• Asia pacific journal of information systems
• /
• v.17 no.2
• /
• pp.123-137
• /
• 2007

The advent of the Internet and related Web technologies has created an easily accessible link between a firm and its customers, and has provided opportunities to a firm to use information technology to support supplementary after-sale services associated with a product or service. It has been widely recognized that supplementary services are an important source of customer value and of competitive advantage as the characteristics of the product itself. Many of these supplementary services are information-based and need not be co-located with the product, so more and more companies are delivering these services electronically. Net-based customer service, which is defined as an Internet-based computerized information system that delivers services to a customer, therefore, is the core infrastructure for supplementary service provision. The importance of net-based customer service in delivering supplementary after-sale services associated with product has been well documented. The strategic advantages of well-implemented net-based customer service are enhanced customer loyalty and higher lock-in of customers, and a resulting reduction in competition and the consequent increase in profits. However, not all customers utilize such net-based customer service. The digital divide is the phenomenon in our society that captures the observation that not all customers have equal access to computers. Socioeconomic factors such as race, gender, and education level are strongly related to Internet accessibility and ability to use. This is due to the differences in the ability to bear the cost of a computer, and the differences in self-efficacy in the use of a technology, among other reasons. This concept, applied to e-commerce, has been called the "e-commerce divide." High Internet penetration is not eradicating the digital divide and e-commerce divide as one would hope. Besides, to accommodate personalized support, a customer must often provide personal information to the firm. This personal information includes not only name and address, but also preferences information and perhaps valuation information. However, many recent studies show that consumers may not be willing to share information about themselves due to concerns about privacy online. Due to the e-commerce divide, and due to privacy and security concerns of the customer for sharing personal information with firms, limited numbers of customers adopt net-based customer service. The limited level of customer adoption of net-based customer service affects the firm profits and the customers' welfare. We use a game-theoretic model in which we model the net-based customer service system as a mechanism to enhance customers' loyalty. We model a market entry scenario where a firm (the incumbent) uses the net-based customer service system in inducing loyalty in its customer base. The firm sells one product through the traditional retailing channels and at a price set for these channels. Another firm (the entrant) enters the market, and having observed the price of the incumbent firm (and after deducing the loyalty levels in the customer base), chooses its price. The profits of the firms and the surplus of the two customers segments (the segment that utilizes net-based customer service and the segment that does not) are analyzed in the Stackelberg leader-follower model of competition between the firms. We find that an increase in adoption of net-based customer service by the customer base is not always desirable for firms. With low effectiveness in enhancing customer loyalty, firms prefer a high level of customer adoption of net-based customer service, because an increase in adoption rate decreases competition and increases profits. A firm in an industry where net-based customer service is highly effective loyalty mechanism, on the other hand, prefers a low level of adoption by customers.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.263-286
• /
• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.