• Journal of Food Hygiene and Safety
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• v.29 no.4
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• pp.299-304
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• 2014

The dose-response models are important for the quantitative microbiological risk assessment (QMRA) because they would enable prediction of infection risk to humans from foodborne pathogens. In this study, we performed a comprehensive literature review and meta-analysis to better quantify this association. The meta-analysis applied a final selection of 193 published papers for total 43 species foodborne disease pathogens (bacteria 26, virus 9, and parasite 8 species) which were identified and classified based on the dose-response models related to QMRA studies from PubMed, ScienceDirect database and internet websites during 1980-2012. The main search keywords used the combination "food", "foodborne disease pathogen", "dose-response model", and "quantitative microbiological risk assessment". The appropriate dose-response models for Campylobacter jejuni, pathogenic E. coli O157:H7 (EHEC / EPEC / ETEC), Listeria monocytogenes, Salmonella spp., Shigella spp., Staphylococcus aureus, Vibrio parahaemolyticus, Vibrio cholera, Rota virus, and Cryptosporidium pavum were beta-poisson (${\alpha}=0.15$, ${\beta}=7.59$, fi = 0.72), beta-poisson (${\alpha}=0.49$, ${\beta}=1.81{\times}10^5$, fi = 0.67) / beta-poisson (${\alpha}=0.22$, ${\beta}=8.70{\times}10^3$, fi = 0.40) / beta-poisson (${\alpha}=0.18$, ${\beta}=8.60{\times}10^7$, fi = 0.60), exponential (r=$1.18{\times}10^{-10}$, fi = 0.14), beta-poisson (${\alpha}=0.11$, ${\beta}=6,097$, fi = 0.09), beta-poisson (${\alpha}=0.21$, ${\beta}=1,120$, fi = 0.15), exponential ($r=7.64{\times}10^{-8}$, fi = 1.00), betapoisson (${\alpha}=0.17$, ${\beta}=1.18{\times}10^5$, fi = 1.00), beta-poisson (${\alpha}=0.25$, ${\beta}=16.2$, fi = 0.57), exponential ($r=1.73{\times}10{-2}$, fi = 1.00), and exponential ($r=1.73{\times}10^{-2}$, fi = 0.17), respectively. Therefore, these results provide the preliminary data necessary for the development of foodborne pathogens QMRA.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.1-24
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• 2021

The Korean government is currently expanding the business startup incubator support program and funds for new ventures with innovative technology in order to spread the second venture boom. However, despite the fact that entrepreneurial education and mentoring that entrepreneurs should have are important parts for the sustainable growth of the startup, some companies selected for government support programs are reluctant to participate in programs such as entrepreneurship education and mentoring for the sole purpose of funding commercialization. This research addressed the effects of entrepreneurial alertness with opportunity awareness as its medium and the small business mentoring service along with the number of times the mentoring has taken place, on the corporate performances. The results of empirical research are as follow: the first one is that scanning-search and evaluation-judgment can influence a company's performance (financial, non-financial) through opportunity recognition, with the exception of association-connection, which is a sub-factor of entrepreneurial alertness. Secondly, it was found to affect a company's financial and non-financial performance through opportunity recognition for financing mentoring, technical support mentoring, and management support mentoring. Thirdly, it was found that the number of mentoring also affects the financial and non-financial performance of a company through opportunity recognition. The implications of this study are that it should be revisited that program managers consider rooms that do not violate the startup founder's strategic decision-making opportunities when designing and operating the program as entrepreneurial alertness sub-factor association-connection does not affect corporate performance through opportunity recognition. This study also emphasizes the need for customized mentoring to meet the outcome goals of each startup, as it has been empirically clarified that the mentoring provided to the startup by the government's support is important. The contribution of this research is that entrepreneurial alertness and opportunity recognition that are treated as important components in research for entrepreneurship, and the factors of mentoring and mentoring frequency that are recognized as important elements in the practical aspect of startup business are clarified theoretically and empirically as an influential factor in corporate performance. And this study also provide a rationale for the startup business support agency supplying mentoring.

• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.17-35
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• 2021

The purpose of this study is to derive implications for the development of the next home economics curriculum by comparing the 'function' and 'achievement standard' presented in the 14 subjects of the 2015 revised middle school common curriculum with the home economics curriculum. For this, keyword network analysis was conducted, and the results are as follows. First, in the 'function' of the 2015 revised middle school common curriculum, 'analysis, use, and expression' were found to be core function keywords with high Degree Centrality and the Eigenvector Centrality. Second, the functional keywords 'understanding, explanation, expression, analysis, and use' in the 'achievement standard' of the 2015 revised middle school common curriculum appeared with high frequency, and 'practice, problem-solving, search and reasoning' which are related to practical problem-solving ability appeared. It was confirmed that 'appreciation, solution and realization', which have relatively high Eigenvector Centrality, were core functional keywords used in the 'achievement standard'. Third, when the 'function' and 'achievement standard' of the 2015 revised middle school home economics curriculum were matched and compared, 7 out of 15 functions were not used in the statement of 'achievement standard', so the connection between 'function' and 'achievement standard' appeared to be insufficient. In addition, the diversity of functional keyword used in the 'achievement standard' was also found insufficient when compared to the middle school common curriculum. Therefore, this study propose strengthening the connectivity of 'function' and 'achievement standard' in the next home economics curriculum, using keywords such as 'analyze', 'express', 'compare', 'understand', 'interpret', 'explore', 'appreciate', and 'solve'.

• Journal of Korea Entertainment Industry Association
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• v.14 no.6
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• pp.53-60
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• 2020

The study is based on the assumption that the trend of phenomena and trends in research are contextually consistent. Therefore the purpose of this study is to explore the trend of dance through the subject analysis of the Korean creative dance study by utilizing text mining. Thus, 1,291 words were analyzed in the 616 journal title, which were established on the paper search website. The collection, refining and analysis of the data were all R 3.6.0 SW. According to the study, keywords representing the times were frequently used before the 2000s, but Korean creative dance research types were also found in terms of education and physical training. Second, the frequency of keywords related to the dance troupe's performance was high after the 2000s, but it was confirmed that Choi Seung-hee was still in an important position in the study of Korean creative dance. Third, an analysis of the overall research subjects of the Korean creative dance study showed that the research on 'Art of Choi Seung-hee in the modern era' was the highest proportion. Fourth, the Hot Topics, which are rising as of 2000, appeared as 'the performance activities of the National Dance Company' and 'the choreography expression and utilization of traditional dance'. However, since the recent trend of the National Dance Company's performance is advocating 'modernization based on tradition', it has been confirmed that the trend of Korean creative dance since the 2000s has been focused on the use of traditional dance motifs. Fifth, the Cold Topic, which has been falling as of 2000, has been shown to be a study of 'dancing expressions by age'. It was judged that interest in research also decreased due to the tendency to mix various dance styles after the establishment of the genre of Korean creative dance.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.27-40
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• 2020

The marathon has long been established as a representative lifestyle for all ages. With the recent expansion of the Work and Life Balance trend across the society, marathon with a relatively low barrier to entry is gaining popularity among young people in their 20s and 30s. By analyzing the issues and related words of the marathon event, we will analyze the spottainment elements of the marathon event that is popular among young people through keywords, and suggest a development plan for the differentiated event. In order to analyze keywords and related words, blogs, cafes and news provided by Naver and Daum were selected as analysis channels, and 'JTBC Marathon' and 'Culture' were extracted as key words for data search. The data analysis period was limited to a three-month period from August 13, 2019 to November 13, 2019, when the application for participation in the 2019 JTBC Marathon was started. For data collection and analysis, frequency and matrix data were extracted through social matrix program Textom. In addition, the degree of the relationship was quantified by analyzing the connection structure and the centrality of the degree of connection between the words. Although the marathon is a personal movement, young people share a common denominator of "running" and form a new cultural group called "running crew" with other young people. Through this, it was found that a marathon competition culture was formed as a festival venue where people could train together, participate together, and escape from the image of a marathon run alone and fight with themselves.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.25-52
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• 2018

This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Global Scholars of Marketing Science
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• v.19 no.4
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• pp.32-43
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• 2009

Trust has been studied extensively in psychology, economics, and sociology, and its importance has been emphasized not only in marketing, but also in business disciplines in general. Unlike past relationships between suppliers and buyers, which take considerable advantage of private networks and may involve unethical business practices, partnerships between suppliers and buyers are at the core of success for industrial marketing amid intense global competition in the 21st century. A high level of mutual cooperation occurs through an exchange relationship based on trust, which brings long-term benefits, competitive enhancements, and transaction cost reductions, among other benefits, for both buyers and suppliers. In spite of the important role of trust, existing studies in buy-supply situations overlook the role of trust and do not systematically analyze the effect of trust on relational performance. Consequently, an in-depth study that determines the relation of trust to the relational performance between buyers and suppliers of business services is absolutely needed. Business services in this study, which include those supporting the manufacturing industry, are drawing attention as the economic growth engine for the next generation. The Korean government has selected business services as a strategic area for the development of manufacturing sectors. Since the demands for opening business services markets are becoming fiercer, the competitiveness of the business service industry must be promoted now more than ever. The purpose of this study is to investigate the effect of the mutual trust between buyers and suppliers on relational performance. Specifically, this study proposed a theoretical model of trust-relational performance in the transactions of business services and empirically tested the hypotheses delineated from the framework. The study suggests strategic implications based on research findings. Empirical data were collected via multiple methods, including via telephone, mail, and in-person interviews. Sample companies were knowledge-based companies supplying and purchasing business services in Korea. The present study collected data on a dyadic basis. Each pair of sample companies includes a buying company and its corresponding supplying company. Mutual trust was traced for each pair of companies. This study proposes a model of trust-relational performance of buying-supplying for business services. The model consists of trust and its antecedents and consequences. The trust of buyers is classified into trust toward the supplying company and trust toward salespersons. Viewing trust both at the individual level and the organizational level is based on the research of Doney and Cannon (1997). Normally, buyers are the subject of trust, but this study supposes that suppliers are the subjects. Hence, it uniquely focused on the bilateral perspective of perceived risk. In other words, suppliers, like buyers, are the subject of trust since transactions are normally bilateral. From this point of view, suppliers' trust in buyers is as important as buyers' trust in suppliers. The suppliers' trust is influenced by the extent to which it trusts the buying companies and the buyers. This classification of trust using an individual level and an organization level is based on the suggestion of Doney and Cannon (1997). Trust affects the process of supplier selection, which works in a bilateral manner. Suppliers are actively involved in the supplier selection process, working very closely with buyers. In addition, the process is affected by the extent to which each party trusts its partners. The selection process consists of certain steps: recognition, information search, supplier selection, and performance evaluation. As a result of the process, both buyers and suppliers evaluate the performance and take corrective actions on the basis of such outcomes as tangible, intangible, and/or side effects. The measurement of trust used for the present study was developed on the basis of the studies of Mayer, Davis and Schoorman (1995) and Mayer and Davis (1999). Based on their recommendations, the three dimensions of trust used for the study include ability, benevolence, and integrity. The original questions were adjusted to the context of the transactions of business services. For example, a question such as "He/she has professional capabilities" has been changed to "The salesperson showed professional capabilities while we talked about our products." The measurement used for this study differs from those used in previous studies (Rotter 1967; Sullivan and Peterson 1982; Dwyer and Oh 1987). The measurements of the antecedents and consequences of trust used for this study were developed on the basis of Doney and Cannon (1997). The original questions were adjusted to the context of transactions in business services. In particular, questions were developed for both buyers and suppliers to address the following factors: reputation (integrity, customer care, good-will), market standing (company size, market share, positioning in the industry), willingness to customize (product, process, delivery), information sharing (proprietary information, private information), willingness to maintain relationships, perceived professionalism, authority empowerment, buyer-seller similarity, and contact frequency. As a consequential variable of trust, relational performance was measured. Relational performance is classified into tangible effects, intangible effects, and side effects. Tangible effects include financial performance; intangible effects include improvements in relations, network developing, and internal employee satisfaction; side effects include those not included either in the tangible or intangible effects. Three hundred fifty pairs of companies were contacted, and one hundred five pairs of companies responded. After deleting five company pairs because of incomplete responses, one hundred five pairs of companies were used for data analysis. The response ratio of the companies used for data analysis is 30% (105/350), which is above the average response ratio in industrial marketing research. As for the characteristics of the respondent companies, the majority of the companies operate service businesses for both buyers (85.4%) and suppliers (81.8%). The majority of buyers (76%) deal with consumer goods, while the majority of suppliers (70%) deal with industrial goods. This may imply that buyers process the incoming material, parts, and components to produce the finished consumer goods. As indicated by their report of the length of acquaintance with their partners, suppliers appear to have longer business relationships than do buyers. Hypothesis 1 tested the effects of buyer-supplier characteristics on trust. The salesperson's professionalism (t=2.070, p<0.05) and authority empowerment (t=2.328, p<0.05) positively affected buyers' trust toward suppliers. On the other hand, authority empowerment (t=2.192, p<0.05) positively affected supplier trust toward buyers. For both buyers and suppliers, the degree of authority empowerment plays a crucial role in the maintenance of their trust in each other. Hypothesis 2 tested the effects of buyerseller relational characteristics on trust. Buyers tend to trust suppliers, as suppliers make every effort to contact buyers (t=2.212, p<0.05). This tendency has also been shown to be much stronger for suppliers (t=2.591, p<0.01). On the other hand suppliers trust buyers because suppliers perceive buyers as being similar to themselves (t=2.702, p<0.01). This finding confirmed the results of Crosby, Evans, and Cowles (1990), which reported that suppliers and buyers build relationships through regular meetings, either for business or personal matters. Hypothesis 3 tested the effects of trust on perceived risk. It has been found that for both suppliers and buyers the lower is the trust, the higher is the perceived risk (t=-6.621, p<0.01 for buyers; t=-2.437, p<0.05). Interestingly, this tendency has been shown to be much stronger for buyers than for suppliers. One possible explanation for this higher level of perceived risk is that buyers normally perceive higher risks than do suppliers in transactions involving business services. For this reason, it is necessary for suppliers to implement risk reduction strategies for buyers. Hypothesis 4 tested the effects of trust on information searching. It has been found that for both suppliers and buyers, contrary to expectation, trust depends on their partner's reputation (t=2.929, p<0.01 for buyers; t=2.711, p<0.05 for suppliers). This finding shows that suppliers with good reputations tend to be trusted. Prior experience did not show any significant relationship with trust for either buyers or suppliers. Hypothesis 5 tested the effects of trust on supplier/buyer selection. Unlike buyers, suppliers tend to trust buyers when they think that previous transactions with buyers were important (t=2.913 p<0.01). However, this study did not show any significant relationship between source loyalty and the trust of buyers in suppliers. Hypothesis 6 tested the effects of trust on relational performances. For buyers and suppliers, financial performance reportedly improved when they trusted their partners (t=2.301, p<0.05 for buyers; t=3.692, p<0.01 for suppliers). It is interesting that this tendency was much stronger for suppliers than it was for buyers. Similarly, competitiveness was reported to improve when buyers and suppliers trusted their partners (t=3.563, p<0.01 for buyers; t=3.042, p<0.01 for suppliers). For suppliers, efficiency and productivity were reportedly improved when they trusted buyers (t=2.673, p<0.01). Other performance indices showed insignificant relationships with trust. The findings of this study have some strategic implications. First and most importantly, trust-based transactions are beneficial for both suppliers and buyers. As verified in the study, financial performance can be improved through efforts to build and maintain mutual trust. Similarly, competitiveness can be increased through the same kinds of effort. Second, trust-based transactions can facilitate the reduction of perceived risks inherent in the purchasing situation. This finding has implications for both suppliers and buyers. It is generally believed that buyers perceive higher risks in a highly involved purchasing situation. To reduce risks, previous studies have recommended that suppliers devise risk-reducing tactics. Moving beyond these recommendations, the present study uniquely focused on the bilateral perspective of perceived risk. In other words, suppliers are also susceptible to perceived risks, especially when they supply services that require very technical and sophisticated manipulations and maintenance. Consequently, buyers and suppliers must solve problems together in close collaboration. Hence, mutual trust plays a crucial role in the problem-solving process. Third, as found in this study, the more authority a salesperson has, the more he or she can be trusted. This finding is very important with regard to tactics. Building trust is a long-term assignment; however, when mutual trust has not been developed, suppliers can overcome the problems they encounter by empowering a salesperson with the authority to make certain decisions. This finding applies to suppliers as well.