The purpose of this study was to develop a smartphone based voice therapy program for patients with voice disorders. Contents of voice therapy were collected through analysis of mobile contents related to voice therapy in Korea, experts and users' demand survey, and the program was developed using Android Studio. Content needed for voice therapy was collected through analysis of mobile contents related to voice therapy. The user satisfaction evaluation for application was conducted for five patient with functional voice disorders. The results showed that the mobile contents related to voice therapy in Korea were mostly related to breathing, followed by voice and singing, but only 13 applications were practically practiced for voice therapy. Expert and user demand surveys showed that the patients and therapists both had a high need for content that could provide voice training in places other than the treatment room. Based on this analysis, 'Home Voice Trainer', an smartphone based voice therapy program, was developed. Home Voice Trainer is an application for voice therapy and management based on Android smartphones. It is designed to train voice therapy activities at home that have been trained offline. In addition, the records of voice training of patients were managed online so that patients can maintain voice improvement through continuous voice consulting even after the end of voice therapy. User evaluations show that patients are satisfied with the difficulty and content of voice therapy programs provided by home voice trainers, but lack of a portion of user interface, such as the portion of home button and interface between screens. Further study suggests the clinical application of home voice trainer to the patients with voice disorders. It is expected that the development study and the clinical application of smart contents related to voice therapy will be actively conducted.
Recently, the proportion of single-person households is on the increase in Korea, expected to reach 34.6% in 2035. Among the single-person households, Young single family households are facing greater difficulties due to high house prices in Korea. The government is expanding its support to Young single family, executing various policies such as public lease housings, private lease housings for youth, youth dormitory, etc. The purpose of this study is to understand the exact housing requirement of Young single family households who have different lifestyles with other age groups and provide base line data for youth house planning which will be in use later on. Study methods are shown below. First, this research studied the status and characteristics of Young single family households by looking into literature. Second, by studying previous studies concerned with life style and housing preferences of youth, the tool for investigating preferences and needs of housing environment by Young single family households was composed. Third, survey on characterstics of space usage, preferences and needs on flat composition, and preferences of interior design were conducted based on lifestyle of Young single family-households. The survey was conducted as an online survey using SNS for 150 Young single family holds from the age of 20 and 39, including students and office workers from December 2018 to January 2019. The results are as following. (1) Looking into the space usage characteristics, considering that various activities other than basic functions take place in bedroom and living room of small-sized Young single family households, we need to consider this additionally when planning the housing. (2) Looking into the preferences and composite needs of flat composition, the subjects demand separate bed room and more living room space, and also demand expansion of living room space where various activities take place and additional storage such as dress room in bed room (3) The preferences toward interior design show preferences toward modern style and achromatic color, a representative color. The subjects also prefer floor finishing materials normally used for living spaces, and indirect, soft lighting that uses wall. Also, there are differences between interior design preferences between students (20's) and office workers(30's) due to their different lifestyles. Research is needed to propose practical residential environment requirements and plans through a case study of actual public rental housing and a wider range of users.
Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.
With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.
YouTube exhibits a hybrid personality, incorporating traits of both over-the-top (OTT) and personal broadcasting platforms. However, limited research has investigated these hybrid characteristics, particularly in the context of paid YouTube channel memberships. Therefore, building upon consumption value theory and prior literature, this study examines the influence of consumption value factors associated with paid YouTube channel memberships on usage satisfaction and continuance intention. Specifically, the study identifies four perceived consumption value factors (functional, social, emotional, and epistemic values) within the paid YouTube channel membership context and assesses their impact on usage satisfaction and continuance intention. Additionally, the study explores the moderating role of conditional value (the experience of watching live streams on paid YouTube channels) in these relationships. Data was collected via an online survey from Korean adults who subscribed to multiple paid YouTube channel memberships, resulting in 274 responses. The proposed hypotheses were tested using structural equation modeling (SEM). The SEM results indicate that all four consumption value factors significantly influence usage satisfaction, with usage satisfaction in turn positively affecting continuance intention. Furthermore, the study reveals that conditional value moderates the relationships between functional/emotional values and usage satisfaction, as well as between usage satisfaction and continuance intention. This study is the first to focus on YouTube channel paid memberships, which encompass characteristics from both OTT and personal broadcasting platforms. It is anticipated that this research will offer insights to personal broadcasters and stakeholders regarding the motivational factors that impact user satisfaction and encourage subscriptions to channel memberships.
Purpose: The purpose of this study was to evaluate the nutritional quality of complementary baby food products sold in Korea according to the baby food stages and food composition. Methods: A total of 1,587 complementary food products sold online and offline between March and December 2021 were investigated. They ranged from liquid meals to solid rice for babies aged 5 to 36 months. Results: The number of intakes per packaged volume was 2.8 in Stage 1, 1.9 in Stage 2, 1.4 in Stage 3, and 1.1 in Stage 4 (p < 0.0001). The dietary variety scores (DVS) of the complementary baby food products were 3.4 in Stage 1, 5.5 in Stage 2, 7.1 in Stage 3, and 9.7 in Stage 4 (p < 0.0001) and showed a significant increase in the later stages. The Korean dietary diversity score (KDDS) significantly increased from 2.3 in Stage 1, to 2.8 in Stage 2, 3.0 in Stage 3, and 3.4 in Stage 4 (p < 0.0001). The higher the baby food stage, the higher the proportion of grains/meat/vegetable ingredients. The ratio of protein intake to Adequate Intake (AI) or Recommended Nutrition Intake (RNI) was higher in products with a KDDS of 3 points or more, or in products with 2 points or fewer in Stages 1 and 2 (p < 0.0001, respectively). The ratio of protein intake to RNI increased as the KDDS score increased in Stages 3 and 4 (p < 0.0001, respectively). For all stages of baby foods, the ratio of protein intake to AI or RNI was high in products that included the meat group (beans, nuts, meat, eggs, fish, and shellfish) (p < 0.0001, respectively). Conclusion: Continuous research and nutritional evaluation are required for establishing nutrient content standards for commercially available baby foods, considering breast milk intake.
Asia-Pacific Journal of Business Venturing and Entrepreneurship
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v.18
no.5
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pp.185-196
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2023
This paper investigates the effects of entrepreneurs' cognitive biases on business opportunity evaluation, given their strong entrepreneurial spirit, which is characterized by innovation, proactivity, and risk-taking. When making decisions related to business activities, entrepreneurs typically make rational judgments based on their knowledge, experience, and the advice of external experts. However, in situations of extreme stress or when quick decisions are required, they often rely on heuristics based on their cognitive biases. In particular, we often see cases where entrepreneurs fail because they make decisions based on heuristics in the process of evaluating and selecting new business opportunities that are planned to guarantee the growth and sustainability of their companies. This study was conducted in response to the need for research to clarify the effects of entrepreneurs' cognitive biases on new business opportunity evaluation, given that the cognitive biases of entrepreneurs, which are formed by repeated successful experiences, can sometimes lead to business failure. Although there have been many studies on the effects of cognitive biases on entrepreneurship and opportunity evaluation among university students and general people who aspire to start a business, there have been few studies that have clarified the relationship between cognitive biases and social networks among entrepreneurs. In contrast to previous studies, this study conducted empirical surveys of entrepreneurs only, and also conducted research on the relationship with social networks. For the study, a survey was conducted using a parallel survey method using online mobile surveys and self-report questionnaires from 150 entrepreneurs of small and medium-sized enterprises. The results of the study showed that 'overconfidence' and 'illusion of control', among the independent variables of entrepreneurs' cognitive biases, had a statistically significant positive(+) effect on business opportunity evaluation. In addition, it was confirmed that the moderating variable, social network, moderates the effect of overconfidence on business opportunity evaluation. This study showed that entrepreneurs' cognitive biases play a role in the process of evaluating and selecting new business opportunities, and that social networks play a role in moderating the structural relationship between entrepreneurs' cognitive biases and business opportunity evaluation. This study is expected to be of great help not only to entrepreneurs, but also to entrepreneur education and policy making, by showing how entrepreneurs can use cognitive biases in a positive way and the influence of social networks.
Asia-Pacific Journal of Business Venturing and Entrepreneurship
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v.19
no.2
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pp.63-79
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2024
The startup ecosystem is experiencing a paradigm shift in founding due to the acceleration of digital transformation, online platform companies have grown significantly into unicorns, but the lack of differentiated approaches and strategic support for deep tech startups has led to the inactivity of the startup ecosystem. is lacking. Therefore, in this study, we proposed ways to develop domestic startup development policies, focusing on the US system, which is an advanced example overseas. Focusing on the definition and characteristics of deep tech startups, current investment status, success stories, support policies, etc., we comprehensively analyzed domestic and international literature and derived suggestions. In particular, he proposed specific ways to improve support policies for domestic deep tech startups and presented milestones for their development. Currently, the United States is significantly strengthening the role of the government in supporting deep tech startups. The US government provides direct financial support to deep tech startups, including detergent support and infrastructure support. It has also established policies to foster deep tech startups, established related institutions, and systematized support. It is worth noting that US universities play a core role in nurturing deep tech startups. Leading universities in the United States operate deep tech startup discovery and development programs, providing research and development infrastructure and technology. It also works with companies to provide co-investment and commercialization support for deep tech startups. As a result, the growth of domestic deep tech startups requires the cooperation of diverse entities such as the government, universities, companies, and private investors. The government should strengthen policy support, and universities and businesses should work together to support R&D and commercialization capabilities. Furthermore, private investors must stimulate investment in deep tech startups. Through such efforts, deep tech startups are expected to grow and Korea's innovation ecosystem will be revitalized.
The purpose of this study is to explore the perceptions of elementary pre-service teachers regarding their interest in science. A survey was conducted among 187 elementary pre-service teachers enrolled at Non-Metropolitan Area A University of Education. Data collection was carried out concurrently with three elementary pre-service teachers who agreed to participate in online interviews. The survey responses provided by the elementary pre-service teachers were analyzed using a qualitative text analysis method. Interest in science was observed to decrease during middle school, followed by the upper grades of elementary school and then the lower grades. The reasons for the decline in interest in science were interpreted as stemming from negative experiences with science education within the context of individual circumstances in the school setting. Strategies to address the decline and enhance interest in science were discussed across individual, family, school, teacher, local community, and national levels, considering both short-term and long-term perspectives. These strategies encompassed various inquiry activities and experiences related to the field of science, engagement in science-related activities, student-centered instruction, teacher professional development, support for elementary students and teachers, and policy measures. The multifaceted approach and efforts aimed to open avenues for positive feedback regarding science on an individual level and foster experiences related to science were interpreted as part of an effort to counteract the decline in interest in science. Lastly, given the current situation of declining interest in science and the need to enhance students' interest, it was implicitly and explicitly discussed that pre-service teachers should focus on improving their expertise in curriculum instruction. This research, by exploring the conceptual characteristics of interest in science, perceptions of changes, and educational needs related to interest in science among elementary pre-service teachers, is expected to have academic significance as foundational research data for the current status of declining interest in science.
SNS enables people to easily connect and communicate with each other. People share information, including personal information, through SNS. Users are concerned about their privacies, but they unconsciously or consciously disclose their personal information on SNS to interact with others. The privacy of a self-disclosed person can be intruded by others. A person can write, fabricate, or distribute a story using the disclosed information of another even without obtaining consent from the information owner. Many studies focused on privacy intrusion, especially from the perspective of a victim. However, only a few studies examined privacy intrusion from the perspective of an intruder on SNS. This study focuses on the intention of privacy intrusion from the perspective of an intruder on SNS and the factors that affect intention. Privacy intrusion intentions are categorized into two types. The first type is intrusion of privacy by writing one's personal information without obtaining consent from the information owner;, whereas the other type pertains to intrusion of privacy by distributing one's personal information without obtaining consent from the information owner. A research model is developed based on motivation theory to identify how these factors affect these two types of privacy intrusion intentions on SNS. From the perspective of motivation theory, we draw one extrinsic motivational factor (response cost) and four intrinsic motivational factors, namely, perceived enjoyment, experience of being intruded on privacy, experience of invading someone's privacy, and punishment behavior. After analyzing 202survey data, we conclude that different factors affect these two types of privacy intrusion intention. However, no relationship was found between the two types of privacy intrusion intentions. One of the most interesting findings is that the experience of privacy intrusion is the most significant factor related to the two types of privacy intrusion intentions. The findings contribute to the literature on privacy by suggesting two types of privacy intrusion intentions on SNS and identifying their antecedents from the perspective of an intruder. Practitioners can also use the findings to develop SNS applications that can improve protection of user privacies and legitimize proper regulations relevant to online privacy.
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