• The Korean Journal of Air & Space Law and Policy
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• v.30 no.2
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• pp.37-81
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• 2015

AirAsia QZ8501 Jet departed from Juanda International Airport in, Surabaya, Indonesia at 05:35 on Dec. 28, 2014 and was scheduled to arrive at Changi International Airport in Singapore at 08:30 the same day. The aircraft, an Airbus A320-200 crashed into the Java Sea on Dec. 28, 2014 carrying 162 passengers and crew off the coast of Indonesia's second largest city Surabaya on its way to Singapore. Indonesia's AirAsia jet carrying 162 people lost contact with ground control on Dec. 28, 2014. The aircraft's debris was found about 66 miles from the plane's last detected position. The 155 passengers and seven crew members aboard Flight QZ 8501, which vanished from radar 42 minutes after having departed Indonesia's second largest city of Surabaya bound for Singapore early Dec. 28, 2014. AirAsia QZ8501 had on board 137 adult passengers, 17 children and one infant, along with two pilots and five crew members in the aircraft, a majority of them Indonesian nationals. On board Flight QZ8501 were 155 Indonesian, three South Koreans, and one person each from Singapore, Malaysia and the UK. The Malaysia Airlines Flight 370 departed from Kuala Lumpur International Airport on March 8, 2014 at 00:41 local time and was scheduled to land at Beijing's Capital International Airport at 06:30 local time. Malaysia Airlines also marketed as China Southern Airlines Flight 748 (CZ748) through a code-share agreement, was a scheduled international passenger flight that disappeared on 8 March 2014 en route from Kuala Lumpur International Airport to Beijing's Capital International Airport (a distance of 2,743 miles: 4,414 km). The aircraft, a Boeing 777-200ER, last made contact with air traffic control less than an hour after takeoff. Operated by Malaysia Airlines (MAS), the aircraft carried 12 crew members and 227 passengers from 15 nations. There were 227 passengers, including 153 Chinese and 38 Malaysians, according to records. Nearly two-thirds of the passengers on Flight 370 were from China. On April 5, 2014 what could be the wreckage of the ill-fated Malaysia Airlines was found. What appeared to be the remnants of flight MH370 have been spotted drifting in a remote section of the Indian Ocean. Compensation for loss of life is vastly different between US. passengers and non-U.S. passengers. "If the claim is brought in the US. court, it's of significantly more value than if it's brought into any other court." Some victims and survivors of the Indonesian and Malaysia airline's air crash case would like to sue the lawsuit to the United States court in order to receive a larger compensation package for damage caused by an accident that occurred in the sea of Java sea and the Indian ocean and rather than taking it to the Indonesian or Malaysian court. Though each victim and survivor of the Indonesian and Malaysia airline's air crash case will receive an unconditional 113,100 Unit of Account (SDR) as an amount of compensation for damage from Indonesia's AirAsia and Malaysia Airlines in accordance with Article 21, 1 (absolute, strict, no-fault liability system) of the 1999 Montreal Convention. But if Indonesia AirAsia airlines and Malaysia Airlines cannot prove as to the following two points without fault based on Article 21, 2 (presumed faulty system) of the 1999 Montreal Convention, AirAsia of Indonesiaand Malaysia Airlines will be burdened the unlimited liability to the each victim and survivor of the Indonesian and Malaysia airline's air crash case such as (1) such damage was not due to the negligence or other wrongful act or omission of the air carrier or its servants or agents, or (2) such damage was solely due to the negligence or other wrongful act or omission of a third party. In this researcher's view for the aforementioned reasons, and under the laws of China, Indonesia, Malaysia and Korea the Chinese, Indonesian, Malaysia and Korean, some victims and survivors of the crash of the two flights are entitled to receive possibly from more than 113,100 SDR to 5 million US$ from the two airlines or from the Aviation Insurance Company based on decision of the American court. It could also be argued that it is reasonable and necessary to revise the clause referring to bodily injury to a clause mentioning personal injury based on Article 17 of the 1999 Montreal Convention so as to be included the mental injury and condolence in the near future.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.199-207
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• 2010

For us, there is virtually no distinction between being a responsible citizen and a successful business... they are one and the same for Wal-Mart today." ~ Lee Scott, al-Mart CEO after the 2005 Katrina disaster; cited in Green to Gold (Esty and Winston 2006). Lee Scott's statement signaled a new era in sustainability as manufacturers and retailers around the globe watched the world's largest mass merchandiser confirm its intentions with respect to sustainability. For decades, the environmental movement has grown, slowly bleeding over into the corporate world. Companies have been born, products have been created, academic journals have been launched, and government initiatives have been undertaken - all in the pursuit of sustainability (Peattie and Crane 2005). While progress has been admittedly slower than some may desire, the emergence and entrance of environmentally concerned mass merchandisers has done much to help with sustainable efforts. To better understand this movement, we incorporate the perspectives of both executives and consumers involved in the consumer packaged goods (CPG) industry. This research relies on three underlying themes: (1) Conceptual and anecdotal evidence suggests that companies undertake sustainability initiatives for a plethora of reasons, (2) The number of sustainability initiatives continues to increase in the consumer packaged goods industries, and (3) That it is, therefore, necessary to explore the role that sustainability plays in the minds of consumers. In light of these themes, surveys were administered to and completed by 143 college students and 101 business executives to assess a number of variables in regards to sustainability including willingness-to-pay, behavioral intentions, attitudes, willingness-to-pay, and preferences. Survey results indicate that the top three reasons why executives believe sustainability to be important include (1) the opportunity for profitability, (2) the fulfillment of an obligation to the environment, and (3) a responsibility to customers and shareholders. College students identified the top three reasons as (1) a responsibility to the environment, (2) an indebtedness to future generations, and (3) an effective management of resources. While the rationale for supporting sustainability efforts differed between college students and executives, the executives and consumers reported similar responses for the majority of the remaining sustainability issues. Furthermore, when we asked consumers to assess the importance of six key issues (healthcare, economy, education, crime, government spending, and environment) previously identified as important to consumers by Gallup Poll, protecting the environment only ranked fourth out of the six (Carlson 2005). While all six of these issues were identified as important, the top three that emerged as most important were (1) improvements in education, (2) the economy, and (3) health care. As the pursuit and incorporation of sustainability continues to evolve, so too will the expected outcomes. New definitions of performance that reflect the social/business benefits as well as the lengthened implementation period are relevant and warranted (Ehrenfeld 2005; Hitchcock and Willard 2006). We identified three primary categories of outcomes based on a literature review of both anecdotal and conceptual expectations of sustainability: (1) improvements in constituent satisfaction, (2) differentiation opportunities, and (3) financial rewards. Within each of these categories, several specific outcomes were identified resulting in eleven different outcomes arising from sustainability initiatives. Our survey results indicate that the top five most likely outcomes for companies that pursue sustainability are: (1) green consumers will be more satisfied, (2) company image will be better, (3) corporate responsibility will be enhanced, (4) energy costs will be reduced, and (5) products will be more innovative. Additionally, to better understand the interesting intersection between the environmental "identity" of a consumer and the willingness to manifest that identity with marketplace purchases, we extended prior research developed by Experian Research (2008). Accordingly, respondents were categorized as one of four types of green consumers (Behavioral Greens, Think Greens, Potential Greens, or True Browns) to garner a better understanding of the green consumer in addition to assisting with a more effective interpretation of results. We assessed these consumers' willingness to engage in eco-friendly behavior by evaluating three options: (1) shopping at retailers that support environmental initiatives, (2) paying more for products that protect the environment, and (3) paying higher taxes so the government can support environmental initiatives. Think Greens expressed the greatest willingness to change, followed by Behavioral Greens, Potential Greens, and True Browns. These differences were all significant at p<.01. Further Conclusions and Implications We have undertaken a descriptive study which seeks to enhance our understanding of the strategic domain of sustainability. Specifically, this research fills a gap in the literature by comparing and contrasting the sustainability views of business executives and consumers with specific regard to preferences, intentions, willingness-to-pay, behavior, and attitudes. For practitioners, much can be gained from a strategic standpoint. In addition to the many results already reported, respondents also reported than willing to pay more for products that protect the environment. Other specific results indicate that female respondents consistently communicate a stronger willingness than males to pay more for these products and to shop at eco-friendly retailers. Knowing this additional information, practitioners can now have a more specific market in which to target and communicate their sustainability efforts. While this research is only an initial step towards understanding similarities and differences among practitioners and consumers regarding sustainability, it presents original findings that contribute to both practice and research. Future research should be directed toward examining other variables affecting this relationship, as well as other specific industries.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.4
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• pp.406-415
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• 2009

Purpose: The purpose of this study was to compare the marginal fitness and fracture load of the zirconia copings according to the design with different thickness and coloration. Material and methods: The evaluation was based on 80 zirconia copings. Zirconia copings were fabricated in design with different thicknesses using CAD/CAM system (Everset, KAVO dental GmbH, Biberach, Germany). The designs of copings were divided into four groups. The first group consisted of copings with uniform thickness of 0.3 mm. The thickness in the second group was 0.3 mm on the buccal surface and 0.6 mm on the lingual surface. The third group consisted of coping with uniform thickness of 0.6 mm. The thickness in the fourth group was 0.6 mm on the buccal surface and 1mm on the lingual surface. Each group consisted of 10 colored and 10 uncolored copings. Half of the copings (40) processed with a milling system according to the specific design were sent to be given a color (A3) through saturation in special dye by a manufacturing company. Just after sintering, the marginal discrepancies of copings were measured on the buccal, lingual, mesial and distal surfaces of metal die, under a Video Microscope System (sv-35, Sometech, Seoul, Korea) at a magnification of $\times$ 100. It was remeasured after the adjusting of the inner surface. Next, all copings were luted to the metal dies using reinforced cement {GC FujiCEM (GC Corp. Tokyo, Japan)} and mounted on the testing jig in a Universal Testing Machine (Instron 4467, Norwood, MA, USA). The results were analyzed statistically using the one-way ANOVA test. Results: The obtained results were as follow: 1. The measured value of marginal discrepancy right after sintering was the greatest in the contraction of the buccal area in all groups, except for group I2. 2. There was no significant difference of marginal fitness among the groups in the colored zirconia group (P<.05). 3. When the marginal fitness among the groups in the uncolored zirconia group was considered, group II2 had the smallest marginal discrepancy. 4. When the colored and uncolored groups with the same design were compared, there was a significant difference between I1 and II1 groups. In group 2, 3, and 4, the uncolored zirconia had the greatest marginal fitness (P<.05). 5. After adjustment of inner surface, there was no significant difference in the marginal fitness in all groups when color and design of the zirconia coping were compared. 6. The fracture load of CAD/CAM zirconia copings showed significant difference in group 1, 2, 3, and 4. I4 and II4 had the strongest fracture load. 7. When groups with different color and same design were compared, all colored groups showed greater fracture load (P>.05), with no significance. Conclusion: There was difference in the marginal fitness according to the design and coloration of zirconia copings right after sintering, but it was decided that the copings may well be used clinically if the inner surface are adjusted. The copings should be thick enough for the reinforcement of fracture strength. But considering the esthetics of the visible surfaces (labial and buccal surface), the thickness of copings may be a little thin, without giving any significant effect on the fracture strength. This type of design may be considered when giving priority to preservation of tooth or esthetics.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.9
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• pp.1172-1181
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• 2007

This research involved 226 Jeonbuk Province school dietitians as subjects to investigate intake and perceptions of the healthy functional foods. Sixty nine percent of the school dietitians didn't even know about the law enforcement concerning the health functional foods. Although 68.1% of the respondents said that they slightly knew about health functional foods, only 25% knew exactly what it was. As shown in the survey, most didn't have the cognitive understanding did not understand which should be obtained by education. Sixty two percent of the answerers said they had experience of taking health various functional food products of various kinds such as supplements (57.9%), red ginseng products (52.9%), and chlorella products (30.0%). The motive of intake was in the order of fatigue restoration (25.7%), sickness prevention (22.9%), and nutrient replenishment (22.9%). A fascinating fact from this study was that the reason for healthy functional product intake was different between groups that was primarily interested in the products and those that was not. For those who had interest, the reason for intake was for sickness prevention. On the other hand, for those who didn't have any interest, the reasons was primarily for fatigue restoration and they were mostly persuaded by close friends and relatives. Main concerns were in the order of side effects (4.72), efficacy after intake (4.59), cleanliness (4.51), reliability of the company (4.29), and price (4.23). In view of the study, it is clear that a lot of people are showing interest in healthy functional food products. However, dietitians who are experts in food and nutrition lacked knowledge and information on healthy functional food.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.2
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• pp.20-31
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• 2022

Purpose Broad Quantification Calibration(B.Q.C) is the procedure for Quantitative Analysis to measure Standard Uptake Value(SUV) in SPECT/CT scanner. B.Q.C was performed with Tc-99m, I-123, I-131, Lu-177 respectively and then we acquired the phantom images whether the SUVs were measured accurately. Because there is no standard for SUV test in SPECT, we used ACR Esser PET phantom alternatively. The purpose of this study was to lay the groundwork for Quantitative Analysis with various isotopes in SPECT/CT scanner. Materials and Methods Siemens SPECT/CT Symbia Intevo 16 and Intevo Bold were used for this study. The procedure of B.Q.C has two steps; first is point source Sensitivity Cal. and second is Volume Sensitivity Cal. to calculate Volume Sensitivity Factor(VSF) using cylinder phantom. To verify SUV, we acquired the images with ACR Esser PET phantom and then we measured SUV_mean on background and SUV_max on hot vials(25, 16, 12, 8 mm). SPSS was used to analyze the difference in the SUV between Intevo 16 and Intevo Bold by Mann-Whitney test. Results The results of Sensitivity(CPS/MBq) and VSF were in Detector 1, 2 of four isotopes (Intevo 16 D1 sensitivity/D2 sensitivity/VSF and Intevo Bold) 87.7/88.6/1.08, 91.9/91.2/1.07 on Tc-99m, 79.9/81.9/0.98, 89.4/89.4/0.98 on I-123, 124.8/128.9/0.69, 130.9, 126.8/0.71, on I-131, 8.7/8.9/1.02, 9.1/8.9/1.00 on Lu-177 respectively. The results of SUV test with ACR Esser PET phantom were (Intevo 16 BKG SUV_mean/25mm SUV_max/16mm/12mm/8mm and Intevo Bold) 1.03/2.95/2.41/1.96/1.84, 1.03/2.91/2.38/1.87/1.82 on Tc-99m, 0.97/2.91/2.33/1.68/1.45, 1.00/2.80/2.23/1.57/1.32 on I-123, 0.96/1.61/1.13/1.02/0.69, 0.94/1.54/1.08/0.98/ 0.66 on I-131, 1.00/6.34/4.67/2.96/2.28, 1.01/6.21/4.49/2.86/2.21 on Lu-177. And there was no statistically significant difference of SUV between Intevo 16 and Intevo Bold(p>0.05). Conclusion Only Qualitative Analysis was possible with gamma camera in the past. On the other hand, it's possible to acquire not only anatomic localization, 3D tomography but also Quantitative Analysis with SUV measurements in SPECT/CT scanner. We could lay the groundwork for Quantitative Analysis with various isotopes; Tc-99m, I-123, I-131, Lu-177 by carrying out B.Q.C and could verify the SUV measurement with ACR phantom. It needs periodic calibration to maintain for precision of Quantitative evaluation. As a result, we can provide Quantitative Analysis on follow up scan with the SPECT/CT exams and evaluate the therapeutic response in theranosis.

• Journal of Global Scholars of Marketing Science
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• v.19 no.4
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• pp.11-23
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• 2009

HSDPA (High-Speed Downlink Packet Access) is a 3.5-generation asynchronous mobile communications service based on the third generation of W-CDMA. In Korea, it is mainly provided in through videophone service. Because of the diffusion of more powerful and diversified services, along with steep advances in mobile communications technology, consumers demand a wide range of choices. However, because of the variety of technologies, which tend to overflow the market regardless of consumer preferences, consumers feel increasingly confused. Therefore, we should not adopt strategies that focus only on developing new technology on the assumption that new technologies are next-generation projects. Instead, we should understand the process by which consumers accept new forms of technology and devise schemes to lower market entry barriers through strategies that enable developers to understand and provide what consumers really want. In the Technology Acceptance Model (TAM), perceived usefulness and perceived ease of use are suggested as the most important factors affecting the attitudes of people adopting new technologies (Davis, 1989; Taylor and Todd, 1995; Venkatesh, 2000; Lee et al., 2004). Perceived usefulness is the degree to which a person believes that a particular technology will enhance his or her job performance. Perceived ease of use is the degree of subjective belief that using a particular technology will require little physical and mental effort (Davis, 1989; Morris and Dillon, 1997; Venkatesh, 2000). Perceived pleasure and perceived usefulness have been shown to clearly affect attitudes toward accepting technology (Davis et al., 1992). For example, pleasure in online shopping has been shown to positively impact consumers' attitudes toward online sellers (Eighmey and McCord, 1998; Mathwick, 2002; Jarvenpaa and Todd, 1997). The perceived risk of customers is a subjective risk, which is distinguished from an objective probabilistic risk. Perceived risk includes a psychological risk that consumers perceive when they choose brands, stores, and methods of purchase to obtain a particular item. The ability of an enterprise to revolutionize products depends on the effective acquisition of knowledge about new products (Bierly and Chakrabarti, 1996; Rothwell and Dodgson, 1991). Knowledge acquisition is the ability of a company to perceive the value of novelty and technology of the outside (Cohen and Levinthal, 1990), to evaluate the outside technology that has newly appeared (Arora and Gambaradella, 1994), and to predict the future evolution of technology accurately (Cohen and Levinthal, 1990). Consumer innovativeness is the degree to which an individual adopts innovation earlier than others in the social system (Lee, Ahn, and Ha, 2001; Gatignon and Robertson, 1985). That is, it shows how fast and how easily consumers adopt new ideas. Innovativeness is regarded as important because it has a significant effect on whether consumers adopt new products and on how fast they accept new products (Midgley and Dowling, 1978; Foxall, 1988; Hirschman, 1980). We conducted cross-national comparative research using the TAM model, which empirically verified the relationship between the factors that affect attitudes - perceived usefulness, ease of use, perceived pleasure, perceived risk, innovativeness, and perceived level of knowledge management - and attitudes toward HSDPA service. We also verified the relationship between attitudes and usage intention for the purpose of developing more effective methods of management for HSDPA service providers. For this research, 346 questionnaires were distributed among 350 students in the Republic of Korea. Because 26 of the returned questionnaires were inconsistent or had missing data, 320 questionnaires were used in the hypothesis tests. In UK, 192 of the total 200 questionnaires were retrieved, and two incomplete ones were discarded, bringing the total to 190 questionnaires used for statistical analysis. The results of the overall model analysis are as follows: Republic of Korea x2=333.27(p=0.0), NFI=0.88, NNFI=0.88, CFI=0.91, IFI=0.91, RMR=0.054, GFI=0.90, AGFI=0.84, UK x2=176.57(p=0.0), NFI=0.88, NNFI=0.90, CFI=0.93, IFI=0.93, RMR=0.062, GFI=0.90, AGFI=0.84. From the results of the hypothesis tests of Korean consumers about the relationship between factors that affect intention to use HSDPA services and attitudes, we can conclude that perceived usefulness, ease of use, pleasure, a high level of knowledge management, and innovativeness promote positive attitudes toward HSDPA mobile phones. However, ease of use and perceived pleasure did not have a direct effect on intention to use HSDPA service. This may have resulted from the fact that the use of video phones is not necessary for everyday life yet. Moreover, it has been shown that attitudes toward HSDPA video phones are directly correlated with usage intention, which means that perceived usefulness, ease of use, pleasure, a high level of knowledge management, and innovativeness. These relationships form the basis of the intention to buy, contributing to a situation in which consumers decide to choose carefully. A summary of the results of the hypothesis tests of European consumers revealed that perceived usefulness, pleasure, risk, and the level of knowledge management are factors that affect the formation of attitudes, while ease of use and innovativeness do not have an effect on attitudes. In particular, with regard to the effect value, perceived usefulness has the largest effect on attitudes, followed by pleasure and knowledge management. On the contrary, perceived risk has a smaller effect on attitudes. In the Asian model, ease of use and perceived pleasure were found not to have a direct effect on intention to use. However, because attitudes generally affect the intention to use, perceived usefulness, pleasure, risk, and knowledge management may be considered key factors in attitude development from which usage intention arises. In conclusion, perceived usefulness, pleasure, and the level of knowledge management have an effect on attitude formation in both Asian and European consumers, and such attitudes shape these consumers' intention to use. Furthermore, the hypotheses that ease of use and perceived pleasure affect usage intention are rejected. However, ease of use, perceived risk, and innovativeness showed different results. Perceived risk had no effect on attitude formation among Asians, while ease of use and innovativeness had no effect on attitudes among Europeans.