• The Journal of Industrial Distribution & Business
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• v.13 no.4
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• pp.1-14
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• 2022

Purpose: The purpose of the study is to explore the application of Augmented Reality (AR) technology to enhance interactivity and decision making via technology-enabled experience particularly, in the context of COVID-19. This study investigated effects of perceived utilitarian value, hedonic value, social value, and perceived risk on customer satisfaction with AR technology that are rarely examined in previous studies. Research design, data and methodology: Online survey data was used in the study. This study applied factor analysis and regression analysis to test the hypotheses and employed ANOVA and mediation effect analysis to explore additional findings. Results: The results suggested that customers' perceived usefulness, arousal, social preference, innovativeness, financial risk, and performance risk have statistically significant effect on customer satisfaction. Conclusions: The findings of the study provided managerial and policy implications to develop and advertise the introduction of AR technology with the emphasis on the practical and utilitarian benefits of the technology. The result of this study highlighted the importance of customer relationship management by providing advanced services to customers through AR technology. This study contributes to technology-enabled CRM literature by providing the empirical result to verify the assumption that AR technology can be an effective tool of firms' CRM strategy

• Health Policy and Management
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• v.16 no.1
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• pp.54-72
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• 2006

This study was conducted as the primary work to develop a customer relationship management (CRM) system to improve the performance of health screening programs. The specific aims of the study was to identify and classify the characteristics of the people who did not receive their health screening using decision trees and to propose management strategies according to their characteristics identified. The data on a total of 5,102,761 subjects of health screening provided by the National Health Insurance Program in the year of 2002 were used. The target variable was whether they underwent their health screening. The input variables included a total of 27. The SAS 9.1 version was used for data preprocessing and statistical analyses. SAS Enterprise Miner was used to develop the decision trees model. The decision trees identified the factors greatly affecting the health screening. In the non-disease group, the highest rate of non-examinees was characterized by: no experience of receiving a health screen, household's age, non-insured episode for the last one year, and patients' age. In the disease group, the one showing the highest rate of non-examinees was characterized by: no experience of receiving a health screening, no experience of going to public health center or midwife clinic for the last one year, and examinees' age. Developing CRM systems for health screening management taking into account the individual characteristics would be considerably helpful to increase the rate of receiving health screening.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• Journal of the East Asian Society of Dietary Life
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• v.17 no.5
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• pp.727-737
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• 2007

The aims of this study was to suggest a new efficiency measurement indicator for evaluating the management efficiency of decision making units(DMUs) in the contract foodservice industry. The data envelopment analysis(DEA) model which considers multiple inputs and outputs and looking for benchmarks, was used to compare the productivity of DMUs. We considered sales, profits, and customer satisfaction as output variables and it adopted food cost, labor cost and administrative expense as input variables. The results of applying DEA revealed relatively efficient types of business and service types. The efficiency of school units was highest and the mired service type was the most efficient one. In this study the CCR model efficiency was analysed with profit and the customer satisfaction index by the matrix method. DEA efficiency was correlated with profit but there was no correlation between DEA efficiency and the customer satisfaction index.

• Journal of Korean Society for Quality Management
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• v.29 no.4
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• pp.54-64
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• 2001

Service industries are rapidly increased and the environment of service is also changing in healthcare settings. Those make them change their management strategies and carry out many researches. Till now, single-item behavioral intention scale or direct measuring has some limitations that we have to consider its efficiency and it is difficult to explain phenomena of broad part in the process of determining behavioral intentions. In this paper, we examine the dimensionality of healthcare service in Korea through the multi-item behavioral intention scale. In addition, we are going to investigate the nature of the relationship between service quality perceptions which have unique construct, and consumer satisfaction judgements in the formation of consumers' behavioral intentions. we are going to conclude that the linear combination model of service quality and customer satisfaction can describe the process of formation of customers' behavioral intentions best and provide some managerial implications to decision makers for redistribution of materials and human resources.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.271-280
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• 2002

Recently, many companies have applied data mining techniques to promote competitive power in the field of their business market. In this study, we address how data mining, that is a technique to enable to discover knowledge from a deluge of data, Is used in an executed project in order to support decision making of an enterprise. Also, we develope scoring model on customer attrition probability for automobile-insurance company using data mining techniques. The development of scoring model in domestic insurance is given as an example concretely.

• 한국디지털정책학회:학술대회논문집
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• 2003.12a
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• pp.11-24
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• 2003

It is far more expensive for companies to acquire new customers than it is to retain customers. As a result, companies are turning to Customer Relationship Management (CRM) in order to make decisions about managing the relationship and the profitability of those customer relationships. CRM is a strategy that integrates the concepts of Knowledge Management, Data Mining and Data Warehousing in order to support the organization's decision -making process to retain long-term and profitable relationships with its customers. This paper examines the design implications that CRM poses to data warehousing. We then present a robust data warehouse schema to support CRM analyses and decisions. For example, the proposed schema could be used to calculate customer profitability and to identify social networks of influence between customers. The paper also discusses future areas for research pertaining to CRM data warehousing and data mining.

• Journal of the Korea Safety Management & Science
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• v.8 no.2
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• pp.103-114
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• 2006

Today the usage of DB already becomes an important issue for many companies' survival. Especially, the companies which have adopted CRM could not gain the return to be expected because of the lack of understanding about the relationship with customers. Professor Schmitt introduced 'the complete CEM(Customer Experience Management) model', but no specific methodology for analysis was introduced. Therefore, in this study we use HOQ, GA, and ANP to build 'the complete CEM model' and present the integrated CEM model based on the integrated analytic process to help the company's decision about the stage to be begun first out of 5 stages of CEM and the priorities of investment in customer experiences.

• Journal of the Korean housing association
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• v.9 no.2
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• pp.51-56
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• 1998

With the improved quality of life standards interior design is recently gaining more concerns than ever before. This requires more well organized and standardized business and marketing in the field. Consequently, a few interior design firms concern about computerizing their marketing process to do this. This paper focuses on developing a computerized consulting system assisting interior design. For this a standardized consulting process is developed. The process includes the input of customer's information, surveying customer's preferences on interior design styles, estimation and budget adjustment, suggesting packages and interior items, and printing consulting results. The system developed is valuable in three different viewpoints: First, from the customer's view it can be a decision-making tool. Second, from the consultant's point of view it is a front-end marking tool. Third, it is an information collector from the viewpoint of an interior design firm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.91-100
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• 2015

In this study, we propose an efficient two-phase heuristic policy, called an acceptance tolerance control policy, for Infrastructure as a Service (IaaS) cloud services that considers both the service provider and customer in terms of profit and satisfaction, respectively. Each time an IaaS cloud service is requested, this policy determines whether the service is accepted or rejected by calculating the potential for realizing the two performance objectives. Moreover, it uses acceptance tolerance to identify the possibility for error with the chosen decision while compensating for both future fluctuations in customer demand and error possibilities based on past decisions. We conducted a numerical experiment to verify the performance of the proposed policy using several actual IaaS cloud service specifications and comparing it with other heuristics.