• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.4
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• pp.1-12
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• 2014

It is essential factor for business operation to raise funds effectively. However, in Korea, many start-ups and small businesses have difficulties in fund-raising. In recent years, crowdfunding, a new method for funding a project of individuals or organizations by raising monetary contributions from a large number of people, has been growing up simultaneously with diffusion of social media. Crowdfunding is on early stage in Korea, and the majority of projects are focused on cultural or art categories. There is high proportion of projects that have social value in start-up sector. Crowdfunding in Korea has great potential because success rate of it is much higher than its of advanced countries, although market size is much smaller than them. The purpose of this paper is to propose success strategies of crowdfunding for start-up through case study. 5 crowdfunding platforms of Korea and Kickstarter, the platform of United States were investigated. Then we checked the figures related to the operation of the whole Korean projects on start-up. Finally, we made comparison between the cases of success and failure by analyzing 8 project characteristics. The study shows that it were the differences in trustworthiness and activeness of project creator, value of reward and efforts for interactivity that have great effects on success of the project. Whereas there was no significant influence of societal contribution and sponsor engagement. The thesis provides success strategies of crowdfunding for start-up as follows. Firstly, creator of the project should make support base by enthusiastic activites before launching funding project. Secondly, there should be contents that can easily show the process of business development in the project information. Thirdly, there must be appropriate design of rewards for each amounts of support money. Finally, efforts for interactivity, such as frequent updates, response for comments and SNS posting, should be followed after the launch of the project.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.84-89
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• 2013

Purpose: Patients' movement during long image acquisition time for the fusion image of PET-CT (Positron Emission Tomography-Computed Tomography) results in unconformity, and greatly affects the quality of the image and diagnosis. The arm support fixtures provided by medical device companies are not manufactured considering the convenience and safety of the patients; the arm and head movements (horizontal and vertical) during PET/CT scan cause defects in the brain fundus images and often require retaking. Therefore, this study aims to develop patient-compensation device that would minimize the head and arm movements during PET/CT scan, providing comfort and safety, and to reduce retaking. Materials and Methods: From June to July 2012, 20 patients who had no movement-related problems and another 20 patients who had difficulties in raising arms due to shoulder pain were recruited among the ones who visited nuclear medicine department for PET Torso scan. By using Patient Holding System (PHS), different range of motion (ROM) in the arm ($25^{\circ}$, $27^{\circ}$, $29^{\circ}$, $31^{\circ}$, $33^{\circ}$, $35^{\circ}$) was applied to find the most comfortable angle and posture. The manufacturing company was investigated for the permeability of the support material, and the comfort level of applying bands (velcro type) to fix the patient's head and arms was evaluated. To find out the retake frequency due to movements, the amount of retake cases pre/post patient-compensation were analyzed using the PET Torso scan data collected between January to December 2012. Results: Among the patients without movement disorder, 18 answered that PHS and $29^{\circ}$ arm ROM were the most comfortable, and 2 answered $27^{\circ}$ and $31^{\circ}$, respectively. Among the patients with shoulder pain, 15 picked $31^{\circ}$ as the most comfortable angle, 2 picked $33^{\circ}$, and 3 picked $35^{\circ}$. For this study, the handle was manufactured to be adjustable for vertical movements. The material permeability of the patient-compensation device has been verified, and PHS and the compensation device were band-fixed (velcro type) to prevent device movements. A furrow was cut for head fixation to minimize the head and neck movements, fixing bands were attached for the head, wrist, forearm, and upper arm to limit movements. The retake frequency of PET Torso scan due to patient movements was 11.06% (191 cases/1,808 patients) before using the movement control device, and 2.65% (48 cases/1,732 patients) after using the device; 8.41% of the frequency was reduced. Conclusion: Recent change and innovation in the medical environment are making expensive medical image scans, and providing differentiated services for the customers is essential. To secure patient comfort and safety during PET/CT scans, ergonomic patient-compensation devices need to be provided. Therefore, this study manufactured a patientcompensation device with vertically adjustable ergonomic ROM according to the patient's body shape and condition during PET Torso scan. The defects in the basal ganglia images due to arm movements were reduced, and retaking was decreased.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.