• Title/Summary/Keyword: Customer rating

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The measurement & Analysis of Voltage Unbalance Factor at LV Customer of Three-Phase Four-Wire System (3상 4선식 저압 수용가의 전압 불평형율 측정분석)

  • Kim, Jong-Gyeum;Park, Young-Jin;Lee, Eun-Woong
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2004.05a
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    • pp.43-47
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    • 2004
  • Most of LV customer has been composed the 3-phase four wire system distribution system which is supplying simultaneously at the 1-phase & 3-phase load. In this system, the composition of the power apparatus system is simple rather than conventional separation mode of the 1-phase & 3-phase, But due to uneven load unbalance or unclean power quality, various kinds such as do-rating or power losses become an issue. In this paper, we measured and analyzed voltage and current waveform in the field, compared with internationally allowable voltage unbalance limits.

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Why Do Customers Purchase from a Website? Activity-based Web Presence Readiness Model

  • Kang, Kyungwoo;Kim, Yong Jin;Shin, Seung Kyoon
    • Asia pacific journal of information systems
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    • v.23 no.4
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    • pp.85-102
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    • 2013
  • This study proposes a web presence readiness model based on pre-payment service functions and a post-payment service function both of which embrace the major concerns of customers in the online purchasing context. Based on the concept of customer utility from the product itself and instrumental utility, the research model suggests four antecedents including, Perceived Economic Benefits, Product Search Support Quality, e-Shopping Method Diversity, and Post-Payment Support Quality. We empirically examined a proposed research model using data collected from online rating company websites. Among the four antecedents, post payment support quality is found to be the most influential determinant of customer evaluation on e-commerce websites. Based on the empirical results, the current study proposes an alternate model of web presence readiness. The findings of this study may provide an insight to field practitioners designing commercial websites. The implications and future research directions are further discussed.

Customer Satisfaction Index of Business & Industry Foodservice Operations in Pusan and Kyeung Nam Area (부산 , 경남지역 사업체 급식소 운영방식에 따른 고객만족지수)

  • Ryu, Eun-Sun
    • Journal of the Korean Dietetic Association
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    • v.4 no.2
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    • pp.152-159
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    • 1998
  • The purpose of this study was to compare the customer satisfaction index(CSI) between 8 self-operated foodservices and 6 contract foodservices in Pusan and the Kyeung Nam area. There were 438 subjects for self-operated foodservices, and 384 for contract foodservices. The questionnaire was used in this study as a survey method and was concerned with quality of food(Ⅰ)(Ⅱ), sanitation, facilities, information service, and employee sevice area. Data from customers were analyzed by using the $SPSSPC^+$ program, and in terms of frequency, and t-test. The results are as follows; 1. Sanitation was the most important factor in both self-operated and contract foodservices. 2. Contract foodservices showed a higher mean rating in both facilities and employee service than did self-operated foodservices in the satisfaction. 3. In self-operated foodservices, men had a significantly(p>0.05) higher CSI in all areas then women, but contract foodservices did not have this difference. 4. Contract foodservices had a higher CSI in quality of food(Ⅰ), sanitation, facilities, information service, and employee service area, and was also in higher total CSI, than self-operated foodservices.

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The Effects of Online Product Reviews on Sales Performance: Focusing on Number, Extremity, and Length

  • PARK, Sunju;CHUNG, Seungwha (Andy);LEE, Seungyong
    • Journal of Distribution Science
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    • v.17 no.5
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    • pp.85-94
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    • 2019
  • Purpose - The purpose of this study is to analyze the impact of customer's communication on sales performance in the online market. Research design, data, and methodology - This study uses linear regression analysis to examine the effects of product review characteristics which are the result of customer's communication, on sales performance by using product reviews of online marketplace Amazon. Result - The increase in the number of product reviews positively affected sales performance. An increase in extreme opinions in the product review has a positive effect on sales performance. The product review length has a negative effect on sales performance. Conclusions - This study has shown the online marketplace customers' communication can influence sales performance using product review big data. This study contributed to the theoretical completeness by analyzing all the products of the book category in Amazon online market. This research will complement the theories regard to the customer behavior affecting sales performance. We expect the empirical analysis result will provide empirical help to sellers, online marketplace operators, and customers. In particular, the number of letters in the product may negatively affect sales performance, so sellers need to consider this effect carefully when exposing product reviews.

AThe Effects of Public Loan Programs in Fishery Industry on Management Performance and Credit Rating Change from a BSC perspective (BSC관점에서 수산정책자금이 경영성과와 신용등급 변화에 미치는 영향)

  • Park, Il-Kon;Jang, Young-Soo
    • The Journal of Fisheries Business Administration
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    • v.47 no.2
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    • pp.43-59
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    • 2016
  • This study investigated the difference of the effects of public loan programs in fishery industry on management performance from a balanced score card (BSC) perspective depending on the type of loan, scale of fund, period of support and business category, using the financial data of fisheries firms having the balance of loan at the end of 2014. The key factors influencing credit rating change were also analyzed after public loan support. From a integrative perspective, results show that the firms supported by working fund have higher management performance than the firms supported by facility fund. The firms received large scale fund showed higher management performance than the firms received small scale fund. While management performance was decreasing or slowing down over time after financial support, management performance of the firms supported by facility fund improved over time. From a non-financial perspective, the firms received facility fund invested more in education and growing perspective than the firms received working fund. As the size of fund increased, the investment in education, growing, internal process and customer increased. Personnel expenses and employee benefits for education and growing has increased over time. However, the firms with facility fund restricted the expenses of education, personnel expenses and employee benefits as time goes by. Because the effects of public loan on credit rating of fisheries corporations have no statistical significance, it has become known that the financial support of public loan program has no influence on the change of credit rating of fisheries corporations. This study attempted performance analysis from a BSC perspective which combine factors of non-financial perspective with factors of financial perspective. Findings from this study suggest the direction of microscopic performance analysis of public loan in fishery industry.

Measuring Consumer Preferences Using Multi-Attribute Utility Theory (다속성 효용이론을 활용한 소비자 선호조사)

  • Ahn, Jae-Hyeon;Bang, Young-Sok;Han, Sang-Pil
    • Asia pacific journal of information systems
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    • v.18 no.3
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    • pp.1-20
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    • 2008
  • Based on the multi-attribute utility theory (MAUT), we present a survey method to measure consumer preferences. The multi-attribute utility theory has been used to make decisions in OR/MS field; however, we show that the method can be effectively used to estimate the demand for new services by measuring individual level utility function. Because conjoint method has been widely used to measure consumer preferences for new products and services, we compare the pros and cons of two consumer preference survey methods. Further, we illustrate how swing weighing method can be effectively used to elicit customer preferences especially for new telecommunications services, Multi-attribute utility theory is a compositional approach for modeling customer preference, in which researchers calculate overall service utility by summing up the evaluation results for each attribute. On the contrary, conjoint method is a decompositional approach, which requires holistic evaluations for profiles. Partworth for each attribute is derived or estimated based on the evaluation, and finally consumer preferences for each profile are calculated. However, if the profiles are quite new and unfamiliar to the survey respondents, they will find it very difficult to accurately evaluate the profiles. We believe that the multi-attribute utility theory-based survey method is more appropriate than the conjoint method, because respondents only need to assess attribute level preferences and not holistic assessment. We chose swing weighting method among many weight assessment methods in multi-attribute utility theory, because it is designed to perform in a simple and fast manner. As illustrated in Clemen and Reilly (2001), to assess swing weights, the first step is to create the worst possible outcome as a benchmark by setting the worst level on each of the attributes. Then, each of the succeeding rows "swings" one of the attributes from worst to best. Upon constructing the swing table, respondents rank order the outcomes (rows). The next step is to rate the outcomes in which the rating for the benchmark is set to be 0 and the rating for the best outcome to be 100, and the ratings for other outcomes are determined in the ranges between 0 and 100. In calculating weight for each attribute, ratings are normalized by the total sum of all ratings. To demonstrate the applicability of the approach, we elicited and analyzed individual-level customer preference for new telecommunication services-WiBro and HSDPA. We began with a randomly selected 800 interviewees, and reduced them to 432 because other remaining ones were related to the people who did not show strong intention for subscription to new telecommunications services. For each combination of content and handset, number of responses which favored WiBro and HSDPA were counted, respectively. It was assumed that interviewee favors a specific service when expected utility is greater than that of competing service(s). Then, the market share of each service was calculated by normalizing the total number of responses which preferred each service. Holistic evaluation of new and unfamiliar service is a tough challenge for survey respondents. We have developed a simple and easy method to assess individual level preference by estimating weight of each attribute. Swing method was applied for this purpose. We believe that estimating individual level preference will be quite flexibly used to predict market performance of new services in many different business environments.

Development of a Book Recommender System for Internet Bookstore using Case-based Reasoning (사례기반 추론을 이용한 인터넷 서점의 서적 추천시스템 개발)

  • Lee, Jae-Sik;Myoung, Hun-Sik
    • The Journal of Society for e-Business Studies
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    • v.13 no.4
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    • pp.173-191
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    • 2008
  • As volumes of electronic commerce increase rapidly, customers are faced with information overload, and it becomes difficult for them to find necessary information and select what they need. In this situation, recommender systems can help the customers search and select the products and services they need more conveniently. These days, the recommender systems play important roles in customer relationship management. In this research, we develop a recommender system that recommends the books to the customers of Internet bookstore. In previous researches on recommender systems, collaborative filtering technique has been often employed. For the collaborative filtering technique to be used, the rating scores on books given by previous purchasers have to be collected. However, the collection of rating scores is not an easy task in reality. Therefore, in this research, we employed case-based reasoning technique that can work only with the book purchase history of customers. The accuracy of recommendation of the resulting book recommender system was about 40% on the level 3 classification code.

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Exploring the Role of Preference Heterogeneity and Causal Attribution in Online Ratings Dynamics

  • Chu, Wujin;Roh, Minjung
    • Asia Marketing Journal
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    • v.15 no.4
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    • pp.61-101
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    • 2014
  • This study investigates when and how disagreements in online customer ratings prompt more favorable product evaluations. Among the three metrics of volume, valence, and variance that feature in the research on online customer ratings, volume and valence have exhibited consistently positive patterns in their effects on product sales or evaluations (e.g., Dellarocas, Zhang, and Awad 2007; Liu 2006). Ratings variance, or the degree of disagreement among reviewers, however, has shown rather mixed results, with some studies reporting positive effects on product sales (e.g., Clement, Proppe, and Rott 2007) while others finding negative effects on product evaluations (e.g., Zhu and Zhang 2010). This study aims to resolve these contradictory findings by introducing preference heterogeneity as a possible moderator and causal attribution as a mediator to account for the moderating effect. The main proposition of this study is that when preference heterogeneity is perceived as high, a disagreement in ratings is attributed more to reviewers' different preferences than to unreliable product quality, which in turn prompts better quality evaluations of a product. Because disagreements mostly result from differences in reviewers' tastes or the low reliability of a product's quality (Mizerski 1982; Sen and Lerman 2007), a greater level of attribution to reviewer tastes can mitigate the negative effect of disagreement on product evaluations. Specifically, if consumers infer that reviewers' heterogeneous preferences result in subjectively different experiences and thereby highly diverse ratings, they would not disregard the overall quality of a product. However, if consumers infer that reviewers' preferences are quite homogeneous and thus the low reliability of the product quality contributes to such disagreements, they would discount the overall product quality. Therefore, consumers would respond more favorably to disagreements in ratings when preference heterogeneity is perceived as high rather than low. This study furthermore extends this prediction to the various levels of average ratings. The heuristicsystematic processing model so far indicates that the engagement in effortful systematic processing occurs only when sufficient motivation is present (Hann et al. 2007; Maheswaran and Chaiken 1991; Martin and Davies 1998). One of the key factors affecting this motivation is the aspiration level of the decision maker. Only under conditions that meet or exceed his aspiration level does he tend to engage in systematic processing (Patzelt and Shepherd 2008; Stephanous and Sage 1987). Therefore, systematic causal attribution processing regarding ratings variance is likely more activated when the average rating is high enough to meet the aspiration level than when it is too low to meet it. Considering that the interaction between ratings variance and preference heterogeneity occurs through the mediation of causal attribution, this greater activation of causal attribution in high versus low average ratings would lead to more pronounced interaction between ratings variance and preference heterogeneity in high versus low average ratings. Overall, this study proposes that the interaction between ratings variance and preference heterogeneity is more pronounced when the average rating is high as compared to when it is low. Two laboratory studies lend support to these predictions. Study 1 reveals that participants exposed to a high-preference heterogeneity book title (i.e., a novel) attributed disagreement in ratings more to reviewers' tastes, and thereby more favorably evaluated books with such ratings, compared to those exposed to a low-preference heterogeneity title (i.e., an English listening practice book). Study 2 then extended these findings to the various levels of average ratings and found that this greater preference for disagreement options under high preference heterogeneity is more pronounced when the average rating is high compared to when it is low. This study makes an important theoretical contribution to the online customer ratings literature by showing that preference heterogeneity serves as a key moderator of the effect of ratings variance on product evaluations and that causal attribution acts as a mediator of this moderation effect. A more comprehensive picture of the interplay among ratings variance, preference heterogeneity, and average ratings is also provided by revealing that the interaction between ratings variance and preference heterogeneity varies as a function of the average rating. In addition, this work provides some significant managerial implications for marketers in terms of how they manage word of mouth. Because a lack of consensus creates some uncertainty and anxiety over the given information, consumers experience a psychological burden regarding their choice of a product when ratings show disagreement. The results of this study offer a way to address this problem. By explicitly clarifying that there are many more differences in tastes among reviewers than expected, marketers can allow consumers to speculate that differing tastes of reviewers rather than an uncertain or poor product quality contribute to such conflicts in ratings. Thus, when fierce disagreements are observed in the WOM arena, marketers are advised to communicate to consumers that diverse, rather than uniform, tastes govern reviews and evaluations of products.

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Real-time CRM Strategy of Big Data and Smart Offering System: KB Kookmin Card Case (KB국민카드의 빅데이터를 활용한 실시간 CRM 전략: 스마트 오퍼링 시스템)

  • Choi, Jaewon;Sohn, Bongjin;Lim, Hyuna
    • Journal of Intelligence and Information Systems
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    • v.25 no.2
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    • pp.1-23
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    • 2019
  • Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

A study on the Prediction Performance of the Correspondence Mean Algorithm in Collaborative Filtering Recommendation (협업 필터링 추천에서 대응평균 알고리즘의 예측 성능에 관한 연구)

  • Lee, Seok-Jun;Lee, Hee-Choon
    • Information Systems Review
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    • v.9 no.1
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    • pp.85-103
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    • 2007
  • The purpose of this study is to evaluate the performance of collaborative filtering recommender algorithms for better prediction accuracy of the customer's preference. The accuracy of customer's preference prediction is compared through the MAE of neighborhood based collaborative filtering algorithm and correspondence mean algorithm. It is analyzed by using MovieLens 1 Million dataset in order to experiment with the prediction accuracy of the algorithms. For similarity, weight used in both algorithms, commonly, Pearson's correlation coefficient and vector similarity which are used generally were utilized, and as a result of analysis, we show that the accuracy of the customer's preference prediction of correspondence mean algorithm is superior. Pearson's correlation coefficient and vector similarity used in two algorithms are calculated using the preference rating of two customers' co-rated movies, and it shows that similarity weight is overestimated, where the number of co-rated movies is small. Therefore, it is intended to increase the accuracy of customer's preference prediction through expanding the number of the existing co-rated movies.