• Information Systems Review
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• v.13 no.1
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• pp.75-96
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• 2011

This research uses coolhunting to predict the future trend of e-Commerce industry. Coolhunting is a method to take Cool Trends which are the future trend through social network analysis for discovering the trendsetter and its collective intelligence. Coolhunting is generally carried out by social network analysis while this research uses NodeXL of social network analysis tools. We designed industrial network research model for relation among e-Commerce corporation, product, the types of customer service and customer service employee to discover the Cool Trends of e-Commerce industry. According to the result of this research, e-Commerce industrial network was being changed from chaos to collective intelligence form. As a analysis result for network influences, we found that Cool Trends of e-Commerce industry invigorate social commerce industry through the collective intelligence focusing intelligence VIP, Excellence, grade of Administrating for women customers(trendsetter) and it promotes semantic consumption from customers and purchasing power will be concentrated on cosmetic, beauty, perfume product categories in social commerce. We propose the strategic direction for e-Commerce corporation and hope that domestic e-Commerce corporation continues to grow and high-quality services are provided for customers.

• Asia pacific journal of information systems
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• v.12 no.4
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• pp.237-253
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• 2002

E-commerce is now widely used as the new business opportunity for most companies in Korea. The purpose of this study is first to find the attributes of the logistics service quality in e-commerce environment. We, then, try to find the relationships among service quality, customer satisfaction and repurchase intentions. The results of empirical study show that five attributes(responsiveness, empathy, reliability, accuracy and tangible) are identified as key factors of service quality. Customer satisfaction has also significant effect on repurchase intentions.

• Journal of Agricultural Extension & Community Development
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• v.11 no.1
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• pp.125-137
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• 2004

The objectives of this investigation were 1) to measure the level of customers satisfaction in agricultural E-Commerce, 2) to identify the variables affecting customer satisfaction, and 3) to suggest ways to improve the level of user satisfaction in agricultural E-Commerce. The major findings of the research can be summarized as follows: 1) The result of ANOVA showed that there is no significant difference in the degree of customer satisfaction among groups of demographic characteristics such as gender. age, marriage, job, the level of education and the level of income. 2) The results of multiple regression analysis showed that four variables had significant effects on the degree of customer satisfaction: internet environment, product information, security, and price. Moreover, the six variables included in this study explained 61.5% of the degree of customer satisfaction.

• Information Systems Review
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• v.1 no.2
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• pp.73-87
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• 1999

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.56-64
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• 2022

Many studies have suggested that e-commerce value creation potential depends on four interdependent factors Lock-In, Complementarity, Efficiency, and Novelty. In order to survive in the recent fierce competition, companies have also secured e-Trust that strengthens long-term business relationships by reducing consumer uncertainty. This study, while analyzing the value creation factors (Lock-in, Complementarity, Efficiency, Novelty, e-Trust) of recent e-commerce (online shopping mall) companies from the point of view of purchase intention, customer value (Functional value, Emotional value, Social value) We present an academic proposition that can also examine the mediating effect of value). First, through previous studies on value-based strategy and value creation in e-commerce, various discussions on the theoretical background necessary for effective value-based strategy establishment and strategy execution of e-commerce (online shopping mall) companies were reviewed. Second, it provides academic discussion and practical implications by presenting academic propositions on the value creation factors of e-commerce (online shopping mall) companies, purchase intentions, and customer value, and confirming the basis through empirical analysis.

• Journal of Information Technology Applications and Management
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• v.17 no.3
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• pp.121-134
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• 2010

Past studies in e-commerce loyalty were mostly focused on the effects of customer satisfaction and trust on loyalty toward online vendors. Few studies investigated the impacts of switching barriers, whilst they are widely proven to affect customer loyalty in offline commerce. Even in a handful of studies that did deal with switching barriers, their treatment of the subject remained at best superficial. This may have to do with the fact that switching costs in e-commerce could be comparatively negligible, as switching to another online vendor often involves one simple mouse click. In this study, we investigated the impact of switching barriers on loyalty under the e-commerce context. Furthermore, the extent of switching barriers which could be affected by those positive factors (most constructs were adopted from IDT) was also examined. The statistical testing results revealed that combined model which includes both the positive factors and the switching barriers explains the loyalty formation process more strongly ($R^2$ = 0.543) than each separated models ($R^2$ = 0.468 for positive factor only model, and $R^2$ = 0.365 for switching barrier only model). While only the two switching barriers such as convenience and emotional were shown to be statistically significant, we found that trust strongly influences customer's emotional barrier, let alone direct impact on loyalty, which thereby influences loyalty. The results offer insights for better understanding switching barriers in e-commerce related applications.

• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.111-121
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• 2015

A correct information of customer returns ratio under e-commerce is not always shared between supply chain (SC) members. Also, it is important issue for SC members to handle the unsold products in a market. This paper discusses the impact of information sharing of customer returns ratio on an optimal sales strategy including resale of customer returns and buyback policy for a SC under e-commerce with a manufacturer and a retailer. A retailer sells a single product and resells the resalable customer returns in the same market. A manufacturer produces the products and buys back the unsold products as to their quality from the retailer. The integrated SC (ISC) determines the optimal product order quantity to maximize the expected profit of the whole SC. The decentralized SC (DSC) makes the optimal decisions for order quantity and the wholesale price of products to maximize the expected profit of each SC member. The effect of information sharing is discussed between SC members under ISC and DSC. The analysis numerically investigates how information sharing of the returns ratio affects the optimal decision and the expected profits under ISC and DSC. Besides, effect of SC coordination to encourage the shift to ISC is discussed.

• Asia-Pacific Journal of Business
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• v.13 no.4
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• pp.61-77
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• 2022

Purpose - The purpose of this study was to explore the relationship between C2C logistics service quality(convenience, responsiveness, reliability, information and empathy) customer satisfaction and customer loyalty. Design/methodology/approach - This study collected 252 survey data from consumers who use C2C platform in China. Then using SPSS 24.0 in order to analyze the data. This study used SPSS 24.0 to perform empirical analysis with 252 people who use C2C platform in China. Findings - First, it was confirmed that the quality of logistics service(convenience, responsiveness, reliability, information and empathy) had a positive effect on customer satisfaction. Second, logistics service quality(convenience, responsiveness, reliability, information and empathy) positively affects the customer loyalty. Third, there is also a strong relationship between customer satisfaction and customer loyalty. Research implications or Originality - It is well known that logistics is an important part of e-commerce. Research on the influence of B2B logistics service quality on customer satisfaction and customer loyalty is more currently, but research on C2C is relatively less. Our study contributes the related research area by doing empirical analysis of the relationship between the logistics service quality and customer satisfaction and customer loyalty to provide development suggestions for C2C sellers.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.30-47
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• 2004

The tremendous growth of the Internet, particularly world wide web, has resulted in significant changes in commerce environments. Internet has brought significant changes in the economics of marketing channels and has led to a redefinition of industry value chain. E-commerce has become so important that countries and firms unprepared for this new trend would lose competitive advantages in the coming century of digital economy. The purpose of this study is to identify logistic!; service quality elements about internet shopping mall customers and examine the relationship among e-commerce logistics service quality, customer satisfaction, relationship quality and repurchase intentions in the area of internet shopping mall. To do so, prior studies on logistics service quality, customer satisfaction, relationship qualify and repurchase intentions were broadly reviewed and the relationship among those four concepts was empirically tested.