• Information Systems Review
• /
• v.25 no.1
• /
• pp.111-128
• /
• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Information Systems Review
• /
• v.19 no.4
• /
• pp.183-209
• /
• 2017

With changes in information technology (IT), many innovative IT-based services, such as AirBnB, have become popular. Switching behavior toward new and innovative services become a major issue for managers who want to attract many customers. In response, many researchers have investigated why customers switch service providers. However, little research has been conducted on the processes of switching behavior for a hedonic service. To fill this research gap, this study aimed to identify the stages of switching behavior based on transtheoretical model. Furthermore, the factors affecting the service switching behavior in stages were identified on the basis of service provider switching model. This study also hypothesized the customer's switching behavior in accommodation sharing economy service and analyzed it empirically. Results showed that the factors affecting switching behavior differ across five stages. The present results can provide a basis to prevent switching behavior and reduce churn by analyzing the difference in switching behavior among stages. This study also helps managers who want to improve organizational performance by enhancing customer retention capability.

• Journal of the Korean Applied Science and Technology
• /
• v.40 no.6
• /
• pp.1498-1505
• /
• 2023

This study attempted to investigate the difference in service orientation according to the individual characteristics of hair salon workers, and to identify the internal marketing factors of hair salon that influence service orientation. Questionnaires for empirical research were collected from hair salon workers in Gyeongnam, and the results of analyzing the collected questionnaires through IBM SPSS Statistics 26 are as follows. First, as a result of analyzing the difference in service orientation according to the individual characteristics of hair salon workers, the '40s or older' group and the 'working period of 10 years or longer' group showed statistically higher service orientation than other groups. Second, as a result of analyzing the causal relationship between internal marketing and service orientation, it was found that welfare, compensation system, education and training of internal marketings had the statistical effect on service orientation, and in particular, the compensation system had the strongest effect on service orientation. Therefore, service orientation for customers should be improved through internal marketing activities that take into account the individual characteristics of hair salon workers. The improvement of service orientation means the customer's intention to reuse, suggesting that ultimately the management performance of hair salon companies can be further improved.

• Journal of Digital Convergence
• /
• v.22 no.3
• /
• pp.1-7
• /
• 2024

This study provides an in-depth analysis of the impact of the deepening reform and opening-up policies announced at the 20th CPC Central Committee's Plenary Session, particularly focusing on the <Foreign Investment Law> and its effects on the tax decisions and organizational restructuring of Korean companies operating in China. Using a comprehensive literature review and policy analysis, the study compares the dual legal structure and tax differences before and after the implementation of the law, assessing how legal unification has influenced the organizational forms and tax strategies of Korean companies. The findings indicate that the <Foreign Investment Law> has played a crucial role in enhancing legal consistency and tax equity between foreign-invested enterprises and domestic enterprises, thereby enabling Korean companies to manage their operations in the Chinese market more stably and efficiently. Additionally, in the context of the ongoing U.S.-China trade conflict, the law's provision of national treatment and tax benefits has proven to be a significant factor in the survival strategy of Korean companies in China. Future research should focus on empirically examining the long-term effects of this law and its impact on actual corporate performance.

• Journal of Service Research and Studies
• /
• v.14 no.3
• /
• pp.172-187
• /
• 2024

This paper deals with asymptotic approximations analysis of M/M/s and M/D/s queues. For M/M/s queue, we observe "economies of scale" under the fixed utilization ρ and the fixed probability α that customer waits in system, how the average system size vary according to the number of servers s increasing. Simulation results show that as s increases, the number of servers who are idling increases, that is, the slack n-E[Q_n] diverges. In addition, through changing the waiting probability α under the M/M/s system, α was not highly sensitive to the behavior of the system size. And, it is shown that using ${\rho}_n\,=\,1-k/\sqrt{n}$ to handle heavy-traffic regime is only appropriate for k = 1 by observing the effect on the performance of the system with different values of k. For the M/D/s queue, two approximations are used to evaluate the expected system size under the fixed ρ and α. Simulations and comparison of these two approximations show that Cosmetatos' approximation performs quite well when the number of servers is small and traffic intensity is heavy, but it overestimates the true value for the large number of servers. Meanwhile, the modified approximation gives good results for the steady state count of the system although the number of servers grows large.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.111-124
• /
• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.125-140
• /
• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• 한국벤처창업학회:학술대회논문집
• /
• 2006.04a
• /
• pp.167-201
• /
• 2006

Korean corporations are transforming their vertical operational structure to a team-based structure to compete in the rapidly changing environment and for improved performance. However, a high percentage of the respondents in KBS said that despite the appearance of the present team structure, the organization operates much like a vertically-structured organization. This result can be attributed to the lack of study and implementation toward the goal of empowerment, the key variable for the success of the team-based structure. This study aims to provide policy suggestions on how to implement the process of empowerment, by investigating the conditions that hinder the process and the attitude of the KBS employees. For the cross-sectional study, this thesis examined the domestic and international references, conducted a survey of KBS employees, personal interviews and made direct observations. Approximately 1,200 copies of the Questionnaire were distributed and 474 were completed and returned. The analysis used SPSS 12.0 software to process the data collected from 460 respondents. For the longitudinal-study, six categories that were common to this study and "The Report of the Findings of KBS Employees' View of the Team Structure" were selected. The comparative study analyzed the changes in a ten-month period. The survey findings showed a decrease of 24.2%p in the number of responses expressing negative views of the team structure and a decrease of 1.29%p in the number of positive responses. The findings indicated a positive transformation illustrating employees' improved understanding and approval of the team structure. However, KBS must address the issue on an ongoing basis. It has been proven that the employee empowerment increases the productivity of the individual and the group. In order to boost the level of empowerment, the management must exercise new, innovative leadership and build trust between the managers and the employees first. Additional workload as a result of shirking at work places was prevalent throughout all divisions and ranks, according to the survey data. This outcome leads to the conclusion that the workload is not evenly distributed or shared. And the data also showed the employees do not trust the assessment and rewards system. More attention and consideration must be paid to the team size and job allocation in order to address this matter; the present assessment and rewards system need to be complemented. The type of leadership varies depending on the characteristics of the organization's structure and employees' disposition. KBS must develop and reform its own management, leadership style to suit the characteristics of individual teams. Finally, for a soft-landing of KBS team structure, in-house training and education are necessary.

• Management & Information Systems Review
• /
• v.23
• /
• pp.41-74
• /
• 2007

In Korea, corporate merger activities were activated since 1980, and nowadays(particuarly since 1986) the changes in domestic and international economic circumstances have made corporate managers have strong interests in merger. Korea and America have different business environments and it is easily conceivable that there exists many differences in motives, methods, and effects of mergers between the two countries. According to recent studies on takeover bids in America, takeover bids have information effects, tax implications, and co-insurance effects, and the form of payment(cash versus securities), the relative size of target and bidder, the leverage effect, Tobin's q, number of bidders(single versus multiple bidder), the time period (before 1968, 1968-1980, 1981 and later), and the target firm reaction (hostile versus friendly) are important determinants of the magnitude of takeover gains and their distribution between targets and bidders at the announcement of takeover bids. This study examines the theory of takeover bids, the status quo and problems of merger in Korea, and then investigates how the announcement of merger are reflected in common stock returns of bidding firms, finally explores empirically the factors influencing abnormal returns of bidding firms' stock price. The hypotheses of this study are as follows ; Shareholders of bidding firms benefit from mergers. And common stock returns of bidding firms at the announcement of takeover bids, shows significant differences according to the condition of the ratio of target size relative to bidding firm, whether the target being a member of the conglomerate to which bidding firm belongs, whether the target being a listed company, the time period(before 1986, 1986, and later), the number of bidding firm's stock in exchange for a stock of the target, whether the merger being a horizontal and vertical merger or a conglomerate merger, and the ratios of debt to equity capital of target and bidding firm. The data analyzed in this study were drawn from public announcements of proposals to acquire a target firm by means of merger. The sample contains all bidding firms which were listed in the stock market and also engaged in successful mergers in the period 1980 through 1992 for which there are daily stock returns. A merger bid was considered successful if it resulted in a completed merger and the target firm disappeared as a separate entity. The final sample contains 113 acquiring firms. The research hypotheses examined in this study are tested by applying an event-type methodology similar to that described in Dodd and Warner. The ordinary-least-squares coefficients of the market-model regression were estimated over the period t=-135 to t=-16 relative to the date of the proposal's initial announcement, t=0. Daily abnormal common stock returns were calculated for each firm i over the interval t=-15 to t=+15. A daily average abnormal return(AR) for each day t was computed. Average cumulative abnormal returns($CART_{T_1,T_2}$) were also derived by summing the $AR_t's$ over various intervals. The expected values of $AR_t$ and $CART_{T_1,T_2}$ are zero in the absence of abnormal performance. The test statistics of $AR_t$ and $CAR_{T_1,T_2}$ are based on the average standardized abnormal return($ASAR_t$) and the average standardized cumulative abnormal return ($ASCAR_{T_1,T_2}$), respectively. Assuming that the individual abnormal returns are normal and independent across t and across securities, the statistics $Z_t$ and $Z_{T_1,T_2}$ which follow a unit-normal distribution(Dodd and Warner), are used to test the hypotheses that the average standardized abnormal returns and the average cumulative standardized abnormal returns equal zero.

• Journal of Distribution Research
• /
• v.17 no.3
• /
• pp.59-92
• /
• 2012

With accumulated research evidence, there is little doubt that leadership behavior is related to a wide variety of positive individual and organizational outcomes. Indeed, leadership behavior has been empirically linked to increased employee satisfaction, organizational commitment, extra effort, turnover intention, organizational citizenship behavior, and overall employee performance. Although leadership behavior has been linked to a number of positive organizational outcomes, research regarding the antecedents of such behavior is limited. Especially there is little research dealing with the antecedents of inter-organizational leadership behavior. This study interests in inter-organizational ethical leadership among marketing channel members. In both the mass media and the academic association, there has been a surge in interest in the ethical and unethical behavior of leaders. Although the corporate scandals in recent years may explain much of the mass media and popular focus, academics' interest has been limited by evidence that ethical leadership behavior is associated with both positive and negative inter-organizational processes and performances. This study tried to contribute to this body of knowledge by examining antecedents of ethical leadership. Ethical leadership is defined "the demonstration of normatively appropriate conduct through personal actions and interpersonal relationships, and the promotion of such conduct to followers through two-way communication, reinforcement, and decision-making." Ethical leaders not only inform individuals of the behefits of ethical behavior and the cost of inappropriate behavior, such leaders also set clear standards and use rewards and fair and balanced punishment to hold followers accountable for their ethical conduct. Despite the assume importance and prominence of ethical leadership among organizations, there are still many questions relating to its antecedents and consequences. One is whether the likelihood of an leading organization being perceived as an ethical leader among other following organizations in marketing channels can be predicted using its characteristics and inter-organizational relationship maintenance skills. Identifying trait and skill antecedents will aid in the development of strategies for selecting and developing ethical leaders and determining the best means to reinforce ethical behaviors. The purpose of this study is to investigate the effects of three categorized variables on ethical leadership of channel leader. To be concrete, this study develops a model of the antecedents of three conceptually distinct forms of channel leader characteristics, such as organizational traits, inter-organizational relationship maintenance strategies, and supplier management strategies, and tests the hypothesized differential effects on ethical leadership of marketing channel leaders. The reason why this study deals with discount store channel is that there is very strong inter-dependence between a discount store and its suppliers. Their strong inter-dependence makes their relationship as the relationship between a leader and suppliers and creates an atmosphere that leadership occur without difficulty. The research model is as follows. For the purpose of empirical testing, 295 respondents of suppliers of discount store channel in Korea were surveyed. The procedures included scale reliability, and discriminant and convergent validity were used to validate measures. Also, the reliability measurements traditionally employed, such as the Cronbach's alpha, were used. All the reliabilities were greater than .70. This study conducted confirmatory factor analyses to assess the validity of our measurements. All items loaded significantly on their respective constructs(with the lowest t-value being 15.2), providing support for convergent validity. We then examined composite reliability and average variance extracted(AVE). The composite reliability of each construct was greater than .70. The AVE of each construct was greater than .50. This study tested research model using Partial Least Square(PLS). The estimation of the structural equation model revealed an acceptable fit of the model to the data($r^2$=.851). Thus, This study concluded that the model fit was considered acceptable. The results of PLS are as follows. The results indicated that conscientiousness, openness, conflict management, social networks, training, fair reward had positive effects on ethical leadership of channel leaders. On the other hand, emotional insecure had negative effect and agreeableness, assurance, and inter-organizational communication had no significant effect on supply chain leadership.