Daily Archives: July 11, 2026

Authors: Ms. Mahima Rana

Abstract: Leadership has a significant impact on employee attitudes, actions, and performance outcomes within firms. This study investigates the impact of different leadership styles transformational, transactional, servant, and situational leadership on employee performance in a variety of organizational circumstances. Using contemporary leadership theories and empirical evidence, the study investigates the mechanisms by which leadership influences individual and team performance, focusing on factors such as motivation, communication, trust, psychological safety, organizational commitment, and adaptive capability. The study found that transformational leadership had the most positive influence on employee performance by encouraging creativity, engagement, and corporate citizenship behaviors. Servant leadership makes a substantial contribution by fostering relationships, empowering employees, and creating supportive work environments, yet transactional leadership is still useful in organized settings where performance is driven by reward and responsibility systems. Situational leadership provides flexibility by tailoring leadership actions to employee preparedness and changing organizational needs. The study also emphasizes the moderating role of organizational culture, industry features, team dynamics, and cross-cultural elements in the leadership-performance relationship. The findings emphasize the necessity of integrating leadership development activities with strategic corporate goals in order to increase staff productivity, well-being, and long-term organizational success. The study adds to the increasing body of leadership literature by offering a thorough grasp of how leadership styles influence employee performance and indicating significant topics for further research.

DOI: http://doi.org/10.5281/zenodo.21308891

Published by: vikaspatanker

Authors: Rajesh Chauhan, Akshay Bhardwaj, Rohit Kumar Verma

Abstract: The spread of false information on digital platforms has surged and there is a growing demand for the adoption of accurate and deployable automated false information detection systems. But models learned in one news domain can easily suffer significant performance drop when transferred to other domains out of the scope of its training. This study compares four classical machine learning models and five deep learning architectures for within and cross domain fake-news detection. Five publicly available benchmark datasets, which include over 150,000 labelled instances, are used for experiments: LIAR, ISOT Fake News, FakeNewsNet GossipCop, WELFake, and Fake and Real News Dataset. Their models are evaluated based on classification accuracy, F1 score, cross domain performance retention, computational cost, data requirements and interpretability. The best fine-tuned RoBERTa model obtained the highest accuracy score of 97.8% on ISOT and 84.9% on the transfer task from ISOT to GossipCop, outperforming the linear SVM model by 13.7 percentage points. However, classical models are still suitable in resource-limited and interpretability sensitive scenarios, and BiLSTM with additive attention is a balanced model. The results show that model selection should not only evaluate the predictive performance of the model but also take into account the operational constraints.

DOI: http://doi.org/10.5281/zenodo.21308343

Published by: vikaspatanker