• Korean small business review
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• v.43 no.4
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• pp.27-57
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• 2021

How does authenticity influence the process that conducts a company's CSR Strategy? Authenticity, an internal/external alignment condition that an employee feels in relation to an organization, means the decision on how true and beneficial to employees through their experiences, such as thoughts and emotions. Also, it can be understood as a process of meaning formation between the organization's strategy to conduct CSR and the perception of employees conducting CSR. To prove the relation between authenticity and CSR clearly, we used various techniques like Text Mining, Topic Modeling and Semantic network analysis about O corporation's 657 review data, from 2015 to 2021. As a result of the analysis, we find out the special issues and types. The analysis shows that the issue concerning the 'external image' is the biggest characteristic of authenticity perception in other conditions. Furthermore, the types of authenticity perception evaluations are largely divided into acceptance and rejection, in detail, five categories. This study indicates that organizations should consider both external and internal conditions when establishing CSR strategies. In addition, it is necessary to be an interactive circular relationship between the organization and employee, collecting and reflecting employee's perceptions. Finally, this study proposes ways to overcome problems related to interaction.

• Korean Association of Arts Management
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• no.58
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• pp.121-152
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• 2021

This study attempted to analyze the effect of the color series of artworks installed as environmental stimuli in commercial spaces on the satisfaction of visitors and the moderating effect of the relationship. To this end, based on the SOR model of Stimulate-Organism-Response applied to burial environment research in the field of environmental psychology, and the preceding research using the SOR model, artwork color series(S)-mood and spaace amenity(O)-A research framework for satisfaction(R) was developed. In the experiment, an online questionnaire was conducted for domestic college students and graduate students by producing images with two conditions depending on the case where warm colors and cold colors were installed for the color series of artworks. As a result of verifying the difference in satisfaction of respondents corresponding to the two conditions through regression analysis, it was found that the warm color(vs. cold color) of the artwork color series induces higher visitor satisfaction. In addition, as a result of verifying the controlling factors of mood and space amenity variables in this relationship of influence, a significant moderating effect was found when the positive mood of warm colors(vs. cold colors) in the artwork color series was felt higher than the average. And, of the four types of space amenity, it was found that a significant moderating effect appeared when only comfort and aesthetics were measured as moderating variables. The result of this study proves that the warm color series of artworks that stimulate the physical environment of commercial spaces has a more positive effect on the satisfaction of visitors than the cold color series, and this is reinforced by positive mood, comfort, and aesthetics. It adds understanding and provides useful implications for marketing strategies for building an effective spatial image.

• Korean Association of Arts Management
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• no.56
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• pp.119-144
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• 2020

Not only the government, but private corporations have contributed a lot to growth and development of culture. Corporations have mainly made charitable, dispensational Macenat activities which are separate from their business activities. Such an one-sided and charitable method of supporting culture cannot last long - Part of the reason why the number of corporations supporting culture is decreasing lies in it. In order to have sustainable partnership with culture, first, corporations should figure out needs of the other party. Second, the activities of corporations to support culture should be corporate citizenship activities which are linked to their business activities. In particular, the existing concepts of CSR and CSV have some limits. CSR separates business activities of corporations and their social contribution activities, and CSV mainly assumes corporate social activities helpful to their business activities. But, the concept of corporate citizenship suggested in this study assumes corporate activities where corporations do their best not only in their business activities, but in solution of social problems. Accordingly, searching for the ways to practice corporate citizenship, this study analyzed global agendas of UN, UNESCO, and UCLG which suggest sustainable development with culture and corporate citizenship activities related with culture among corporations in Korea and other countries. The findings and hints of the analysis are as follows. First, corporate citizenship activities can contribute to building of unique images of corporations and improvement of brand identities. Second, such activities can help corporations to be born again as life style companies by using local cultures and their attractiveness. Third, corporations should have partnership with cultural associations creating shared values and provide them with continuous and stable support. And, cultural associations should try to grow with corporations through efforts to develop attractive contents and programs harmonious with management purposes of corporations.

• Korean Association of Arts Management
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• no.54
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• pp.33-57
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• 2020

This study aimed to empirically analyze the effect of the appreciation of artwork in the workplace on creativity. To this end, two virtual workspace images with and without artwork were created, and an online survey was conducted with office workers. A regression analysis was performed on the results to investigate whether and how much the appreciation and recognition of artwork was effective for the creativity. As a result, among the factors of recognition according to the appreciation of artwork, "intellectual development" and "thinking" showed positive effects on the five sub-factors of creativity at work, such as original flexibility, alternative problem-solving skills, pursuit of adventure and freedom, individual independence, and exploratory immersion. Unlike most previous studies, however, "understanding" had a negative effect on original flexibility. In conclusion, it was found that some of the factors of the appreciation and recognition of artwork had a positive effect on creativity at work. This study provides implications that the appreciation of artwork in the workplace is effective for improving creativity at work and that it is important for each company to develop a streamlined approach based on its goal of pursuing a creative environment. In addition, it is expected that this study will contribute to the widespread use of artwork sharing services at workplaces as well as encouraging more empirical studies to be done on the effect of the services.

• Korean Association of Arts Management
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• no.58
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• pp.49-88
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• 2021

Contemporary literary writers engage in multiple jobs and activities in the changing industrial and institutional environments to manage careers and produce literary value. The notion of art entrepreneurship envisages writers as the actors pursuing optimal rewards at both literary and economic levels by applying creative knowledge and skills to the management of career. In contrast, the notion of creative labor argues that writers go through career insecurity as they pursue self-fulfillment through work. This paper critically reviews two notions and suggests the notion of cultural intermediaries to better understand their production of literary value within the variety of relational contexts where they are situated. This paper analyzes the structures and characteristics of a wide range of intermediary practices by literary writers. Based on the analysis, I argue that the autonomy of literary value the writers construct and their status of entrepreneur-labourer are contingent upon the relational contexts within which they practice the intermediary work. I also suggest that literary writers by utilizing a variety of tactics cope with the changes that shape the autonomy of literature and invent new roles and identities as cultural intermediaries. Furthermore, literary writers develop not only self-management skills to adapt to the changing environments but also the collective capacity to cope with the constraints derived from the structural change of literary production and circulation. Finally, I argue that the art management discipline can reflect upon and support the creative endeavors of literary writers by embracing the critical understanding of structural changes suggested by the disciplines of humanities and social sciences.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.403-410
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• 2022

Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.57-74
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• 2022

Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.