• Title/Summary/Keyword: Group-Buying Social Commerce

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A Study on US Consumers' Loyalty to Online Shopping Mall : Focused on Group Buying Social Commerce (미국 소비자의 온라인 쇼핑몰 충성도 연구 : 공동구매형 소셜커머스를 중심으로)

  • Cho, Yun-Jin
    • Journal of Convergence for Information Technology
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    • v.9 no.2
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    • pp.75-84
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    • 2019
  • The purpose of this paper is to examine the factors influencing on loyalty in US online shopping malls. The study proposed a model to investigate the relationship among quality of sites, satisfaction, attitude, and loyalty. The hypotheses were examined by analyzing a structural equation model. 280 US samples were used for the final analysis. The results show that this model demonstrates good fit for the samples. The ease of use was found to be a significant variable in their satisfaction, while it did not have the direct effect on attitude toward the sites. The information quality was found to be a crucial variable in consumers' satisfaction and attitude toward the site. Satisfaction directly affected attitude as well as loyalty, and attitude also directly affected loyalty. Thus, the structural relationship among the variables of customers' loyalty was verified. This research provides practical insights into US consumer behaviors that would be beneficial to marketers when they make decisions for the US e-commerce market.

Online to Offline Convergent Ecosystem: a Case Study of Dianping.com (온라인과 오프라인을 융북합 생태계: Dianping.com 사례연구)

  • Zhang, Chao;Wan, Lili
    • Journal of Digital Convergence
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    • v.13 no.6
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    • pp.105-111
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    • 2015
  • In this highly competitive century, selling products and service through Internet and smart phones offers both opportunities and challenges. Online commerce is expanding it's wings to the offline market. The connection between online market and offline market is called O2O(Offline to Online) market. In this study we examine the best practice case study of an Internet company's successful efforts to connect users and offline merchants. Based on Dianping.com success story in China, a successful framework for building online to offline ecosystem is examined. Dianping.com successful experience may provide suggestions for other online companies operate in the convergent field.

The Research on Recommender for New Customers Using Collaborative Filtering and Social Network Analysis (협력필터링과 사회연결망을 이용한 신규고객 추천방법에 대한 연구)

  • Shin, Chang-Hoon;Lee, Ji-Won;Yang, Han-Na;Choi, Il Young
    • Journal of Intelligence and Information Systems
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    • v.18 no.4
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    • pp.19-42
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    • 2012
  • Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.