• Journal of the Ergonomics Society of Korea
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• v.18 no.3
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• pp.55-72
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• 1999

Evaluation of user interfaces has to be performed in an intuitive and subjective manner by experts especially when the problem comes to the complexity and consistency of sophisticated interface procedures. The manual analysis and evaluation of logical interfaces also tends to be slow and laborious. To make the evaluation more formal and objective, the criteria and the procedure with which the evaluation can proceed must be explicitly prescribed. Furthermore, to make the formal procedure as practical as subjective expert evaluation, the criteria must reflect the user knowledge of the target tasks since the task knowledge plays the role of a basis for users to understand the interface organizations and procedures. This paper describes ESTIM, a support system for task-based evaluation of user interface, that includes the interface evaluation criteria and implies an evaluation procedure. The support system can be used either in an interactive manner by the analyst during the evaluation or in an automatic evaluation mode. It was verified that the result of automatic evaluation by ESTIM matched the results of expert evaluation fairly well.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.1
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• pp.260-273
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• 2013

This study aims to reflect experts' opinions in analyzing a design thinking as foundation of multidisciplinary education. For this purpose, a delphi survey was conducted with 20 experts in three sessions from May 1 to June 25, 2012. To analyze the collected data, descriptive statistics, including frequency, percentage, the mean, and standard deviation were implemented, and internal reliability test on the survey instrument was carried out for statistical processing. The main results are as follows : First, the delphi analysis on intuitive thinking of design thinking suggested 7 items(to pursue the possibility of outside, to pursue the possibility of applying new forms of technology, content planning, facing a complex real-world phenomena etc.). Second, the delphi analysis on logical thinking of design thinking suggested 7 items(executed repeatedly, reasoning and verification, artificial intelligence, a decision support system etc.) Third, the delphi analysis on subjective thinking of design thinking suggested 9 items(user experience measuring, user satisfaction ratings, user requirements analysis, user interface design, behavioral responses of the human etc.). Fourth, the delphi analysis on objective information of design thinking suggested 8 items(information management system, simulation, production process, information exchange and sharing etc.). According to the results of the delphi analysis, design thinking can be seen as the foundation of multidisciplinary education. Suggestions were made for discussion about the main results and further researches.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.395-401
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• 2014

As social network service becomes common, the consumers use many discount coupons with which they can purchase goods via social commerce. Although, the quantities of coupons offered from social commerce are currently on the sharp increase, customized coupon service that reflects user preference is not offered. This paper proposes a coupon service method reflecting user's subjective inclination targeting food coupons to offer customized coupon service for social commerce. Towards this end, this paper conducts hierarchization of the factors that become standard in selecting coupons including food types, food prices, discount rates and the number of buyers. And then, this study classifies, extracts and offers the coupons using Fuzzy-AHP, a decision making support method that reflects subjective inclination. From the user satisfaction results on the extracted coupons, the users are generally satisfied: very satisfactory with 45%, satisfactory with 33% and fair with 22%, and there was no experiment participant, who was dissatisfied.

• Korean Journal of Social Welfare Studies
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• v.43 no.2
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• pp.5-29
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• 2012

Previous performance evaluation focusing on objective data of service agency has the limitations that did not reflect user-centered evaluation. With the expansion of voucher service, importance of perspective of service user such as satisfaction index is increasing. As voucher service has been delivered by the financial burden of government and user, we need the performance evaluation system that reflects the both performance indices to meet the accountability of two stake-holders. So this study focuses on deriving integrated evaluation system developing systems what mixed objective and subjective performance. Data used in this study is collected form 70 social service agencies that deliver voucher service and 1445 service users. Using General Satisfaction Index and Efficiency Index by DEA, this study analysed the correlation between efficiency and satisfaction index, and integrated performance evaluation model is constructed through portfolio map. This study has the following implication. This study theoretically explains the relation of objective performance and subjective performance and gives practical guidance in performance evaluation criterion and interpretation of performance.

• Journal of Korean Library and Information Science Society
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• v.28
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• pp.447-475
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• 1998

We are seeing a shift of the focus of information service from system-centered(data-centered or intermediary-centered) a n.0, pproach to user-centered a n.0, pproach. Sense-making theory is one of the user- centered a n.0, pproaches. This study focused on user-centered information service paradigm concepts of Dervin's sense-making theory and interpreting sense-making theory in the practical context of library services. The sense-making theory is basically a cognitive a n.0, pproach to information-seeking, in that it recognizes information as something that involves internal cognitive processes. The user of information becomes the focus in this sense-making model. The sense-making theory sees information as subjective, situational, and cognitive. It focuses on understanding information within specific contexts and on understanding how information needs develop and how they are satisfied. It regards the user not as a passive receive of external information but as the center in a active, ongoing process of change. It related to the information needs of users. Dervin's method for studying information needs employs the 'situation-gap-use' metaphor. All information needs stem from a discontinuity or 'gap! in one's knowledge. Dervin believes that information needs can be addressed by understanding the process that each individual goes through in experiencing a gap, in trying to resolve it, and in gaining something (especially new knowledge) from the experience. Sense-making is a process; 'sense' is the product of this process. Sense includes 'knowledge'. However, it includes a host of other subjective factors that reflect an individual's interpretations of a situation including intuitions, opinions, hunches, effective responses, evaluations, questions, etc. For several reasons, however, difficulties are encountered if one attempts a more substantial assessment of the theory. First, it has not yet crystallized into a well-defined theory. Secondly, though the theory has aroused general interest, e.g. within LIS, detailed discussion of its strong and weak points is still lacking. A third source of difficulty is that the basic assumptions of the theory have been explicated metaphorically. If sense-making theory is to be really useful, it needs to be interpreted for library settings. But we have found no attempt to construct an information service based on the theory. Because the research tradition is still relatively young, there are many unanswered questions connected, e.g. with the design of information systems. In any case, e.g. intermediary access systems would be more efficient and effective it their planning could be founded on the user-centered a n.0, pproach.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.461-468
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• 2017

The current available recommendation systems for mobile games have a couple of problems. First, there is no knowing whether they make a pattern recommendation for games that actual users prefer or for games that they are simply interested in. It is also impossible to know the subjective preference of users in a direct manner. An AHP(Analytic Hierarchy Process)-based recommendation system for mobile games was thus developed to reflect the subjective preference of users directly, but it had its own problem since the degree of preference could vary among users in spite of the same scale for their preferable items. In an effort to solve those problems, this study implemented a recommendation system for mobile games by applying triangular fuzzy numbers of the Fuzzy-AHP technique and the independence of evaluation items in the Bayesian Network. The findings show that the proposed recommendation system recorded the highest accuracy of recommendation results and the highest level of user satisfaction.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.222-226
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• 2010

With the continuously increasing volume of e-commerce transactions, it is now popular to buy some products and to evaluate them on the World Wide Web. The product reviews are very useful to customers because they can make better decisions based on the indirect experiences obtainable through these reviews. However, since online shopping malls do not provide ranking results, it is not easy for users to read all the relevant review documents effectively. Product reviews include subjective and emotional opinions. Thus, the review search is different from the general web search in terms of ranking strategy. In this paper, we propose an effective method of ranking the reviews that can reflect user's intention by using opinion mining techniques. The proposed method analyzes product reviews with query words, and sentimental polarity of subjective opinions. Through diverse experiments, we show that our proposed method outperforms conventional ones.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.289-296
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• 2015

The explosive growth of information has been difficult for users to get an appropriate information in time. The various ways of new services to solve problems has been provided. As customized service is being magnified, the personalized recommendation system has been important issue. Collaborative filtering system in the recommendation system is widely used, and it is the most successful process in the recommendation system. As the recommendation is based on customers' profile, there can be sparsity and cold-start problems. In this paper, we propose personalized movie recommendation system using collaborative filtering techniques and context-based techniques. The context-based technique is the recommendation method that considers user's environment in term of time, emotion and location, and it can reflect user's preferences depending on the various environments. In order to utilize the context-based technique, this paper uses the human emotion, and uses movie reviews which are effective way to identify subjective individual information. In this paper, this proposed method shows outperforming existing collaborative filtering methods.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.