• The Korean Journal of Franchise Management
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• v.8 no.4
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• pp.21-32
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• 2017

Purpose - In hotel business, how to build the relationship between leader and employees is very important, because it affects on the customer satisfaction. Thus, this research examines the effect of authentic leadership on job performance in the context of hotel industry and identifies mediating roles of organizational engagement, job satisfaction, and creativity in the relationship between authentic leadership and job performance. This study suggests the guidelines for how hotel companies should improve employee productivity and build a desirable organizational culture by presenting employee attitudes and behavioral models that explain the relationship between leaders and employees. Research design, data, and methodology - This study examines the structural relationship between authentic leadership, organizational engagement, job satisfaction, creativity, and job performance from the employee's perspective. Authentic leadership divide into four sub-dimensions such as self-awareness, balanced process of informations, internalized moral perspective, and relational transparency. In order to test the purposes of this study, research model and hypotheses were developed. All constructs were measured with multiple items developed and tested in the previous studies. The data were collected from 114 franchise hotel employees and were analyzed using SPSS 21.0 and Smart PLS 3.0. program. Result - The results of this study are as follows. First, authentic leadership have significant impacts on organizational engagement and creativity, but does not have impact on job satisfaction directly. Second, organizational engagement have significant impacts on job satisfaction and job performance, but does not have impact on creativity directly. Third, job satisfaction has significant impact on creativity, but does not have impact on job performance. Fourth, creativity has significant impact on job performance. Conclusions - The findings of this study indicate that hotel leaders should properly implement the authentic leadership and consider how to build a corporate culture to improve an organizational and employee productivity through authentic leadership. Due to the nature of the hotel industry, which relies heavily on human resources, hotel companies must manage their employees with authenticity in order to increase organizational engagement, job satisfaction, and creativity that affect hotel and employee productivity. If hotel employees perceive their leader's authentic leadership, they show more organizational engagement that increases creativity and leads to job performance. Finally, hotel employees can propose creative ideas only if they will be satisfied with their jobs. Therefore, the leader should develop non-monetary or monetary reward system for the employees and, make an efforts to foster creativity of the employees.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.158-177
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• 2024

This paper proposes and tests a theoretical model of the relational link between a novel form of customer-perceived fairness for a reward design (distributive peer justice climate) and C2C helping intention via community identification and online C2C interaction (friend-, neighboring customer-, audience-interaction) qualities in a collective consumption context (MMORPG). To test hypotheses, we amassed survey data within a collective consumption context (massively multiplayer online role-playing games, MMORPGs). We used structural equation modeling in analyzing the survey data. The results reveal that user-perceived distributive peer justice climate for a reward design enhances their C2C helping intention via community identification and C2C interactions in MMORPG contexts. Collective consumption-type service managers should focus on promoting the user-perceived distributive peer justice climate for their reward system to enhance users' present C2C co-creation experience (community identification, C2C interaction) and future C2C co-creation behavior (helping intention). By adopting an intra-unit level distributive justice concept (customer-perceived distributive peer justice climate) to a reward design in a collective consumption context (MMORPGs), this study informed collective consumption-type service managers of the importance of its management.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.115-132
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• 2008

Metal industry is one of the most representative heavy industries and the median sales volume of steel and nonferrous metal companies is over one billion dollars in the case America [Forbes 2006]. As seen in the recent business market situation, an increasing number of industrial manufacturers and suppliers are moving from adversarial to cooperative exchange attitudes that support the long-term relationships with their customers. This article presents the results of an empirical study of the antecedent factors of business relationships in metal industry of the United States. Commitment has been reviewed as a significant and critical variable in research on inter-organizational relationships (Hong et al. 2007, Kim et al. 2007). The future stability of any buyer-seller relationship depends upon the commitment made by the interactants to their relationship. Commitment, according to Dwyer et al. [1987], refers to "an implicit or explicit pledge of relational continuity between exchange partners" and they consider commitment to be the most advanced phase of buyer-seller exchange relationship. Bonds are made because the members need their partners in order to do something and this integration on a task basis can be either symbiotic or cooperative (Svensson 2008). To the extent that members seek the same or mutually supporting ends, there will be strong bonds among them. In other words, the principle that affects the strength of bonds is 'economy of decision making' [Turner 1970]. These bonds provide an important idea to study the causes of business long-term relationships in a sense that organizations can be mutually bonded by a common interest in the economic matters. Recently, the framework of structural bonding has been used to study the buyer-seller relationships in industrial marketing [Han and Sung 2008, Williams et al. 1998, Wilson 1995] in that this structural bonding is a crucial part of the theoretical justification for distinguishing discrete transactions from ongoing long-term relationships. The major antecedent factors of buyer commitment such as technology, CLalt, transaction-specific assets, and importance were identified and explored from the perspective of structural bonding. Research hypotheses were developed and tested by using survey data from the middle managers in the metal industry. H1: Level of technology of the relationship partner is positively related to the level of structural bonding between the buyer and the seller. H2: Comparison level of alternatives is negatively related to the level of structural bonding between the buyer and the seller. H3: Amount of the transaction-specific assets is positively related to the level of structural bonding between the buyer and the seller. H4: Importance of the relationship partner is positively related to the level of structural bonding between the buyer and the seller. H5: Level of structural bonding is positively related to the level of commitment to the relationship. To examine the major antecedent factors of industrial buyer's structural bonding and long-term relationship, questionnaire was prepared, mailed out to the sample of 400 purchasing managers of the US metal industry (SIC codes 33 and 34). After a follow-up request, 139 informants returnedthe questionnaires, resulting in a response rate of 35 percent. 134 responses were used in the final analysis after dropping 5 incomplete questionnaires. All measures were analyzed for reliability and validity following the guidelines offered by Churchill [1979] and Anderson and Gerbing [1988]., the results of fitting the model to the data indicated that the hypothesized model provides a good fit to the data. Goodness-of-fit index (GFI = 0.94) and other indices ( chi-square = 78.02 with p-value = 0.13, Adjusted GFI = 0.90, Normed Fit Index = 0.92) indicated that a major proportion of variances and covariances in the data was accounted for by the model as a whole, and all the parameter estimates showed statistical significance as evidenced by large t-values. All the factor loadings were significantly different from zero. On these grounds we judged the hypothesized model to be a reasonable representation of the data. The results from the present study suggest several implications for buyer-seller relationships. Theoretically, we attempted to conceptualize the antecedent factors of buyer-seller long-term relationships from the perspective of structural bondingin metal industry. The four underlying determinants (i.e. technology, CLalt, transaction-specific assets, and importance) of structural bonding are very critical variables of buyer-seller long-term business relationships. Our model of structural bonding makes an attempt to systematically examine the relationship between the antecedent factors of structural bonding and long-term commitment. Managerially, this research provides industrial purchasing managers with a good framework to assess the interaction processes with their partners and, ability to position their business relationships from the perspective of structural bonding. In other words, based on those underlying variables, industrial purchasing managers can determine the strength of the company's relationships with the key suppliers and its state of preparation to be a successful partner with those suppliers. Both the supplying and customer companies can also benefit by using the concept of 'structural bonding' and evaluating their relationships with key business partners from the structural point of view. In general, the results indicate that structural bonding gives a critical impact on the level of relationship commitment. Managerial implications and limitations of the study are also discussed.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of Distribution Research
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• v.15 no.1
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• pp.73-101
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• 2010

Using data collected from franchisees from Busan, we empirically examined the relationship both structural (tie strength), relational (trust), and cognitive (shared value) of social capital and between those dimensions and the patterns of economic performance and noneconomic performance (relationship continuity intention). So we established 9 hypotheses to test the structural relationship among dimensions of social capital and performances like below. H1: A franchisee's perceived tie strength with its franchisor will positively influence its trust in the franchisor. H2: A franchisee's perceived shared value with its franchisor will positively influence its trust in the franchisor. H3: A franchisee's perceived tie strength with its franchisor will positively influence its economic performance. H4: A franchisee's perceived shared value with its franchisor will positively influence its economic performance. H5: A franchisee's perceived trust in its franchisor will positively influence its economic performance. H6: A franchisee's perceived tie strength with its franchisor will positively influence its relationship continuity intention with the franchisor. H7: A franchisee's perceived shared value with its franchisor will positively influence its relationship continuity intention with the franchisor. H8: A franchisee's perceived trust in its franchisor will positively influence its relationship continuity intention with the franchisor. H9: A franchisee's perceived economic performance will positively influence its relationship continuity intention with the franchisor. The conceptual model specifying the relationship among dimensions of social capital and performances is presented in Fig. 1. Tests of the hypotheses were performed using a structural equation model. This model also reflected a good fit to the data ($\chi^2$=101.12 df=62 p=0.004, RMSEA=0.050 GFI=0.936 AGFI=0.895 NFI=0.959 CFI=0.986). The standardized solution estimated by the AMOS 7 program was for interpreting the structural results (Table 1). As was expected, tie strength and shared value were founded to be significant predictors of trust (H1 supported; H2 supported). Tie strength and trust have a significant positive effect on economic performance (H3 supported; H5 supported). But shared value have not a significant effect on economic performance (H4 Rejected). Tie strength were not associated with relationship continuity intention (H6 Rejected). While on the other higher shared value, trust, and economic performance have a significant effect on the relationship continuity intention (H7 supported; H8 supported; H9 supported). The results show integratedly that, first, tie strength does not affect directly but affects indirectly on relationship continuity intention via trust and economic performance. Second, shared goals affect directly and do indirectly via trust on relationship continuity intention. But shared goals does not affect via economic performance on relationship continuity intention. Finally, the study suggests important implications for both research and practice for franchise system especially.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.67-74
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• 2016

Drone images with high spatial resolution are emerging as an alternative to previous studies with extraction limits in high density forests. Individual tree in the dense forests were extracted from drone images. To detect the individual tree extracted through the image segmentation process, the image segmentation results were compared between the combination of DSM and all R,G,B band and the combination of DSM and R,G,B band separately. The changes in the tree density of a deciduous forest was experimented by time and image. Especially the image of May when the forests are dense, among the images of March, April, May, the individual tree extraction rate based on the trees surveyed on the site was 50%. The analysis results of the width of crown showed that the RMSE was less than 1.5m, which was the best result. For extraction of the experimental area, the two sizes of medium and small trees were extracted, and the extraction accuracy of the small trees was higher. The forest tree volume and forest biomass could be estimated if the tree height is extracted based on the above data and the DBH(diameter at breast height) is estimated using the relational expression between crown width and DBH.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.130-142
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• 2008

Different from other ontology languages, TopicMap is capable of integrating numerous amount of heterogenous information resources using the locational information without any information transformation. Although many conventional editors have been developed for topic maps, they are standalone-type only for writing XTM documents. As a result, these tools request too much time for handling large-scale data and provoke practical problems to integrate with legacy systems which are mostly based on relational database. In this paper, we model a large-scale topic map structure based on XTM 1.0 into RDB structure to minimize the processing time and build up the ontology in legacy systems. We implement a topic map platform called X-TOP that can enhance the efficiency of ontology construction and provide interoperability between XTM documents and database. Moreover, we can use conventional SQL tools and other application development tools for topic map construction in X-TOP. The X-TOP is implemented to have 3-tier architecture to support flexible user interfaces and diverse DBMS. This paper shows the usability of X-TOP by means of the comparison with conventional tools and the application to healthcare cancer ontology management.

• Journal of Distribution Science
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• v.13 no.11
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• pp.79-92
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• 2015

Purpose - The aim of this study is to develop an empirical model of the effects of traditional market service quality factors on customer value, relationship quality, and behavior. The specific objectives of the study are as follows: 1) to classify study objects into cultural tourism markets and non-cultural tourism markets as well as to verify the differences in service quality among the two markets and, 2) to present practical service marketing methods that fit with the characteristics of the traditional markets by amending the five quality evaluation items of SERVQUAL (a multiple-item scale for measuring service quality)to suit the characteristics of the traditional markets and establish the relationship among customer value, relationship quality, and behavior intention. Research design, data, and methodology - The study methods of empirical investigation are as follows. First, this study selected for a study object the Suwon Paldalmun Gate Market to represent the cultural tourism market, and general traditional markets to represent the non-cultural tourism market. This study also conducted personal interviews in order to increase the response rate and collected a total of 418 responses between March 18, 2014 and April 05, 2014. The total of 418 responses used for this study excluded 14 responses that had either misleading information or missing values. Results - This study verified the perceived differences of service quality based on traditional market specialization through an independent sample t-test. It appeared that the perceived service quality of the cultural tourism market was generally higher than that of the non-cultural tourism market. This study executed a path analysis in order to examine the effects of service quality factors on customer value, relationship quality, and behavior intention. This study also comprehensively analyzed the specialized market and non-specialized market separately. Although there were some differences among the results, the overall results were uniform. It appeared that convenience, reliability, and empathy, among the service quality factors, exerted meaningful effects on customer value. On the other hand, convenience, reliability, responsiveness, and empathy, excluding the tangibles, exerted meaningful effects on the relationship quality. In addition, it appeared that all service quality factors exerted meaningful effects on the customer value, relationship quality, and behavior intention. Therefore, the study verified that all of the hypotheses formulated in the study were generally adopted. Conclusions - The implication of this study may be classified into academic and practical implication as follows. With respect to the academic implication, it seems that this study is among the early studies to verify the differences between the cultural tourism market and the non-cultural tourism market. The practical implication of this study is that the perceived service quality, such as convenience, reliability, responsiveness, and tangibles, excluding empathy, was higher in the cultural tourism market than in the non-cultural tourism market. This means that customer satisfaction is enhanced by governmental aid such as hardware, software, and information and communications technology.

• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.73-80
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• 2006

Mitologia is about visualizing relations of information in user-oriented method. Most information given in life has invisible relations with each other. By analyzing the common characters and relations of information, we can not only measure the importance of the information but also grasp the overall properties of the information. Especially human relations are the major concerns of social network having several visualization methodologies shown by analyzing relations of each individual in society. We applied social network theory to grasp relationships between characters in Greek mythology representing a limited society. But the current tools of social network analysis have limits that they show the information one-sided way because of the ignorance of user-oriented design. Mitologia attempts to suggest the visual structure model more effective and easy to understand in analyzing data. We extracted connections among myth characters by evaluating classes, frequencies of appearance and emotional links they have. And we raised the understanding of users with furnishing the proper interaction to the information. The initial interface offers 4 kinds of indexes helping to access character nodes easily, while zoom-in function can be used for the detailed relations. The Zoom-in is quite different from usual filtering methods. It makes the irrelative information invisible so that users can find out the characters' relation more easily and quickly. This project suggests the layout to show overall information relationships and the appropriate interactions to present detailed information at the same time.