• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.123-138
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• 2024

Experiential social entrepreneurship education offers participants opportunities for active engagement in social entrepreneurial activities. Highlighting the significance of psychosocial experiences within the program, this study examines work meaningfulness discovered in this process as the antecedent to forming social entrepreneurial intention. Furthermore, drawing on social cognitive career theory that emphasizes the role of agency in career decisions, we propose social entrepreneurial self-efficacy as the underlying mechanism and social class as the moderating factor in the relationship between work meaningfulness and social entrepreneurial intention formation. The propositions were tested with a two-wave survey dataset collected among 145 university students taking part in an experiential social entrepreneurship program in South Korea. Our results indicate that work meaningfulness positively affects social entrepreneurial self-efficacy, which subsequently promotes social entrepreneurial intention. Moreover, when participants' social class, measured by either household income or perceived rank, is lower, the positive effect of work meaningfulness on social entrepreneurial self-efficacy as well as intention is amplified. Theoretically, these findings shed light on the crucial role of work meaningfulness in strengthening potential entrepreneurs' agency in the domain of social entrepreneurship, particularly for those from lower classes. Practically, we provide guidelines for designing an inclusive experiential social entrepreneurship program that allows participants to find meaningfulness by realizing their strengths and justifying their prosocial contribution.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.7
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• pp.876-885
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• 2017

This study aimed to identify the characteristics of Home Meal Replacement (HMR) product purchases and the need for HMR product development for Hong Kong consumers in order to suggest market segmentation strategies according to consumers' dietary lifestyle. For this, an online survey was conducted on a panel of 521 Hong Kong consumers with HMR purchase experience registered at a specialized organization. Data analysis was performed using SPSS (ver. 23.0). HMR purchase characteristics of Hong Kong consumers according to dietary lifestyle showed significant differences in all items, including 'number of purchases', 'purchase location', 'cost of single purchase', and 'reason for purchase'. According to dietary lifestyle, participants were divided into three clusters: 'High interest', 'normal interest', and 'low interest'. In the case of 'high interest in dietary life group', 'low-sodium food' was the most common, followed by 'heating food', 'low sugar food', and 'low calorie food'. In the case of 'moderate interest in dietary life group', 'low-sodium food' was the most common, followed by 'low sugar food', 'low calorie food', and 'nutritious meal'. In the case of 'low interest in dietary life group', 'low sugar food' was the most common, followed by 'low-sodium food', 'various new menu', and 'easy-to-carry dehydrated food'. For the 'high interest' group, the highest proportion of consumers were male in between the ages of 20 to 29, married, and worked in an office job. The 'high interest' consumers also showed a tendency to pay '15,000 to 20,000 KRW' per single purchase. The 'normal interest' group consisted of an even proportion of male and female consumers, with the most common age range being from 30 to 39 years, and most were married. These consumers preferred to spend 'less than 10,000 KRW' or '10,000 KRW to 15,000 KRW' per single purchase, which is in the lower price range for HMR purchases. The 'low interest in dietary life group' had more females gender-wise, were unmarried, and worked in an office job, For a single purchase, the 'low interest' group chose to pay less than 10,000 KRW, which is relatively lower than the other two clusters. The results of this study can be used as baseline data for building marketing strategies for HMR product development. It can also provide basic data and directions for new HMR export products that reflect consumer needs in order to create a market segmentation strategy for industrial applications.

• Korean Journal of Heritage: History & Science
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• v.52 no.3
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• pp.172-187
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• 2019

Knowledge of intangible cultural heritage is usually disseminated through word-of-mouth and actions rather than written records. Thus, people assemble to teach others about it and form communities. Accordingly, to understand and spread information about intangible cultural heritage properly, it is necessary to understand not only their attributes but also a community's relational characteristics. Community members include specialized transmitters who work under the auspices of institutions, and general transmitters who enjoy intangible cultural heritage in their daily lives. They converse about intangible cultural heritage in close relationships. However, to date, research has focused only on professionals. Thus, this study focused on the roles of general transmitters of intangible cultural heritage information by investigating intangible cultural heritage communities centering around Jeongseon Arirang; a social network analysis was performed. Regarding the research objectives presented in the introduction, the main findings of the study are summarized as follows. First, there were 197 links between 74 members of the Jeongseon Arirang Transmission Community. One individual had connections with 2.7 persons on average, and all were connected through two steps in the community. However, the density and the clustering coefficient were low, 0.036 and 0.32, respectively; therefore, the cohesiveness of this community was low, and the relationships between the members were not strong. Second, 'Young-ran Yu', 'Nam-gi Kim' and 'Gil-ja Kim' were found to be the prominent figures of the Jeongseon Arirang Transmission Community, and the central structure of the network was concentrated around these three individuals. Being located in the central structure of the network indicates that a person is popular and ranked high. Also, it means that a person has an advantage in terms of the speed and quantity of the acquisition of information and resources, and is in a relatively superior position in terms of bargaining power. Third, to understand the replaceability of the roles of Young-ran Yu, Nam-gi Kim, and Gil-ja Kim, who were found to be the major figures through an analysis of the central structure, structural equivalence was profiled. The results of the analysis showed that the positions and roles of Young-ran Yu, Nam-gi Kim, and Gil-ja Kim were unrivaled and irreplaceable in the Jeongseon Arirang Transmission Community. However, considering that these three members were in their 60s and 70s, it seemed that it would be necessary to prepare measures for the smooth maintenance and operation of the community. Fourth, to examine the subgroup hidden in the network of the Jeongseon Arirang Transmission Community, an analysis of communities was conducted. A community refers to a subgroup clearly differentiated based on modularity. The results of the analysis identified the existence of four communities. Furthermore, the results of an analysis of the central structure showed that the communities were formed and centered around Young-ran Yu, Hyung-jo Kim, Nam-gi Kim, and Gil-ja Kim. Most of the transmission TAs recommended by those members, students who completed a course, transmission scholarship holders, and the general members taught in the transmission classes of the Jeongseon Arirang Preservation Society were included as members of the communities. Through these findings, it was discovered that it is possible to maintain the transmission genealogy, making an exchange with the general members by employing the present method for the transmission of Jeongseon Arirang, the joint transmission method. It is worth paying attention to the joint transmission method as it overcomes the demerits of the existing closed one-on-one apprentice method and provides members with an opportunity to learn their masters' various singing styles. This study is significant for the following reasons: First, by collecting and examining data using a social network analysis method, this study analyzed phenomena that had been difficult to investigate using existing statistical analyses. Second, by adopting a different approach to the previous method in which the genealogy was understood, looking at oral data, this study analyzed the structures of the transmitters' relationships with objective and quantitative data. Third, this study visualized and presented the abstract structures of the relationships among the transmitters of intangible cultural heritage information on a 2D spring map. The results of this study can be utilized as a baseline for the development of community-centered policies for the protection of intangible cultural heritage specified in the UNESCO Convention for the Safeguarding of Intangible Cultural Heritage. To achieve this, it would be necessary to supplement this study through case studies and follow-up studies on more aspects in the future.

• Korean Journal of Heritage: History & Science
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• v.39
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• pp.351-378
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• 2006

With the development of media, the methods for the documentation of intangible cultural heritage have been also developed and diversified. As well as the previous analogue ways of documentation, the have been recently applying new multi-media technologies focusing on digital pictures, sound sources, movies, etc. Among the new technologies, the documentation of intangible cultural heritage using the method of 'Motion Capture' has proved itself prominent especially in the fields that require three-dimensional documentation such as dances and performances. Motion Capture refers to the documentation technology which records the signals of the time varing positions derived from the sensors equipped on the surface of an object. It converts the signals from the sensors into digital data which can be plotted as points on the virtual coordinates of the computer and records the movement of the points during a certain period of time, as the object moves. It produces scientific data for the preservation of intangible cultural heritage, by displaying digital data which represents the virtual motion of a holder of an intangible cultural heritage. National Research Institute of Cultural Properties (NRICP) has been working on for the development of new documentation method for the Important Intangible Cultural Heritage designated by Korean government. This is to be done using 'motion capture' equipments which are also widely used for the computer graphics in movie or game industries. This project is designed to apply the motion capture technology for 3 years- from 2005 to 2007 - for 11 performances from 7 traditional dances of which body gestures have considerable values among the Important Intangible Cultural Heritage performances. This is to be supported by lottery funds. In 2005, the first year of the project, accumulated were data of single dances, such as Seungmu (monk's dance), Salpuri(a solo dance for spiritual cleansing dance), Taepyeongmu (dance of peace), which are relatively easy in terms of performing skills. In 2006, group dances, such as Jinju Geommu (Jinju sword dance), Seungjeonmu (dance for victory), Cheoyongmu (dance of Lord Cheoyong), etc., will be documented. In the last year of the project, 2007, education programme for comparative studies, analysis and transmission of intangible cultural heritage and three-dimensional contents for public service will be devised, based on the accumulated data, as well as the documentation of Hakyeonhwadae Habseolmu (crane dance combined with the lotus blossom dance). By describing the processes and results of motion capture documentation of Salpuri dance (Lee Mae-bang), Taepyeongmu (Kang seon-young) and Seungmu (Lee Mae-bang, Lee Ae-ju and Jung Jae-man) conducted in 2005, this report introduces a new approach for the documentation of intangible cultural heritage. During the first year of the project, two questions have been raised. First, how can we capture motions of a holder (dancer) without cutoffs during quite a long performance? After many times of tests, the motion capture system proved itself stable with continuous results. Second, how can we reproduce the accurate motion without the re-targeting process? The project re-created the most accurate motion of the dancer's gestures, applying the new technology to drew out the shape of the dancers's body digital data before the motion capture process for the first time in Korea. The accurate three-dimensional body models for four holders obtained by the body scanning enhanced the accuracy of the motion capture of the dance.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.