• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.99-124
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• 2012

Our society has long been talking about necessity for innovation. Since companies in particular need to carry out business innovation in their overall processes, they have attempted to apply many innovation factors on sites and become to pay more attention to their innovation. In order to achieve this goal, companies has applied various information technologies (IT) on sites as a means of innovation, and consequently IT have been greatly developed. It is natural for the field of IT to have faced another revolution which is called cloud computing, which is expected to result in innovative changes in software application via the Internet, data storing, the use of devices, and their operations. As a vehicle of innovation, cloud computing is expected to lead the changes and advancement of our society and the business world. Although many scholars have researched on a variety of topics regarding the innovation via IT, few studies have dealt with the issue of could computing as IT. Thus, the purpose of this paper is to set the variables of innovation attributes based on the previous articles as the characteristic variables and clarify how these variables affect "Performance Expectancy" of companies and the intention of using cloud computing. The result from the analysis of data collected in this study is as follows. The study utilized a research model developed on the innovation diffusion theory to identify influences on the adaptation and spreading IT for cloud computing services. Second, this study summarized the characteristics of cloud computing services as a new concept that introduces innovation at its early stage of adaptation for companies. Third, a theoretical model is provided that relates to the future innovation by suggesting variables for innovation characteristics to adopt cloud computing services. Finally, this study identified the factors affecting expectation and the intention to use the cloud computing service for the companies that consider adopting the cloud computing service. As the parameter and dependent variable respectively, the study deploys the independent variables that are aligned with the characteristics of the cloud computing services based on the innovation diffusion model, and utilizes the expectation for performance and Intention to Use based on the UTAUT theory. Independent variables for the research model include Relative Advantage, Complexity, Compatibility, Cost Saving, Trialability, and Observability. In addition, 'Acceptance for Adaptation' is applied as an adjustment variable to verify the influences on the expected performances from the cloud computing service. The validity of the research model was secured by performing factor analysis and reliability analysis. After confirmatory factor analysis is conducted using AMOS 7.0, the 20 hypotheses are verified through the analysis of the structural equation model, accepting 12 hypotheses among 20. For example, Relative Advantage turned out to have the positive effect both on Individual Performance and on Strategic Performance from the verification of hypothesis, while it showed meaningful correlation to affect Intention to Use directly. This indicates that many articles on the diffusion related Relative Advantage as the most important factor to predict the rate to accept innovation. From the viewpoint of the influence on Performance Expectancy among Compatibility and Cost Saving, Compatibility has the positive effect on both Individual Performance and on Strategic Performance, while it showed meaningful correlation with Intention to Use. However, the topic of the cloud computing service has become a strategic issue for adoption in companies, Cost Saving turns out to affect Individual Performance without a significant influence on Intention to Use. This indicates that companies expect practical performances such as time and cost saving and financial improvements through the adoption of the cloud computing service in the environment of the budget squeezing from the global economic crisis from 2008. Likewise, this positively affects the strategic performance in companies. In terms of effects, Trialability is proved to give no effects on Performance Expectancy. This indicates that the participants of the survey are willing to afford the risk from the high uncertainty caused by innovation, because they positively pursue information about new ideas as innovators and early adopter. In addition, they believe it is unnecessary to test the cloud computing service before the adoption, because there are various types of the cloud computing service. However, Observability positively affected both Individual Performance and Strategic Performance. It also showed meaningful correlation with Intention to Use. From the analysis of the direct effects on Intention to Use by innovative characteristics for the cloud computing service except the parameters, the innovative characteristics for the cloud computing service showed the positive influence on Relative Advantage, Compatibility and Observability while Complexity, Cost saving and the likelihood for the attempt did not affect Intention to Use. While the practical verification that was believed to be the most important factor on Performance Expectancy by characteristics for cloud computing service, Relative Advantage, Compatibility and Observability showed significant correlation with the various causes and effect analysis. Cost Saving showed a significant relation with Strategic Performance in companies, which indicates that the cost to build and operate IT is the burden of the management. Thus, the cloud computing service reflected the expectation as an alternative to reduce the investment and operational cost for IT infrastructure due to the recent economic crisis. The cloud computing service is not pervasive in the business world, but it is rapidly spreading all over the world, because of its inherited merits and benefits. Moreover, results of this research regarding the diffusion innovation are more or less different from those of the existing articles. This seems to be caused by the fact that the cloud computing service has a strong innovative factor that results in a new paradigm shift while most IT that are based on the theory of innovation diffusion are limited to companies and organizations. In addition, the participants in this study are believed to play an important role as innovators and early adapters to introduce the cloud computing service and to have competency to afford higher uncertainty for innovation. In conclusion, the introduction of the cloud computing service is a critical issue in the business world.

• Journal of Preventive Medicine and Public Health
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• v.31 no.1 s.60
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• pp.139-156
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• 1998

Diving related disease including decompression sickness is an important occupational health problem and diving fishermen remain a fairly hazardous occupation in Korea. To prevent diving related disease, we investigate diving patterns, incidence of diving related diseases, and contributing factors of 433 diving fishermen of three coast interviewing and mailing questionnaire in 1996. Mean age of divers was 39.7 years, ranged from 24 to 58 years, 92.8% of these were male, and 58.4% of divers were high school graduates. Mean duration of work as a diver was 12.9 years, ranged from 2 to 40 years. It was found that 70.4% of divers were using hookah system, 22.2% of helmet, and only 2.5% SCUBA. About half of them have learned diving skills from other divers. The peak season of diving was from April to June and mean working days were 20.3 days per month during the peak season. On the average, the divers dived 5-6 times, ranged from 1 to 10 times a day with 51.1 minutes of diving time, ranged from 20 to 120 minutes, at 30 m or 40 m in depth, and 35.5 minute of interval on surface. Most divers ascended slowly making decompression stop, yet the decompression profile used was not based on any scientific knowledge except for their own experiences. It appeared that each diving system had slightly different diving patterns. There were 282(65.0%) divers that suffered from DCS in 1995 and 31.2% of divers were given recompression therapy at a medical facility since they worked as diving fishermen. Skin and musculoskeletal complaints were common symptoms of DCS and 39% of divers experienced a voiding difficulty. In univariate analysis, females have an increased frequency of DCS(93% vs 66% for males). Old age, long duration of work, helmet diving, diving time, diving depth, repetitive diving, and blow up were all contributing factors to DCS. It was found that most diving patterns exceed no decompression limit and did not use the standard decompression table. This suggests that most of divers are at high risk of developing diving related disease with prolonged dives and lengthy repetitive diving in deep depth. Considering the diving patterns and economic aspect of professional diving, the incidence of DCS among diving fishermen in Korea will not decrease in the near future. These findings suggest that periodical health surveillance for divers, and education of health and safety are important for reducing the risk of diving related disease in the population of diving fishermen.

• Journal of Society of Preventive Korean Medicine
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• v.12 no.1
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• pp.19-48
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• 2008

In order to verify the risk of onset of the illnesses based on gender, age, and monthly income 1,739 subjects from Hongcheon county, Gangwon province were selected. Questionnaire on demographic sociology, health condition, existence of illnesses(cancer, hypertension, stroke, diabetes, arthritis, cardiac disorders), and usage of public health services was surveyed from October 1, 2006 to October 20, 2006. Following conclusions were reached on the basis of the questionnaire : - For demographic sociological peculiarities, gender, age, occupation, and education level were evenly distributed. Most were under normal marriage(67.38%), health insurance(86.39%), 494(36.0%) individuals with less than monthly income of 1 million won, 494(36.0%) individuals with monthly income between 1 and 2 million won, 219(16.0%) with monthly income between 2 and 3 million won, and 164(12.0%) individuals with more than 3 million won, thus showing relatively low income. - For health status, 1,199(70.28%) individuals are non-smokers, 209(45.63%) individuals smoke $10{\sim}20$ cigarettes a day, 754(44.02%) individuals exercise less than twice a week are the major sector of the population. 1,518(88.10%) individuals have regular checkup more than once and 1,131(65.49%) stated their health condition less than average. - For comparison of existence of illnesses between genders, there was no statistical significance on cancer, stroke, and diabetes. But statistical significance was shown on hypertension(P value 0.025), arthritis(P value 0.000), and cardiac disorders(P value 0.016). Statistical significance was seen in the age comparison, and OR(confidence interval) drastically increased with increase in age. - There was no difference between the primary health clinic(P value 0.000), most visited clinic(P value 0.000), selection criteria(P value 0.000), and satisfaction on efficacy(P value 0.000). There was a tendency preferring hospital than public health center with increase in income. - For correlation between the existence of illnesses among different income levels, except for cancer(P value 0.172), statistical significance was seen in hypertension(P value 0.000), stroke(P value 0.003), diabetes (P value 0.001), arthritis(P value 0.000), and cardiac disorders(P value 0.000). The number of individuals suffering from illnesses and ratio all decreased for all illnesses with increase in income. - After adjusting confounding factors(gender, age, income, marriage, occupation, education) and male (1) as the standard, OR (confidence interval) of cancer, hypertension, stroke, diabetes, arthritis, cardiac disorders were 0.47(0.11${\sim}$2.05), 1.27(0.89${\sim}$1.81), 0.58(0.21${\sim}$1.59), 0.71(0.41${\sim}$1.23), 1.79(1.34${\sim}$2.39, P<0.01), and 1.46(0.72${\sim}$2.96), respectively. Risk of arthritis is significantly high in female and 20's (1) as the standard, OR(confidence interval) of cancer, hypertension, stroke, diabetes, arthritis, cardiac disorders were 1.01(0.96${\sim}$1.07), 1.06(1.04${\sim}$1.07, P<0.01), 1.05(1.01${\sim}$1.10, P<0.01), 1.06(1.03${\sim}$1.08, P<0.01), 1.05(1.03${\sim}$1.06, P<0.01), and 1.06(1.04${\sim}$1.09, P<0.01), respectively. Risk of onset for illnesses significantly increased with yearly aging except for cancer. - For comparison between monthly income after adjusting confounding factors(gender, age, income, marriage, occupation, education), with less than 1 million won (1) as the standard, OR(confidence interval) of cancer for 1 to 2 million won, 2 to 3 million won, and more than 3 million won were 0.23(0.03${\sim}$2.16), 2.53(0.41${\sim}$15.43), and 1.73(0.15${\sim}$19.50), respectively. OR(confidence interval) of hypertension were 1.12(0.76 ${\sim}$1.66), 0.68(0.34${\sim}$1.34), and 2.04(1.08${\sim}$3.86, P<0.01), respectively. OR(confidence interval) of stroke were 0.96(0.30${\sim}$3.08) for 1 to 2 million won, and 0.80(0.08${\sim}$8.46) for 2 to 3 million won. OR(confidence interval) of diabetes were 0.73(0.38${\sim}$1.38), 0.65(0.24${\sim}$1.71), and 0.69(0.24${\sim}$2.01), respectively. The values were 0.76(0.55${\sim}$1.03), 1.14(0.75${\sim}$1.73), and 0.90(0.56${\sim}$1.46), respectively for arthritis. OR(confidence interval) of cardiac disorders were 1.15(0.53${\sim}$2.48), 0.63(0.13${\sim}$3.12), and 1.20(0.28${\sim}$5.14), respectively. Risks of cancer, hypertension, stroke, diabetes, arthritis, and cardiac disorders were dependent of monthly income, and stroke and diabetes decreased with increase in income. Summarizing above data, arthritis was significantly higher in women and increase in age by each year brought significant increase in the chance of onset in hypertension, stroke, diabetes, arthritis, and cardiac disorders except for cancer. Stroke and diabetes decreased with increase in income. Above findings can be applied and reflected in public health policies at the national level, and it can also be applied at the personal level for individual health maintenance and prevention.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.