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With the rapid growth of online grocery shopping in South Korea, e-grocery retailers have faced intense competition. Consequently, they attempt to differentiate themselves by offering diversified delivery services, such as providing early morning delivery services or eco-friendly packaging. The purpose of this paper is to analyze consumer preferences with regards to delivery services with different delivery times and packaging types targeting South Korea’s online grocery markets. Specifically, six delivery types consisting of combinations of two delivery time options (dawn, daytime) and three packaging types (paper box, market cooler bag, personal icebox) are examined. A survey was conducted in July 2020 with 218 consumers who regularly buy fresh food online. The collected data were analyzed by means of a conjoint analysis and ANOVA. The present study finds that customers most strongly prefer dawn delivery using a personal icebox, followed by dawn delivery using a market cooler bag, with daytime delivery using a paper box being the least preferable. Furthermore, consumers value the packaging type more than the delivery time when selecting a delivery service. Lastly, the preferences of consumers regarding delivery service types differ according to their characteristics. This study is expected to contribute to the establishment of a delivery strategy for online grocery companies.

In the light of the increased interplay between customers and companies that touches on the diverse aspects (economic, legal, and ethical) of corporate management, it is increasingly important to ask whether customers consider not only the citizenship behaviour of companies but also their own citizenship behaviour as a precursor for ethical consumption. In this respect, this study attempts to answer this question by adopting two of the actors expressing citizenship behaviour, namely customer and company. This study attempts to answer the following three research questions. (1) Do the attitudes of corporate citizenship and customer citizenship correlate with each other? (2) Do two kinds of citizenship attitudes create a positive ethical company attitude? (3) Does an ethical company attitude result in ethical consumption?

본 연구는 최근 콜센터가 전문화된 인력에 대한 필요성이 계속 증가할 전망임에도 불구하고 비정규직을 활용한 탄력적인 인력 운영제도, 온라인 상담이 이루어지는 기계화된 작업 환경, 감정노동 등의 근무조건으로 단순한 근무 환경 조성만으로는 상담원들이 직무에 대한 전문성을 높이는데 어려움이 있음을 전제하고 직무적합성에 대한 심층적인 분석에 중요한 것으로 판단하여 이에 초점을 맞추어 개인의 성격 특성에 따른 직무적합성을 실증 분석하였다. 현재까지 콜센터 상담원을 대상으로 실증적인 분석을 한 연구는 부재한 영역으로 McCrae and Costa의 Big 5 모델에 기초하여 5가지 성격 특성 요인들과 직무적합성간의 영향관계를 각각 규명하였고 또한 기존에 주로 직무스트레스, 감정소진, 조직유효성에 국한되어 다루어진 경향이 있었던 직무적합성과의 영향관계에 있는 종속변수에 대해 본 연구에서는 콜센터를 운영하는 서비스기업에 맞게 조직 성과변수를 고객지향성으로, 개인 성과변수로 직무만족을 설정하여 직무적합성과의 영향관계를 분석하였다. 콜센터 상담원 283명의 표본을 대상으로 분석한 실증분석 연구결과를 요약하면 다음과 같다. Big 5 성격유형 5가지 요인 중 성실성, 외향성, 친화성은 직무적합성과 유의한 정(+)의 영향관계가 있는 것으로 밝혀졌으며 개방성은 부(-)의 영향이 유의미하게 나타났고 개방성은 직무적합성과 유의미한 영향관계가 없는 것으로 나타났다. 또한, 직무적합성은 고객지향성과의 영향관계와 직무만족과의 영향관계에서 모두 정(+)의 영향관계가 유의미하게 규명되었다. 본 연구는 기존 직무적합성 요인에 대한 연구와 온라인 환경에서 서비스 직무를 수행하는 콜센터 상담원 역량과 관련한 연구영역의 확장 차원에서 의미를 지니며 분석한 연구결과를 토대로 콜센터 상담원의 채용 또는 인적자원관리 전략 수립에 유용한 자료를 제공하고자 한다.

Online customer reviews are a sustainable form of word of mouth (WOM) which play an increasingly important role in e-commerce. However, low quality reviews can often cause inconvenience to review readers. The purpose of this paper is to automatically predict the helpfulness of reviews. This paper analyzes the characteristics embedded in product reviews across five different product types and explores their effects on review helpfulness. Furthermore, four data mining methods were examined to determine the one that best predicts review helpfulness for each product type using five real-life review datasets obtained from Amazon.com. The results show that reviews for different product types have different psychological and linguistic characteristics and the factors affecting the review helpfulness of them are also different. Our findings also indicate that the support vector regression method predicts review helpfulness most accurately among the four methods for all five datasets. This study contributes to improving efficient utilization of online reviews.

According to the `2013 Performance Assessment Report on the Financial Program` from the National Assembly Budget Office, the unfilled recruitment ratio of Software(SW) Developers in South Korea was 25% in the 2012 fiscal year. Moreover, the unfilled recruitment ratio of highly-qualified SW developers reaches almost 80%. This phenomenon is intensified in small and medium enterprises consisting of less than 300 employees. Young job-seekers in South Korea are increasingly avoiding becoming a SW developer and even the current SW developers want to change careers, which hinders the national development of IT industries. The Korean government has recently realized the problem and implemented policies to foster young SW developers. Due to this effort, it has become easier to find young SW developers at the beginning-level. However, it is still hard to recruit highly-qualified SW developers for many IT companies. This is because in order to become a SW developing expert, having a long term experiences are important. Thus, improving job continuity intentions of current SW developers is more important than fostering new SW developers. Therefore, this study surveyed the job continuity intentions of SW developers and analyzed the factors associated with them. As a method, we carried out a survey from September 2014 to October 2014, which was targeted on 130 SW developers who were working in IT industries in South Korea. We gathered the demographic information and characteristics of the respondents, work environments of a SW industry, and social positions for SW developers. Afterward, a regression analysis and a decision tree method were performed to analyze the data. These two methods are widely used data mining techniques, which have explanation ability and are mutually complementary. We first performed a linear regression method to find the important factors assaociated with a job continuity intension of SW developers. The result showed that an `expected age` to work as a SW developer were the most significant factor associated with the job continuity intention. We supposed that the major cause of this phenomenon is the structural problem of IT industries in South Korea, which requires SW developers to change the work field from developing area to management as they are promoted. Also, a `motivation` to become a SW developer and a `personality (introverted tendency)` of a SW developer are highly importantly factors associated with the job continuity intention. Next, the decision tree method was performed to extract the characteristics of highly motivated developers and the low motivated ones. We used well-known C4.5 algorithm for decision tree analysis. The results showed that `motivation`, `personality`, and `expected age` were also important factors influencing the job continuity intentions, which was similar to the results of the regression analysis. In addition to that, the `ability to learn` new technology was a crucial factor for the decision rules of job continuity. In other words, a person with high ability to learn new technology tends to work as a SW developer for a longer period of time. The decision rule also showed that a `social position` of SW developers and a `prospect` of SW industry were minor factors influencing job continuity intensions. On the other hand, `type of an employment (regular position/ non-regular position)` and `type of company (ordering company/ service providing company)` did not affect the job continuity intension in both methods. In this research, we demonstrated the job continuity intentions of SW developers, who were actually working at IT companies in South Korea, and we analyzed the factors associated with them. These results can be used for human resource management in many IT companies when recruiting or fostering highly-qualified SW experts. It can also help to build SW developer fostering policy and to solve the problem of unfilled recruitment of SW Developers in South Korea.

Objective: The paper studies the new learning technique called cost-sensitive case-based reasoning (CSCBR) incorporating unequal misclassification cost into CBR model. Conventional CBR is now considered as a suitable technique for diagnosis, prognosis and prescription in medicine. However it lacks the ability to reflect asymmetric misclassification and often assumes that the cost of a positive diagnosis (an illness) as a negative one (no illness) is the same with that of the opposite situation. Thus, the objective of this research is to overcome the limitation of conventional CBR and encourage applying CBR to many real world medical cases associated with costs of asymmetric misclassification errors. Methods: The main idea involves adjusting the optimal cut-off classification point for classifying the absence or presence of diseases and the cut-off distance point for selecting optimal neighbors within search spaces based on similarity distribution. These steps are dynamically adapted to new target cases using a genetic algorithm. We apply this proposed method to five real medical datasets and compare the results with two other cost-sensitive learning methods-C5.0 and CART. Results: Our finding shows that the total misclassification cost of CSCBR is lower than other cost-sensitive methods in many cases. Even though the genetic algorithm has limitations in terms of unstable results and over-fitting training data, CSCBR results with GA are better overall than those of other methods. Also the paired t-test results indicate that the total misclassification cost of CSCBR is significantly less than C5.0 and CART for several datasets. Conclusion: We have proposed a new CBR method called cost-sensitive case-based reasoning (CSCBR) that can incorporate unequal misclassification costs into CBR and optimize the number of neighbors dynamically using a genetic algorithm. It is meaningful not only for introducing the concept of cost-sensitive learning to CBR, but also for encouraging the use of CBR in the medical area. The result shows that the total misclassification costs of CSCBR do not increase in arithmetic progression as the cost of false absence increases arithmetically, thus it is cost-sensitive. We also show that total misclassification costs of CSCBR are the lowest among all methods in four datasets out of five and the result is statistically significant in many cases. The limitation of our proposed CSCBR is confined to classify binary cases for minimizing misclassification cost because our proposed CSCBR is originally designed to classify binary case. Our future work extends this method for multi-classification which can classify more than two groups.

Although data mining and knowledge discovery techniques have recently been used to diagnose human disease, little research has been conducted on disease diagnostic modelling using human gene information. Furthermore, to our knowledge, no study has reported on diagnosis models using single nucleotide polymorphism (SNP) information. A disease diagnosis model using data mining techniques and SNP information should prove promising from a practical perspective as more information on human genes becomes available. Data mining and knowledge discovery techniques can be put to practical use detecting human disease, since a haplotype analysis using high-density SNP markers has gained great attention for evaluating human genes related to various human diseases. This paper explores how data mining and knowledge discovery can be applied to medical informatics using human gene information. As an example, we applied case-based reasoning to a cancer detection problem using human gene information and SNP analysis because case-based reasoning has been applied in medicine relatively less often than other data mining techniques. We propose a modified case-based reasoning method that is appropriate for associated categorical variables to use in detecting gastric cancer.

Case-based reasoning (CBR) models often solve problems by retrieving multiple previous cases and integrating those results. However, conventional CBR makes decisions by comparing the integrated result with the cut-off point irrespective of the degree of the adjacency between them. This can cause increasing misclassification error for the target cases adjacent to the cut-off point, since the results of previous cases used to produce those results are relatively inconsistent with each other. In this article, we suggest a new interactive CBR model called grey-zone case-based reasoning (GCBR) that makes decisions focusing additional attention on the cases near the cut-off point by interactive communication with users. GCBR classifies results automatically for the cases placed outside the cut-off point boundary area. On the other hand, it communicates with users to make decision for the cases placed inside the area by verifying characteristics of the dataset. We suggest the architecture of GCBR and implement its prototype.

This paper proposes a regression case based reasoning (RCBR) which applies different weights to independent variables before finding similar cases. The traditional CBR model has focused on finding similar cases from a case base without considering the importance of independent variables. Thus, when extracting similar cases the traditional CBR has put same weights on each independent variable. The proposed regression CBR (RCBR) finds a relative importance of independent variables from the relationship between independent variables and a dependent variable using a regression analysis and puts relative weights using regression coefficients on independent variables. Then, it selects nearest neighbor or similar cases using weighted independent variables through the traditional CBR machine and updates weights dynamically for the next target case and again performs the traditional CBR machine.

Case-based reasoning (CBR) has been used in various problem-solving areas such as financial forecasting, credit analysis and medical diagnosis. However, conventional CBR has the limitation that it has no criterion for choosing the nearest cases based on the probabilistic similarity of cases. It uses a fixed number of neighbors without considering an optimal number for each target case, so it does not guarantee optimal similar neighbors for various target cases. This leads to the weakness of lowering predictability due to deviation from desired similar neighbors. In this paper we suggest a new case extraction technique called statistical case-based reasoning. The main idea involves a dynamic adaptation of the optimal number of neighbors by considering the distribution of distances between potential similar neighbors for each target case. In order to do this, our technique finds the optimal distance threshold and selects similar neighbors satisfying the distance threshold criterion. We apply this new method to five real-life medical data sets and compare the results with those of the statistical method, logistic regression; we also compare the results with the learning methods C5.0, CART, neural networks and conventional CBR. The results of this paper show that the proposed technique outperforms those of many other methods, it overcomes the limitation of conventional CBR, and it provides improved classification accuracy.

Conventional case-based reasoning (CBR) does not perform efficiently for high volume dataset because of case-retrieval time. Some previous researches overcome this problem by clustering a case-base into several small groups, and retrieve neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performances than the conventional CBR. This paper suggests a new case-based reasoning method called the Clustering-Merging CBR (CM-CBR) which produces similar level of predictive performances than the conventional CBR with spending significantly less computational cost.

The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.