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    Title: 「台版ChatGPT」發展研究:探索TAIDE採用與公眾意見
    The development of the 'Taiwanese ChatGPT': Exploring public opinions of TAIDE adoption
    Authors: 饒珮琪
    Jao, Pei-Chi
    Contributors: 林翠絹
    Lin, Tsui-Chuan
    饒珮琪
    Jao, Pei-Chi
    Keywords: 生成式AI
    台版ChatGPT
    TAIDE
    公眾意見
    情感分析
    創新擴散理論
    活動系統框架
    Generative AI
    Taiwanese ChatGPT
    TAIDE
    Public opinion
    Sentiment Analysis
    Rogers’ Diffusion of Innovation (DOI) Theory
    Activity System Framework
    Date: 2024
    Issue Date: 2024-11-01 11:02:59 (UTC+8)
    Abstract: 台灣的生成式人工智慧（GenAI）應用尚處於早期階段。由台灣的國家科學及技術委員會發起的「可信任生成式AI對話引擎 (TAIDE)」是使用繁體中文和具台灣文化特色內容訓練的大型語言模型（LLM）。發展TAIDE的目標是建立一個本土的繁體中文語言模型，以對抗中國簡體中文模型中隱含的意識形態和文化偏見的主導地位。本研究運用創新擴散理論（Rogers，2003）和活動系統理論（Nah et al.，2023）結合的分析框架，從多方利害關係人和網路輿論的角度檢視TAIDE的發展。混合方法研究包括專家訪談和社群媒體資料情緒分析。前者訪問了 TAIDE 開發者和組織採用者，探討模型的特徵、外部因素以及採用和試用過程中的挑戰。後者針對人工智慧相關的 Facebook 討論社團及 PTT 論壇中，針對 TAIDE 的討論內容進行情緒分析。

    專家訪談顯示，公共和私營部門組織都處於採用的初始階段（即議題設定階段和合理化/適應階段），但尚未正式採用 TAIDE。除了處理繁體中文的能力之外，確保資料安全性和地端運算的客製化功能是該模型的關鍵優勢，儘管其使用者介面與使用的複雜性帶來了挑戰。對於 TAIDE 的特點，受訪者認為「可客製化」和「低運算成本」是組織採用的關鍵相對優勢。但其介面不易用、下載與使用方式複雜，需要專業的AI服務團隊協助採用。 TAIDE 模式對台灣文化和繁體中文的關注與組織價值觀一致。這些因素對於組織採用此模型的決定至關重要。

    關於TAIDE計畫的社群媒體輿情分析涵蓋了兩個時期的網路討論。在TAIDE模型正式發布之前，Facebook的討論主要以中性至正面的情緒推廣TAIDE計畫，而PTT的討論則基本上持負面態度，批評政府對資金的處理方式。 TAIDE模型正式發表後，Facebook內容轉向技術面和應用程式方面的討論，正面情緒略有上升。然而，PTT 討論仍然很具批判性，人們對 TAIDE 的有效性和資源管理持續持懷疑態度。
    訪談和輿情分析的結果揭示了不同利害關係人的不同看法。從社群媒體輿論分析，有人表示支持「台灣ChatGPT」的發展，也有人質疑該計畫的投資。相較之下，TAIDE模型開發人員強調，該計畫對於確保大型語言模型的繁體中文內容品質、資料安全和組織的模型客製化需求是必要的。社群媒體情緒分析顯示公眾看法存在分歧，Facebook 表現出中性至正面的情緒，而 PTT 仍然對政府主導的計畫持批評態度。 Facebook 用戶支援 TAIDE 的技術潛力，而 PTT 用戶則批評政府支出和效率低下。儘管組織採用者讚揚了 TAIDE 的實用性，但對其長期可持續性和傳播的擔憂仍然存在。彌合利害關係人和公眾認知之間的差距對於持續採用和支持至關重要。

    關鍵字：生成式人工智慧、台灣ChatGPT、TAIDE、公眾意見、情感分析、創新擴散（DOI）理論、活動系統框架
    Generative AI (GenAI) adoption in Taiwan is in its early stages. "Trustworthy AI Dialogue Engine (TAIDE)," initiated by Taiwan’s National Science and Technology Council, is characterized by a Large Language Model (LLM) trained with the traditional Chinese language and Taiwanese cultural nuances. The goal of developing TAIDE is to establish a home ground traditional Chinese LLM model to counter the dominance of the ideological and cultural biases embedded in China’s simplified Chinese models. This study applies an analytical framework integrating the Diffusion of Innovation (DOI) Theory (Rogers, 2003) and the Activity System theory (Nah et al., 2023) to examine TAIDE’s development from the perspectives of multi- stakeholders and online public opinion. The mixed method research encompasses expert interviews and social media data sentiment analysis. The former interviewed TAIDE developers and organizational adopters to explore the model's characteristics, external factors, and challenges during adoption and trials. The latter analyzes the sentiments in AI-related Facebook groups and PTT forums.

    The expert interviews show that both public and private sector organizations are in the initial stage of adoption (i.e. agenda setting and matching), but not yet implementing TAIDE. Apart from the ability to process traditional Chinese, ensuring data security and customization function from on-premise computing feature were key advantages of the model, though its user interface and complexity posed challenges. With respect to TAIDE’s characteristics, the interviewees identified "customizability" and "low computing cost" as key relative advantages for organizational adoption. However, its interface is not easy to use and is complex to download, which relies on professional AI service teams to assist the adoption. The TAIDE model’s focus on Taiwanese cultures and the Traditional Chinese language was compatible with organizational values. These factors have been crucial to the organization's decision to adopt the model.

    The analysis on social media public opinions about TAIDE project covers two periods of online discussion. Before the official release of the TAIDE model, Facebook discussions focused on promoting the TAIDE project with neutral to positive sentiments, while PTT discussions were largely negative, criticizing the government’s handling of funds. After the official release of the TAIDE model, Facebook content shifted to technical aspects and applications, showing a slight increase in positive sentiment. However, PTT discussions remained critical, with continued skepticism about TAIDE’s effectiveness and resource management.
    The results of the interview and public opinion analysis revealed different views from different stakeholders. From the analysis of public opinion on social media, some expressed the support for the development of “Taiwanese ChatGPT”, while others questioned the investment in the project. In contrast, the TAIDE model developer stressed that the project was necessary for ensuring Traditional Chinese content quality in LLMs, data security, and model customization needs in organizations. Social media sentiment analysis revealed divergent public perceptions, with Facebook showing neutral to positive sentiments and PTT remaining critical of the government- led project. Facebook users supported TAIDE’s technical potential, while PTT users criticized government spending and inefficiencies. Although organizational adopters have praised TAIDE’s usefulness, concerns about its long-term sustainability and diffusion remain. It is essential to bridging the gap between stakeholders and public perceptions is essential for sustained adoption and support.

    Keywords: Generative AI, Taiwanese ChatGPT, TAIDE, Public opinion, Sentiment Analysis, Rogers’ Diffusion of Innovation (DOI) Theory, Activity System Framework
    Reference: 1. Acerbi, A., & Stubbersfield, J. M. (2023). Large language models show human-like content biases in transmission chain experiments. Proceedings of the National Academy of Sciences, 120(44). https://doi.org/10.1073/pnas.2313790120
    2. Ahn, M. J., & Chen, Y. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 39(2), 101664. https://doi.org/10.1016/j.giq.2021.101664
    3. Allen, D., Karanasios, S., & Slavova, M. (2011). Working with activity theory: Context, technology, and information behavior. Journal of the American Society for Information Science and Technology, 62(4), 776–788. https://doi.org/10.1002/asi.21441
    4. Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Shishakly, R., Lutfi, A.,
    Alrawad, M., Al Mulhem, A., Alkhdour, T., & Al-Maroof, R. S. (2022). Measuring institutions’ adoption of artificial intelligence applications in online learning environments: Integrating the innovation diffusion theory with technology adoption
    rate. Electronics, 11(20), 3291. https://doi.org/10.3390/electronics11203291
    5. Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly, 37(4), 101490. https://doi.org/10.1016/j.giq.2020.101490
    6. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, 82-115.
    7. Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. (2020). I, chatbot: Modeling the
    determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473
    8. Agarwal, P., Swami, S. and Malhotra, S.K. (2024), "Artificial Intelligence Adoption in the Post COVID-19 New-Normal and Role of Smart Technologies in Transforming Business: a Review", Journal of Science and Technology Policy Management, Vol. 15 No. 3, pp. 506- 529. https://doi.org/10.1108/JSTPM-08-2021-0122
    9. Artificial Intelligence Foundation. (2024). 台灣產業 AI 化大調查: 生成式 AI 浪潮下的 企業競賽 [Taiwan industry AI transformation survey: The corporate competition in the wave of generative AI]. Artificial Intelligence Foundation. https://aif.tw/event/ai- research/file/2023%20 台灣產業%20AI%20 化大調查.pdf
    10. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation- confirmation model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921
    11. Bingcheng Wang, Pei-Luen Patrick Rau & Tianyi Yuan (2022): Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale, Behaviour & Information Technology, DOI: 10.1080/0144929X.2022.2072768
    12. Campion, A., Gasco-Hernandez, M., Jankin Mikhaylov, S., & Esteve, M. (2020a). Overcoming the challenges of Collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review, 40(2), 462-477. https://doi.org/10.1177/0894439320979953
    13. Campion, A., Hernandez, M., Mikhaylov Jankin, S., & Esteve, M. (2020b). Managing artificial intelligence deployment in the public sector. Computer, 53(10), 28-37. https://doi.org/10.1109/mc.2020.2995644
    14. Cao, Y.-H. (2024, May 3). TAIDE 一年有成 公私協力共同推進具臺灣特色之大型語言 模型[One year of TAIDE: Public-private collaboration advances a large language model with Taiwanese characteristics]. National Science and Technology
    Council. https://www.nstc.gov.tw/folksonomy/detail/dd2d9d72-8f7b-44dd-976c- 438d5ce683af?l=ch
    15. Chang, A. (2024, May 3). 國科會 TAIDE 練功 1 年會說台語 立委質詢稿可快速生 成 [National Science and Technology Council's TAIDE project masters Taiwanese in one year: Legislative inquiry drafts can be quickly generated. Economic Daily News]. https://money.udn.com/money/story/5613/7940347
    16. Chao, L.-Y. (2024, May 17). 中興大學「神農 TAIDE」 讓 AI 查找解決農業問題 [Shennong TAIDE from National Chung Hsing University helps AI solve agricultural problems]. CNA. https://www.cna.com.tw/news/ahel/202405170288.aspx
    17. Chen, G. (2024, April 15). 國科會釋出 TAIDE-LX-7B 模型，具繁體中文生成能力、支 援五大功能 [National Science and Technology Council releases TAIDE-LX-7B model with Traditional Chinese generation capability and support for five major functions]. TechNews 科技新報. https://technews.tw/2024/04/15/national-science-and-technology- council-releases-taide-lx-7b-model/
    18. Chen, M. (2023, November 13). Taiwan brings in generative AI to help students learn English. Source Asia. https://news.microsoft.com/source/asia/features/taiwan-brings-in- generative-ai-to-help-students-learn-english/
    19. Chen, T., Gascó-Hernandez, M., & Esteve, M. (2023). The adoption and implementation of artificial intelligence chatbots in public organizations: Evidence from U.S. state governments. The American Review of Public Administration.
    https://doi.org/10.1177/02750740231200522
    20. Chou, C.-H. (2023). 生成式 AI 監管方向初探-美、歐、日之路徑分析 [Preliminary
    exploration of generative AI regulation: Analysis of paths in the US, EU, and Japan]. 科技 法律透析 [Technology Law Review], 35(10), 63-
    70. https://www.airitilibrary.com/Article/Detail?DocID=a0000571-N202403210019-00013
    21. Choung, H., David, P., & Ross, A. (2022). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727-1739. https://doi.org/10.1080/10447318.2022.2050543
    22. Creery, J. (2024, January 30). Taiwan builds own AI language model to counter China’s influence. The Japan Times. https://www.japantimes.co.jp/business/2024/01/26/tech/taiwan- ai-model-counter-china/
    23. Dong, X., & Lian, Y. (2021). A review of social media-based public opinion analyses: Challenges and recommendations. Technology in Society, 67,
    101724. https://doi.org/10.1016/j.techsoc.2021.101724
    24. Donna L. Russell, & Art Schneiderheinze. (2005). Understanding Innovation in Education Using Activity Theory. Journal of Educational Technology & Society, 8(1), 38–53. http://www.jstor.org/stable/jeductechsoci.8.1.38
    25. Economic Daily News. (2023, October 5). Hami 書城推 AI 導讀旅遊好書 再送日本來回 機票 [Hami Bookstore launches AI-guided tour of good books and gives away round-trip tickets to Japan]. https://money.udn.com/money/story/122328/7485510
    26. Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki, Finland: Orienta-Konsultit.
    27. Executive Yuan, Republic of China (Taiwan). (2023, August 31). Cabinet approves draft guidelines for use of generative AI by Executive Yuan and its subordinate agencies. Executive Yuan, R.O.C.
    (Taiwan). https://english.ey.gov.tw/Page/61BF20C3E89B856/d57a1a70-c0b0-4481-9845- a6265437a8ff
    28. García-Peñalvo, F., & Vázquez-Ingelmo, A. (2023). What do we mean by GenAI? A systematic mapping of the evolution, trends, and techniques involved in generative AI. International Journal of Interactive Multimedia and Artificial Intelligence, 8(4), 7. https://doi.org/10.9781/ijimai.2023.07.006
    29. Gartner. (2023). Generative AI: What is it, tools, models, applications and use cases. https://www.gartner.com/en/topics/generative-ai
    30. Gesk, T. S., & Leyer, M. (2022). undefined. Government Information Quarterly, 39(3), 101704. https://doi.org/10.1016/j.giq.2022.101704
    31. Global Government Forum. (2023a, September 17). Canadian public servants issued guidelines for using generative AI. Civil Service & Public Sector News | Global Government Forum. https://www.globalgovernmentforum.com/canadian-public-servants- issued-guidelines-for-using-generative-ai/
    32. Global Government Forum. (2023b, July 11). Australia issues guidance on how public servants can use AI in government. Civil Service & Public Sector News | Global Government Forum. https://www.globalgovernmentforum.com/australia-issues-guidance- on-how-public-servants-can-use-ai-in-government/
    33. Ghimire, A., & Edwards, J. (2024). Generative AI adoption in the classroom: A contextual exploration using the technology acceptance model (TAM) and the innovation diffusion theory (IDT). 2024 Intermountain Engineering, Technology and Computing
    (IETC). https://doi.org/10.1109/ietc61393.2024.10564292
    34. Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2023). Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance. Frontiers in Computer Science, 5. https://doi.org/10.3389/fcomp.2023.1096257
    35. Hornyak. (2023, September 14). Why Japan is building its own version of ChatGPT. Nature. https://www.nature.com/articles/d41586-023-02868-z
    36. Horowitz, M. C., & Kahn, L. (2021). What influences attitudes about artificial intelligence adoption: Evidence from U.S. local officials. PLOS ONE, 16(10), e0257732. https://doi.org/10.1371/journal.pone.0257732
    37. Hsieh, Y.-T., & Chao, C.-H. (2023). 生成式 AI 潛藏的道德風險與資安危機 [Ethical risks and cybersecurity crises hidden in generative AI]. 會計研究月刊 [Accounting Research Monthly], 450, 82-87. https://doi.org/10.6650/ARM.202305_(450).0012
    38. Hsu, Y. (2024, May 6). TAIDE 台灣大型語言模型釋出!TAIDE 如何下載、有哪 3 大特 色? [TAIDE: Taiwan large language model released! How to download TAIDE and its 3 key features]. FutureCity. https://futurecity.cw.com.tw/article/3407
    39. Hsu, W. N. (2024b, February 23). 國產 AI 對話引擎 7B 模型四月結案 未來提供外界使 用校正 [Domestic AI dialogue engine 7B model to conclude in April and provide external usage calibration in the future]. United News
    Network. https://udn.com/news/story/7266/7788400
    40. Huang, P.-C. (2024). 當電腦選不出好的花生──從幾個新聞事件淺談金融業使用生成式 AI 科技所面臨的法律議題[When computers can't choose good peanuts: A discussion on legal issues faced by the financial industry using generative AI technology]. 當代法律 [Contemporary Law], 28, 60-67. https://www.airitilibrary.com/Article/Detail?DocID=P20240521001-N202405210008- 00007
    41. IDC. (2023, December 6). IDC 公布 2024 年台灣資通訊(ICT)市場重點趨勢預測 [IDC announces 2024 Taiwan ICT market key trend forecast]. IDC: The premier global market intelligence company. https://www.idc.com/getdoc.jsp?containerId=prAP51484623
    42. Issa, H., Jabbouri, R., & Palmer, M. (2021). An artificial intelligence (ai)-readiness and adoption framework for Agritech firms. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3971188
    43. Kaptelinin, V., & Nardi, B. (2018). Activity theory as a framework for human-technology interaction research. Mind, Culture, and Activity, 25(1), 3-5.
    44. Kar, A. K., & Kushwaha, A. K. (2021). Facilitators and barriers of artificial intelligence adoption in business – Insights from opinions using big data analytics. Information Systems Frontiers, 25(4), 1351-1374. https://doi.org/10.1007/s10796-021-10219-4
    45. Kalyan Prasad Agrawal (28 Jul 2023): Towards Adoption of Generative AI in Organizational Settings, Journal of Computer Information Systems, DOI: 10.1080/08874417.2023.2240744
    46. Khandelwal, K., Tonneau, M., Bean, A. M., Kirk, H. R., & Hale, S. A. (2024). Indian-bhed: A dataset for measuring India-centric biases in large language models. Proceedings of the 2024 International Conference on Information Technology for Social Good, 10, 231-
    239. https://doi.org/10.1145/3677525.3678666
    47. Kotwel. (2024). How Neural Networks Learn from Training Data. https://kotwel.com/how- neural-networks-learn-from-training-data/
    48. Lai, Y.-C. (2023). 生成式 AI 的教學實踐初探:人類為創作主體之觀點 [Initial exploration of generative AI in teaching practice: The perspective of humans as the main creators]. 臺灣傳播學刊 [Taiwan Communication Journal], 44, 115-
    126. https://www.airitilibrary.com/Article/Detail?DocID=P20240119001-N202401190012- 00008
    49. Leont’ev, A.N. (1978). Activity, consciousness, and personality. Upper Saddle River, NJ: Prentice-Hall.
    50. Li, C. (2024, March 20). TAIDE 完成階段性任務!近期開源釋出 7B 模型 [TAIDE completes a milestone! 7B model to be open-sourced soon]. Yahoo 奇摩新聞. https://tw.news.yahoo.com/taide 完成階段性任務-近期開源釋出 7b 模型-095205346.html
    51. Li, L., Lee, K. Y., Emokpae, E., & Yang, S. (2021). What makes you continuously use chatbot services? Evidence from Chinese online travel agencies. Electronic Markets, 31(3), 575-599. https://doi.org/10.1007/s12525-020-00454-z
    52. Li, Y. J. (2023, August). Challenges, opportunities, and hidden cybersecurity risks of generative AI for government departments. Digital Government Summit.
    53. Liao, P.-C. (2023, December 27). Why must Taiwan develop large language models? An interview with Professor Tsai Tzong-Han, head of the core model training and RLHF team for the "TAIDE Generative AI Dialogue Engine." Science and Technology Vista. https://scitechvista.nat.gov.tw/Article/C000003/detail?ID=9f256eca-4480-44dc-a25b- 94f73bf4e0cc
    54. Liao, Y .-L., & Lu, S.-C. (2024). 人工智慧之著作權法律專題:生成式 AI 的著作權侵權 風險 [Legal issues of copyright in artificial intelligence: Risks of copyright infringement in generative AI]. 理律新知 [Lihpao New Knowledge], 2024(1), 19-
    20. https://www.airitilibrary.com/Article/Detail?DocID=P20201214002-N202403160010- 00012
    55. Lin, Y. (2024a, January 31). Can Taiwan’s first Mandarin LLM prevent an invasion of Chinese Mandarin AI? Common Wealth
    Magazine. https://english.cw.com.tw/article/article.action?id=3614
    56. Lin, Y .-J. (2024b). 透過生成式 AI 幫助學生成為更好的學習者 [Helping students become better learners through generative AI]. 師友雙月刊 [Teacher & Friend Bi-monthly], 644, 56-61. https://www.airitilibrary.com/Article/Detail?DocID=P20190807001- N202403260015-00013
    57. Lun-Wei Ku and Hsin-Hsi Chen (2007). Mining Opinions from the Web: Beyond Relevance Retrieval. Journal of American Society for Information Science and Technology, Special Issue on Mining Web Resources for Enhancing Information Retrieval, 58(12), pages 1838-1850.
    58. Lund, B., Omame, I., Tijani, S., & Agbaji, D. (2020). Perceptions toward artificial intelligence among academic library employees and alignment with the diffusion of innovations’ adopter categories. College & Research Libraries,
    865. https://doi.org/10.5860/crl.81.5.865
    59. Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1), 101774. https://doi.org/10.1016/j.giq.2022.101774
    60. McDonald, R. M., & Eisenhardt, K. M. (2020). Parallel Play: Startups, Nascent Markets, and Effective Business-model Design. Administrative Science Quarterly, 65(2), 483-523. https://doi.org/10.1177/0001839219852349
    61. Misuraca, G., Van Noordt, C., & Boukli, A. (2020). undefined. Proceedings of the 13th International Conference on Theory and Practice of Electronic
    Governance. https://doi.org/10.1145/3428502.3428513
    62. Nah, F. F.-H., Cai, J., Zheng, R., & Pang, N. (2023). An activity system-based perspective of generative AI: Challenges and research directions. AIS Transactions on Human- Computer Interaction, 15(3), pp. 247-267. DOI: 10.17705/1thci.00190
    63. Nah, F.-H, Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277-304. https://doi.org/10.1080/15228053.2023.2233814
    64. Naous, T., Ryan, M. J., Ritter, A., & Xu, W. (2023). Having beer after prayer? measuring cultural bias in large language models. arXiv preprint arXiv:2305.14456.
    65. Oliver Neumann, Katharina Guirguis & Reto Steiner (20 Mar 2022): Exploring artificial intelligence adoption in public organizations: a comparative case study, Public Management Review, DOI: 10.1080/14719037.2022.2048685
    66. Powell, K. (2024, January 11). Generative AI opening next era of drug discovery. NVIDIA Blog. https://blogs.nvidia.com/blog/drug-discovery-bionemo-generative-ai/
    67. Putri, R. A., Haznitrama, F. G., Adhista, D., & Oh, A. (2024). Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese. arXiv preprint arXiv:2402.17302.
    68. Rogers, E. M. (1962). Diffusion of innovations. Free Press.
    69. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
    70. Rozado, D. (2023). The political biases of ChatGPT. Social Sciences, 12(3),
    148. https://doi.org/10.3390/socsci12030148
    71. Sætra, H. S. (2023). Generative AI: Here to stay, but for good?
    https://doi.org/10.2139/ssrn.4330244
    72. Salewski, L., Alaniz, S., Rio-Torto, I., Schulz, E., & Akata, Z. (2024). In-Context
    Impersonation Reveals Large Language Models' Strengths and Biases. Advances in Neural
    Information Processing Systems, 36.
    73. Schaefer, C., Lemmer, K., Samy Kret, K., Ylinen, M., Mikalef, P., & Niehaves, B. (2021).
    Truth or dare? – How can we influence the adoption of artificial intelligence in municipalities? Proceedings of the Annual Hawaii International Conference on System
    Sciences. https://doi.org/10.24251/hicss.2021.286
    74. Schepman, A., & Rodway, P. (2022). The general attitudes towards artificial intelligence
    scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724-2741. https://doi.org/10.1080/10447318.2022.2085400
    75. Schmager, S., Grøder, C. H., Parmiggiani, E., Pappas, I., & Vassilakopoulou, P. (2023). What do citizens think of AI adoption in public services? Exploratory research on citizen attitudes through a social contract lens. Proceedings of the Annual Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2023.544
    76. Soni, Vishvesh. (2023). Impact of Generative AI on Small and Medium Enterprises' Revenue Growth: The Moderating Role of Human, Technological, and Market Factors.
    77. Spiller, M., McIntosh, B. S., Seaton, R. A., & Jeffrey, P. (2012). Implementing pollution source control—Learning from the innovation process in English and Welsh water companies. Water Resources Management, 27(1), 75-94. https://doi.org/10.1007/s11269- 012-0161-7
    78. Tainan City Government Education Bureau. (2019, January 8). AI 教育 科技新都 臺南市 長黃偉哲率先公布將研發 ChatGPT 教學模組 [AI Education, Technology New Capital: Mayor Huang Wei-che of Tainan City takes the lead in announcing the development of ChatGPT teaching modules]. Tainan City Government
    Website. https://www.tainan.gov.tw/News_Content.aspx?n=13370&s=8245570
    79. Taide. (2023, November 1). 「TAIDE 公部門應用工作坊」:TAIDE 引擎開放測試，公部門可信賴生成式 AI 應用發想[TAIDE Public Sector Application Workshop: TAIDE engine open testing, trustworthy generative AI application ideas for the public sector]. TAIDE Taiwanese Native Large Language
    Model. https://taide.tw/index/newsList/newsDetail/4b1141818b89803d018b89c4755a01b4
    80. TAIDE. (2024). Key Research and Development Activities Timeline. https://en.taide.tw
    81. TAKEUCHI, K., & WATANABE, N. (2023, July 27). ChatGPT tech to be adopted by
    Japan government for administrative tasks. Nikkei Asia. https://asia.nikkei.com/Business/Technology/ChatGPT-tech-to-be-adopted-by-Japan- government-for-administrative-tasks
    82. Teale. (2023, November 28). Generative AI has ‘significant, beneficial’ uses for state government. Route Fifty. https://www.route-fifty.com/emerging-tech/2023/11/generative-ai- has-significant-beneficial-uses-state-government/392311/
    83. Ting, Y. Z., & Lin, Z. L. (2020). Exploring the application potential of artificial intelligence in enhancing government public governance. Archives Semi-Annual, 19(2), 24-41. https://www.airitilibrary.com/Article/Detail?DocID=P20190408001-202012- 202101140011-202101140011-24-41
    84. Tsao, Y.-H. (2024, May 3). 台版 ChatGPT 周年成果曝光 吳政忠宣布 TAIDE 計畫再延 一年 [The first-year results of the Taiwanese version of ChatGPT revealed; Wu Zhengzhong announces one-year extension of the TAIDE project]. Commercial
    Times. https://www.ctee.com.tw/news/20240503700603-430104
    85. Von Garrel, J., & Mayer, J. (2023). Artificial intelligence in studies—use of ChatGPT and AI-based tools among students in Germany. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02304-7
    86. Wang, S. H. (2023, December 26). 【2023 年度風雲人物 6】AI 會說台灣人的中文 台 語模型也完備「真歡喜看著你」 [Top Figures of 2023: AI can speak Taiwanese and complete the Taiwanese model "I'm really happy to see you"]. Mirror
    Weekly. https://www.mirrormedia.mg/story/20231226pol007
    87. Wang, W.-T. (2024, May 23). TAIDE model infused with Taiwanese flavor: Cho Jung-tai hopes to introduce it into public sectors to provide convenient services. Radio Taiwan International. https://money.udn.com/money/story/5613/7940347
    88. Wang, C.-H., & Tsai, C. (2023). Social Media as Mobilizer and Demobilizer: Evidence From Taiwan. Sage Open, 13(1). https://doi.org/10.1177/21582440231156806
    89. Wen, Y.-H. (2024). Exploring the application of generative AI tools on teaching Chinese writing: A case of ChatGPT. 語文教育論壇 [Language Education Forum], 31, 8-29. https://doi.org/10.6750/LEF.202406_(31).02
    90. Wray, S. (2023, September 14). City CIOs urged to lay the foundations for generative AI. Cities Today. https://cities-today.com/city-cios-urged-to-lay-the-foundations-for-generative- ai/
    91. Yahoo News. (2023, November 29). Yahoo. Yahoo News. https://tw.news.yahoo.com/yahoo 公布 2023「生成式 ai 台灣消費者大調查」-015925458.html
    92. Yang, W. (2024, April 15). 不讓簡體中文獨占 國科會今釋出具台灣特色大型語言模型 [Preventing simplified Chinese dominance: National Science and Technology Council releases large language model with Taiwan characteristics]. Radio Taiwan
    International. https://www.rti.org.tw/news/view/id/2202639
    93. Yigitcanlar, T., Agdas, D., & Degirmenci, K. (2022). Artificial intelligence in local governments: Perceptions of city managers on prospects, constraints and choices. AI & SOCIETY, 38(3), 1135-1150. https://doi.org/10.1007/s00146-022-01450-x
    94. Zuiderwijk, A., Chen, Y., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3),
    101577. https://doi.org/10.1016/j.giq.2021.101577
    Description: 碩士
    國立政治大學
    全球傳播與創新科技碩士學位學程
    111ZM1001
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111ZM1001
    Data Type: thesis
    Appears in Collections:[Master’s Program in Global Communication and Innovation Technology] Theses

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