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Social-Media-Based Learning (SMBL) is the use of social-media-based platforms, such as Twitter, Google Plus, Facebook, and YouTube, for learning purposes. It facilitates interactive, participative, and cooperative learning among people in real time. This study aimed to determine the factors influencing SMBL in higher education institutions and to assess the mediating effect of trust on the target platform. To this end, we developed a model by combining the theoretical constructs of the unified theory of acceptance and use of technology theory with the concept of “trust.” We used the structural equation modeling method (partial least squares analysis) to analyze data collected using an online survey of 300 participants that included university students and faculties of higher education institutions in Bangladesh. In addition, we used importance-performance map analysis to determine constructs having relatively high importance, but showing relatively low performance. The study revealed that performance expectancy, effort expectancy, social influence, and facility conditions have a significant impact on social media usage intention. Furthermore, trust partially mediates the direct impact of performance expectancy, effort expectancy, and social influence on social media usage intention. The findings imply that trust has higher importance but relatively low performance.
Tazizur Rahman; Yang Sok Kim; Mijin Noh; Choong Kwon Lee. A study on the determinants of social media based learning in higher education. Educational Technology Research and Development 2021, 69, 1325 -1351.
AMA StyleTazizur Rahman, Yang Sok Kim, Mijin Noh, Choong Kwon Lee. A study on the determinants of social media based learning in higher education. Educational Technology Research and Development. 2021; 69 (2):1325-1351.
Chicago/Turabian StyleTazizur Rahman; Yang Sok Kim; Mijin Noh; Choong Kwon Lee. 2021. "A study on the determinants of social media based learning in higher education." Educational Technology Research and Development 69, no. 2: 1325-1351.
This study determines the influential factors responsible for social commerce adoption in an emerging economy. Our model combines constructs from social commerce constructs, social presence theory, and flow theory with the factors of trust and perceived risk management. We use structural equation modeling to analyze the data collected via an online survey of 300 participants who are members of social network sites in Bangladesh. The results indicate that social commerce constructs and social presence have significant impacts on trust and perceived risk management except for forums and communities, and the trust relationship, which influences social commerce use intention, thereby influencing social commerce use behavior. The findings also reveal that flow has a significant impact on intention to use.
Tazizur Rahman; Yang Sok Kim; Mijin Noh; Choong Kwon Lee. Determinants of social commerce adoption in an emerging economy. Service Business 2020, 14, 479 -502.
AMA StyleTazizur Rahman, Yang Sok Kim, Mijin Noh, Choong Kwon Lee. Determinants of social commerce adoption in an emerging economy. Service Business. 2020; 14 (4):479-502.
Chicago/Turabian StyleTazizur Rahman; Yang Sok Kim; Mijin Noh; Choong Kwon Lee. 2020. "Determinants of social commerce adoption in an emerging economy." Service Business 14, no. 4: 479-502.
Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.
Hyunwoo Hwangbo; Yangsok Kim. Session-Based Recommender System for Sustainable Digital Marketing. Sustainability 2019, 11, 3336 .
AMA StyleHyunwoo Hwangbo, Yangsok Kim. Session-Based Recommender System for Sustainable Digital Marketing. Sustainability. 2019; 11 (12):3336.
Chicago/Turabian StyleHyunwoo Hwangbo; Yangsok Kim. 2019. "Session-Based Recommender System for Sustainable Digital Marketing." Sustainability 11, no. 12: 3336.
This study presents a real-world collaborative filtering recommendation system implemented in a large Korean fashion company that sells fashion products through both online and offline shopping malls. The company’s recommendation environment displays the following unique characteristics: First, the company’s online and offline stores sell the same products. Second, fashion products are usually seasonal, so customers’ general preference changes according to the time of year. Last, customers usually purchase items to replace previously preferred items or purchase items to complement those already bought. We propose a new system called K-RecSys, which extends the typical item-based collaborative filtering algorithm by reflecting the above domain characteristics. K-RecSys combines online product click data and offline product sale data weighted to reflect the online and offline preferences of customers. It also adopts a preference decay function to reflect changes in preferences over time, and finally recommends substitute and complementary products using product category information. We conducted an A/B test in the actual operating environment to compare K-RecSys with the existing collaborative filtering system implemented with only online data. Our experimental results show that the proposed system is superior in terms of product clicks and sales in the online shopping mall and its substitute recommendations are adopted more frequently than complementary recommendations.
Hyunwoo Hwangbo; Yang Sok Kim; Kyung Jin Cha. Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications 2018, 28, 94 -101.
AMA StyleHyunwoo Hwangbo, Yang Sok Kim, Kyung Jin Cha. Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications. 2018; 28 ():94-101.
Chicago/Turabian StyleHyunwoo Hwangbo; Yang Sok Kim; Kyung Jin Cha. 2018. "Recommendation system development for fashion retail e-commerce." Electronic Commerce Research and Applications 28, no. : 94-101.
Nowadays, information technology (IT) outsourcing companies face enduring demands to reduce cost while increasing productivity. This pressure leads many IT outsourcing companies to rely on outsourcing arrangements with IT personnel suppliers. In order to maximise efficiency, outsourcing companies have focused on fostering high-performing suppliers through improved collaboration and mutual relations. However, it is very difficult to advance to a long-term partnership using the existing outsourcing process because of insufficient collaboration between IT outsourcing companies and their suppliers. Based on collaboration perspective of supply chain management (SCM), this study identifies the critical success factors for collaborative strategic partnerships and presents an evaluation framework for assessing and managing suppliers. We have developed an organisational process model for Supplier relationship management (SRM)-based collaboration which includes some of the key constructs from the previous studies and interviews with the IT outsourcing industry people. In this study, we will identify four types of strategic suppliers and suggest approaches for improving collaborative relationship between an IT outsourcing company and its partner companies. In addition, to validate the feasibility of the proposed model, we applied it to a well-known Korean IT outsourcing company ‘A’.
Kyung-Jin Cha; Yang Sok Kim. Critical success factors for mutual collaboration with suppliers in IT outsourcing industry: a case study of a top IT outsourcing company in Korea. Enterprise Information Systems 2016, 12, 76 -95.
AMA StyleKyung-Jin Cha, Yang Sok Kim. Critical success factors for mutual collaboration with suppliers in IT outsourcing industry: a case study of a top IT outsourcing company in Korea. Enterprise Information Systems. 2016; 12 (1):76-95.
Chicago/Turabian StyleKyung-Jin Cha; Yang Sok Kim. 2016. "Critical success factors for mutual collaboration with suppliers in IT outsourcing industry: a case study of a top IT outsourcing company in Korea." Enterprise Information Systems 12, no. 1: 76-95.
A common perception is that online dating systems “match” people on the basis of profiles containing demographic and psychographic information and/or user interests. In contrast, product recommender systems are typically based on Collaborative Filtering, suggesting purchases not based on “content” but on the purchases of “similar” users. In this paper, we study Collaborative Filtering for people-to-people recommendation in online dating, comparing this approach to a baseline Profile Matching method. Initial data analysis highlights the problem of over-recommending popular users, a standard problem for Collaborative Filtering applied to product recommendation, but more acute in people-to-people recommendation. We address this problem with a two-stage recommender process that employs a Decision Tree derived from interactions data as a “critic” to re-rank candidates generated by Collaborative Filtering. Our baseline Profile Matching method dynamically chooses, for each user, attributes that contribute most significantly to successful interactions with candidates having the best matching attribute value. The key evaluation metric is success rate improvement, the increase in the chance of a user having a successful interaction when acting on recommendations. Our methods were first evaluated on historical data from a large online dating site and then trialled live over a 9 week period providing recommendations via e-mail to a large number of users. The trial confirmed the consistency of the analysis on historical data and the ability of our Collaborative Filtering method to generate suitable candidates over an extended period. Moreover, the Collaborative Filtering method gives a higher success rate improvement than Profile Matching.
A. Krzywicki; W. Wobcke; Y.S. Kim; X. Cai; M. Bain; A. Mahidadia; P. Compton. Collaborative Filtering for people-to-people recommendation in online dating: Data analysis and user trial. International Journal of Human-Computer Studies 2014, 76, 50 -66.
AMA StyleA. Krzywicki, W. Wobcke, Y.S. Kim, X. Cai, M. Bain, A. Mahidadia, P. Compton. Collaborative Filtering for people-to-people recommendation in online dating: Data analysis and user trial. International Journal of Human-Computer Studies. 2014; 76 ():50-66.
Chicago/Turabian StyleA. Krzywicki; W. Wobcke; Y.S. Kim; X. Cai; M. Bain; A. Mahidadia; P. Compton. 2014. "Collaborative Filtering for people-to-people recommendation in online dating: Data analysis and user trial." International Journal of Human-Computer Studies 76, no. : 50-66.