對於在21世紀中競爭的企業與組織，數位轉型已經不再是選擇而是勢在必行，隨著大多數的消費者的需求，環境快速的變遷及競爭對手不斷創新，維持原有的商業模式將會降低競爭力而被其他公司或組織取代。2019年末COV-19疫情爆發，有些公司企業或店家因為疫情帶來的影響紛紛停止營業，然而有些公司卻反而營利增加，由此可知數位轉型的重要性。使用數位科技，改變企業內部營運模式、與顧客互動關係及營運模式，創造價值並解決問題即為數位轉型。然而大多數企業高層管理者再導入新技術並進行數位轉型是會產生許多的猶豫，因此了解企業目前所在的數位轉型階段，就能降低數位轉型的風險。本研究透過搜索近十年來數位轉型相關的專利及文獻，利用文字探勘、機器學習的技術，建立符合台灣智慧商業(零售業與製造業)的數位轉型階段，除了分析亞洲矽谷推動的智慧商業數位轉型案例，以106至109年經濟部智慧商業服務推動應用案例為分析資料集，進行所有案例(65個案例)之產業型態定義及歸類，再依照數位轉型階段定義，以文字探勘、資料探勘、機器學習演算法與資訊技術，進行個案智慧科技應用之種類與數位轉型階段之判認，也搜尋其他產業的數位轉型文獻案例(150個案例)進行分析，藉此了解當前智慧商業數位轉型狀態在各產業中的特質及精進策略。最後，藉由辨認的結果，對台灣未來智慧商業數位轉型的趨勢進行探討，深入了解其導入技術及未來趨勢走向。

In the 21st century, digital transformation is no longer a choice but an imperative action for companies and organizations. With the demands of most customers, rapid changes in the environment and continuous innovation of competitors, maintaining the original business model might reduce competitiveness and even be replaced by other companies or organizations. In the end of 2019, the COVID-19 epidemic broke out. Some companies or stores shut down due to the impact of the epidemic, but some companies gain more profits instead of going out of business, it shows the importance of digital transformation. The process of using digital technologies to change the internal business model, interaction with customers and business model called digital transformation. However, most managers will hesitate to introduce new technologies and carry out digital transformation. Therefore, it’s important to determine the status and trend of digital transformation while developing strategies.
In this research, by searching patents and references related to digital transformation in the past ten years, using text mining and machine learning technologies to define a digital transformation stage with Taiwan, investigating 65 cases of intelligent business DT project reports (2017~2020) to identify their industry types and DT classification and 100 cases from the Internet. Then, according to the technology mining (including text and data mining and machine learning modeling) of these DT project reports, the study discovers the intelligent business development trends, critical DT technologies, and successful applications in industries. Then, based on the results of the identification, analyzing the trend of the digital transformation of Taiwan's smart business in the future.

1. Agarwal, R., Gao, G., DesRoches, C., & Jha, A. K. (2010). Research commentary—The digital transformation of healthcare: Current status and the road ahead. Information Systems Research, 21(4), 796-809.
2. Alekseevna, T. E., Yakovlevna, R. E., & Vasilievich, R. D. (2017, October). The concept of digital transformation of the society. In Paper Presented at the 2017 Tenth International Conference Management of Large-Scale System Development (MLSD).
3. Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA), 6(1).
4. Ani Bhalekar & Karel Eloot (2019, July 16). A scalable IIoT tech stack starts with business-focused use cases. Retrieved from McKinsey & Company Web site: https://www.mckinsey.com/business-functions/operations/our-insights/operations-blog/a-scalable-iiot-tech-stack-starts-with-business-focused-use-cases
5. Brown, P. F., Desouza, P. V., Mercer, R. L., Pietra, V. J. D., & Lai, J. C., “Class-based n-gram models of natural language,” Computational linguistics, 18(4), 467-479, 1992.
6. Barnewold, L., & Lottermoser, B. G. (2020). Identification of digital technologies and digitalisation trends in the mining industry. International Journal of Mining Science and Technology, 30(6), 747-757.
7. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
8. Bolton, R. N. (2019). Responsible research in retailing: is your research really useful?. Journal of Retailing, 95(3), 3.
9. Bulović, V., & Čović, Z. (2020, September). The Impact of Digital Transformation on Sustainability in Fashion Retail. In 2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY) (pp. 000149-000154). IEEE.
10. Butler, T. (2020). What's Next in the Digital Transformation of Financial Industry?. IEEE Computer Architecture Letters, 22(01), 29-33
11. CGI (2020). 2020 CGI Client Global Insights for Manufacturing, WHITE PAPER. Retrieved from CGI website: https://www.cgi.com/en/white-paper/manufacturing/2020-cgi-client-global-insights-manufacturing
12. CGI (2020). 2020 CGI Client Global Insights for Retail and Consumer Services, WHITE PAPER. Retrieved from CGI website: https://www.cgi.com/en/white-paper/retail-and-consumer-services/2020-cgi-client-global-insights-retail-and-consumer-services
13. Changes In Food Retail Foot Traffic During COVID-19. McMillan Doolittle, Transforming Retail, (2020).Retrieved from https://www.mcmillandoolittle.com/changes-in-food-retail-foot-traffic-during-covid-19/
14. Chang, C. C., & Lin, C. J. (2001). Training v-support vector classifiers: theory and algorithms. Neural computation, 13(9), 2119-2147.
15. Dumais, S. T., Furnas, G. W., Landauer, T. K., Deerwester, S., & Harshman, R. (1988, May). Using latent semantic analysis to improve access to textual information. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 281-285).
16. Dumais, S. T. (2004). Latent semantic analysis. Annual review of information science and technology, 38(1), 188-230.
17. Ercan, G. & Cicekli, I., “Using lexical chains for keyword extraction,” Information Processing & Management,” 43(6), 1705-1714, 2007.
18. Gartner (2014). Gartner's 2014 Hype Cycle for Emerging Technologies Maps the Journey to Digital Business. Retrieved from Gartner Web site: https://www.gartner.com/en/newsroom/press-releases/2014-08-11-gartners-2014-hype-cycle-for-emerging-technologies-maps-the-journey-to-digital-business
19. Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: a multidisciplinary perspective.
20. Grewal, D., Noble, S. M., Roggeveen, A. L., & Nordfalt, J. (2020). The future of in-store technology. Journal of the Academy of Marketing Science, 48(1), 96-113.
21. Hoffman, D. L., & Novak, T. P. (2018). Consumer and object experience in the internet of things: An assemblage theory approach. Journal of Consumer Research, 44(6), 1178-1204.
22. Hofmann, T. (2013). Probabilistic latent semantic analysis. arXiv preprint arXiv:1301.6705.
23. Hofmann, T. (2000). Learning the similarity of documents: An information-geometric approach to document retrieval and categorization. In Advances in neural information processing systems (pp. 914-920).
24. Inman, J. J., & Nikolova, H. (2017). Shopper-facing retail technology: A retailer adoption decision framework incorporating shopper attitudes and privacy concerns. Journal of Retailing, 93(1), 7-28.
25. Jorge Lopez (2014). Where Are You On The Digital Business Development Path? Retrieved form Gartner Web site: https://www.forbes.com/sites/gartnergroup/2014/08/26/where-are-you-on-the-digital-business-development-path/?sh=386856374d47
26. Kutnjak, A., Pihiri, I., & Furjan, M. T. (2019, May). Digital Transformation Case Studies Across Industries–Literature Review. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 1293-1298). IEEE.
27. Kramer, O. (2013). K-nearest neighbors. In Dimensionality reduction with unsupervised nearest neighbors (pp. 13-23). Springer, Berlin, Heidelberg.
28. Kraus, S., Schiavone, F., Pluzhnikova, A., & Invernizzi, A. C. (2021). Digital transformation in healthcare: Analyzing the current state-of-research. Journal of Business Research, 123, 557-567.
29. Kumar, V., Anand, A., & Song, H. (2017). Future of retailer profitability: An organizing framework. Journal of Retailing, 93(1), 96-119.
30. Li, D., Li, S., Li, W., Wang, W. & Qu, W., (2010, July), “A semi-supervised key phrase extraction approach: learning from title phrases through a document semantic network,” In Proceedings of the ACL 2010 conference short papers, pp. 296-300, Association for Computational Linguistics.
31. Liu, Z., Li, P., Zhang, Y., & Sun, M., “Clustering to find exemplar terms for keyphrase extraction,” Proceeding, In Conference on Empirical Methods in Natural Language Processing, Volume 1-Volume 1 (pp. 257-266). Association for Computational Linguistics, 2009.
32. McKinsey & Company (2020). Accelerating retail transformation to win digital-first consumers. Retrieved from McKinsey & Company Web site: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/accelerating-retail-transformation-to-win-digital-first-consumers
33. Meade Monger (2017). Digital transformation: a view across industries. Retrieved from ALixPartners Web site: https://www.alixpartners.com/
34. Monika A. (2019). Examples of Digital Transformation Across Industries. Retrieved from Digital Doughnut Web site: https://www.digitaldoughnut.com/articles/2019/may/digital-transformation-across-industries
35. Nagpal, A., Jatain, A., & Gaur, D., “Review based on Data Clustering Algorithms,” IEEE Conference on Information and Communication Technologies, 2013.
36. Reinartz, W., Wiegand, N., & Imschloss, M. (2019). The impact of digital transformation on the retailing value chain. International Journal of Research in Marketing, 36(3), 350-366.
37. Roggeveen, A. L., & Sethuraman, R. (2019). Using Commentaries, Special Sections, and Special Issues to Enhance Research into Important and Cutting Edge Topics in this Dynamic Field of Retailing. Journal of Retailing, 95(4), 109.
38. Riasanow, T., Jäntgen, L., Hermes, S., Böhm, M., & Krcmar, H. (2020). Core, intertwined, and ecosystem-specific clusters in platform ecosystems: analyzing similarities in the digital transformation of the automotive, blockchain, financial, insurance and IIoT industry. Electronic Markets, 1-16.
39. Salton, G. & McGill, M. J., “Introduction to modern information retrieval,” McGraw Hill Book Company, 1983.
40. Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., Er, M. J., Ding, W. P. & Lin, C. T., “A review of clustering techniques and developments,” Neurocomputing, 267, 664-681, 2017.
41. Siddiqi, S., & Sharan, A. (2015). Keyword and keyphrase extraction techniques: a literature review. International Journal of Computer Applications, 109(2).
42. Trappey, A. J., Trappey, C. V., Govindarajan, U. H., & Jhuang, A. C. (2018). Construction and validation of an ontology-based technology function matrix: technology mining of cyber physical system patent portfolios. World Patent Information, 55, 19-24.
43. Trappey, A. J., (2017). Ontology-based Technology Function Matrix for Patent Analysis of Additive Manufacturing in the Dental Industry. International Journal of Manufacturing Research 12(1):1. DOI:10.1504/IJMR.2017.10003723
44. Uzun, Y., “Keyword extraction using Naive Bayes,” In Bilkent University, Department of Computer Science, Turkey, 2005.
45. Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2019). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901.
46. Von Leipzig, T., Gamp, M., Manz, D., Schöttle, K., Ohlhausen, P., Oosthuizen, G., ... & von Leipzig, K. (2017). Initialising customer-orientated digital transformation in enterprises. Procedia Manufacturing, 8, 517-524.
47. W. Y., Ma, & K. J., Chen, (2003). Introduction to CKIP Chinese Word Segmentation System for the First International Chinese Word Segmentation Bakeoff.
48. Williams, T., & Betak, J. (2018). A Comparison of LSA and LDA for the Analysis of Railroad Accident Text. Procedia computer science, 130, 98-102
49. Wu, Z., Liu, H., & Chen, L. (2014). The simply implement of effective naïve bayes web news text classification model. Statistical and Application, 3, 30-35.
50. Zhou, S., Xu, X., Liu, Y., Chang, R., & Xiao, Y. (2019). Text similarity measurement of semantic cognition based on word vector distance decentralization with clustering analysis. IEEE Access, 7, 107247-107258.
51. Zhang, K., Xu, H., Tang, J., & Li. J., “Keyword extraction using support vector machine,” In international conference on web-age information management pp. 85-96, Springer, Berlin, Heidelberg, 2006.
52. Zhang, C., Wang, X., Yu, S., & Wang, Y. (2018, June), “Research on Keyword Extraction of Word2vec Model in Chinese Corpus,” In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), pp. 339-343, IEEE.
53. Zimmermann, A., Schmidt, R., Sandkuhl, K., Jugel, D., Bogner, J., & Möhring, M. (2018, October). Evolution of enterprise architecture for digital transformation. In 2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 87-96). IEEE.
54. ADVANTECH，2019。邁向工業4.0最佳化階段，導入人工智慧成關鍵。取自https://www.advantech.com/resources/news/c7e92884-c3d8-46b3-bd9d-3ea502aa952e
55. Benson，2020。什麽是智慧製造？淺談製造業的數位轉型(上/中/下)。取自https://benson-lee.blog/2019/11/06/%e8%a3%bd%e9%80%a0%e6%a5%ad%e7%9a%84%e6%95%b8%e4%bd%8d%e8%bd%89%e5%9e%8b%e6%98%af%e8%a6%81%e8%bd%89%e6%88%90%e4%bb%80%e9%ba%bc%ef%bc%9f-%e4%b8%8a/
56. Benson，2020。如何規劃智慧製造？三個階段一步一步來。取自https://benson-lee.blog/2019/12/24/%E5%A6%82%E4%BD%95%E8%A6%8F%E5%8A%83%E6%99%BA%E6%85%A7%E8%A3%BD%E9%80%A0%EF%BC%9F%E6%99%BA%E6%85%A7%E8%A3%BD%E9%80%A0%E7%9A%84%E4%B8%89%E9%9A%8E%E6%AE%B5/
57. CIO雜誌編輯部，2020。2019-20 CIO大調查報告，CIO IT經理人。取自https://www.cio.com.tw/wp-content/uploads/2019-20ciosurvey.pdf
58. David (2015)。企業數位轉型路線圖的思考與繪製。取自科技政策研究與資訊中心，科技產業資訊室。https://iknow.stpi.narl.org.tw/Post/Read.aspx?PostID=11716
59. David (2015)。傳統ICT公司如何在物聯網時代轉型-數位企業發展路徑圖與啟示。取自科技政策研究與資訊中心，科技產業資訊室。https://iknow.stpi.narl.org.tw/Post/Read.aspx?PostID=10529
60. David, H.,2019. 邁向智慧製造的新階段，Applied materials。取自https://www.appliedmaterials.com/zh-hant/nanochip/nanochip-fab-solutions/jan-2020/intelligent-manufacturing
61. Deloitte (2020)。2020零售力量與趨勢展望勤業眾信消費產業預測報告。取自https://www2.deloitte.com/tw/tc/pages/consumer-business/articles/rp200428-2020-retail-industry-trend.html
62. Larry Lien, 2020。數位轉型 ? 2021台灣企業通往成功的轉型策略。取自https://www.hububble.co/blog/digitaltransformation/
63. Masa Chen. (2021). 數位轉型 徹底理解數位轉型-定義、案例、以及轉型策略。取自https://oosga.com/digital-transformation/
64. Masa Chen (2019)。數位行銷-徹底掌握2020數位行銷趨勢與策略。取自OOSGA。https://oosga.com/digital-marketing/
65. Oracle (2017)。台灣企業數位轉型為什麼拼不過別人?因為46%企業老闆跟不上時代。取自TechOrange。https://buzzorange.com/techorange/2017/07/04/oracle-research/
66. Yang, C. Y. (2015). 基於專利技術功效矩陣之科技機會探索方法. 臺灣大學資訊管理學研究所學位論文, 1-63.
67. 天下雜誌，2020。2020 全球製造新秩序，掌握企業智能製造落地四大關鍵。取自 https://topic.cw.com.tw/event/2018ibm/article/index1/article7.html
68. 天下雜誌，2020。台灣製造業突圍，轉型智慧製造的四大挑戰。取自https://topic.cw.com.tw/event/2018ibm/article/index1/article8.html
69. 王智仁 (2021)。疫情觸動數位轉型契機 數位經濟時代來臨。取自網管人。https://www.netadmin.com.tw/netadmin/zh-tw/snapshot/8EFE19E4B76D465C94C1A862B0A8280F
70. 白寧超，自然語言處理理論與實戰，2018。
71. 向永清，2019。工廠數字化與智能化的四個階段。取自https://kknews.cc/zh-tw/tech/2yppgl9.html
72. 向德成. (2020). 現在台灣工廠智動化需求. 機械工業雜誌, (446), 2-6.
73. 江逸之，2019。2019年2000大企業數位轉型與人才調查，天下雜誌。取自https://topic.cw.com.tw/cw/pdf/191211.pdf
74. 全球餐飲瘋科技。數位時代，2020。
75. 吳志平. (2019). 傳統產業需要什麼樣的智慧工廠?. 機械工業雜誌, (437), 5-8.
76. 吳佳翰，2020。2020年趨勢解析《風險諮詢服務篇》新興科技應用的四大關鍵風險管理。取自https://www2.deloitte.com/tw/tc/pages/risk/articles/2020-outlook-risk.html
77. 決勝新金融。數位時代，No.317。2020。
78. 余至浩，2017。製造業數位轉型如何找對第一步。取自https://www.ithome.com.tw/news/119660
79. 李思壯, & 黃彥男. (2019). 數位時代之數位隱私保護. 國土及公共治理季刊, 7(4), 30-39.
80. 林柏君 (2020)。疫情衝擊下看中小企業數位轉型發展。經濟前瞻，190，58-61。
81. 林蒧均, & 陳信宏. (2019). 數位經濟對勞動力發展的影響與轉型芻議. 經濟前瞻, (183), 78-83.
82. 林柏君. (2019). 數位科技對中小企業數位轉型重要性之探討. 經濟前瞻, (183), 29-29.
83. 林伯勳. (2018). 製造業數位轉型策略之個案研究-以 A 公司為例.
84. 林良憲, 張予潔, 蔡煥文, 林宏偉, 王莉珮, & 蔡昀翰. (2018). 公版工業物聯網平台技術. 電腦與通訊, (175), 43-48.
85. 林立峰, 方譽荃, & 池冠儀. (2019). 5G 智慧工廠試驗場域與應用案例驗證. 電工通訊季刊, 15-25.
86. 洪哲倫、張志宏、林宛儒. (2019) 工業 4.0 與智慧製造的關鍵技術：工業物聯網與人工智慧. 科儀新知, 第221期, 19-25.
87. 高敬原 (2020)，7成企業數位轉型腳步跟不上!該從哪裡著手?5面相組織改造怎麼做? 取自數位時代。https://www.bnext.com.tw/article/58314/digital-transformation-2020.
88. 陳君毅，2019。Dell最新數位轉型調查，用數字揭露台灣企業內心的焦慮。數位時代。取自https://www.bnext.com.tw/article/53114/dell-digital-transformation-2019
89. 陳信宏, & 歐宜佩. (2020). 以數位轉型為基礎的高齡社會創新政策. 國土及公共治理季刊, 8(1), 20-33.
90. 陳祈廷，2018。擁抱數位轉型趨勢，面向智能製造新時代。取自https://www.digiwin.com/tw/blog/4/942.html
91. 陳家明. (2020). 智慧製造的超前佈署, 數位分身 AI 打造虛實整合工廠. 機械工業雜誌, (452), 99-106.
92. 許婉琪 (2019，12月27日)，趨勢觀察-數位轉型三階段，經濟日報，A12產業追蹤。
93. 張寶誠，2020。製造業數位轉型觀點：數位優化v.s.數位聯結，經濟日報。取自https://mymkc.com/article/content/24327
94. 超零售時代。數位時代No.300，2019/05。
95. 經濟部技術處，2019。產業技術白皮書2018/2019。
96. 經濟部技術處，2020。產業技術白皮書2019/2020。
97. 楊惟中, 何啟弘, 陳弘展, 車慧中, & 賴以軒. (2003)。結合發明法則之專利技術功效佈局分析(碩博士論文). 取自file:///C:/Users/eblab/Downloads/%E6%95%88%E4%BD%88%E5%B1%80%E5%88%86%E6%9E%90.pdf
98. 詹文男. (2019). 數位轉型-思維與行動，財團法人資訊工業策進會，產業情報研究所。取自https://www.ima.org.tw/uploadFiles/download/12/3.%E4%BC%81%E6%A5%AD%E6%95%B8%E4%BD%8D%E8%BD%89%E5%9E%8B%E6%96%B0%E8%A6%96%E7%95%8C-%E8%A9%B9%E6%96%87%E7%94%B7-%E5%B7%B2%E8%BD%89%E6%AA%94.pdf
99. 鄧怡菁，2020。中小企業數位轉型路徑初探。MKC管理知識中心。取自https://mymkc.com/article/content/23359
100. 數位時代，2017。政策加碼推動AI，開啟台灣產業轉型的機會之窗。取自https://www.bnext.com.tw/article/45162/policy-promote-theopeningoftaiwan%27s-industrialopportunities
101. 數位時代，2019。MarTech行銷科技新浪潮。數位時代，No.304。
102. 劉朱松，2020。數位轉型 製造業六成未跟進，工商時報。取自https://ctee.com.tw/news/industry/360620.html
103. 劉文臻. (2018). 金融科技 (Fintech) 發展下以顧客為中心的數位轉型策略-以個案公司為例.
104. 歐宜佩, & 陳信宏. (2018). 近期數位轉型發展趨勢之觀察.中華經濟研究院. 上網日期: 2019年, 3.
105. 蔡鳳凰. (2020). 面對後疫情趨勢我國遠距醫療產業發展. 經濟前瞻, (192), 118-122.
106. 蔡志宏, 周士雄, & 莊順斌. (2018). 我國數位科技引領產業創新之現況與展望: 以臺灣 AI 行動計畫為例. 國土及公共治理季刊, 6(4), 16-27.
107. 薛力豪，鍾佩昕，&黃昱綸.(2020). 智慧醫療專利技術分析. 智慧財產權月刊. VOL.258, 62-94
108. 王力弘.(2015).社群媒體新詞偵測系統 以PTT八卦版為例. 國立政治大學，碩士論文.