• Asia pacific journal of information systems
• /
• v.34 no.1
• /
• pp.150-190
• /
• 2024

This article proposes a framework to elucidate the structural dynamics of the generative AI ecosystem. It also outlines the practical application of this proposed framework through illustrative policies, with a specific emphasis on the development of the Korean generative AI ecosystem and its implications of platform strategies at AI platform-squared. We propose a comprehensive classification scheme within generative AI ecosystems, including app builders, technology partners, app stores, foundational AI models operating as operating systems, cloud services, and chip manufacturers. The market competitiveness for both app builders and technology partners will be highly contingent on their ability to effectively navigate the customer decision journey (CDJ) while offering localized services that fill the gaps left by foundational models. The strategically important platform of platforms in the generative AI ecosystem (i.e., AI platform-squared) is constituted by app stores, foundational AIs as operating systems, and cloud services. A few companies, primarily in the U.S. and China, are projected to dominate this AI platform squared, and consequently, they are likely to become the primary targets of non-market strategies by diverse governments and communities. Korea still has chances in AI platform-squared, but the window of opportunities is narrowing. A cautious approach is necessary when considering potential regulations for domestic large AI models and platforms. Hastily importing foreign regulatory frameworks and non-market strategies, such as those from Europe, could overlook the essential hierarchical structure that our framework underscores. Our study suggests a clear strategic pathway for Korea to emerge as a generative AI powerhouse. As one of the few countries boasting significant companies within the foundational AI models (which need to collaborate with each other) and chip manufacturing sectors, it is vital for Korea to leverage its unique position and strategically penetrate the platform-squared segment-app stores, operating systems, and cloud services. Given the potential network effects and winner-takes-all dynamics in AI platform-squared, this endeavor is of immediate urgency. To facilitate this transition, it is recommended that the government implement promotional policies that strategically nurture these AI platform-squared, rather than restrict them through regulations and stakeholder pressures.

• Journal of Venture Innovation
• /
• v.7 no.2
• /
• pp.41-56
• /
• 2024

Recently, generative AI services, including ChatGPT, have garnered significant attention. These services appealed not only to digital natives, such as Generation Z, but also to digital immigrants, including seniors. This study aimed to analyze the factors affecting seniors' behavioral intention of generative AI services. A survey targeting seniors was conducted, resulting in 250 valid responses. The data were analyzed using multiple regression analysis. For this purpose, performance expectancy, effort expectancy, social influence, requisite knowledge, biophysical aging restrictions of seniors based on MATOA (Model for the Adoption of Technology by Older Adults), a research model on technology acceptance by seniors and AI hallucinations of generative AI services were set as independent variables. The empirical results were as follows: performance expectancy and social influence had a significant positive impact on seniors' behavioral intention of generative AI services. Additionally, requisite knowledge positively influenced seniors' behavioral intention of generative AI services, while biophysical aging restrictions had a significant negative effect. However, effort expectancy and AI hallucinations did not show a significant influence on seniors' behavioral intention of generative AI services. The variables were ranked by influence as follows: performance expectancy, social influence, requisite knowledge, and biophysical aging restrictions. Based on these research results, academic and practical implications were presented.

• Information Systems Review
• /
• v.25 no.2
• /
• pp.125-144
• /
• 2023

Generative Artificial Intelligence (AI) technology is gaining global attention as it can automatically generate sentences, images, and voices that humans previously generated. In particular, ChatGPT, a representative generative AI service, shows proactivity and accuracy differentiated from existing chatbot services, and the number of users is rapidly increasing in a short period of time. Despite this growing interest in generative AI services, most preceding studies are still in their infancy. Therefore, this study utilized LDA topic modeling and keyword network diagrams to derive success factors for generative AI services and to propose successful business strategies based on them. In addition, using ChatGPT, a new research methodology that complements the existing text-mining method, was presented. This study overcomes the limitations of previous research that relied on qualitative methods and makes academic and practical contributions to the future development of generative AI services.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.1
• /
• pp.591-601
• /
• 2024

As generative AI-based educational content and services targeting child users rapidly increase, the need for research related to children's perception of generative AI is increasing. Accordingly, this study sought to determine the type of generative AI recognized by children and whether cognitive, behavioral, and emotional properties were assigned to it. To understand this, we collected responses through workshop activities to create storybooks with children, semi-structured interviews, and drawing. As a result, children viewed generative AI as an artifact with a high cognitive level, but it was not a type of existing artifact.

• Journal of Venture Innovation
• /
• v.6 no.4
• /
• pp.153-170
• /
• 2023

Generative AI services, including ChatGPT, were becoming increasingly active. This study aimed to empirically analyze the factors that promoted and hindered the diffusion of such services from a consumer perspective. Accordingly, a research model was developed based on the Value-based Adoption Model (VAM) framework, addressing both benefit and sacrifice factors. Benefits identified included usefulness and enjoyment, while sacrifices were security and hallucination. The study analyzed how these factors affected the intention to use generative AI services. A survey was conducted among college students for empirical analysis, and 200 valid responses were analyzed. The analysis utilized structural equation modeling with AMOS 24. The empirical results showed that usefulness and enjoyment had a significant positive impact on perceived value, while security and hallucination had a significant negative impact. The order of influence on perceived value was usefulness, hallucination, security, and then enjoyment. Perceived value had a significant positive impact on usage intention. Moreover, perceived value was found to mediate the relationship between usefulness, enjoyment, security, hallucination, and the intention to use generative AI services. These findings expanded the research horizon academically by validating the effectiveness of generative AI services based on existing models and demonstrated the continued importance of usefulness in a practical context.

• Journal of Information Technology Services
• /
• v.22 no.6
• /
• pp.17-35
• /
• 2023

This study investigated how helpful the use of generative AI such as ChatGPT is in conducting engaged learning at each university. In this study, based on the experiences of users using generative AI technology, we analyzed the relationship between usability and ease in consideration of the characteristics of learners, and examined whether there is an intention to continue using generative AI technology in the future. In this study, in order to verify the factors affecting the intention to use ChatGPT technology in order to solve the problems given in the participating classes, we examined previous papers based on the Technology Acceptance Model (TAM) and the Information System Success Model (IS), extracted the factors affecting the intention of ChatGPT technology, and presented the research model and hypothesis. Empirical research on the continuous use of generative AI in participatory learning using ChatGPT was conducted to determine whether it is suitable for long-term and continuous use in the educational environment, and whether it is sustainable by examining the intention of learners to continue using it. First, user satisfaction was positively related to the intention to continue using generative AI technology. Second, if the user experience has a great influence on the intention to continue using ChatGPT technology, and users gain experiences such as usefulness, interest, and effective response in the process of using the technology, the evaluation of the technology is positively formed and the intention to continue using it is high. Third, the ease of use of the technology also showed that it was intended to be used continuously when an environment was provided in which users could easily and conveniently utilize generative AI technology.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.2
• /
• pp.471-485
• /
• 2024

This study explores the effective utilization of generative AI for conducting in-depth interviews within the lean startup environment. With recent technological advancements, the application of generative AI in enhancing operational productivity has been on the rise across various organizations, and this trend extends to the lean startup milieu. The research develops specific guidelines and a guidebook aimed at assisting practitioners in lean startups to conduct in-depth interviews using AI, even amidst the constraints of limited time and capital. The proposed guidebook facilitates practitioners to swiftly design and conduct interviews, thereby promoting an agile and flexible working environment within lean startups. Moreover, this study investigates practical methods for applying text-based generative AI services like ChatGPT 4 and Luyten in the fields of design and interviewing, thereby contributing to the academic discussion and practical implementation in these areas. The significance of this research lies in its potential to broaden the horizon of scholarly debate and practical application of generative AI in lean startups.

• International journal of advanced smart convergence
• /
• v.12 no.3
• /
• pp.186-191
• /
• 2023

This study would investigate the generative AIs currently in service in the era of hyperscale AIs and explore measures for the use of generative AIs, focusing on 'ChatGPT,' which has received attention as a leader of generative AIs. Among the various generative AIs, this study selected ChatGPT, which has rich application cases to conduct research, investigation, and use. This study investigated the concept, learning principle, and features of ChatGPT, identified the algorithm of conversational AI as one of the specific cases and checked how it is used. In addition, by comparing various cases of the application of conversational AIs such as Google's Bard and MS's NewBing, this study sought efficient ways to utilize them through the collected cases and conducted research on the limitations of conversational AI and precautions for its use. If connected to city-related databases, it can provide information on city infrastructure, transportation systems, and public services, so residents can easily get the information they need. We want to apply this research to enrich the lives of our citizens.

• Journal of Internet Computing and Services
• /
• v.25 no.4
• /
• pp.109-119
• /
• 2024

This study aims to contribute to the initial research on on-device AI in an environment where generative AI-based services on mobile and other on-device platforms are increasing. To derive success strategies for generative AI-based chatbot services in a mobile environment, over 200,000 actual user experience review data collected from the Google Play Store were analyzed using the LDA topic modeling technique. Interpreting the derived topics based on the Information System Success Model (ISSM), the topics such as tutoring, limitation of response, and hallucination and outdated informaiton were linked to information quality; multimodal service, quality of response, and issues of device interoperability were linked to system quality; inter-device compatibility, utility of the service, quality of premium services, and challenges in account were linked to service quality; and finally, creative collaboration was linked to net benefits. Humanization of generative AI emerged as a new experience factor not explained by the existing model. By explaining specific positive and negative experience dimensions from the user's perspective based on theory, this study suggests directions for future related research and provides strategic insights for companies to improve and supplement their services for successful business operations.

• Journal of Korean Society of Archives and Records Management
• /
• v.23 no.4
• /
• pp.179-200
• /
• 2023

Records management institutions in Korea generally face a situation where they lack the workforce to manage the vast amount of electronic records. If electronic records management tasks and archival information services can be automated and intelligentized, the workload can be reduced and the service satisfaction of users can be improved. Therefore, this study proposes to utilize "generative AI" technology in records management practice. To achieve this, the study first examined previous research that aimed to intelligently automate various tasks in the field of records management. The fundamental concepts of generative AI were subsequently outlined, and domestic cases of generative AI applications were investigated. Next, the scope of applying generative AI to the field of records management was defined, and specific utilization strategies were proposed based on this. Regarding the strategies, the effectiveness was verified by presenting results from applying commercial generative AI services or citing examples from other fields. Lastly, the benefits and implications of using generative AI technology in the field of records management, as well as limitations that must be addressed in advance, were presented. This study holds significance in that it identified tasks within the field of records management where generative AI technology can be integrated and proposed effective utilization strategies tailored to those tasks.