It utilizes the influence-based social exchange and two-step flow of information theories to collect reliable user data
SEOUL, South Korea, Feb. 3, 2023 /PRNewswire/ — There is a need to improve the quality of user data for making effective business decisions. To this end, researchers in Korea propose a network effect-based reasoning model that directly collects meaningful data by applying the influence-based social exchange and two-step flow of information theories. The model can enhance business intelligence and facilitate big data analysis.
In the age of big data, corporations are increasingly using business intelligence—collecting, analyzing, and utilizing user data—for decision-making. It has been made possible through social media platforms and technological innovations, which enable easy data accumulation and storage at low costs.
The effectiveness of business intelligence depends upon the quality of data. Big data has a lot of noise, and only meaningful inputs can lead to meaningful outputs. Therefore, reliable user data is essential. The existing ways to improve the reliability of already collected data, however, are not efficient.
To address this issue, Dr. Kihwan Nam, Assistant professor of Management Information Systems in the Business School of Dongguk University, Korea, and his colleague have recently presented a new theoretical model that secures meaningful information by improving data reliability during the collection stage. Their study reporting the model was made available online on 1 June 2022 and published in Volume 205 of Expert Systems With Applications on 1 November 2022.
“The model, based on the network effect, applies both the influence-based social exchange theory and the two-step flow of information theory. In it, initially, a user evaluates and recommends a product to a group. Its recommendation range depends on the user’s influence. The user, aware of their influence, will try to increase it by getting positive feedback from the group, thereby acting as an ‘opinion leader’ and generating data that reliably indicates popular products,” explains Dr. Nam.
The researchers verified the effectiveness of their reasoning model by collecting user data from an online media content platform and applying the model to its recommendation system. They found that its performance—data quality and user satisfaction—improved two to five times. That led to an upsurge in the number of users on the platform.
Dr. Nam is hopeful about the future potential of their work. “It demonstrates theoretical, technical, and practical aspects of reliable data collection, making it valuable for corporations. Not only will our proposed model enhance business intelligence, but it will also help extract meaningful information for various other applications involving big data analysis.”
Authors: Nohkyum Kyeong1, Kihwan Nam2
Title of original paper: Mechanism design for data reliability improvement through network-based reasoning model
Journal: Expert Systems With Applications
*Corresponding author’s email: [email protected]
*Corresponding author’s home page: https://sites.google.com/view/namki
About Dongguk University
About the author
Kihwan Nam is an Assistant Professor at the Management Information Systems, Business School, Dongguk University, and Adjunct Professor at the College of Business, Korea Advanced Institute of Science and Technology (KAIST). His research interests include Quantitative Marketing, Recommender System, Big Data Analytics, Data Mining, Statistical Analysis, and Applications of Machine Learning and Deep Learning in Business Analytics and Econometric Model. He is also a data scientist. In addition to his academic research, he is making a positive contribution to both academia and industry by successfully carrying out various projects in a big international company.
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SOURCE Dongguk University