• Management & Information Systems Review
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• v.38 no.1
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• pp.121-139
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• 2019

This study aims to identify the change of customer participation(CP) which is essential to the service industry as the relationship between the customer and the employee develops. The latent growth modeling analysis based on the longitudinal data is utilized to examine the pattern of the change. This is based on the fact that CP needs to be understood in the relationship and is to confirm the change in CP by the development of the relationship. Given the dynamics of the relationship, we intend to overcome the limitations of previous cross-sectional researches by revealing the trajectory of CP in the relationship through the longitudinal data. We also want to examine which variables in the relationship can facilitate changes of CP. Research has shown that CP is significantly changed with the development of the relationship when we analyzed it through latent growth modeling. This confirms that CP needs to be understood in the relationship. In addition, 'relationship proneness' variable and 'dependence to provider' variable have positive effects on the initial values of CP, but they have not been established to promote the changes of CP. Consequently, when considering the dynamics of relationships, it is important to recognize that CP is also dynamic. This study sought to get out of the cross-sectional and fragmented understanding of CP that is dynamic. Through this, we would like to propose the successful operation of the customer management program of service firms in relation to CP. This will lead to the success of service encounter where appropriate CP levels at each stage of relationship development can be achieved.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.47-57
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• 2019

Purpose: This paper aims to investigate the concentration of fine particulate matter (PM2.5) in the Seoul area by predicting unhealthy days due to PM2.5 and comparing the regional differences. Methods: The extreme value theory is adopted to model and compare the PM2.5 concentration in each region, and each best model is selected through the goodness of fitness test. The maximum likelihood estimation technique is applied to estimate the parameters of each distribution, and the fitness of each model is measured by the mean absolute deviation. The selected model is used to estimate the number of unhealthy days (above $75{\mu}g/m^3$ PM2.5 concentrations) in each region, with which the actual number of unhealthy days are compared. In addition, the level of PM2.5 concentration in each region is analyzed by calculating the return levels for periods of 6 months, 1 year, 3 years, and 5 years. Results: The Mapo (MP) area revealed the most unhealthy days, followed by Gwanak (GW) and Yangcheon (YC). On the contrary, the number of unhealthy days was low in Seodaemun (SDM), Songpa (SP) and Gangbuk (GB) areas. The return level of PM2.5 was high in Gangnam (GN), Dongjak (DJ) and YC. It will be necessary to prepare for PM2.5 than other regions. On the contrary, Gangbuk (GB), Nowon (NW) and Seodaemun (SDM) showed relatively low return levels for PM2.5. However, in most of the regions of Seoul, PM25 is generated at a very poor level ($75{\mu}g/m^3$) every 6months period, and more than $100{\mu}g/m^3$ PM2.5 occur every 3 years period. Most areas in Seoul require more systematic management of PM2.5. Conclusion: In this paper, accurate prediction and analysis of high concentration of PM2.5 were attempted. The results of this research could provide the basis for the Seoul Metropolitan Government to establish policies for reducing PM2.5 and measuring its effects.

• Management & Information Systems Review
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• v.37 no.4
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• pp.63-74
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• 2018

Recently, in the asset management industry, the smart beta strategy, which has an intermediate nature between passive and active strategies, is attracting attention. In this smart beta strategy, value, momentum, low volatility, and quality index are widely used. In this study, we analyzed the quality index which is not clear and complicated to calculate. According to the MSCI methodology, the quality index was calculated using three variables: return on equity, debt to equity, and earnings variability. In addition, we use the index using only return on equity variable, the index using only two variables of return on equity and debt to equity, and the KOSPI index as comparison targets for the quality index. In order to evaluate the performance of the indices used in the analysis, the arithmetic mean return, the coefficient of variation, and the geometric mean return were used. In addition, Fama and French (1993) model, which is widely used in related studies, was used as a pricing model to test whether abnormal returns in each index are occurring. The results of the empirical analysis are as follows. First, in all period analysis, quality index was the best in terms of holding period returns. Second, the quality index performed best in the currency crisis and the global financial crisis. Third, abnormal returns were not found in all indices before the global financial crisis. Fourth, in the period after the global financial crisis, the quality index has the highest abnormal return.

• Management & Information Systems Review
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• v.37 no.4
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• pp.75-92
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• 2018

The Purpose of this study was to verify that the corporate social contribution activity(eco-friendly product production, donation activity, volunteer activity, fair trade had a positive influence on the corporate credibility, the intention of price premium and the persistent purchase intention. And also this study examined the differences of corporate credibility and price premium according to consumer's gender. To accomplish the purpose of this paper, I performed literature review which relates to corporate social contribution activity, the corporate credibility, the price premium and the persistent purchase intention, and also performed empirical research. I produced questionnaire which investigates the relation between the factors which influence corporate social contribution activity, the corporate credibility, the price premium and the persistent purchase intention. SPSS 21.0 and Lisrel 8.7 were used to analyze the collected data and to identify the influence relationships. The findings of this paper are as follows: First, the corporate social contribution activity(eco-friendly product production, fair trade) did significant effect on the corporate credibility and the price premium. Second, the intention to pay premium price had affected the intention of persistent purchasing but corporate credibility had affected the intention of persistent purchasing through the intention to pay premium price. Third, the difference in preception of men and woman about the corporate social contribution activity. Among the corporate social contribution activity, women's perception of the relationship between volunteer activity and fair trade on corporate reliability was higher than that of men. Fourth, among the corporate social contribution activity, women's perception of the relationship between volunteer activity and donation activity on the intention of price premium was higher than that of men.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.528-534
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• 2019

This study analyzes the factors affecting China's carbon emissions from 1985 to 2016. In recent years, the whole industries of China are in the midst of industrialization and have several problems. Now, the low-carbon economy has become the main task of China's economic development. This study analyzes the factors affecting China 's carbon emissions by selecting relevant data onto the Chinese yearbook and using a time series model. The analysis shows that related industries continue to innovate and increase the use of green energy such as electricity, but coal is still the largest share of the energy consumed. As energy use efficiency increases and industrial R&D investment increases year by year, carbon emissions are increasing every year. In addition, there is a stereotype that industry is the biggest factor affecting carbon emissions. The research found that the impact of the industry on China's carbon emissions is declining gradually. While controlling industrial carbon emissions, keeping continue to improve technology development and focusing on carbon emissions from other industries are critical to reduce overall carbon emissions. Based on the empirical results, if we can change stereotypes starting from the nature of the data, we will quickly reach a low carbon sustainable development economy.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.127-134
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• 2019

With the advancement of IT technology, the amount of data generated has been growing exponentially every year. As an alternative to this, research on distributed systems and in-memory based big data processing schemes has been actively underway. The processing power of traditional big data processing schemes enables big data to be processed as fast as the number of nodes and memory capacity increases. However, the increase in the number of nodes inevitably raises the frequency of failures in a big data infrastructure environment, and infrastructure management points and infrastructure operating costs also increase accordingly. In addition, the increase in memory capacity raises infrastructure costs for a node configuration. Therefore, this paper proposes an in-memory-based hybrid big data processing scheme for improve the big data processing rate. The proposed scheme reduces the number of nodes compared to traditional big data processing schemes based on distributed systems by adding a combiner step to a distributed system processing scheme and applying an in-memory based processing technology at that step. It decreases the big data processing time by approximately 22%. In the future, realistic performance evaluation in a big data infrastructure environment consisting of more nodes will be required for practical verification of the proposed scheme.

• Management & Information Systems Review
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• v.37 no.4
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• pp.257-270
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• 2018

Although healing is now developing an interest in extending tourism, there have been few studies in this area. Especially late-night tour is closely related to healing experience, but there are little researches that help us understand healing in late-night tour. This study was conducted to examine the relationships among late-night scapes(light aesthetics, sharing mood, culture uniqueness), healing experience(stress reduce and psychological happiness), attitude toward late-night tour. We collected data involving various late-night tourist, and used 198 respondents to analyze these data using LISREL structural modeling. Light aesthetics and sharing mood had positive effects on stress remove, but Culture uniqueness didn't have a signigicant effect on stress remove, unlikely the prediction. And all late-night shape had positive effects on psychological happiness. Also we tested the effects of the healing experience(stress remove and psychological happiness) on attitude. In the final section, we discussed several limitations of our study and suggested directions for future research. We concluded with a discussion of managerial implications, including the potential to advance understanding late-night tourist and implying an enhanced ability to satisfy target consumers of late-night tour.

• Management & Information Systems Review
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• v.37 no.4
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• pp.271-287
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• 2018

Salespersons play an important role in strengthening a firm's marketing competitiveness advantage because they provide service through the direct contact with customers. This research tests several hypothesized relationships between their career performances(incomes, job satisfaction and organizational commitment) and marketing comprtitiveness advantage. The data used in this study to measure the hypotheses is 'Human Capital Enterprise Panel Data 2015(5th) that was collected by Korea Vocational Training Institute. The results showed that the earnings was not significantly different depending on salesperson's self-directed career management. The job satisfaction and organizational commitment of the salesperson positively affected firm's marketing competitiveness advantage, but the effects of salesperson job satisfaction and organizational commitment on firm's marketing competitiveness advantage were not significantly different depending on salesperson's self-directed career management. This study confirmed that it is necessary to regard career movement as the significant resources of the marketing competitiveness advantage as self-directed career management rather than a negative point of view.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.25-32
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• 2020

The seed, the most basic component, is an important factor in increasing production and efficiency in agriculture. Seeds with superior genes can be expected to improve agricultural productivity, crop survival, and reproduction. Currently, however, screening of superior seeds depends mostly on manual work, which requires a lot of time and manpower. In this paper, we propose a system that can extract the characteristics of seed phenotypes by using computer image processing technology, so that even a small number of people and a short period of time are needed to extract the characteristics of seeds. The proposed system detects individual seeds from images containing large quantities of seeds, and extracts and stores various characteristics such as representative colors, area, perimeter and roundness for each individual seed. Due to the regularity of input images, the accuracy of individual seed extraction in the proposed system is 99.12% for soybean seeds and 99.76% for rice seeds. The extracted data will be used as basic data for various data analyses that reflect the opinions of experts in the future, and will be used as basic data to determine the expressive nature of each seed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.