• Korean Journal of Human Ecology
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• v.15 no.1
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• pp.141-148
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• 2006

The purpose of this study is to analyze the dimensions of clothing involvement of female undergraduates in Busan and some influential factors of TV on the clothing involvement. This research also shows the correlations among variables and the differences in clothing involvement and TV media-related influential factors by the customer types. The results of this study are as follows: 1. The clothing involvement is categorized into five factors: pleasure, fashionableness, symbolism, danger awareness, and disinterest in clothing. Four TV media-related influential factors are identified: conformity to entertainers' clothing, interest in entertainers, interest in TV-watching, and imitation of entertainers. 2. An analysis of correlation between clothing involvement and TV media-related variables shows that there is a correlation between conformity to entertainers' clothing and various clothing involvement factors as fashionableness, symbolism and pleasure. A factor of interest in TV-watching is related to symbolism and interest in clothing. A factor of imitation of entertainers is related to fashionableness and disinterest in clothing. 3. An analysis of the differences by the customer types leads to four types of categorization. Type 1 shows low interests in clothing and TV media-related variables. Type 2 demonstrates high clothing involvement and interest in TV-watching. Type 3 is classified as a high involvement/ conformity group, which not only shows high involvement in pleasure and symbolism of clothing but also high conformity to entertainers' clothing. And lastly, type 4 is an disinterest group but it shows high interest in TV-watching, entertainers and imitation of entertainers.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.43-61
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• 2016

Customer reviews help potential customers make purchasing decisions. However, the prevalence of reviews on websites push the customer to sift through them and change the focus from a mere search to identifying which of the available reviews are valuable and useful for the purchasing decision at hand. To identify useful reviews, websites have developed different mechanisms to give customers options when evaluating existing reviews. Websites allow users to rate the usefulness of a customer review as helpful or not. Amazon.com uses a ratio-type helpfulness, while Yelp.com uses a count-type usefulness index. This usefulness index provides helpful reviews to future potential purchasers. This study investigated the effects of sentiment and readability on useful votes for customer reviews. Similar studies on the relationship between sentiment and readability have focused on the ratio-type usefulness index utilized by websites such as Amazon.com. In this study, Yelp.com's count-type usefulness index for restaurant reviews was used to investigate the relationship between sentiment/readability and usefulness votes. Yelp.com's online customer reviews for stores in the beverage and food categories were used for the analysis. In total, 170,294 reviews containing information on a store's reputation and popularity were used. The control variables were the review length, store reputation, and popularity; the independent variables were the sentiment and readability, while the dependent variable was the number of helpful votes. The review rating is the moderating variable for the review sentiment and readability. The length is the number of characters in a review. The popularity is the number of reviews for a store, and the reputation is the general average rating of all reviews for a store. The readability of a review was calculated with the Coleman-Liau index. The sentiment is a positivity score for the review as calculated by SentiWordNet. The review rating is a preference score selected from 1 to 5 (stars) by the review author. The dependent variable (i.e., usefulness votes) used in this study is a count variable. Therefore, the Poisson regression model, which is commonly used to account for the discrete and nonnegative nature of count data, was applied in the analyses. The increase in helpful votes was assumed to follow a Poisson distribution. Because the Poisson model assumes an equal mean and variance and the data were over-dispersed, a negative binomial distribution model that allows for over-dispersion of the count variable was used for the estimation. Zero-inflated negative binomial regression was used to model count variables with excessive zeros and over-dispersed count outcome variables. With this model, the excess zeros were assumed to be generated through a separate process from the count values and therefore should be modeled as independently as possible. The results showed that positive sentiment had a negative effect on gaining useful votes for positive reviews but no significant effect on negative reviews. Poor readability had a negative effect on gaining useful votes and was not moderated by the review star ratings. These findings yield considerable managerial implications. The results are helpful for online websites when analyzing their review guidelines and identifying useful reviews for their business. Based on this study, positive reviews are not necessarily helpful; therefore, restaurants should consider which type of positive review is helpful for their business. Second, this study is beneficial for businesses and website designers in creating review mechanisms to know which type of reviews to highlight on their websites and which type of reviews can be beneficial to the business. Moreover, this study highlights the review systems employed by websites to allow their customers to post rating reviews.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.232-234
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• 2022

Big data is widely used in customer relationship management, relationship marketing, financial business improvement, credit information and risk management. Moreover, as non-face-to-face financial transactions have become more active recently due to the COVID-19 virus, the use of financial big data is more demanded in terms of relationships with customers. In terms of customer relationship, financial big data has arrived at a time that requires an emotional rather than a technical approach. In relational marketing, it was necessary to emphasize the emotional aspect rather than the cognitive, rational, and rational aspects. Existing traditional financial data was collected and utilized through text-type customer transaction data, corporate financial information, and questionnaires. In this study, the customer's emotional image data, that is, atypical data based on the customer's cultural and leisure activities, is acquired through SNS and the customer's activity image is analyzed with an artificial intelligence CNN algorithm. Activity analysis is again applied to the annotated AI, and the AI big data model is designed to analyze the behavior model shown in the annotation.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.371-378
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• 2018

The purpose of this study is to classify the customers according to the characteristics of the relational benefits and to compare the performances of the sub-groups. As a result of the research, the group type according to the relational benefits was subdivided into 3 groups, and each group was named emotional relational group, continuous relational group and intermittent relational group considering customer characteristics. First, the emotional relational group is the group that emphasizes the empathy and assurance between the service provider and the customer, and the continuous relational group is the group with the highest social, confidence and economic benefits. The intermittent relational group was simply a transaction-oriented group. This implies that a differentiated customer management strategy is needed for each relational benefit group based on customer experience in medical services.

• Korean Institute of Interior Design Journal
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• v.26 no.6
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• pp.62-70
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• 2017

This study has a purpose that definitize correlationship of spatial structure and sales strategies in mixed-use complexes which established in Korea since year 2000. For this purpose, this study tries to analysis spatial structure of mixed-use facilities through space syntax theory and makes an typological categorization based on their space composition. As a result of categorization, Korean mixed-use facilities can be classed as three types. First type is integrated stacking type. Second one is parallel connecting type. Third type is room to room type. According to differences between categories, their spatial structures show different meaning with different sales strategies. Integrated stacking type sales luxury brands and expensive stuffs and it also has deep and complicated structure. That means theses spaces usually make a strategy of exclusion. Parallel connecting type sales special categories and it also has shallow and easy spatial structure. That means these facilities want to make a smooth connection between apposed volumes. Room to room type sales cheap and fancy objects and it also has simple and shallow structure. That means simple spatial structure can be lead contact customer to stuffs. These results mean spatial structure have a effect on design of sales space.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.523-533
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• 2006

We analyze two finite buffers queueing system with priority scheduling dependent upon queue length. Customers are classified into two types ( type-l and type-2 ) according to their characteristics. Here, the customers can be considered as traffics such as voice and data in telecommunication networks. In order to support customers with characteristics of burstiness and time-correlation between interarrival, the arrival of the type-2 customer is assumed to be an Markov- modulated Poisson process(MMPP). The service order of customers in each buffer is determined by the queue length of two buffers. Methods of embedded Markov chain and supplementary variable give us information for queue length of two buffers. Finally, performance measures such as loss and mean delay are derived.

• Korean Management Science Review
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• v.13 no.3
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• pp.49-73
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• 1996

Common problems have been observed in the existing Information Requirements Analysis (IRA) methodologies such as BSP(IBM), CSF(Rockart), or ISP(Information Engineering), etc. These methodologies require too much time and human resources for the IRA process, involves high complexity, and their IRA outcomes are frequently biased by the subjective views of the people involved. In this paper, we introduce a new methodology for the enterprise wide information requirements analysis utilizing templates and nine closed-ended questions. Extending the BIAIT(Business Information Analysis and Integration Technique) method, this methodology presents nine question about each firm's order type(7Q's for customer side and 2Q's for supplier side) to derive information processing characteristics of the firms. As the central component of this methodology, data and process templates for the manufacturing industry were established. To validate the methodology, suitability of the data and process templates were examined through six real world business cases.

• Journal of applied mathematics & informatics
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• v.38 no.3_4
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• pp.351-373
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• 2020

There is an extensive literature on retrial queueing models. While a majority of the literature on retrial queueing models focuses on the retrial times to be exponentially distributed (so as to keep the state space to be of a reasonable size), a few papers deal with nonexponential retrial times but with some additional restrictions such as constant retrial rate, only the customer at the head of the retrial queue will attempt to capture a free server, 2-state phase type distribution, and finite retrial orbit. Generally, the retrial queueing models are analyzed as level-dependent queues and hence one has to use some type of a truncation method in performing the analysis of the model. In this paper we study a retrial queueing model with threshold-type policy for orbiting customers in the context of nonexponential retrial times. Using matrix-analytic methods we analyze the model and compare with the classical retrial queueing model through a few illustrative numerical examples. We also compare numerically our threshold retrial queueing model with a previously published retrial queueing model that uses a truncation method.

• Journal of the Korean Home Economics Association
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• v.46 no.10
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• pp.107-117
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• 2008

The purpose of this study was to analyze customer attraction effects of restaurant promotion for kids. The questionnaires were distributed to 300 parents who have children under 12 years old and a total of 269 questionnaires were collected and used in analysis(usage rate 90.0%). The data was analyzed using SPSS 12.0 for descriptive analysis and paired t-test. The major results were as follows; First, customers mainly got information on coupon from mail. In case of premium and membership, the information was obtained when customers were visiting restaurants. Second, among the types of promotion for kids, coupon was mostly preferred by customers. 'Discount coupon' for the coupon, 'free gift' for the premium and 'pay-back' program for the membership were more preferable than other types of kids promotion. Third, A 'discount coupon' was more effective than 'free coupon'(p<.0l) in term of customer inducement. In case of premium, 'free gift' was more effective than 'lottery'(p<.05) and in case of membership, the customer attraction effect was not significantly different according to the types of membership. In case of coupon and membership, it was more effective in family restaurant than fastfood restaurant(p<.05), but the effect of premium was not significantly different in family vs fastfood restaurant. The results of the study revealed that 'coupon' was the most preferred type of promotion for kids and specifically customers preferred 'discount coupon', 'free gift' and 'pay-back program'.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.97-107
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• 2020

This paper shows the result of a survey about the users using Didi Chuxing in China to analyze the effect of the service quality and technology acceptance model (TAM) on customer satisfaction. There are 314 valid responsiveness out of the 414 for 9 days, which had been analyzed with SPSS and AMOS. In the analysis result, the academic implications are as follows. First, it was analyzed that the quality of service had a positive(+) effect on the technology acceptance model. Second, it was analyzed that the technology acceptance model had a positive effect on the attitude and customer satisfaction. Third, in the analysis result of the moderate effect, attitude and customer satisfaction vary partially according to whether types of reliability. The practical implications are as follows: First, it is necessary to provide service quality factors to customers. Second, It is necessary to suggest ways that customers can gain reliability. These findings also have limitations and research directions. First, the variety of samples is needed. Second, an analysis is required according to the type of vehicle use. Therefore, future research and analysis will need to be subdivided.