Review of Applied Management and Social Sciences (RAMSS) (E-Journal)
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The Role of Social Support Networks in Reducing Workplace Burnout
Get PDFThis research aims to analyze the role of social support networks in minimizing workplace burnout among managers working in small organizations in the provinces of Punjab, Sindh, and Khyber Pakhtunkhwa (KPK) of Pakistan. A total of 198 managers participated in this research and provided data through self-administered questionnaires. The study uses quantitative methods, such as correlation analysis, regression analysis, and ANOVA statistics, to analyze the inter-relationships between emotional, instrumental, and informational support and workplace burnout. Based on the data analysis, it can be noticed that each category of social support has lowered the levels of burnout. However, emotional social support had the highest negative correlation at r = -0.635. Regression analysis supports the hypothesis as p-values ? 0.000 ensure all the relationships studied are statistically significant. ANOVA analysis shows that the cultural, social, and economic dynamics of a region are also responsible for significant dimensions in determining the effectiveness of support networks. The study suggests that tailored interventions, which include awareness campaigns and workplace-based programs, are essential for the effective prevention of burnout
Using AI to Inform Evidence-based Decision-Making in Educational Policy
Get PDFThis study evaluates the role of artificial intelligence in evidence-based decision-making in educational policy, with a quantitative research design to measure the perceptions and experiences of 200 educators at public and private universities in Khyber Pakhtunkhwa and Punjab, Pakistan. Random sampling was applied to the participants so as to ensure even distribution and remove bias. Data were collected through self-report questionnaires that consisted of both closed-ended questions and questions that used the Likert scale. Statistical methods of data analysis applied included correlation analysis, r = 0.78, p < 0.001, regression analysis, ? = 0.65, R² = 0.58, p < 0.001, and t-tests, t(198) = 2.85, p = 0.005. Such techniques have been applied to determine the relationship,?predictive factors, and group differentiation associated with perceptions of the effectiveness of AI in educational policy-making. The study shows that artificial intelligence improves decision-making and?corrects the imbalances in education greatly. The findings do stress?the demands of embedding artificial intelligence into education systems even while confronting issues such as algorithmic bias and data privacy. It sheds light on how AI could impact?educational policy, and it provides guidance on the careful implementation of this tech
Assessing the Role of Social Media in Shaping Quality of Interpersonal Communication among Students at Universities
Get PDFThe current descriptive study aimed to examine the role of social media on students' interpersonal communication skills at universities The study population covered 5,768 students who were studying regularly at three public universities: Emerson University Multan, Bahauddin Zakariya University Multan, and The Women University Multan, Punjab, Pakistan. A sample of (n=200) two hundred students, including sixty males (M=60) and one hundred forty females (F=140), were chosen through simple random sampling from various departments within the social science faculty to accomplish this study. A questionnaire was designed to obtain quantitative data for the current study. The descriptive statistics standard deviation, mean, percentages, and frequencies as well as inferential statistics Pearson coefficient correlation regression and t-test was employed to examine the data. The findings indicated a positive perspective of students towards social media in shaping their communication skills and a significant positive correlation between social media usage and interpersonal communication skills was observed in the study. The study recommended that counselling services for students should be offered to maximizes the positive impression of social media's effect on communication patterns at universities
The Role of AI in Supporting Inclusive Education: Addressing Divers Learning Needs Through Intelligent Tutoring System
Get PDFThe aim of this study is to look at AI-based ITS in inclusive schools and notice their impact on student learning, mainly considering grades, how engaged the students are, and their level of motivation. The study made use of a quantitative approach by obtaining information from 250 students attending classes that use ITS. It was discovered in the study that the use of AI in instruction improves students’ academic outcomes. Also, it was found from the ANOVA analysis that students who regularly engage with ITS are involved and motivated more in their studies than other students. Similar findings are noted in the moderation analysis, where ladies and students with disabilities are influenced differently by ITS, meaning that ITS tools can be updated to work for these groups and therefore ensure equal opportunities in education for everyone. Since the research included almost every type of student and was done in a careful manner, someone would find the results to be realistic and worth considering. According to the findings, Intelligent Tutoring Systems give students additional support and help them perform better in school. It points out that AI can be used in schools whenever students’ abilities to learn are not equal. Since ITS offers concrete advice and lessons suited to each student, everyone enjoys more academic success and a sense of fairness and greater involvement. Besides, it appears that teaching teachers to use AI in classrooms increases their rewards from ITS and guarantees they act ethically. Basically, the study proposes more development and proper use of AI in education to make sure that people of all learning backgrounds can benefit from them
Forecasting Stock Index Movement using Tree Based Classifiers: Evidence from an Emerging Economy
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The given research addresses the issue of predicting the KSE-100 index through decision tree or random forest machine learning algorithms in an emerging market. The research attempts to build a complex analysis framework by including both types of technical indicators, e.g., stochastic %K and fundamental variables, e.g., oil and gold prices. The findings assert that the random forest model was better than the decision tree since the former has a prediction rate of 76% odds to 73%. Technical indicators are shown to be more effective as compared to the fundamental but combining them only provides a slight improvement in the predictive ability. The findings emphasize the prospect of ensemble learning techniques in the detection of non-linear market trends and associating investment strategies and risk management practices. The paper also proposes that emerging markets due to their structural inefficiencies can provide even more predictability thus adding to the growing body of research on the use of machine learning in forecasting financial markets
Key Drivers of Behavioral Biases of Individual Investors: A case of Pakistani Investors
Get PDFThis study investigates the strength of behavioral biases in individual investors. A questionnaire was developed and distributed to individual investors and there selection was based on convenience sampling method. Multi criteria decision making technique of AHP was used for determining the strength of the behavioral biases of individual investors towards overall behavior of investors. Results revealed that hindsight, anchoring, representative, and loss aversion biases were most prominent biases towards shaping the behavior of individual investors along with conservatism bias which was significant and positively correlated with overall behavior of individual investors
The Psychological Impact of AI - generated Feedback on Learner self- concept and Motivation
Get PDFThe current research was aimed at examining how AI-based feedback can influence emotionally positive or negative responses of students and their engagement in learning in higher education. Based on a quantitative research design, statistical analysis methods were used to operate on 270 teachers within the Pakistani educational landscape with the help of a structured questionnaire. The results indicated that the tone and quality of AI generated feedback could have a significant impact on motivation and confidence rates of students and their engagement in learning behaviors. Mediation analysis demonstrated that emotional response was also a vital component in describing the relationship between the level of feedback quality and engagement. The implementation of clear and timely AI feedback in accordance with the satisfaction of teachers might lead to an increase in the focus of the students or the alleviation of anxiety and trigger active learning, but in some cases, misdesigned information unsettled the students or even irritated them. According to this information, there is a necessity that is to be taken into account in order to identify the proper solution to introduce the AI technology in the classroom without causing the crisis with the assistance of training and establishing relations with the technical support. The discussion has emphasized the need to adapt AI feedback types to different needs of the current learning process and balance the presence of technology and human communication in order to establish further inclusion and accessibility. AI feedback has a potential to be the practical approach to education that would improve learning results provided it is applied in a moral and innovative manner as demonstrated by the researchers conducting the stud
Reframing Work Motivation: An Interdisciplinary Model of Autonomy, Purpose Alignment, and Global Voice
Get PDFIn a digitalized world, traditional thinking about employee motivation is no longer adequate. This paper introduces a new interdisciplinary model – Meaning-Driven Motivation (MDM) – that synthesizes three critical dimensions: personal needs, for autonomy and competence;?organizational purpose fit; and global voice and trust. In this manner, through review of interdisciplinary empirical and conceptual literature (2020–2025) spanning psychology, human resource management (HRM), and international relations (IR),?the model provides an explanation of how these concepts overlap to form a sustainable motivational dynamic. The MDM approach implies that work motivation is best understood not only in terms of individual motives for agency but also through alignment with organizational purpose and meaningful participation in transnational structures of voice and trust. In this study recommendations for international enterprises, virtual teams and multicultural settings are discussed, suggesting a research agenda and practical implications for development of inclusive, ethically grounded work systems
Stress and Spending Dynamics: An Economically Framed Study of Emotional Expenditures in Youth
Get PDFIt is a quantitative research study that explores the connection between psychological stress, emotional spending, and digital impacts on the youth spending behaviour based on the data gathered on 291 respondents (15-25 years old). Using a structured questionnaire and stratified random sampling, the study investigated the role of academic pressure, social comparison, family expectations, and digital engagement in impulsive and emotional spending. The descriptive statistics demonstrated that the degree of digital influence stresses (M = 3.91) and the significant impulsive spending tendencies (M = 3.62) were high. Correlation analysis indicated that there were positive significant relationships between all stressors and impulsive spending with the strongest relationship between digital influence stress (r =.55, p <.01). A multiple regression analysis also revealed that the level of stress (b = .49), relief after spending (b =.36), and using spending as a coping strategy (b =.32) were also significant predictors of frequency of emotional spending and accounted 56% of the variance in emotional spending (R2 = .56, p =.001). The results of ANOVA established that there were significant differences in impulsive digital spending in the levels of digital influence with the youth with high levels of digital influence exhibiting the highest level of impulsive digital spending (F = 19.37, p < .001). Altogether, the results indicate that psychological stressors and fortified by digital environment, emotional spending among young people can be greatly influenced by specific financial education and emotional control training and ethical digital design, which will help youth make healthier consumer choices
Financial Inclusion and Economic Growth: A Case of Pakistan
No full textPakistan is among the economies where financial inclusion is very low. This paper measures the influence of financial inclusion (FI) on economic growth (EG) of Pakistan over the period from 1982 to 2020. The study employs Autoregressive Distributed Lag (ARDL) method and bounds test approach to investigate the impact of FI on EG in the long run and short run. PCA (principle component analysis) is used to create the FI index, which measures the financial services availability in the Pakistani economy. It has been found that FI has a significant and positive impact on EG of Pakistan. The conclusions of the study recommend that provision of the financial services across the country must be improved. Policymakers can design policies on the basis of financial services for improvement in economic growth of the country