Since South Korea has an economic structure that has a characteristic which market-dependent on overseas, the tourism industry is considered as a very important industry for the national economy, such as improving the country's balance of payments or providing income and employment increases. Accordingly, the necessity of more accurate forecasting on the demand in the tourism industry has been raised to promote its industry. In the related research, economic variables such as exchange rate and income have been used as variables influencing tourism demand. As information technology has been widely used, some researchers have also analyzed the effect of media on tourism demand. It has shown that the media has a considerable influence on traveler's decision making, such as choosing an outbound destination. Furthermore, with the recent availability of online information searches to obtain the latest information and two-way communication in social media, it is possible to obtain up-to-date information on travel more quickly than before. The information in online media such as blogs can naturally create the Word-of-Mouth effect by sharing useful information, which is called eWOM. Like all other service industries, the tourism industry is characterized by difficulty in evaluating its values before it is experienced directly. And furthermore, most of the travelers tend to search for more information in advance from various sources to reduce the perceived risk to the destination, so they can also be influenced by online media such as online news. In this study, we suggested that the number of online media posting, which causes the effects of Word-of-Mouth, may have an effect on the number of outbound travelers. We divided online media into public media and private media according to their characteristics and selected online news as public media and blog as private media, one of the most popular social media in tourist information. Based on the previous studies about the eWOM effects on online news and blog, we analyzed a relationship between the volume of eWOM and the outbound tourism demand through the panel model. To this end, we collected data on the number of national outbound travelers from 2007 to 2015 provided by the Korea Tourism Organization. According to statistics, the highest number of outbound tourism demand in Korea are China, Japan, Thailand, Hong Kong and the Philippines, which are selected as a dependent variable in this study. In order to measure the volume of eWOM, we collected online news and blog postings for the same period as the number of outbound travelers in Naver, which is the largest portal site in South Korea. In this study, a panel model was established to analyze the effect of online media on the demand of Korean outbound travelers and to identify that there was a significant difference in the influence of online media by each time and countries. The results of this study can be summarized as follows. First, the impact of the online news and blog eWOM on the number of outbound travelers was significant. We found that the number of online news and blog posting have an influence on the number of outbound travelers, especially the experimental result suggests that both the month that includes the departure date and the three months before the departure were found to have an effect. It is shown that online news and blog are online media that have a significant influence on outbound tourism demand. Next, we found that the increased volume of eWOM in online news has a negative effect on departure, while the increase in a blog has a positive effect. The result with the country-specific models would be the same. This paper shows that online media can be used as a new variable in tourism demand by examining the influence of the eWOM effect of the online media. Also, we found that both social media and news media have an important role in predicting and managing the Korean tourism demand and that the influence of those two media appears different depending on the country.
A systemic approach to behavior on the basis of human psychology is needed for behavior-centered space design. Also, the recognition that human and environment, in all, have complementarity is needed- human and space shall be understood as a general phenomenon, supposing interaction. Design of behavior-oriented space means configuration and coordination of physical subjects as well as understanding, analysis and reflection of psychological and behavioral phenomena. It is analysis of a private individual as well as understanding of interaction between human groups, as well. In respect of space recognition, analysis not on material movement but on energy circulation and variable is important. It means that the understanding of user's behavior and psychology does not orient reasonable purpose just for convenience. That is, such understanding intends to understand behavioral patterns and psychological phenomena between space and human beyond the decomposition of structure of human and space into physical elements and the design based on standardized data. Thereby, more human-oriented space design might be implemented by the understanding of behavioral essence. Also, a user-centered design process from another viewpoint might be created, and the general amenity among man, space and environment - better environmental quality - might be produced. For this, the consciousness of human activity that is, activity system shall be ahead of it, and the approaches for design shall be implemented into a process not in predictive ideas but in semi-scientific system. On the basis of the above view, this study was attempted to investigate the orientation of design to recognize space as another life, and explore a process where it is drawn into a design language on the basis of human behavior. If the essence of space behavior and the activity system are analyzed through user observation and it is reflected upon a space design program and then developed into a formative language, a new design process on human and environment might be produced. In conclusion, the reflection of user's behavior and psychology into design, contrary to existing public space design based on physical data, can orient quality improvement of human life and ultimately be helpful to the proposition, 'humanization of space'.
It is a global trend that the water management policy is shifting from a water quality-oriented assessment to the aquatic ecosystem-based assessment. The majority of aquatic ecosystem assessment systems were developed solely based on physicochemical factors (e.g., water quality and bed structure) and a limited number of organisms (e.g., plankton and benthic organisms). Only a few systems use plants for a health assessment, although plants are sensitive indicators reflecting long-term disturbances and alterations in water regimes. The development of an assessment system is underway to evaluate and manage lakes as ecosystem units in the Korean Ministry of Environment. We reviewed the existing multivariate health assessment methods of other leading countries, and discussed their applicability to Korean lakes. The application of multivariate assessment methods is costly and time consuming, in addition to the correlation problem among variables. However, a single variable is not available at this moment, and the multivariate method is an appropriate system due to its multidimensional evaluation and cumulative data generation. We, therefore, discussed multivariate assessment methods in three steps: selecting metrics, scoring metrics and assessing indices. In the step of selecting metrics, the best available metrics are species-related variables, such as composition and abundance, as well as richness and diversity. Indicator species, such as sensitive species, are the most frequently used in other countries, but their system of classification in Korea is not yet complete. In terms of scoring metrics, the lack of reference lakes with little anthropogenic impact make this step difficult, and therefore, the use of relative scores among the investigated lakes is a suitable alternative. Overall, in spite of several limitations, the development of a plant-based multivariate assessment method in Korea is possible using mostly field research data. Later, it could be improved based on qualitative metrics on plant species, and with the emergence of further survey data.
Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.
Currently, thanks to the major stride made in developing wired and wireless communication technology, a variety of IT services are available on land. This trend is leading to an increasing demand for IT services to vessels on the water as well. And it is expected that the request for various IT services such as two-way digital data transmission, Web, APP, etc. is on the rise to the extent that they are available on land. However, while a high-speed information communication network is easily accessible on land because it is based upon a fixed infrastructure like an AP and a base station, it is not the case on the water. As a result, a radio communication network-based voice communication service is usually used at sea. To solve this problem, an additional frequency for digital data exchange was allocated, and a ship ad-hoc network (SANET) was proposed that can be utilized by using this frequency. Instead of satellite communication that costs a lot in installation and usage, SANET was developed to provide various IT services to ships based on IP in the sea. Connectivity between land base stations and ships is important in the SANET. To have this connection, a ship must be a member of the network with its IP address assigned. This paper proposes a SANET-CC protocol that allows ships to be assigned their own IP address. SANET-CC propagates several non-overlapping IP addresses through the entire network from land base stations to ships in the form of the tree. Ships allocate their own IP addresses through the exchange of simple requests and response messages with land base stations or M-ships that can allocate IP addresses. Therefore, SANET-CC can eliminate the IP collision prevention (Duplicate Address Detection) process and the process of network separation or integration caused by the movement of the ship. Various simulations were performed to verify the applicability of this protocol to SANET. The outcome of such simulations shows us the following. First, using SANET-CC, about 91% of the ships in the network were able to receive IP addresses under any circumstances. It is 6% higher than the existing studies. And it suggests that if variables are adjusted to each port's environment, it may show further improved results. Second, this work shows us that it takes all vessels an average of 10 seconds to receive IP addresses regardless of conditions. It represents a 50% decrease in time compared to the average of 20 seconds in the previous study. Also Besides, taking it into account that when existing studies were on 50 to 200 vessels, this study on 100 to 400 vessels, the efficiency can be much higher. Third, existing studies have not been able to derive optimal values according to variables. This is because it does not have a consistent pattern depending on the variable. This means that optimal variables values cannot be set for each port under diverse environments. This paper, however, shows us that the result values from the variables exhibit a consistent pattern. This is significant in that it can be applied to each port by adjusting the variable values. It was also confirmed that regardless of the number of ships, the IP allocation ratio was the most efficient at about 96 percent if the waiting time after the IP request was 75ms, and that the tree structure could maintain a stable network configuration when the number of IPs was over 30000. Fourth, this study can be used to design a network for supporting intelligent maritime control systems and services offshore, instead of satellite communication. And if LTE-M is set up, it is possible to use it for various intelligent services.
Franchise businesses in South Korea have contributed to economic growth and job creation, and its growth potential remains very high. However, despite such virtues, domestic franchise businesses face many problems such as the instability of franchisor's business structure and weak financial conditions. To solve these problems, the government enacted legislation and strengthened franchise related laws. However, the strengthening of laws regulating franchisors had many side effects that interrupted the development of the franchise business. For example, legal regulations regarding franchisors have had the effect of suppressing the franchisor's leadership activities (e.g. activities such as the ability to advocate the franchisor's policies and strategies to the franchisees, in order to facilitate change and innovation). One of the main goals of the franchise business is to build cooperation between the franchisor and the franchisee for their combined success. However, franchisees can refuse to follow the franchisor's strategies because of the current state of franchise-related law and government policy. The purpose of this study to explore the effects of franchisor's leadership style on franchisee's commitment in a franchise system. We classified leadership styles according to the path-goal theory (House & Mitchell, 1974), and it was hypothesized and tested that the four leadership styles proposed by the path-goal theory (i.e. directive, supportive, participative and achievement-oriented leadership) have different effects on franchisee's commitment. Another purpose of this study to explore the how the level of franchisee's self-efficacy influences both the franchisor's leadership style and franchisee's commitment in a franchise system. Results of the present study are expected to provide important theoretical and practical implications as to the role of franchisor's leadership style, as restricted by government regulations and the franchisee's self-efficacy, which could be needed to improve the quality of the long-term relationship between the franchisor and franchisee. Quoted by Northouse(2007), one problem regarding the investigation of leadership is that there are almost as many different definitions of leadership as there are people who have tried to define it. But despite the multitude of ways in which leadership has been conceptualized, the following components can be identified as central to the phenomenon: (a) leadership is a process, (b) leadership involves influence, (c) leadership occurs in a group context, and (d) leadership involves goal attainment. Based on these components, in this study leadership is defined as a process whereby franchisor's influences a group of franchisee' to achieve a common goal. Focusing on this definition, the path-goal theory is about how leaders motivate subordinates to accomplish designated goals. Drawing heavily from research on what motivates employees, path-goal theory first appeared in the leadership literature in the early 1970s in the works of Evans (1970), House (1971), House and Dessler (1974), and House and Mitchell (1974). The stated goal of this leadership theory is to enhance employee performance and employee satisfaction by focusing on employee motivation. In brief, path-goal theory is designed to explain how leaders can help subordinates along the path to their goals by selecting specific behaviors that are best suited to subordinates' needs and to the situation in which subordinates are working (Northouse, 2007). House & Mitchell(1974) predicted that although many different leadership behaviors could have been selected to be a part of path-goal theory, this approach has so far examined directive, supportive, participative, and achievement-oriented leadership behaviors. And they suggested that leaders may exhibit any or all of these four styles with various subordinates and in different situations. However, due to restrictive government regulations, franchisors are not in a position to change their leadership style to suit their circumstances. In addition, quoted by Northouse(2007), ssubordinate characteristics determine how a leader's behavior is interpreted by subordinates in a given work context. Many researchers have focused on subordinates' needs for affiliation, preferences for structure, desires for control, and self-perceived level of task ability. In this study, we have focused on the self-perceived level of task ability, namely, the franchisee's self-efficacy. According to Bandura (1977), self-efficacy is chiefly defined as the personal attitude of one's ability to accomplish concrete tasks. Therefore, it is not an indicator of one's actual abilities, but an opinion of the extent of how one can use that ability. Thus, the judgment of maintain franchisee's commitment depends on the situation (e.g., government regulation and policy and leadership style of franchisor) and how it affects one's ability to mobilize resources to deal with the task, so even if people possess the same ability, there may be differences in self-efficacy. Figure 1 illustrates the model investigated in this study. In this model, it was hypothesized that leadership styles would affect the franchisee's commitment, and self-efficacy would moderate the relationship between leadership style and franchisee's commitment. Theoretically, quoted by Northouse(2007), the path-goal approach suggests that leaders need to choose a leadership style that best fits the needs of subordinates and the work they are doing. According to House & Mitchell (1974), the theory predicts that a directive style of leadership is best in situations in which subordinates are dogmatic and authoritarian, the task demands are ambiguous, and the organizational rule and procedures are unclear. In these situations, franchisor's directive leadership complements the work by providing guidance and psychological structure for franchisees. For work that is structured, unsatisfying, or frustrating, path-goal theory suggests that leaders should use a supportive style. Franchisor's Supportive leadership offers a sense of human touch for franchisees engaged in mundane, mechanized activity. Franchisor's participative leadership is considered best when a task is ambiguous because participation gives greater clarity to how certain paths lead to certain goals; it helps subordinates learn what actions leads to what outcome. Furthermore, House & Mitchell(1974) predicts that achievement-oriented leadership is most effective in settings in which subordinates are required to perform ambiguous tasks. Marsh and O'Neill (1984) tested the idea that organizational members' anger and decline in performance is caused by deficiencies in their level of effort and found that self-efficacy promotes accomplishment, decreases stress and negative consequences like depression and emotional instability. Based on the extant empirical findings and theoretical reasoning, we posit positive and strong relationships between the franchisor's leadership styles and the franchisee's commitment. Furthermore, the level of franchisee's self-efficacy was thought to maintain their commitment. The questionnaires sent to participants consisted of the following measures; leadership style was assessed using a 20 item 7-point likert scale developed by Indvik (1985), self-efficacy was assessed using a 24 item 6-point likert scale developed by Bandura (1977), and commitment was assessed using a 6 item 5-point likert scale developed by Morgan & Hunt (1994). Questionnaires were distributed to Korean optical franchisees in Seoul. It took about 20 days to complete the data collection. A total number of 140 questionnaires were returned and complete data were available from 137 respondents. Results of multiple regression analyses testing the relationships between the each of the four styles of leadership shown by the franchisor as independent variables and franchisee's commitment as the dependent variable showed that the relationship between supportive leadership style and commitment ($\beta$=.13, p<.001),and the relationship between participative leadership style and commitment ($\beta$=.07, p<.001)were significant. However, when participants divided into high and low self-efficacy groups, results of multiple regression analyses showed that only the relationship between achievement-oriented leadership style and commitment ($\beta$=.14, p<.001) was significant in the high self-efficacy group. In the low self-efficacy group, the relationship between supportive leadership style and commitment ($\beta$=.17, p<.001),and the relationship between participative leadership style and commitment ($\beta$=.10, p<.001) were significant. The study focused on the franchisee's self-efficacy in order to explore the possibility that regulation, originally intended to protect the franchisee, may not be the most effective method to maintain the relationships in a franchise business. The key results of the data analysis regarding the moderating role of self-efficacy between leadership behavior style as proposed by path-goal and commitment theory were as follows. First, this study proposed that franchisor should apply the appropriate type of leadership behavior to strengthen the franchisees commitment because the results demonstrated that supportive and participative leadership styles by the franchisors have a positive influence on the franchisee's level of commitment. Second, it is desirable for franchisor to validate the franchisee's efforts, since the franchisee's characteristics such as self-efficacy had a substantial, positive effect on the franchisee's commitment as well as being a meaningful moderator between leadership and commitment. Third, the results as a whole imply that the government should provide institutional support, namely to put the franchisor in a position to clearly identify the characteristics of their franchisees and provide reasonable means to administer the franchisees to achieve the company's goal.
This paper attempts to identify key determinants of long run movements of real M2 by using the Johansen procedure for estimating and testing cointegration relations. It turns out that the real M2 equation has been stable over the long run despite rapid changes in financial structure since 1975. Moreover, the real M2 equation can be reduced to a velocity equation with the opportunity cost variable, expected inflation less the weighted average rate paid on M2 deposits, being the key determinant. However, it does not work to use a market interest rate such as the yield on corporate bonds in place of expected inflation for calculation of the opportunity cost. In the U.S., a market interest rate can be used, but not in Korea. Presumably, two somewhat different reasonings can be used to explain this result. One is that the yield on corporate bonds may not adequately reflect the inflationary expectations due to regulations on movements in interest rates. The other is that M2 deposits are not readily substitutable with such assets as corporate bonds because of market segmentations, regulations, and so on. From the policymaker's point of view, this implies that the inflation rate is an important indicator of a policy response. On the other hand, policymakers do not regard movements of the yield on corporate bonds as an important policy indicator. Altogether, the role of interest rates has been quite limited in Korea because of incomplete interest rate liberalization, an underdeveloped financial system, implementation procedures of policy measures, and so on. The result that M2 velocity has a positive cointegration relation with expected inflation minus the average rate on M2 implies that frequent adjustments of the regulated rates on M2 will be necessary as market conditions change. As the expected inflation gets higher, M2 velocity will eventually increase, given that the rates on M2 do not change. This will cause higher inflation. If interest rates are liberalized, then increases in market interest rates will result in lagged increases in deposits rates on M2. However, in Korea a substantial portion of deposit rates are regulated and will not change without the authority's initiatives. A tight monetary policy will cause increases in a few market interest rates. But the market mechanism, upward pressure for interest rate adjustments, never reaches regulated deposit rates. Hence the overall effects of tight monetary policy diminish considerably, only causing distortions in the flow of funds. Therefore, frequent adjustments of deposit rates are necessary as market conditions such as inflationary expectations change. Then it becomes important for the policymaker to actively engage in adjusting regulated deposit rates, because the financial sector in Korea is not fully developed.
This study examines the spatial characteristics of postal office patronage in rural areas. in the light of future possible relocation and closures of the postal facilities. Most of private services have flown out small rural central places due to the decrease of supporting population, and there consequently remain only a few public services including government-run post offices at the Myon seats, the lowest level among rural central places in Korea. The small local population and its further decline undermine the rationale for maintaining such public services in depleted rural areas. For the worse of it, the government recently plans to transform the postal system to a quasi-private, corporational structure. One can fear that the profit-seeking nature of the new postal corporation will inevitably force to close many of such small rural facilities. The study first analysed nation-wide censuses of postal offices for the years of 1986 and 1992. The postal services examined are per capita number of postal stamps and revenue stamps sold, and letters, parcels, telegrams and monetary transactions handled at the post offices. It is found that, while the usage of postal services has increased substantially throughout the nation during the period of 1986-1992, the increment has largely been occurred by urban post offices rather than by those in Gun seats (i.e., rural counties); and that the gap of the service levels between urban and rural post offices is ever widening. The study further examined the service differentials among the post offices within rural counties to find that those post offices adjacent to the county (Gun) seats and larger urban centers rendered less amount of services than remote rural post offices, indicating that rural residents tend to partonize larger centers more and more than local Myon seats. At the second stage of the study, questionnaire surveys were conducted in Muju, Kimpo, and Hongsung-Gun's. These three counties are meant to represent respectively the remote, suburban, and intermediary counties in Korea. The analyses of survey data reveal that the postal hinterlands of the county seats extend to much of nearby Myons, the subdivisions of a Gun. It is also found that the extent of postal hinterlands of the three counties and the magnitude of patronage and quite different from each other depending upon the topography, population density, and the propinquity of the counties to metropolitan centers. The findings suggest to reappraise the current flat allocation scheme of public facilites to each of rural subdivisions throughout the nation. A detailed analysis on the travel behavior of the survey respondents yields that age is the most salient variable to distinguish activity spaces of rural residents. The activity spaces of older respondents tend to be more limited within their Myon, whereas those of younger respondents extend across the Myon boundary, toward the central towns and even distant larger cities. The very existence of several activity spaces in rural areas calls for an attention in the future locational decisions of public facilities. The locational criteria, employed by the Ministry of Communication of Korean government to establish a post office, are the size of hinterland population and the distance from adjacent postal facilities. The present study findings suggest two additional criteria: the order in rural central place hierarchy and the propinquity to the upper-level centers of the central hierarchy. These old and new criteria are complementary each other in that the former criteria are employed to determine new office locations, whereas the latter are appropriate to determine facility relocation and closures.
This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.
Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.
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