Nikolaos Varotsis | Technological Change | Best Researcher Award

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Dr. Nikolaos Varotsis | Technological Change | Best Researcher Award

Lonian University | Greece

Dr. Nikolaos Varotsis is a multidisciplinary researcher whose work spans tourism management, behavioral economics, and knowledge management, focusing on how individuals, tourists, and organizations make decisions under complex socio-economic and informational conditions. With 203 citations, an h-index of 8, and an i10-index of 8, his research output demonstrates strong and steadily growing academic influence in both tourism studies and behavioral sciences. His contributions advance understanding of tourist information search behavior, destination brand equity, cultural tourism development, and digital entrepreneurship within the tourism sector. Drawing from behavioral economics and social simulation methodologies, Dr. Nikolaos Varotsis investigates mental accounting, organizational motivation, tax behavior, and the interplay of psychological, economic, and social factors influencing tax planning and tax compliance. His work also provides significant insights into telecommuting performance, work–family conflict, and public sector attitudes during the COVID-19 era, along with fiscal foresight models, shadow economy reduction strategies, and e-payment institutionalization. In tourism research, he has produced influential studies on information service management, wedding tourism decision motives, alternative tourism models for island destinations, and quality standards in hospitality services. His publications appear in respected journals such as Cogent Business & Management, Journal of Convention & Event Tourism, Nonlinear Dynamics Psychology and Life Sciences, Journal of Economic Structures, Digital Policy, Regulation and Governance, and Theoretical Economics Letters. Dr. Nikolaos Varotsis diverse research interests continue to evolve around tourism behavior, social simulations, organizational change, knowledge management, and the integration of behavioral insights into tourism innovation and public administration.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

  • Varotsis, N., & Mylonas, N. (2024). A systematic literature review on information service management and information-seeking behavior in tourism. Cogent Business & Management, 11(1), 2385731.

  • Mylonas, N., Varotsis, N., & Vozinidou, I.-M. (2024). Unveiling the relationship between travel decision motives and destination brand equity in wedding tourism. Journal of Convention & Event Tourism, 25(2), 233–248.

  • Kontogeorgis, G., & Varotsis, N. (2022). Cultural tourism in developed island tourist destinations: The development of an alternative tourism model in Corfu. Journal of Environmental Management and Tourism, 13(2), 490–503.

  • Varotsis, N. (2022). A fiscal policy foresight tax model, shadow economy reduction, and e-payment institutionalization as a result of knowledge management. Theoretical Economics Letters, 12(6), 1710–1728.

  • Varotsis, N. (2022). Exploring the influence of telework on work performance in public services: Experiences during the COVID-19 pandemic. Digital Policy, Regulation and Governance, 24(3), 248–263.

Sukumar Letchmunan | Computer Science | Best Researcher Award

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Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

University Sains Malaysia | Malaysia

Dr. Sukumar Letchmunan is an accomplished researcher and academic at Universiti Sains Malaysia, specializing in Software Engineering, Machine Learning, Software Metrics, and Service-Oriented Software Engineering. His research primarily focuses on developing intelligent computational models and data-driven frameworks that address complex real-world problems in domains such as crime prediction, healthcare analytics, uncertainty modeling, and sustainable digital systems. Over the years, Dr. Sukumar Letchmunan has made significant contributions to artificial intelligence applications through the integration of fuzzy logic, evidential theory, and deep learning, as reflected in his high-impact publications in leading journals including IEEE Access, ACM Transactions on Knowledge Discovery from Data, Knowledge-Based Systems, and Applied Soft Computing. His collaborations have produced novel methods for multi-view evidential clustering, belief function-based uncertainty representation, and medical decision-making systems, demonstrating his expertise in handling imprecision in data-centric environments. Notably, his works on AI-driven crime forecasting, machine learning-based diabetes prediction, and deep neural network analysis for human activity recognition have been widely cited, influencing interdisciplinary research across computer science and healthcare informatics. With a strong record of international collaboration, particularly with researchers from China, Turkey, and Saudi Arabia, Dr. Sukumar Letchmunan continues to advance the frontier of trustworthy and interpretable AI systems. His recent works in fuzzy similarity measures, transformer-based spatiotemporal modeling, and green decision-making frameworks exemplify his commitment to enhancing computational intelligence for sustainable and socially relevant applications. With 1,298 citations, an h-index of 19, and an i10-index of 22, Dr. Sukumar Letchmunan research impact underscores his influential role in advancing next-generation AI methodologies and their meaningful application to global scientific and societal challenges.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

1. Butt, U. M., Letchmunan, S., Ali, M., Hassan, F. H., Baqir, A., & Sherazi, H. H. R. (2021). Machine learning based diabetes classification and prediction for healthcare applications. Journal of Healthcare Engineering, 2021(1), 9930985.

2. Khaw, T. Y., Teoh, A. P., Abdul Khalid, S. N., & Letchmunan, S. (2022). The impact of digital leadership on sustainable performance: A systematic literature review. Journal of Management Development, 41(9–10), 514–534.

3. Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-temporal crime hotspot detection and prediction: A systematic literature review. IEEE Access, 8, 166553–166574.

4. Liu, Z., & Letchmunan, S. (2024). Enhanced fuzzy clustering for incomplete instance with evidence combination. ACM Transactions on Knowledge Discovery from Data, 18(3), 1–20.

5. Liu, Z., Huang, H., Letchmunan, S., & Deveci, M. (2024). Adaptive weighted multi-view evidential clustering with feature preference. Knowledge-Based Systems, 294, 111770.