• Asia pacific journal of information systems
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• v.24 no.4
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• pp.559-575
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• 2014

Individual investors are increasingly flocking to message boards to seek, clarify, and exchange information. Businesses like Seekingalpha.com and business magazines like Fortune are evaluating, synthesizing, and reporting the comments made on message boards or blogs. In March of 2012, Yahoo! Finance Message Boards recorded 45 million unique visitors per month followed by AOL Money and Finance (19.8 million), and Google Finance (1.6 million) [McIntyre, 2012]. Previous studies in the finance literature suggest that online communities often provide more accurate information than analyst forecasts [Bagnoli et al., 1999; Clarkson et al., 2006]. Some studies empirically show that the volume of posts in online communities have a positive relationship with market activities (e.g., trading volumes) [Antweiler and Frank, 2004; Bagnoli et al., 1999; Das and Chen, 2007; Tumarkin and Whitelaw, 2001]. The findings indicate that information in online communities does impact investors' investment decisions and trading behaviors. However, research explicating the correlation between information on online communities and stock market activities (e.g., trading volume) is still evolving. Thus, it is important to ask whether a volume of posts on online communities influences trading volumes and whether trading volumes also influence these communities. Online stock message boards offer two different types of information, which can be explained using an economic and a psychological perspective. From a purely economic perspective, one would expect that stock message boards would have a beneficial effect, since they provide timely information at a much lower cost [Bagnoli et al., 1999; Clarkson et al., 2006; Birchler and Butler, 2007]. This indicates that information in stock message boards may provide valuable information investors can use to predict stock market activities and thus may use to make better investment decisions. On the other hand, psychological studies have shown that stock message boards may not necessarily make investors more informed. The related literature argues that confirmation bias causes investors to seek other investors with the same opinions on these stock message boards [Chen and Gu, 2009; Park et al., 2013]. For example, investors may want to share their painful investment experiences with others on stock message boards and are relieved to find they are not alone. In this case, the information on these stock message boards mainly reflects past experience or past information and not valuable and predictable information for market activities. This study thus investigates the two roles of stock message boards-providing valuable information to make future investment decisions or sharing past experiences that reflect mainly investors' painful or boastful stories. If stock message boards do provide valuable information for stock investment decisions, then investors will use this information and thereby influence stock market activities (e.g., trading volume). On the contrary, if investors made investment decisions and visit stock message boards later, they will mainly share their past experiences with others. In this case, past activities in the stock market will influence the stock message boards. These arguments indicate that there is a correlation between information posted on stock message boards and stock market activities. The previous literature has examined the impact of stock sentiments or the number of posts on stock market activities (e.g., trading volume, volatility, stock prices). However, the studies related to stock sentiments found it difficult to obtain significant results. It is not easy to identify useful information among the millions of posts, many of which can be just noise. As a result, the overall sentiments of stock message boards often carry little information for future stock movements [Das and Chen, 2001; Antweiler and Frank, 2004]. This study notes that as a dependent variable, trading volume is more reliable for capturing the effect of stock message board activities. The finance literature argues that trading volume is an indicator of stock price movements [Das et al., 2005; Das and Chen, 2007]. In this regard, this study investigates the correlation between a number of posts (information on stock message boards) and trading volume (stock market activity). We collected about 100,000 messages of 40 companies at KOSPI (Korea Composite Stock Price Index) from Paxnet, the most popular Korean online stock message board. The messages we collected were divided into in-trading and after-trading hours to examine the correlation between the numbers of posts and trading volumes in detail. Also we collected the volume of the stock of the 40 companies. The vector regression analysis and the granger causality test, 3SLS analysis were performed on our panel data sets. We found that the number of posts on online stock message boards is positively related to prior stock trade volume. Also, we found that the impact of the number of posts on stock trading volumes is not statistically significant. Also, we empirically showed the correlation between stock trading volumes and the number of posts on stock message boards. The results of this study contribute to the IS and finance literature in that we identified online stock message board's two roles. Also, this study suggests that stock trading managers should carefully monitor information on stock message boards to understand stock market activities in advance.

• Asia pacific journal of information systems
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• v.23 no.2
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• pp.1-20
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• 2013

Social commerce has been gaining explosive popularity, with typical examples of the model such as Groupon and Level Up. Both local business owners and consumers can benefit from this new e-commerce model. Local business owners have a chance to access potential customers and promote their products in a way that could not have otherwise been easily possible, and consumers can enjoy discounted offerings. However, questions have been increasingly raised about the value and future of the social commerce model. A recent survey shows that about a third of 324 business owners who ran a daily-deal promotion in Groupon went behind. Furthermore, more than half of the surveyed merchants did not express enthusiasm about running the promotion again. The same goes for the case in Korea, where more than half of the surveyed clients reported no significant change or even decrease in profits compared to before the use of social commerce model. Why do local business owners fail to exploit the benefits from the promotions and advertisements through the social commerce model and to make profits? Without answering this question, the model would fall under suspicion and even its sustainability might be challenged. This study aims to look into problems in the current social commerce transactions and provide implications for the social commerce model, so that the model would get a foothold for next growth. Drawing on justice theory, this study develops theoretical arguments for the effects of transaction characteristics on consumers' distributive justice and purchase intention in the social commerce. Specifically, this study focuses on two characteristics of social commerce transactions-the discount rate and the purchase rate of products-and investigates their effects on consumers' perception of distributive justice for discounted transactions in the social commerce and their perception of distributive justice for regular-priced transactions. This study also examines the relationship between distributive justice and purchase intention. We conducted an online experiment and gathered data from 115 participants to test the hypotheses. Each participant was randomly assigned to one of nine manipulated scenarios of social commerce transactions, which were generated based on the combination of three levels of purchase rate (high, medium, and low) and three levels of discount rate (high, medium, and low). We conducted MANOVA and post-hoc ANOVA to test hypotheses about the relationships between the transaction characteristics (purchase rate and discount rate) and distributive justice for each of the discounted transaction and the regular-priced transaction. We also employed a PLS analysis to test relations between distributive justice and purchase intentions. Analysis results show that a higher discount rate increases distributive justice for the discounted transaction but decreases distributive justice for the regular-priced transaction. This, coupled with the result that distributive justice for each type of transaction has a positive effect on the corresponding purchase intention, implies that a large discount in the social commerce may be helpful for attracting consumers, but harmful to the business after the promotion. However, further examination reveals curvilinear effects of the discount rate on both types of distributive justice. Specifically, we find distributive justice for the discounted transaction increases concavely as the discount rate increases while distributive justice for the regular-priced transaction decreases concavely with the dscount rate. This implies that there exists an appropriate discount rate which could promote the discounted transaction while not hurting future business of regular-priced transactions. Next, the purchase rate is found to be a critical factor that facilitates the regular-priced transaction. It has a convexly positive influence on distributive justice for the transaction. Therefore, an increase of the rate beyond some threshold would lead to a substantial level of distributive justice for the regular-priced transaction, threrby boosting future transactions. This implies that social commerce firms and sellers should employ various non-price stimuli to promote the purchase rate. Finally, we find no significant relationship between the purchase rate and distributive justice for the discounted transaction. Based on the above results, we provide several implications with future research directions.

• The Korean Journal of Nuclear Medicine
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• v.36 no.6
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• pp.357-367
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• 2002

Purpose: While cerebral blood flow and cerebrovascular reserve could be evaluated with basal/acetazolamide Tc-99m-HMPAO SPECT in cerebrovascular disease, objective quantification is necessary to assess the efficacy of the revascularization. In this study we adopted the SPM method to quantify basal cerebral blood flow and cerebrovascular reserve on basal/acetazolamide SPECT in assessment of the patients who underwent bypass surgery for linternal carotid artery (ICA) stenosis. Materials and Methods: Twelve patients ($51{\pm}15$ years) with ICA stenosis were enrolled. Tc-99m-HMPAO basal/acetazolamide perfusion SPECT was peformed before and after bypass surgery. After spatia1 and count normalization to cerebellum, basal cerebral blood flow and cerebrovascular reserve were compared with 21 age-matched normal controls and postoperative changes of regional blood flow and reserve were assessed by Statistical Parametric Mapping method. Mean pixel values of each brain region were calculated using probabilistic anatomical map of lobes. Perfusion reserve was defined as the % changes after acetazolamide over basal counts. Results: Preoperative cerebral blood flow and cerebrovascular reserve were significantly decreased in involved ICA territory, comparing with normal control (p<0.05). Postoperative improvement of cerebral blood flow and cerebrovascular reserve was observed in grafted ICA territories, but cerebrovasculr reserve remained with significant difference with normal control. Improvement of the cerebrovascular reserve was most prominent in the superior temporal and the angular gyrus, nearest to the anastomosis sites. Conclusion: Using SPM quantification method on hasal/acetazolamide Tc-99m-HMPAO SPECT, the cerebral blood flow and cerebrovascular reserve could be assessed before revascularization and so could the efficacy of the bypass surgery.

• Journal of Korean Home Economics Education Association
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• v.30 no.3
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• pp.43-66
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• 2018

The purpose of this study was to conduct the instruction for 'Family Life Planning' based on backward design and measured the learners' understanding and satisfaction for testing validity. In short, the result of this study are as follows: In this study, first of all, the students could explain significant concepts, knowledge, and principles for the planning of family life; they could interpret and apply them; they have perspectives on them; they could empathize them; and they could have self-knowledge. The students could also accomplish high achievements for important concepts related to the field of family life planning. In conclusion, this study showed that the developed instruction was very effective for the students to achieve fruitful results, accelerating the learners' persistent understanding. Second, the learners had high satisfaction on the instruction of Family Life Planning based on backward design with the average score of 3.68 out of the perfect score 4. The students could be satisfied with the developed instruction since they could have high interest in the class thanks to diverse learning materials, and they could take an active part in the learning tasks based on group activities and questions. Also they could apply the contents that they learned through task performances to new situation and context. Therefore, this study proved that the developed instruction enhanced the learners' satisfaction on class.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.49-58
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• 2010

Corporate social responsibility has become a very important issue for researchers (Greenfield, 2004; Maignan & Ralston, 2002; McWilliams et al., 2006; Pearce & Doh 2005), and many consider it necessary for businesses to define their role in society and apply social and ethical standards to their businesses (Lichtenstein et al., 2004). As a result, a significant number of retailers have adopted CSR as a strategic tool to promote their businesses. To this end, this study sought to discover U.S. consumers' attitudes and behavior in ethical purchasing and consumption based on their subjective perception and evaluation of a retailer. The objectives of this study include: 1) determine the participants awareness of retailers corporate social responsibility; 2) assess how participants evaluate retailers corporate social responsibility; 3) examine whether participants evaluation process of retailers CSR influence their attitude toward the retailer; and 4) assess if participants attitude toward the retailers CSR influence their purchase behavior. This study does not focus on actual retailers' CSR performance because a consumer's decision making process is based on an individual assessment not an actual fact. This study examines US college students' awareness and evaluations of retailers' corporate social responsibility (CSR). Fifty six college students at a major Southeastern university participated in the study. The age of the participants ranged from 18 to 26 years old. Content analysis was conducted with open coding and focused coding. Over 100 single-spaced pages of written responses were collected and analyzed. Two steps of coding (i.e., open coding and focused coding) were conducted (Esterberg, 2002). Coding results and analytic memos were used to understand participants' awareness of CSR and their ethical purchasing behavior supported through the selection and inclusion of direct quotes that were extracted from the written responses. Names used here are pseudonyms to protect confidentiality of participants. Participants were asked to write about retailers, their aware-ness of CSR issues, and to evaluate a retailer's CSR performance. A majority (n = 28) of respondents indicated their awareness of CSR but have not felt the need to act on this issue. Few (n=8) indicated that they are aware of this issue but not greatly concerned. Findings suggest that when college students evaluate retailers' CSR performance, they use three dimensions of CSR: employee support, community support, and environmental support. Employee treatment and support were found as an important criterion in evaluation of retailers' CSR. Respondents indicated that their good experience with a retailer as an employee made them have a positive perception and attitude toward the retailer. Regarding employee support four themes emerged: employee rewards and incentives based on performance, working environment, employee education and training program, and employee and family discounts. Well organized rewards and incentives were mentioned as an important attribute. The factors related to the working environment included: how well retailers follow the rules related to working hours, lunch time and breaks was also one of the most mentioned attributes. Regarding community support, three themes emerged: contributing a percentage of sales to the local community, financial contribution to charity organizations, and events for community support. Regarding environments, two themes emerged: recycling and selling organic or green products. It was mentioned in the responses that retailers are trying to do what they can to be environmentally friendly. One respondent mentioned that the company is creating stores that have an environmentally friendly design. Information about what the company does to help the environment can easily be found on the company’s website as well. Respondents have also noticed that the stores are starting to offer products that are organic and environmentally friendly. A retailer was also mentioned by a respondent in this category in reference to how the company uses eco-friendly cups and how they are helping to rebuild homes in New Orleans. The respondents noticed that a retailer offers reusable bags for their consumers to purchase. One respondent stated that a retailer uses its products to help the environment, through offering organic cotton. After thorough analysis of responses, we found that a participant's evaluation of a retailers' CSR influenced their attitudes towards retailers. However, there was a significant gap between attitudes and purchasing behavior. Although the participants had positive attitudes toward retailers CSR, the lack of funds and time influenced their purchase behavior. Overall, half (n=28) of the respondents mentioned that CSR performance affects their purchasing decisions making when shopping. Findings from this study provide support for retailers to consider their corporate social responsibility when developing their image with the consumer. This study implied that consumers evaluate retailers based on employee, community and environmental support. The evaluation, attitude and purchase behavior of consumers seem to be intertwined. That is, evaluation is based on the knowledge the consumer has of the retailers CSR. That knowledge may influence their attitude toward the retailer and thus influence their purchase behavior. Participants also indicated that having CSR makes them think highly of the retailer, but it does not influence their purchase behavior. Price and convenience seem to surpass the importance of CSR among the participants. Implications, recommendations for future research, and limitations of the study are also discussed.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Aquaculture
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• v.11 no.4
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• pp.529-546
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• 1998

Gonadal part that developed by indifferentiation period for 6 months after hatching is made as gonad and fat body. These gonad are thin semi-transparant and undistinguished germ cell. Germinal epithelium is distinguished by development of gonad epithelial tissue from 7 months after hatching. Sex differentiation is begun by oogonia develoment at 8 months after hatching. Primary oocytes grow over germinal epithelium of gonadal cavity, at 9 months after hatching, gonadal cavity become ovarian cavity as they increasing. As soon as oocytes at 13 months after hatching are filled with the whole part of gonad, degeneration of oocyte is begun. And then, gonad has cavity tissue, a small number of oocyte are located in gonadal cavity. At 15 months after hatching, new primary oocyte develop and cavity of ovarian tissue in the central of ovarian cavity. Spermatogonia multiplicate and cavity tissue consist of testicular tissue. These gonad become hermaphrodite and then ditermine the sex of female and male. These results show the red sea bream is juvenile hermaphrodite and undif-ferentiated gonochoristic teleost. Male and female differentiation type of gonad is divided in undifferentiation stage, oogonia-like stage, ovary-like stage, ovary development stage, hermaphroditic testis stage, hermaphroditic ovary stage, and testis development stage. Undifferentiation stage is continued total lenth 18cm at 13 months after hatching. ovary-like stage is continued total length 11~18cm at 13 months after hatching. Ovary-like stage is continued total length 14~26cm at 10~14 months after hatching. Ovary development stage begins from total length 20cm, 14 months after hatching. At 20 months after hatching, 44 percent of total sampled individuals had ovary. Hermaphroditic ovary stage first begins total length 19~20 cm at 15 months after hatching, but it is not observed total length 28~29cm at 20months after hatching. Hermaphroditic testis stage first begins total length 21~22cm at 20months after hatching and is continued for 20months. Testis development stage first begins total length 20~21cm at 20 months after hatching, and is occupied 33 percent total length 28~29cm at 20 months. The beginning of sex differentiation more than 50 percent is from total length 16cm at 11 months after hatching. Sex determination begins total length 20cm, 14months after hatching in female and total length 20cm, 15 months after hatching in male. Sex determination more than 50 percent begins total length 23cm,, 17 months after hatching. Undifferentiated gonadal part of red sea bream consist gonad and fat body. As differentiation is going on and gonad is growing, fat body shrinks. This appearence is showed the same tendency in 3-year old red sea bream. 1.9mm larvae after hatching grow about 19mm larvae for 47 days. The relationship between the total length and body weight of larvae and juveniles in $BW=4.45{\times}10^{-6}TL^{3.4718}$ r=0.9820. Fishes in cage culture grow to maximum total length 28.4cm. The relationship between the total length and body weight of these fishes is $BW=2.36{\times}10^{-2}TL^{2.9180}$, r=0.9971. Undifferentiated gonadal part of red sea bream consist gonad and fat body. As differentiation is going on and gonad is growing, fat body shrinks.