• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.219-226
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• 2010

Recently most parking control systems provide customers with various services, but most of the services are just the extension of parking spaces, automatic parking control system and so on. It is essential to use the satisfaction degree as the extent that customer are satisfied with parking control system to improve the quality of the system services and diversify the system services. The degree of satisfaction is different from customer to customer in same condition and can be represented as linguistic variables. In this paper, we present therefore a technique that quantify how much customer are satisfied with parking control system and fuzzy inference system architecture as a solution that can help us to make a efficient decision for these parking problems. In this architecture, inference engine using fuzzy logic compares context data with the rules in the fuzzy rule-based system, gets the sub-results, aggregates them and defuzzifies the aggregated result using MATLAB application programming to obtain crisp value. Fuzzy inference system architecture presented in this paper, can be used as a efficient method to analyze the satisfaction degree which is represented as fuzzy linguistic variables by human emotion. And it can be used to improve the satisfaction degree of not only parking system but also other service systems of various domains.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• The Journal of Information Systems
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• v.21 no.3
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• pp.117-136
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• 2012

Given to the prevalence of social commerce such as Groupon, Ticketmonster, and Coupang, it has become critical to understand customer purchasing behavior in social commerce environments. When consumers make purchasing decisions in social commerce, they often act impulsively. This is because social commerce is a deal-of-the-day website that features discounted gift certificates usable at local companies. However, the vast majority of social commerce research has viewed consumer decision-making as a rational process. This study develops a theoretical framework to investigate key drivers of customer's impulsive purchasing in social commerce. This study identifies flow, positive emotion, negative emotion, social commerce attractiveness, and discounted price as the key antecedents of impulsive purchasing. Data collected from 164 users who had prior purchasing experiences with social commerce were empirically tested against the research model using partial least squares analysis. The analysis results indicate that flow plays an important role in facilitating customers' impulsive purchasing in social commerce environments. Moreover, the findings show the exact roles of positive emotion, negative emotion, social commerce attractiveness, and discounted price on consumer's impulsive purchasing.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.11
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• pp.63-71
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• 2010

FormFactors is one of the most critical design factors in early development of mobile phone, and proper selection of FormFactors is necessary for smoothness of product development and customer satisfaction. Especially, emergence of mobile convergence added so various functions besides its original call function that importance of selecting FormFactors has increased because multiform FormFactors are needed. However there is problem such as frequent change of decision making in existing process because established process picks out FormFactors sensibly and arbitrarily through idea pull and so forth. We proposed FormFactors selection process by Axiomatic Design, set approach method and block approach method for reasonable and systematical FormFactors selection. First of all, we set the purpose of mobile phone development, and it is examined by Axiomatic Design. FormFactors design matrix is deduced through this process, the numbers of axes and rails are proposed using set approach method, and then patterns of FormFactors are embodied by block approach method. Particularly process application was tried through case study of mobile phone development, and we ensured that new FormFactors can be presented to a designer by systematical verification if change of customer requirements occurs through our process.

• The Journal of Bigdata
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• v.2 no.2
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• pp.9-16
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• 2017

Global Platform Solution Company (aka Amazon, Google, MS, IBM) who has cloud platform, are driving AI and Big Data service on their cloud platform. It will dramatically change Enterprise business value chain and infrastructures in Supply Chain Management, Enterprise Resource Planning in Customer relationship Management. Enterprise are focusing the channel with customers and Business Partners and also changing their infrastructures to platform by integrating data. It will be Digital Transformation for decision support. AI and Deep learning technology are rapidly combined to their data driven platform, which supports mobile, social and big data. The collaboration of platform service with business partner and the customer will generate new ecosystem market and it will be the new way of enterprise revolution as a part of the 4th industrial revolution.

• The Research Journal of the Costume Culture
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• v.21 no.1
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• pp.129-138
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• 2013

Consumers' perception of risk plays a major role in how they make online purchase decisions. Since online shopping is perceived to be riskier than in-store shopping, consumers engage in a variety of risk reduction strategies such as searching online for alternative products and alternative e-tailers. This study examines the influence of risk involvement on risk reduction strategies and customer satisfaction. It discusses three aspects of risk reduction strategies: time spent in making a purchasing decision, searching for alternative e-tailers, and searching for alternative products. Data from 294 female shoppers who had experience in purchasing fashion products online was analyzed. This study found that risk involvement had a positive influence on the time spent in making decisions, while the influence of risk involvement on searching for alternative retailers and alternative products was not significant. However, consumer satisfaction was negatively related to search for alternative retailers and positively related to risk involvement. This study provides a better understanding of customers' risk involvement and risk reduction strategies in online shopping. This information would be beneficial for marketers and retailers to reduce customer perception of risks and to promote online sales.

• Journal of Distribution Science
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• v.17 no.8
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• pp.87-97
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• 2019

Purpose - This study focuses on the effectiveness of the athlete endorsement on the purchase intention of customers and investigates the attributes of the athlete identification. The Athlete Identification is defined by the expertise, trustworthiness, attractiveness, toughness and transgression. Athlete identification and athlete endorsement help marketers with a thorough look at the trend of using famous sports player to promote products. Research design and methodology - 450 questionnaires were delivered to respondents in Ho Chi Minh city and 433 were returned completed. Descriptive statistics, reliability, exploratory factor analysis, confirmatory factor analysis and structural equation modeling were conducted to test the relationship between independent and dependent variables. Results - The expertise, trustworthiness, attractiveness and toughness exert positive impacts on athlete identification. Transgression affects negatively athlete identification and athlete endorsement. This research confirmed results of previous studies. Conclusions - The athlete first needs to create their own Identification from a set of attributes in order to be out-standing in the sport and leisure industry before becoming an endorser for a particular product. From the company's perspectives, decision makers should choose an acclaimed sports player to boost the purchase intention of consumers.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.377-393
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• 2020

Freight management systems require a new business model for rapid decision making to improve their business processes by dynamically analyzing the previous experience data. Moreover, the amount of data generated by daily business activities to be analyzed for making better decisions is enormous. Online-to-offline or offline-to-online (O2O) is an electronic commerce (e-commerce) model used to combine the online and physical services. Data analysis is usually performed offline. In the present paper, to extend its benefits to online and to efficiently apply the big data analysis to the freight management system, we suggested a system architecture based on O2O services. We analyzed and extracted the useful knowledge from the real-time freight data for the period 2014-2017 aiming at further business development. The proposed system was deemed useful for truck management companies as it allowed dynamically obtaining the big data analysis results based on O2O services, which were used to optimize logistic freight, improve customer services, predict customer expectation, reduce costs and overhead by improving profit margins, and perform load balancing.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.315-317
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• 2001

This paper studies for the optimal operation of BESS. The goal must be optimized electricity charge of the customer sides owned time-of-use rates in this paper. Therefore, the least of cost is caused by BESS installation, Multi-Pass Dynamic Programming (MPDP) algorithm is applied to the customer for the optimal operation determination in this paper. It is to solve the optimal solution under the constraints. No matter how become one stage in general, problem is divided into several stage in series in this algorithm. Regardless of the decision step, MPDP is only accomplished based on the state of stage in the present. To investigate the efficiencies of the algorithm, it is applied the typical load curve to the cutomer owned Time-Of-Use(TOU). Result shows that the maximun economic benefits of the battery energy storage system can be achieved by the purposed algorithm.

• Journal of the Korea Safety Management & Science
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• v.19 no.3
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• pp.137-150
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• 2017

Logistic enterprises want to be competitive enterprises in fierce logistic market and worry about the securement of discriminative competitiveness for it. The standards for the judgement of logistic industry's maintenance of competitiveness are not only economic feasibility of logistic costs but also the satisfaction of users because well-established service system for variety and enhancement of logistic needs. Some of the quality attributes sufficiently satisfy expectation of customers, but not guarantee high-quality satisfaction. Therefore, it's difficult to grasp quality attributes with the existing approach of perceived service quality. Quality attribute model suggested by Kano is widely used as the concept is accurate, there is high possibility to be used at the stage of product/service planning, and it can be easily applied. Kano model has a limitation that quality attributes are classified with mode and the differences between strong property of the quality attribute and week property in quality attributes were ignored. Therefore, Timko calculated customer satisfaction coefficient with the result of Kano's survey and effects of customer satisfaction and unsatisfaction through relations between satisfaction coefficient and unsatisfaction coefficient. The purposes of this study are to use ASC, the average of satisfaction coefficient and unsatisfaction, as the satisfaction of quality characteristics, decide the importance of quality characteristics with TOPSIS, a representative multi-standard decision-making method, and calculate strategy improvement propriety of logistic service quality.