DOI QR코드

DOI QR Code

The Impact of Generative AI's Technical Characteristics and Librarians' Personal Traits on Intention to Use Generative AI

생성형 AI의 기술적 특성과 사서의 개인적 특성이 생성형 AI 사용의도에 미치는 영향

  • Seonghee Kim ;
  • Seung Min Lee
  • 김성희 (중앙대학교 사회과학대학 문헌정보학과) ;
  • 이승민 (중앙대학교 일반대학원 문헌정보학과)
  • Received : 2024.05.17
  • Accepted : 2024.05.25
  • Published : 2024.06.30

Abstract

This study investigated the impact of the technical characteristics of Generative AI (GAI) and librarians' personal traits on their intention to use GAI. Personalization, interaction, and context awareness were considered as technical characteristics of GAI that influence the intention to use GAI, while innovativeness and frequency of GAI use were considered as librarians' personal traits. The study targeted 187 librarians working in libraries, and 165 questionnaires were collected and analyzed. The results showed that the technical characteristics of GAI had a statistically significant impact on the intention to use GAI. Additionally, librarians' personal traits, namely innovativeness and frequency of GAI use, were also found to have a significant impact on the intention to use GAI. The findings of this study can be used as valuable information to help librarians increase their intention to use GAI and improve the quality and satisfaction of library services.

본 연구는 생성형 인공지능(Generative AI)의 기술적 특성과 도서관 사서의 개인적 특성이 생성형 AI 사용의도에 미치는 영향을 분석하였다. 이를 위해 본 연구는 생성형 AI 사용의도에 영향을 미치는 요인으로 개인화, 상호작용, 맥락 인지를 생성형 AI의 기술적 특성으로 투입하고, 혁신성과 사용빈도를 사서의 개인적 특성으로 투입하였다. 연구대상은 도서관에서 재직 중인 사서 187명이 대상이며, 이 중 165부의 설문지를 수집하여 분석에 사용하였다. 연구결과, 생성형 AI의 기술적 특성은 생성형 AI의 사용의도에 통계적으로 유의미한 영향을 미치는 것으로 나타났고, 사서의 개인적 특성인 혁신성과 생성형 AI 사용빈도 역시 모두 생성형 AI의 사용의도에 유의미한 영향을 미친 것으로 나타났다. 본 연구의 결과는 도서관 사서들이 생성형 AI 사용의도를 높여 도서관 서비스의 질과 만족도를 제고하는 중요한 기초자료로 활용될 것이다.

Keywords

Acknowledgement

이 논문은 2023년도 중앙대학교 연구 장학기금 지원에 의한 것임.

References

  1. Ahn, Jeonghee & Park, Hye Ok (2023). Development of a case-based nursing education program using generative artificial intelligence. The Journal of Korean Academic Society of Nursing Education, 29(3), 234-246. http://dx.doi.org/10.5977/jkasne.2023.29.3.234
  2. Ahn, Kwang Ho & Lim, Byung Hoon (2013). Social Research Method And Analysis. Paju: Hakhyunsa.
  3. Ahn, Shang He & Lee, Min-Hwa (2016). Fourth industrial revolution impact: How it changes jobs. Korean Academic Society of Business Adiministration, 2344-2363.
  4. Choi, Jiwoong (2023). Evaluating the Current State of ChatGPT and Its Disruptive Potential: an Empirical Study of Korean Users. Master's thesis, The Graduate School of Business Seoul National University.
  5. Digital Platform Government Committee (2024). Guidelines for the Introduction and Utilization of Large-Scale AI in the Public Sector.
  6. Hwangbo, Yun & Bae, Keun-Suk (2017). Impact of corporate entrepreneurship, human resource innovation on the firms' innovation activities and nonfinancial performance: a exploratory research of KOSDAQ Companies. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 12(4), 1-14. http://dx.doi.org/10.16972/apjbve.12.4.201708.1
  7. Jeong, Cheonsu (2023). Generative AI service implementation using LLM application architecture: based on RAG model and LangChain framework. Journal of Intelligence and Information Systems, 29(4), 129-164. https://doi.org/10.13088/jiis.2023.29.4.129
  8. Kim, Ji Soo, Kang, Su Jung, & Kwon, Sun Young (2023). A study on the recognition of teacher librarians on the introduction of ChatGPT in school library. Journal of the Korean Society for Library and Information Science, 57(2), 349-377. http://dx.doi.org/10.4275/KSLIS.2023.57.2.349
  9. Kim, Chang-Su, Lee, Sung-Ho, & Oh, Eun-Hae (2011). The impact of interaction factors of digital contents on flow and use Intention. the Journal of the Korea Contents Association, 11(9), 212-224. https://doi.org/10.5392/JKCA.2011.11.9.212
  10. Kim, Chan-Won (2024). University learners' intention to use ChatGPT using the extended technology acceptance model: focusing on personal innovativeness, perceived trust, and perceived risk. The Journal of the Korea Contents Association, 24(2), 462-475. http://dx.doi.org/10.5392/JKCA.2024.24.02.462
  11. Kim, Cheul-Hee (2022). A Study on the Influence of Entrepreneurship and Human Resource Innovation upon Entrepreneur's Business Performance: The Mediating Effects of Corporate Innovation Activities. Ph.D. Dissertation, Graduate School of Catholic Kwandong University.
  12. Kim, Hyo-Jung & Rha, Jong-Youn (2017). Impacts of the O2O mobile order and pay services continued use intention: usage frequency moderating effect. Journal of Consumption Culture, 20(3), 199-226. http://dx.doi.org/10.17053/jcc.2017.20.3.010
  13. Kim, Hyun-Jung & Lee, Jea-Woog (2021). Impact of multiview e-sports broadcasting service' affordance on perceived innovativeness and continuous use intention. Korean Journal of Sport Management, 26(1), 124-137. https://doi.org/10.31308/KSSM.26.1.124
  14. Kim, Min (2008). A Study on the Effect of Individualization on User's Satisfaction on blog Sites. Master's thesis, Graduate School of Industrial Art at Hongik University.
  15. Kim, Soo-Sang, Jang, Won-Jung, Hugo Mariano, & Gim, Gwang-Yong (2019). An exploratory study on factors affecting intention to use of AI speaker. The Journal of Information Technology and Architecture, 16(1), 71-86. http://dx.doi.org/10.22865/jita.2019.16.1.71
  16. Kim, Young Doo (2023). A study on user characteristics of consumer cooperatives and consumer problem based on visit frequency. Journal of Consumer Policy Studies, 54(1), 31-62. http://dx.doi.org/10.15723/jcps.54.1.202304.31
  17. Kim, Youngeun & Park, Ji-Hong (2021). Factors influencing the intention of knowledge sharing in public libraries: based on self-determination theory. Journal of the Korean Biblia Society for Library and Information Science, 32(1), 247-265. http://dx.doi.org/10.14699/kbiblia.2021.32.1.247
  18. Korea Information Society Development Institute (2023). Policy Report. The Changes and Policy Tasks Brought by Generative AI: Jobs and Labor (23-03-03).
  19. Lee, Hang & Kim, Joon-Hwan (2023). Effects of UTAUT on the digital literacy and acceptance intention of ChatGPT users. The Society of Convergence Knowledge Transactions, 11(2), 33-43. http://dx.doi.org/10.22716/sckt.2023.11.2.014
  20. Lee, Han-Saem & You, Ji-Won (2024). Exploring college students' educational experiences and perceptions of Generative AI: The case of a university. The Korea Contents Society, 24(1), 428-437. http://dx.doi.org/10.5392/JKCA.2024.24.01.428
  21. Lee, Ji-Eun, Shin, Minsoo, & Woo, Jung-Eun (2010). A study of factors affecting mobile widget-based personalized services. Journal of Information Technology Services, 9(2), 21-42.
  22. Lee, Jin (2024). Determinants of Continuous Use of Generative AI: Focusing on the Extended Technology Acceptance Model (ETAM) and AI Literacy. Master's thesis, The Graduate School Hanyang University.
  23. Lee, Jung-Ran, Yoo, Dongkeun, & Lee, Yong-Ki (2004). The effect of web interactivity of e-brand on relationship quality and customer loyalty. Journal of the Korean Operations Research and Management Science Society, 29(4), 73-93.
  24. Lee, Kyung-Sun (2024). Evolutionary Directions and Policy Challenges of Generative AI Technology (2024-01). Korea Information Society Development Institute.
  25. Lee, Seung-Bae (2019). A study on the effect of technology acceptance attitudes of smart order system users on their behavior of use. Journal of the Korea Management Engineers Society, 24(4), 105-121. http://dx.doi.org/10.35373/KMES.24.4.7
  26. Lee, Seungjoon, Lee, Jeha, & Chung, Doohee (2021). A study on the factors affecting the acceptance intention of chatbot service in the financial industry. Journal of Korea Technology Innovation Society, 24(5), 845-869. http://doi.org/10.35978/jktis.2021.10.24.5.845
  27. Lee, Yunhee (2023). A Study on the Antecedents and the Outcome of Generative AI Use: Focusing on the Moderating Effect of Metacognition. Ph.D. Dissertation, Graduate School of Business IT Kookmin University.
  28. Li, Li, Liu, Shanshan, & Lee, Jong-yoon (2023). A study on the intent of continuous viewing of AI announcer: focusing on the extended technology acceptance model (ETAM). Korean Journal of Communication Studies, 31(2), 79-100. http://doi.org/10.23875/kca.31.2.4
  29. Liu, Yu-Xi & Cheng, Su-Yang (2023). A study on the effect of AI news recommendation service factors on users' continuous use intention: focusing on the technology acceptance model. The Journal of the Korea Contents Association, 23(4), 39-52. http://dx.doi.org/10.5392/JKCA.2023.23.04.039
  30. Ministry of Science and ICT (2024.04.04). Launch of the top-level AI Governance "AI High-Level Consultative Council". KDI Economic Information Center. Available: https://eiec.kdi.re.kr/policy/materialView.do?num=249970
  31. Park, Jun Cheul & Park, Se Rin (2023). The effect of personal characteristics on ChatGPT attitude and intention to use. Journal of the Korea Management Engineers Society, 28(3), 33-46. http://dx.doi.org/10.35373/KMES.28.3.3
  32. Park, Ok Nam (2018). A study on the changes of libraries and directions of librarian education in the era of the fourth industrial revolution. Journal of the Korean Society for Library and Information Science, 52(1), 285-311. https://doi.org/10.4275/KSLIS.2018.52.1.285
  33. Park, Sung-Je & Lee, Jea-Woog (2018). The effect of service quality and user innovativeness of VR sports broadcasting on acceptance Iintention: focusing on the extended technology acceptance model. Journal of Sport and Leisure Studies, 71, 269-282. http://dx.doi.org/10.51979/KSSLS.2018.02.71.269
  34. Park, Tae-Yeon, Han, Hui-Jeong, Oh, Hyo-Jung, & Yang, Dongmin (2018). A study on the librarian's key tasks of the era of the 4th Industrial Revolution. Journal of Korean Library and Information Science Society, 49(2), 327-356. http://dx.doi.org/10.16981/kliss.49.2.201806.327
  35. Seok, Gwang Hee (2023). A Study on the Library Service Plan Using Artificial Intelligence (AI) Technology and the Enhancement of Professionalism of Librarians. Master's thesis, Chinju National University of Education.
  36. Su, Wenshuai & Kim, Hag-Min (2022). The influence of features of text-based chatbots in social media on user intention. The E-business Studies, 23(5), 107-125.
  37. Suh, Euy Hoon (2018). IBM SPSS Statistics. Seoul: Freeacademy.
  38. Suh, Woong & Jang, Subin (2024). Analysis of factors influencing the acceptance of generative AI to reduce teacher administrative work. The Journal of Korean Association of Computer Education, 27(1), 39-50. http://dx.doi.org/10.32431/kace.2024.27.1.003
  39. Sung, Dong gue (2012). A study of The Relationship between Consumer Innovation and Brand choice Model. Master's thesis, Konkuk University.
  40. Yang, Byunghwa (2013). The roles of switching barriers and personality traits on the link between customer loyalty and complaining behavior. The Korean Journal of Consumer and Advertising Psychology, 14(1), 129-153. http://dx.doi.org/10.21074/kjlcap.2013.14.1.129
  41. Yang, Hee-Tae (2023). Exploratory study on enhancing national competitiveness through Generative AI: focusing on collaboration between large and small-medium enterprises. Korean Management Consulting Review, 23(5), 269-281.
  42. Yu, Hyelee & Min, Young (2023). A study on intentions to use Generative AI chatbot ChatGPT: adding affordances to the technology acceptance model. Korean Journal of Broadcasting & Telecommunications Research, 124, 141-169. http://dx.doi.org/10.22876/kjbtr.2023..124.005
  43. Yu, Hyelee (2023). A Study on the User Intention of Generative AI Chatbot 'ChatGPT': Adding Affordances to the Technology Acceptance Model. Master's thesis, Graduate School of Media & Communication Korea University.
  44. Yun, Sung Im (2024). A Study on the Impact on Behaviral Intention Generative AI(GenAI) Services: Forcusing on UTAUT2. Ph.D. Dissertation, Graduate School of Dongguk University.
  45. Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies, 25, 2899-2918. https://doi.org/10.1007/s10639-019-10094-2
  46. Baek, T. H. & Morimoto, M. (2012). Stay away from me: Examining the determinants of consumer avoidance to personalized advertising. Journal of Advertising, 41(1), 59-76. https://doi.org/10.2753/JOA0091-3367410105
  47. Baek, T. H. & Kim, M. (2023). Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence. Telematics and Informatics, 83. https://doi.org/10.1016/j.tele.2023.102030
  48. Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 1-15. https://doi.org/10.1080/14703297.2023.2195846
  49. Kim, E. J., Kim, J. J., & Han, S. H. (2021). Understanding student acceptance of online learning systems in higher education: Application of social psychology theories with consideration of user innovativeness. Sustainability, 13(2), 896. https://doi.org/10.3390/su13020896
  50. Kline, R. B. (2015). Principles and practice of structural equationmodeling (4th ed.). New York: Guilford. Press.
  51. Larsen, B. & Narayan, J. (2023, January 09). Generative AI: A game-changer that society and industry need to be ready for. World Economic Forum. Available: https://www.weforum.org/agenda/2023/01/davos23-generative-ai-a-game-changer-industries-and-society-code-developers/
  52. Lin, X., Shao, B., & Wang, X. (2021). Employees' perceptions of chatbots in B2B marketing: Affordances vs. disaffordances. Industrial Marketing Management, 101(1), 45-56. https://doi.org/10.1016/j.indmarman.2021.11.016
  53. Lund, B. D. & Wang, T. (2023). Chatting about ChatGPT: how may AI and GPT impact academia and libraries?. Library hi tech news, 40(3), 26-29. https://doi.org/10.1108/LHTN-01-2023-0009
  54. Moussawi, S. (2018, June 18-20). User experiences with personal intelligent agents: A sensory, physical, functional and cognitive affordances view. Paper presented at the ACM SIGMIS Conference on Computers and People Research, Buffalo, NY. https://doi.org/10.1145/3209626.3209709
  55. Mygland, M. J., Schibbye, M., Pappas, I. O., & Vassilakopoulou, P. (2021, September 1-3). Affordances in human-chatbot interaction: A review of the literature. Paper presented at the 20th Conference on e-Business, e-Services and e-Society, Galway, Ireland.
  56. Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical System, 3, 121-154. https://doi.org/10.1016/j.iotcps.2023.04.003
  57. Russell, S. J. & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Pearson.
  58. Stoeckli, E., Dremel, C., & Uebernickel, F. (2020). How affordances of chatbots cross the chasm between social and traditional enterprise systems. Electron Markets, 30, 369-403. https://doi.org/10.1007/s12525-019-00359-6
  59. Waizenegger, L., Seeber, I., Dawson, G., & Desouza, K. (2020, January 7-10). Conversational agents: Exploring generative mechanisms and second-hand effects of actualized technology affordances. Paper presented at the 53rd Hawaii International Conference on System Sciences, Maui, HI.
  60. Yilmaz, O. & Bayraktar, D. M. (2014). Teachers' attitudes towards the use of educational technologies and their individual innovativeness categories. Procedia-social and Behavioral Sciences, 116, 34 58-3461. https://doi.org/10.1016/j.sbspro.2014.01.783