Scientists at Dongguk University Propose a Network Effect-Based Model That Enhances Business Intelligence

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.

Recommended AI: Himax Partners with Novatek to Unveil Industry Leading Ultralow Power Pre-Roll AI Solution for Battery Surveillance Camera

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.

Recommended AI: aelf Announces the form of aelf DAO, Enhancing Decentralization of Governance and Ecosystem Growth

“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.”

Recommended AI: GoodFirms Unlocks the Best Task Management Software with Rich Features

[To share your insights with us, please write to sghosh@martechseries.com]