Yonsei University · 社会科学
Professor Il Im's research lab specializes in information systems, e-commerce, and digital marketing, with a focus on user behavior in online environments. The lab investigates personalized recommendation systems, particularly collaborative filtering, and examines how user characteristics, product domains, and search behaviors influence system accuracy and effectiveness. It also explores the role of social media and online networks in entrepreneurial opportunity identification, customer complaint management, and knowledge sharing. The lab’s work bridges technology, consumer behavior, and business strategy in digital marketplaces.
Figures are computed from collected data and may differ slightly.
How businesses resolve customer-complaining behavior effectively has been considered a "defensive marketing" strategy or a "zero-defections" strategy, which diminishes customer dissatisfaction. Handling customer dissatisfaction accompanies Web customer complaint management, which might be the critical issue for online customer service solutions and e-CRM (electronic customer relationship management). In this paper; the authors (1) investigate the current sources and causes of online complaints;
Social media platforms are an indispensable part of entrepreneurship practices. They offer entrepreneurs a platform for business growth and brand development. However, little is known about the effect of social media use on identifying entrepreneurial opportunities. Utilizing social cognition theory, this research focuses on how certain factors—prior knowledge, alertness, and social media—may impact two aspects of opportunity: the discovery and the creation of entrepreneurial opportunity. This s
Collaborative filtering (CF) is a personalization technology that generates recommendations for users based on others' evaluations. CF is used by numerous e-commerce Web sites for providing personalized recommendations. Although much research has focused on refining collaborative filtering algorithms, little is known about the effects of user and domain characteristics on the accuracy of collaborative filtering systems. In this study, the effects of two factors—product domain and users' search m
Using a database of 11,001 unique sponsored search keywords, we investigate the relationship between the characteristics of keywords oriented around deal-seeking and brand-seeking and consumer search behaviors and buying propensities. On the basis of the search depth versus search breadth framework, we hypothesize that deal-seeking keywords elicit a search of greater breadth, whereas brand-seeking keywords induce a search of greater depth. We also explore the moderating effect of product type (s
People use social media to help them solve problems and enhance performance. Users may engage in diverse social interactions online, increasing their knowledge base through both weak and strong ties. In this study we investigate the impact of social networks on job performance, focusing on knowledge quality and diversity based on social network theory and the knowledge-based view. The empirical analysis reveals that the strength of social network ties is strongly associated with knowledge qualit
In todayiÌs networked business environment, with the endless increase in available information, relevant information is becoming more and more difficult to find. Collaborative filtering (CF) generates recommendations for users based on othersiÌ evaluations. CF has great potential to improve information search and knowledge reuse. Previous studies have mostly focused on the improvement of CF algorithms. Little research has been done on the effect of users and types of product domains on the per
As the number of business process reengineering (BPR) projects increase in industry, there seems to be a large potential to reduce cost and increase quality through the use of software tools. Currently, more than 50 BPR tools are available, but there has been little research to determine which features of BPR tools are important for BPR success and how important BPR tools are in BPR projects. Research in the related area of tools for computer-aided software engineering (CASE) shows that software
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