The 8th ICBIM 2024| Invited Speakers

Prof. Noriyuki Suyama, Toyo University, Japan

Noriyuki Suyama was born in Tokyo. He is a Professor in Toyo University. His publications and research interest focus primarily on Global Marketing and Customer Relationship Management with quantitative methods. He received an undergraduate degree in Economics from Sophia University in Tokyo, M.B.A. in Marketing from University of Rochester, NY, M.S. in Econometrics and Ph.D. in Marketing from Senshu University in Tokyo.
He started business career at Daimaru Co. Ltd., one of major department store chains in Japan and was mainly involved in merchandising and marketing activities as a manager. He moved to Rakuten Inc., No.1 electric commerce firm in Japan and was double-assigned to general manger positions in merchandising division and client marketing division. He was also engaged in commercial real estate management and food business as a general manager and a CEO, respectively in overseas markets. His overseas assignment totals more than 10 years, mainly in Southeast Asia.
Currently, he is also an adjunct faculty of Marketing Research and International Marketing at Metropolitan University of Tokyo and Sophia University, respectively.
Dr. Suyama belongs to Japanese Society of Marketing and Distribution, Japan Marketing Academy, Japan Society for Southeast Asia Studies, Fashion Business Association and Japan Halal Association. He is a member of Gerson Lehrman Group (GLG) Council, who consults with clients.

Relationship between Weather Conditions and Fashion Business in Japan and Implications for the Future

Promotion of Science (JSPS) through a Grant-in-Aid for Scientific Research. In the first year of the project, I here discussed "The relationship between weather conditions and the fashion business in Japan and its implications for the future," and wrote as an invited paper on the topic. In the apparel industry, there is still a huge of problems in the form of mass production, mass consumption, and mass disposal as negative assets, as well as environmental problems (mass use of water, carbon dioxide emissions, use of pesticides and other chemicals, etc.), marine pollution (microfibers, water pollution, etc.), and labor problems (poor environment, human rights issues, etc.). On the other hand, marketing activities based on future weather conditions may possibly lead to new business models, such as securing appropriate inventories, avoiding missed sales, improving the efficiency of production activities in the supply chain, maximizing sales by adjusting the sell-by period, and optimizing the timing of advertising promotions.

Assoc. Prof. Paniti Netinant, Rangsit University, Thailand

Paniti Netinant is a native of Bangkok. He is an Associate Professor of Computer Science at Rangsit University. He is currently the associate dean of the graduate school at Rangsit University. He is responsible for information technology and international affairs at the graduate school. He graduated with second class honors from Bangkok University in Thailand with a bachelor's degree in computer science. He earned a master's degree in computer science and a doctorate in computer science from the Illinois Institute of Technology in Chicago, Illinois, United States of America. He was a recipient of research grants from the Thai government. As a senior consultant, he worked in both the private and public sectors. Additionally, he is an associate editor for the SCOPUS-indexed Journal of Current Science and Technology and serves as Editor-in-Chief of the Journal of Digital Business and Social Science. He is particularly interested in data modeling and framework development, information technology design and development, the Internet of Things, information layers and services, and information management. He has recently chaired international conferences such as ICSIM 2022, ICEEG 2022, and ICFECT 2022 and served on their technical committees. He has published in several international journals and proceedings, including ACM Communications, ACM Computing Surveys, ACM Proceedings, IEEE Proceedings, Journal of Information and Communication Technology, TEM Journal, and Journal of Current Science and Technology.

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Asst. Prof. Michael Pace, Texas A&M University, USA

Dr. Michael Pace (USA) is an Executive Assistant Professor at Texas A&M University’s Mays Business School and Faculty Affiliate of Texas A&M’s Energy Institute. His responsibilities include instruction in Project Management, Strategy, Entrepreneurship, and Managing Sustainable Business; leading study abroad programs to multiple locations (his goal is to teach on each continent); and advising student organizations (Phi Beta Lambda & the Aggie Product Management Club). He has delivered keynotes, workshops, and training worldwide on project management, especially on method customization; written several books & articles on project management; and founder of the consulting firm Diverging Roads. Outside of academia, Dr. Pace has spent almost 2 decades building or fixing project management functions across a diverse set of clients - including financial institutions, government agencies, biotech firms, and telecommunication companies. Dr. Pace is President of IPMA-USA.

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Assoc. Prof. Ronald A. Monzon, Caraga State University Cabadbaran Campus, Philippines

Professor Ronald Ablan Monzon was born in Manay, Davao Oriental, Philippines on May 28, 1973. He obtained his BS in Computer Engineering and BS in Electronics Communication Engineering degrees at Saint Joseph Institute of Technology in 1996 and 1999 respectively. He earned his MS in Information Technology at Surigao State College of Technology. At present, he is pursuing his Doctor in Information Technology at the Technological Institute of the Philippines.
Prof. Monzon is the current Chairperson of the Department of Information Technology, College of Engineering and Information Technology at Caraga State University Cabadbaran City. He has twenty-six years’ experience of in teaching and training in the field of information technology. He is one of the active members of the Accrediting Agency of Chartered Colleges and Universities in the Philippines (AACCUP). He has served as an accreditor often (10) prestigious state colleges and universities in the Philippines.
Added to these achievements, he has presented and published journals at local, regional, national, and international levels. His research interests include data mining and machine learning. Prof. Monzon is known to his colleagues and students as a responsible, trustworthy, and supportive mentor. Moreover, he is willing to work under pressure, dedicated to his work, and perform duties beyond the regular teaching hours.

Topic: Academic Performance during Face-To-Face, Online Classes, and Blended Classes: a correlational study using Bayesian approach

In the modern era, education has undergone a significant transformation with the emergence of various teaching methodologies. Face-to-face classes have long been the traditional mode of instruction, but with advancements in technology, online and blended classes have gained popularity. This correlational study aims to explore the impact of these different modes on academic performance using a Bayesian approach. The study will examine how students' academic performance varies across face-to-face, online, and blended classes. By collecting data on student grades and attendance records, researchers will be able to establish correlations between class format and academic success. It has been found that the students achieved higher grades and better performance in the online classes compared to face-to-face and blended learning environments. It is evident that online classes offer numerous advantages, such as flexibility and convenience. Students can access course materials and lectures at their own pace, allowing them to tailor their learning experience according to their individual needs. The Bayesian approach is chosen due to its ability to incorporate prior knowledge into statistical analysis. This method allows for more accurate predictions by combining existing information with new data collected during the study.