Published Papers

Fifty percent of emissions must be cut by 2030 and net zero emissions by 2050 to keep global warming below 1.5°C. Transport is a major component of tourism’s contribution to climate change. Therefore, encouraging tourists to use less energy intensive public transport is an important strategy in reducing tourism emissions. Despite the crucial role of public transport for sustainable tourism, the area remains substantially under researched and theoretically uninformed. To fill the research gap, an extended-value-belief-norm (EVBN) model was created and assessed, including environmental, social, and governance (ESG) factors, air quality, and climate change, and comparing the Sustainable Development Goals (SDGs) groups of fair distribution, efficient allocation, and sustainable scale. Results revealed that an EVBN model well explains tourist behavior for public transport, showing the significant distinct effect of ESG components and SDG groups in the research model, and providing theoretical and managerial insights with respect to tourist use of public transport.

Since reusable launch vehicles have revolutionized access to space, space tourism has received enormous policy and research attention. However, such growth is occurring within a wider context of concerns over climate change, emissions, and space debris. Although the space industries have enormous environmental impacts, few studies have been undertaken on the sustainability of space tourism. Therefore, we aim to create and assess an extended value-beliefs-norms theory with environmental, social, and governance (ESG) factors, trust in artificial intelligence (AI), and the benefits of AI, in comparing three types of space tourism (Earth, suborbital, and orbital). To achieve the goals, multi-method analyses of 1,000 respondents were applied, including partial least squares-structural equation modeling, multi-group analysis, fuzzy-set Qualitative Comparative Analysis, and deep learning. Results revealed that the extended value-belief-norm model well explains space tourist behavior, ESG also has significant roles on the research model, and the three types have unique characteristics.

The study of public transport and tourism, especially domestic tourism, is relatively under-researched, particularly in relation to emerging transport technologies, such as artificial intelligence (AI), and environmental, social, and governance (ESG). To bridge this gap, an integrated research model is created and tested with ESG, air quality, climate change, and AI, applying multi-analysis methods of partial least squares-structural equation modelling (PLS-SEM), multi-group analysis (MGA), and fuzzy-set qualitative comparative analysis (fsQCA) in an Asian context. The three methods provide a well-rounded perspective of the factors that influence tourists’ public transport use. Symmetric methods of SEM and MGA identifies key variables and their relationships, while the fsQCA reveals complex combinations of conditions. Results reveal that environmental and social ESG as well as climate change mitigation and sustainable mobility are significant for use of public transport by domestic tourists. High and low AI knowledge groups also have distinctive public transport use characteristics.

Increasing tourist use of public transport is a potentially significant means of reducing greenhouse gas emissions. There are limited theoretically informed studies that focus on domestic tourist use of public transport, particularly in an Asian cultural context (e.g. South Korea). To bridge the research gap, this study applies and tests an extended value-attitude-behaviour (EVAB) theory, including personal and social norms and subjective well-being, along with artificial intelligence (AI) benefits as a moderator based on partial least squares-structural equation modelling, multi-group analysis, fuzzy-set qualitative comparative analysis and deep learning in South Korea. The high and low AI benefit groups are compared to each other according to multi-analysis methods. Results revealed that the EVAB model well explains travelers’ behaviour with public transport and AI benefits partially moderate the research model, showing some unique differences.

Industry 4.0 technology enables luxury fashion brands in the virtual market toquantify the value of digital items in the metaverse; thus, brands can maintain theirreputations, ensure consistent and integrated luxury brand marketing, and attractnew consumers in the virtual market. Understanding consumer behavior towardbuying digital assets (i.e., nonfungible tokens [NFTs]) is important. By usingblockchain‐based NFTs as a way to verify the authenticity of digital assets in thevirtual market, luxury brands can maintain their reputations and help consumersprotect their digital assets. Thus, developing global marketing strategies supportedby this technology is important for the success of luxury fashion brands in themetaverse. We conducted analyses to explore consumer behavior in the metaversewith regard to blockchain‐based luxury NFTs. The findings reveal the psychologicalevaluation process as a mechanism that drives consumer behavior toward NFTluxury brand fashion items in global virtual markets. The empirical findings alsoextend the application of game theory and prospect theory by revealing thepsychological evaluation of risks associated with (not) buying luxury fashion NFTs asanother mechanism driving consumer behavior in the metaverse.

There is limited theoretically informed research on sustainability in the space tourism market. An extended model of goal-directed behavior (EMGB) is created and verified with individual and organizational interventions, comparing space tourism experienced and non-experienced travelers. Results revealed that individual intervention has fully significant effects on all MGB constructs, while organizational intervention has partially significant impacts, which in turn influence desire relevant to behavioral intention. The findings from fsQCA showed the different profiles of experienced and non-experienced tourists.

Online for offline (O4O) ecommerce is a new multichannel strategy that is being adopted worldwide, whereby successful online companies launch offline retail stores, such as Amazon Go and Alibaba's Hema Fresh Supermarket. Despite this new retail trend, it is unclear whether this new business model is sustainable. Therefore, the purpose of this empirical study is to identify factors affecting consumers’ intention to continue using online and offline channels established by successful online brands using an O4O strategy, and to test the halo effect of online stores going offline. This study reveals: (a) how consumers’ online utilitarian and hedonic values and parasocial relationships affect their intention to continue to visit online stores; (b) the relationship between consumers’ intention to visit online stores and their intention to visit offline stores; and (c) the mediating roles of utilitarian and hedonic values in parasocial relationships between online and offline stores. The findings of this study confirm that utilitarian value, hedonic value, and parasocial relationships have significant effects on intention to continue visiting online and offline stores. Also, consumers’ intention to continue to visit online stores strongly influences the perceived utilitarian and perceived hedonic value of the offline store. However, no significant direct effect was found related to their intention to continue visiting offline stores. These results suggest that the perceived utilitarian and hedonic value of the online store can expand and sustain enterprises adopting the multichannel strategy. 

This study examines consumers' evaluations of product consumption values, purchase intentions and willingness to pay for fashion products designed using generative adversarial network (GAN), an artificial intelligence technology. This research investigates differences between consumers' evaluations of a GAN-generated product and a non-GAN-generated product and tests whether disclosing the use of GAN technology affects consumers' evaluations. Sample products were developed as experimental stimuli using cycleGAN. Data were collected from 163 members of Generation Y. Participants were assigned to one of the three experimental conditions (i.e. non-GAN-generated images, GAN-generated images with disclosure and GAN-generated images without disclosure). Regression analysis and ANOVA were used to test the hypotheses. Functional, social and epistemic consumption values positively affect willingness to pay in the GAN-generated products. Relative to non-GAN-generated products, willingness to pay is significantly higher for GAN-generated products. Moreover, evaluations of functional value, emotional value and willingness to pay are highest when GAN technology is used, but not disclosed. This study evaluates the utility of GANs from consumers' perspective based on the perceived value of GAN-generated product designs. Findings have practical implications for firms that are considering using GANs to develop products for the retail fashion market.

The rapid growth of artificial intelligence (AI) technology has prompted the development of AI-based intelligent products. Accordingly, various technology acceptance theories have been used to explain acceptance of these products. This comparative study determines which models best explain consumer acceptance of AI-based intelligent products and which factors have the greatest impact in terms of purchase intention. We assessed the utility of the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Value-based Adoption Model (VAM) using data collected from a survey sample of 378 respondents, modeling user acceptance in terms of behavioral intention to use AI-based intelligent products. In addition, we employed decomposition analysis to compare each factor included in these models in terms of influence on purchase intention. We found that the VAM performed best in modeling user acceptance. Among the various factors, enjoyment was found to influence user purchase intention the most, followed by subjective norms. The findings of this study confirm that acceptance of highly innovative products with minimal practical value, such as AI-based intelligent products, is more influenced by interest in technology than in utilitarian aspects.

The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.

This study aims to learn lessons from the sanctioned cases in violation of the EU General Data Protection Regulation (hereinafter the ‘GDPR’) in (1) finance, (2) health and (3) commerce sectors in the EU. We collected 994 cases in which fines were imposed for violation of the GDPR from the GDPR Enforcement Tracker and analyzed them using the keyword network and text mining analysis methods. First, we identified general causes of sanctions in each sector, and second, characterized and compared the causes of sanctions according to sizes of companies categorized into small, medium, and large in each sector. Through the examination of these analyses, a set of pragmatic implications has been deduced, pertinent to Korean enterprises operating within similar sectors. Furthermore, implications also extend to the Korean governmental sphere, offering insights for policy formulation, as well as to the academic community, serving as a basis for subsequent theoretical investigations.

As consumers' expectations for the safety of imported food increase, the importance of safety inspection on imported food is also increasing. However, legacy randomized testing in a manual manner is not very effective. Even though several machine learning methods have been proposed to improve randomized testing, it has been difficult to propose a model with good performance due to the dearth of features provided by the competent department. Moreover, the data for the judgment of nonconformity of imported food has severe data imbalance. Hence, the purpose of this study is to propose a machine learning model that detects unsuitable food suitable for the characteristics of data possessed by government offices on imported food. First, the components of food import/export customs clearance data that affect the judgment of nonconformity were examined and derived variables were newly created. Second, to select features for machine learning, class imbalance and nonlinearity were considered when performing exploratory analysis on imported food-related data. Third, we try to compare the performance and interpretability of each model by applying various machine learning techniques. The result of the performance analysis suggests that the proposed model is superior to the model being used by the Ministry of Food and Drug Safety. In particular, the ensemble model shows better performance than other models.

In order to prepare ethical and legal standards for the use of artificial intelligence, the international community and major countries are proposing ethical principles, guidelines for use, and legislative proposals. In particular, due to the characteristics of artificial intelligence, whose performance is dependent on learning data, issues of invasion of personal information and privacy, fairness and bias, and discrimination are constantly being raised, and discussion on the possibility of explaining the results of artificial intelligence and attributable to it. Accordingly, research on various issues related to the use of artificial intelligence technology is being conducted in the field of law. Therefore, to examine the research trends related to artificial intelligence in domestic and foreign law fields, this study collected academic papers that include the keywords of artificial intelligence, machine learning, and deep learning for the last ten years from 2010 to 2020. For this, we targeted academic journals registered with the National Research Foundation of Korea and the Web of Science database. And we employed keyword network analysis and topic modeling based on the BERT model by collecting author selection research keywords and abstracts (176 domestic cases, 275 foreign cases). As a result of the study, domestic and foreign artificial intelligence-related legal research topics after 2018 have increased in various ways, and there has been a tendency to gradually develop from specific categories and issues on the use of artificial intelligence to general legal and policy discussions. Therefore, this study aims to promote a comprehensive understanding of the future AI-related legislative direction and industrial changes through the results of this study.

At the crossroads of social change caused by the spread of the Fourth Industrial Revolution and the prolonged COVID-19, the Korean government announced the Digital New Deal policy on July 14, 2020. The Digital New Deal policy's primary goal is to create new businesses by accelerating digital transformation in the public sector and industries around data, networks, and artificial intelligence technologies. However, in a rapidly changing social environment, information asymmetry of the future benefits of technology can cause differences in the public's ability to analyze the direction and effectiveness of policies, resulting in uncertainty about the practical effects of policies. On the other hand, the media leads the formation of discourse through communicators' role to disseminate government policies to the public and provides knowledge about specific issues through the news. In other words, as the media coverage of a particular policy increases, the issue concentration increases, which also affects public decision-making. Therefore, the purpose of this study is to verify the dynamic relationship between the media coverage and the stock market on the Korean government's digital New Deal policy using Granger causality, impulse response functions, and variance decomposition analysis. To this end, the daily stock turnover ratio, daily price-earnings ratio, and EWMA volatility of digital technology-based companies related to the digital new deal policy among KOSDAQ listed companies were set as variables. As a result, keyword search volume, daily stock turnover ratio, EWMA volatility have a bi-directional Granger causal relationship with media coverage. And an increase in media coverage has a high impact on keyword search volume on digital new deal policies. Also, the impulse response analysis on media coverage showed a sharp drop in EWMA volatility. The influence gradually increased over time and played a role in mitigating stock market volatility. Based on this study's findings, the amount of media coverage of digital new deals policy has a significant dynamic relationship with the stock market.

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 AutoRegression) 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 four years from January 1, 2016, 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.

China's rapid development and commercialization of high-tech technologies in the fourth industrial revolution has led to effective technology exchanges between Korean and Chinese firms becoming more important to Korea's mid-term and long-term industrial development. However, there is still a lack of empirical research on how technology exchanges between Korean and Chinese firms proceed and their effectiveness. In response, this study conducted a social network analysis based on text mining data of Korea-China business technology exchange and cooperation articles introduced in the news from 2018 to March 2020 on the current status and effects of Korea-China technology exchanges related to the fourth industrial revolution, and conducted a regression analysis how network centrality effect on the firm performance. According to the results, most of the Korean major electronic firms are actively networking with Chinese firms and institutions, showing high centrality in the centrality index. Korean telecommunication firms showed high betweenness centrality and subgraph centrality, and Korean Internet service providers and broadcasting contents firms showed high eigenvector centrality. In addition, Chinese firms showed higher betweenness centrality than Korean firms, and Chinese service firms showed higher closeness centrality than manufacturing firms. As a result of regression analysis, this network centrality had a positive effect on firm performance. To the best of our knowledge, this is the first to analyze the impact of the technical cooperation between Korean and Chinese firms under the fourth industrial revolution context. This study has theoretical implications that suggested the direction of social network analysis-based empirical research in global firm cooperation. Also, this study has practical implications that the guidelines for network analysis in setting the direction of technical cooperation between Korea and China by firms or governments.

Recently, as artificial intelligence such as deep learning has developed, it is beginning to be applied to product production, such as creating new ideas or media. Correspondingly, the expectation for increasing productivity of new products is increasing. However, there is still a negative opinion on the use of deep learning such as deep fake, and it is still not obvious whether the technology will be accepted. In addition, little has been known about the interests of Christians in their evaluation and acceptance of deep learning-based products. As a follow-up study of religiousness on the acceptance of artificial intelligence products, the purpose of this study is to verify the moderating effect of christian religiousness on deep learning-based products. Especially, this paper empirically analyzes whether christian religiousness plays a moderating effect on the effect of consumer’s consumption value on its acceptance. To this end, we surveyed 163 consumers in their 20s and 30s about the appraisal of apparel products made by deep learning technology called cycleGAN. As a result, Christians significantly influenced product acceptance in functional values and non-Christian in social and emotional values. On the other hand, it was found that the intention of recommendation had a significant influence on the intention of recommendation for the product in the functional value and social value in the case of Christians and in the emotional value in the case of Christians.

This study examines the effect of founders' strategic orientation and social capital on the performance of startups in Korean contexts. Founders' strategic orientation is proposed as the main factor to reduce the risk of failure and to increase the potential of future growth of the startup. Three main components of strategic orientation, entrepreneurial, market, technology, were simultaneously tested with a sample of Korean startups. Furthermore, founders' social capital, networks with supporting entities and the level of chemistry within founding members were also proposed to be important factors. We also tested possible mediating effects of social capital on the main impact of strategic orientation. With a sample of 79 startups founded less than 5 years in Seoul Metropolitan Areas, we found that technology orientation of founders only significantly increases the performance of startups, but both entrepreneurial and market orientation do not. Regarding social capital effect, the external networks of the founding members are effective as proposed, and also the high cohesiveness of founding members increases the startup performance. However, the mediating role of social capital is just partially confirmed. The implications of our findings, in both sense of firm's strategy and startup policy, are discussed. This study is expected to be a bridge connecting firms' strategy with startup policy-making in Korea.

Recently, artificial intelligence (AI)-enabled products and services such as smartphones, smart speakers, chatbots are being released due to advances in AI technology. Thus researchers making effort to reveal that consumers’ intention to adopt AI-enabled products. Yet, little is known about the intended adoption of AI-enabled products. Because most of studies has been not consideredthe perceived utility value of consumers for each attribute by classified based on the characteristics of AI-enabled products. Therefore, the purpose of this study is to investigate the difference in importance between attributes that affect the intention to adopt of AI-enabled products. For this, first, identified and classified the attributes of AI-enabled products based on IS Success Model of DeLone and McLean. Second, measured the utility value of each attribute on the adoption of AI-enabled products through conjoint analysis. And we employed construal level theory to see whether there are differences in the relative importance of AI-enabled products attributes depending on the temporal distance. Third, we segmented the market based on the utility value of each respondent through cluster analysis and tried to understand the characteristics and needs of consumers in each segment market. We expect to provide theoretical implications for conceptually structured attributes and factors of AI-enabled products and practical implications for how development efforts of AI-enabled products are needed to reach consumers need for each segment.