 0.001) and Technical Anomalies (beta = 0.139, p = 0.003). This indicates that greedy investors channel their desire for profit into observable market patterns, leading to specific, and potentially irrational, financial decisions.

This paper compares the performance of two machine learning algorithms, Random Forest and Logistic Regression, in predicting student course completion in online university courses using behavioral data. Behavioral data, including interaction logs and submission records, has proven to be crucial in identifying students at risk of non-completion. The study evaluates the models using standard classification metrics such as accuracy, precision, recall, and F1-score, based on real-world data from online courses. Both models demonstrate exceptionally high predictive accuracy, with Logistic Regression achieving perfect classification and Random Forest closely following. While Logistic Regression is favored for its simplicity and interpretability, Random Forest excels in handling complex, non-linear relationships within the data. The analysis of feature importance reveals that student engagement, particularly through viewing and passing course materials, is a strong predictor of course completion. These findings offer significant practical implications for online education, supporting early interventions to enhance student retention. However, limitations such as the absence of certain behavioral data and the linear assumption in Logistic Regression suggest areas for future research. Expanding the dataset to include discussion forums, peer interactions, or additional machine learning models may provide deeper insights into improving student success in online courses.

This paper compares the performance of two machine learning algorithms, Random Forest and Logistic Regression, in predicting student course completion in online university courses using behavioral data. Behavioral data, including interaction logs and submission records, has proven to be crucial in identifying students at risk of non-completion. The study evaluates the models using standard classification metrics such as accuracy, precision, recall, and F1-score, based on real-world data from online courses. Both models demonstrate exceptionally high predictive accuracy, with Logistic Regression achieving perfect classification and Random Forest closely following. While Logistic Regression is favored for its simplicity and interpretability, Random Forest excels in handling complex, non-linear relationships within the data. The analysis of feature importance reveals that student engagement, particularly through viewing and passing course materials, is a strong predictor of course completion. These findings offer significant practical implications for online education, supporting early interventions to enhance student retention. However, limitations such as the absence of certain behavioral data and the linear assumption in Logistic Regression suggest areas for future research. Expanding the dataset to include discussion forums, peer interactions, or additional machine learning models may provide deeper insights into improving student success in online courses.