在全球化與多語環境交織的國際商展情境中，AI 翻譯已成為推動展覽服務數位轉型的關鍵技術，但過去研究多聚焦系統效能或語料建置，對台灣與港澳觀展族群在使用者體驗與科技接受歷程上的實證探討仍付之闕如。為填補此缺口，本研究結合科技接受模型與 UX 理論，旨在檢驗 AI 翻譯技術特性如何透過感知易用性與可用性影響使用者體驗。研究採混合方法設計：量化部分回收 719 份有使用AI 翻譯經驗的有效問卷並以結構方程模型驗證假設，模型適配度良好；質化部分則對8位實際參觀國際商展的受訪者進行訪談並以內容分析交互佐證。結果顯示，AI 翻譯特性顯著提升感知易用性與可用性，兩者又分別強化使用者體驗；訪談亦指出AI翻譯可節省資訊搜尋時間並帶來正向情感體驗，但在專業術語處理與高噪音場域仍需改進。實務上建議展覽主辦單位與系統開發者整合 RAG 術語庫、強化噪音抑制與即時回饋機制，以進一步提升易用性與信任感；政策層面可將 AI 翻譯納入智慧展覽服務指引，打造更友善的多語溝通環境。理論上，本研究驗證結構方程模型在 AI 翻譯場域的適用性，驗證感知易用性與可用性於 AI 翻譯特性與使用者體驗之間的中介角色，並為未來多語人機互動研究提供新的視角與實務啟示。

In the context of international trade exhibitions shaped by globalization and multilingual environments, AI translation has emerged as a key technology driving the digital transformation of exhibition services. However, past research has largely focused on system performance or corpus construction, with limited empirical studies examining user experience and technology acceptance processes among exhibition attendees from Taiwan, Hong Kong, and Macau. To address this gap, this study integrates the Technology Acceptance Model (TAM) with user experience (UX) theory to examine how the characteristics of AI translation technologies influence user experience through perceived ease of use and perceived usefulness. A mixed-methods research design was adopted. The quantitative phase collected 719 valid survey responses from participants who had prior experience with AI translation, and structural equation modeling (SEM) was used to test the hypotheses, yielding a well-fitting model. The qualitative phase involved interviews with eight exhibition attendees from Taiwan, Hong Kong, and Macau, and content analysis was employed to cross-validate findings. Results indicate that the characteristics of AI translation significantly enhance perceived ease of use and perceived usefulness, both of which, in turn, positively influence user experience. Interviews also revealed that AI translation reduces information-search time and fosters positive emotional experiences. However, challenges remain in handling professional terminology and operating effectively in noisy environments. Practically, it is recommended that exhibition organizers and system developers integrate Retrieval-Augmented Generation (RAG) terminology databases, enhance noise suppression, and implement real-time feedback mechanisms to further improve usability and trust. At the policy level, incorporating AI translation into smart-exhibition service guidelines could help create a more multilingual-friendly communication environment. Theoretically, this study confirms the applicability of SEM in the field of AI translation, validates the mediating roles of perceived ease of use and perceived usefulness between AI translation characteristics and user experience, and offers new perspectives and practical implications for future research on multilingual human–machine interaction.

第一章 緒論	7
1.1 研究背景與動機	7
1.2 研究目的	11
1.3 研究流程	11
1.4 研究範圍	12
第二章 文獻探討	14
2.1 AI 翻譯技術的發展	14
2.1.1 機器翻譯	14
2.1.2 文本翻譯	18
2.1.3 語音翻譯	21
2.1.4 發展	24
2.2 各國AI翻譯發展現況	27
2.3 科技接受模型（TAM）與 AI 翻譯的使用者體驗	34
2.4 小結	39
第三章 研究方法	41
3.1 研究假說	41
3.2 研究架構	42
3.3 研究變項操作性定義與測量工具	44
3.3.1 自變數	45
3.3.2 中介變數	47
3.3.3 依變數	49
3.4 研究設計	50
3.5 資料分析方法	52
3.5.1 敘述性統計分析	52
3.5.2 信效度分析	52
3.5.3 相關分析	54
3.5.4 結構方程模型	54
第四章 資料分析與結果	55
4.1 樣本結構分佈	56
4.2 信效度分析	62
4.3 相關分析	74
4.4 結構模式之配適度檢驗	75
第五章 結論	76
5.1 結論	78
5.2 實務意涵	79
5.3 研究限制與未來研究方向	81

文獻
1.	吳明隆（2006）。問卷統計分析實務：SPSS 操作與應用。臺北市：五南圖書出版公司。
2.	邱皓政（2000）。量化研究與統計分析：SPSS 中文視窗版資料分析範例解析。臺北市：五南圖書出版公司。

References
1.	Albayati, H. (2024). Investigating undergraduate students’ perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Computers and Education: Artificial Intelligence, 6, Article 100203. 
2.	Amershi, Saleema, Daniel S. Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi T. Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, & Eric Horvitz. (2019).
3.	Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Paper 3). ACM. 
4.	Bahdanau, Dzmitry, Kyunghyun Cho, & Yoshua Bengio. (2015). Neural machine translation by jointly learning to align and translate. 3rd International Conference on Learning Representations (ICLR) – Conference Track Proceedings.
5.	Bar-Hillel, Yehoshua. (1960). The present status of automatic translation of languages. Advances in Computers, 1, 91–163.
6.	Brown, Peter F., John Cocke, Stephen A. Della Pietra, Vincent J. Della Pietra, Fredrick Jelinek, John D. Lafferty, Robert L. Mercer, & Paul S. Roossin. (1990). A statistical approach to machine translation. Computational Linguistics, 16(2), 79–85. 
7.	Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, & Dario Amodei. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. 
8.	Brynjolfsson, Erik, & Andrew McAfee. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
9.	Chang, Yi-Cheng, & Chia-Ling Lo. (2022). Effects of AI translation quality on user trust: An empirical study. In Proceedings of the 2022 International Conference on Applied Artificial Intelligence (pp. 55–60).
10.	Cooper, Gregory S. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32, 444–452. 
11.	Cronbach, Lee J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
12.	Dalgıç, A., Yaşar, E., & Demir, M. (2024). ChatGPT and learning outcomes in tourism education: The role of digital literacy and individualized learning. Journal of Hospitality, Leisure, Sport & Tourism Education, 34, Article 100481.
13.	Davis, Fred D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
14.	Devlin, Jacob, Ming-Wei Chang, Kenton Lee, & Kristina Toutanova. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 4171–4186). Association for Computational Linguistics.
15.	Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, & Neil Houlsby. (2021). An image is worth 16 × 16 words: Transformers for image recognition at scale. International Conference on Learning Representations. 
16.	Fornell, Claes, & David F. Larcker. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
17.	Fukuda, Ryo, Yuta Nishikawa, Yasumasa Kano, Yuka Ko, Tomoya Yanagita, Kosuke Doi, Mana Makinae, Sakriani Sakti, Katsuhito Sudoh, & Satoshi Nakamura. (2023). NAIST simultaneous speech translation system for IWSLT 2023. In Proceedings of the 20th International Conference on Spoken Language Translation (pp. 330–340). 
18.	Gefen, David, Elena Karahanna, & Detmar W. Straub. (2003). Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management, 50(3), 307–321.
19.	Gupta, Kartik K., Rafal Haque, Asif Ekbal, Pushpak Bhattacharyya, & Andy Way. (2020). Modelling source- and target-language syntactic information as conditional context in interactive neural machine translation. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (pp. 195–204).
20.	Hair, Joseph F., William C. Black, Barry J. Babin, & Rolph E. Anderson. (2010). Multivariate data analysis (7th ed.). Pearson.
21.	Hassenzahl, Marc. (2008). User experience (UX): Towards an experiential perspective on product quality. In Proceedings of the 20th International Conference of the Association Francophone d’Interaction Homme-Machine (pp. 11–15).
22.	Herbig, Nico, Santanu Pal, Josef van Genabith, & Antonio Krüger. (2019). Multi-modal approaches for post-editing machine translation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Paper 231). 
23.	Hossain, Tanzila, & Khurshid Muhammad. (2023). Transformers in the real world: A survey on NLP applications. Information, 14(4), 242.
24.	Hovy, Dirk, & Sharon L. Spruit. (2016). The social impact of natural language processing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 591–598).
25.	Huffaker, Jason S., & Sai R. Gouravajhala. (2022). Shaping trust in machine translation suggestions through AI-assisted context building (Technical report).
26.	Humphreys, Linda, Xuan Chen, & Yi Wang. (2023). Mitigating cognitive load in machine translation post-editing: An eye-tracking study. Translation & Interpreting Studies, 18(2), 210–229.
27.	Hutchins, John W. (1986). Machine translation: Past, present, future. Ellis Horwood.
28.	Hutchins, John W. (2007). Machine translation: A concise history (Unpublished manuscript). 
29.	Johnson, Melvin, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, & Jeffrey Dean. (2017). Google’s multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 5, 339–351. 
30.	Kaiser, Henry F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36.
31.	Karlsson, Felix. (2020). User-centered visualizations of transcription uncertainty in AI-generated subtitles of news broadcast (Master’s thesis). Uppsala University.
32.	Kasneci, Enkelejda, Kathrin Sessler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, Stephan Krusche, Gitta Kutyniok, Tilman Michaeli, Claudia Nerdel, Jürgen Pfeffer, Oleksandra Poquet, Michael Sailer, Albrecht Schmidt, Tina Seidel, Matthias Stadler, Jochen Weller, Jochen Kuhn, & Gjergji Kasneci. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. 
33.	Li, Dong, Yonghui Gao, Chenxi Zhu, Qian Wang, & Rui Wang. (2023). Improving speech recognition performance in noisy environments by enhancing lip-reading accuracy. Sensors, 23(4), 2053. 
34.	Lin, Tianyang, Yuxin Wang, Xiangyang Liu, & Xipeng Qiu. (2021). A survey of transformers. arXiv:2106.04554. 
35.	Liu, Yinhan, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, & Luke Zettlemoyer. (2020). Multilingual denoising pre-training for neural machine translation. Transactions of the Association for Computational Linguistics, 8, 726–742. 
36.	Minsky, Marvin. (1968). Semantic information processing. MIT Press.
37.	Nguyen, Minh-Tien, Dat P. Nguyen, Tuan-Hai Luu, Xuan-Quang Nguyen, Tung-Duong Nguyen, & Jeff Yang. (2024). Improving speech recognition with jargon injection. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue (pp. 490–499).
38.	Och, Franz J., & Hermann Ney. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1), 19–51.
39.	Oncevay, Arturo, Charese Smiley, & Xiaomo Liu. (2025). The impact of domain-specific terminology on machine translation for finance in European languages. In Proceedings of NAACL-HLT 2025 (pp. 2758–2775).
40.	OpenAI. (2023). GPT-4 technical report. arXiv:2303.08774.
41.	Pallant, Julie. (2013). SPSS survival manual (5th ed.). McGraw-Hill.
42.	Papineni, Kishore, Salim Roukos, Todd Ward, & Wei-Jing Zhu. (2002). BLEU: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (pp. 311–318).
43.	Park, Jungwoo, Sang-Hyun Han, Hyun-Kyu Kim, Yonghwan Cho, & Weon-Gyu Park. (2013). Developing elements of user experience for mobile phones and services: Survey, interview, and observation approaches. Human Factors and Ergonomics in Manufacturing & Service Industries, 23(4), 279–293.
44.	Raffel, Colin, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, & Peter J. Liu. (2020). Exploring the limits of transfer learning with a unified text-to-text Transformer. Journal of Machine Learning Research, 21(140), 1–67.
45.	Raschka, Sebastian. (2023, January 14). Understanding and coding self-attention, multi-head attention, causal-attention, and cross-attention in LLMs. Ahead of AI. 
46.	Rei, Ricardo, Craig Stewart, António C. Farinha, & Alon Lavie. (2020). COMET: A neural framework for MT evaluation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 2685–2702).
47.	Sennrich, Rico, Barry Haddow, & Alexandra Birch. (2016). Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 1715–1725).
48.	Slocum, Jonathan. (1985). A survey of machine translation: Its history, current status, and future prospects. Computational Linguistics, 11(1), 1–17.
49.	Sullivan, Michele, Andrew Kelly, & Patrick McLaughlan. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1), 1–10.
50.	Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, & Illia Polosukhin. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30 (pp. 5998–6008). 
51.	Venkatesh, Viswanath, & Fred D. Davis. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
52.	Vieira, Lucas N. (2019). Post-editing of machine translation. In M. O’Hagan (Ed.), The Routledge handbook of translation and technology (pp. 319–335). Routledge.
53.	Weaver, Warren. (1949/1955). Translation. In W. N. Locke & A. D. Booth (Eds.), Machine translation of languages (pp. 15–23). MIT Press. (Original memorandum published 1949).
54.	Wilks, Yorick. (1978). Machine translation and artificial intelligence: Implementing machine aids to translation. In Proceedings of the International Conference on Translating and the Computer. Aslib.
55.	Wu, Yonghui, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, & Jeffrey Dean. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144.
56.	Yan, Meng, Shasha Zhao, & Hongfei Li. (2020). Cognitive effort in post-editing of machine translation: Evidence from eye-tracking. Journal of Chinese Information Processing, 34(6), 56–64.

 
網路資料
1.	A*STAR Institute for Infocomm Research (2020) Annual report FY 2019/2020.https://www.a-star.edu.sg/docs/librariesprovider1/default-document-library/annualreports/astar-fy2020-annual-report.pdf 
2.	Acolad (2025) How AI interpreting revolutionized a multilingual product launch [Case study].https://www.acolad.com/en/services/interpreting/ai-interpreting-events.html 
3.	ALPAC (1966) Languages and machines: Computers in translation and linguistics [Technical report].https://www.mt-archive.net/50/ALPAC-1966.pdf
4.	The Business Research Company (2025) Corporate event global market report 2025. https://www.thebusinessresearchcompany.com/report/corporate-event-global-market-report 
5.	China Speech Valley (2023) Environment | China Speech Valley & Quantum Center, Hefei. https://www.china.org.cn/hefeihightech/node_9007338_3.html 
6.	CNA 中央社 (2023 年 12 月 5 日) 觀光署整備 AI 翻譯櫃台迎接國際旅客。https://www.cna.com.tw/news/ahel/202312050046.aspx 
7.	DeepL (2021, April 15) Press release: DeepL sets record accuracy in tech translations. https://www.deepl.com/en/press-release 
8.	DeepL (2024) Privacy / Data protection https://support.deepl.com/hc/en-us/articles/360020556980-Privacy-Data-protection 
9.	DeepTranslate Ltd (2024) Official website. https://deeptranslate.hk/en/
10.	European Commission (2020) eTranslation documentation portal. https://commission.europa.eu/resources/etranslation_en
11.	Fo Guang University (佛光大學) (2025) AI 導入多國語言學習、佛大與愛比科技攜手打造國際化學習環境。https://www.cna.com.tw/postwrite/chi/395681 
12.	Google (2024) Privacy & Terms. https://policies.google.com/privacy
13.	HKTDC Research (2021) Age no issue for entrepreneurs (DeepTranslate 專訪)。https://hkmb.hktdc.com/en/1X0ALT5G/article/age-no-issue-for-entrepreneurs
14.	International Congress and Convention Association (ICCA) (2025)Understanding the international associations meetings industry. https://www.iccaworld.org/about-icca/association-meetings-industry/ 
15.	Interprefy (2025, January 21) How finance and insurance leaders can improve global collaboration. https://www.interprefy.com/resources/blog/how-finance-and-insurance-leaders-can-improve-global-collaboration 
16.	OpenAI (2023) GPT-4 technical report. https://arxiv.org/abs/2303.08774
17.	Skift Meetings (2024, August 22) Why AI is the future of translation at multilingual meetings. https://meetings.skift.com/2024/08/22/new-report-why-ai-is-the-future-of-translation-at-multilingual-meetings/ 
18.	Smart LAB Hong Kong (2024) Machine translation to improve productivity (Solution S-0433). https://www.smartlab.gov.hk/en/it-solutions/s-0433
19.	Smart Nation Singapore (2019) National AI Strategy (NAIS). https://www.smartnation.gov.sg/nais/
20.	Statista (2021) Global machine translation market size 2020–2026. https://www.statista.com/statistics/731843/worldwide-machine-translation-market-size/
21.	Taiwan Tourism Administration (2023) 觀光署整備 AI 翻譯櫃台迎接國際旅客。https://admin.taiwan.net.tw/News/NewsTravel?a=35&id=29922
22.	Technavio (2024) Events industry market—size, share & growth analysis 2025-2029. https://newsroom.technavio.org/events-industry-market-industry-analysis
23.	UFI—The Global Association of the Exhibition Industry (2024) UFI Global Exhibition Barometer: Exhibition industry set to reach record revenue levels in 2024. https://www.ufi.org/mediarelease/ufi-global-barometer-indicates-that-the-exhibition-industry-will-grow-to-record-levels-in-2024/ 
24.	UNICEF (2023) Bhashini AI: Making languages more accessible with digital technology.https://www.unicef.org/digitalimpact/bhashini-ai-making-languages-more-accessible-digital-technology
25.	WIRED (2021, July 15) Review: Waverly Labs Ambassador Interpreter. https://www.wired.com/review/waverly-labs-ambassador-interpreter/
26.	Wordly, Inc (2024) State of live AI translation: Research report. https://www.wordly.ai/resources/wordly-ai-translation-research-2024 
27.	YouTube Help Center (2023) Use automatic captioning and translation. https://support.google.com/youtube/answer/6373554