技術汰換的快速與整體環境需求的增加，促使學術界與產業界展開產學合作。在產學合作領域中，學校和企業最先面臨的挑戰為選擇大學研究成果商業化策略。產學合作必須考量技術和企業的性質以及法令的規範，才能以合適的商業化策略帶來雙贏的合作關係。過去的研究提出許多類型的產學合作模式以及影響產學合作模式的因素，但是至今仍未有明確選擇大學研究成果商業化策略之決策模式。
本研究提出以機器學習改善內容分析法的方式作為研究工具。由於內容分析法能夠將訪談、個案或報導等非數據型態的質性資料簡化和量化成少量的重要概念，所以被廣泛的使用。然而，內容分析法往往耗費許多人力和時間於分析質性資料上。因此，本研究運用機器學習的學習和修正特性改善內容分析法，縮短研究者處理質性資料的時間，並且使用易於理解的決策樹模型呈現內容分析法的分析結果。
本研究以台灣國立成功大學產學合作案例為實證分析對象。首先，透過內容分析法分析產學合作相關人員的訪談內容，接著藉由機器學習提升內容分析法處理質性資料的速度，最後使用決策樹建構大學研究成果商業化策略之決策模式。本研究經由內容分析法與機器學習之整合能有助於研究者分析質性資料，同時憑藉決策樹協助研究者辨認出影響產學合作模式的重要因素，因此期望決策模式之建立能夠作為管理者決策時的參考。

As the rapid replacement of technology and the increased requirement for environment, academia and industry start to develop academy-industry collaboration. For universities and firms, one of the key challenges on academy-industry collaboration is choosing commercialization strategies for the research outcomes of university. Previous studies have proposed many types of academy-industry collaboration models and determinants of academy-industry collaboration models, but there is little empirical evidence to suggest which model to choose.
This study presents a research tool for content analysis using machine learning technique. Because content analysis can distil qualitative data into fewer categories, it is a widely used research methodology. However, content analysis costs researchers a lot of time to analyze the qualitative data. Therefore, this study uses machine learning to reduce the time and cost of undertaking content-analysis processes, and employs decision tree to report the results of content analysis.
The sample of this study is the case of academy-industry collaboration in the National Cheng Kung University. First, we use content analysis to analyze transcripts, which from the interview of industry-university collaboration. Second, we adopt machine learning to improve the processes in content analysis. Finally, we employ decision tree to construct the decision model of commercialization strategies for the research outcomes of university. By the integration of content analysis and machine learning, decision tree can help researchers identify the important factors of academy-industry collaboration models. The result shows decision model of academy-industry collaboration to manager for reference.

Alpaydin, E. Introduction to machine learning. Cambridge, Massachusetts: MIT Press. 2004.
Bazeley, P. Qualitative data analysis with NVivo. Sage. 2007.
Bercovitz, J. & Feldman, M. Entpreprenerial universities and technology transfer: A conceptual framework for understanding knowledge-based economic development. 31: 175-188. 2006.
Berelson, B. Content analysis in communication research. Glencoe, IL: Free Press. 1952.
Bloedon, R. V. & Stokes, D. R. Making university-industry collaborative research succeed. Research-Technology Management, 37(2): 44-48. 1994.
Bowers, J. W. Methods of research in communication. Boston: Hougton Miffinco Press. 1970.
Bowman, E. H. Content-analysis of annual reports for corporate-strategy and risk. Interfaces, 14(1): 61-71. 1984.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. Classification and regression trees. Belmont: Wadsworth. 1984.
Bruneel, J., D'Este, P., & Salter, A. Investigating the factors that diminish the barriers to university-industry collaboration. Research Policy, 39(7): 858-868. 2010.
Calas, M. B. & Smircich, L. Voicing seduction to silence leadership. Organization Studies, 12(4): 567-602. 1991.
Chen, M. S., Han, J. W., & Yu, P. S. Data mining: an overview from a database perspective. Ieee Transactions on Knowledge and Data Engineering, 8(6): 866-883. 1996.
Dane, F. C. Research method. California: Brooks/Cole. 1990.
Debackere, K. & Veugelers, R. The role of academic technology transfer organizations in improving industry science links. Research Policy, 34(3): 321-342. 2005.
Demantaras, R. L. A distance - based attribute selection measure for decision tree induction. Machine Learning, 6(1): 81-92. 1991.
Downe-Wamboldt, B. Content analysis: method, applications, and issues. Health Care for Women International, 13(3): 313-321. 1992.
Druskat, V. U. & Wheeler, J. V. Managing from the boundary: The effective leadership of self-managing work teams. Academy of Management Journal, 46(4): 435-457. 2003.
Duriau, V. J., Reger, R. K., & Pfaffer, M. D. A content analysis of the content analysis literature in organization studies - Research themes, data sources, and methodological refinements. Organizational Research Methods, 10(1): 5-34. 2007.
Dutton, D. M. & Conroy, G. V. A review of machine learning. Knowledge Engineering Review, 12(4): 341-367. 1997.
Elo, S. & Kyngas, H. The qualitative content analysis process. Journal of Advanced Nursing, 62(1): 107-115. 2008.
Eltinge, E. M. & Roberts, C. W. Linguistic content-analysis - a method to measure science as inquiry in textbooks. Journal of Research in Science Teaching, 30(1): 65-83. 1993.
Eom, B. Y. & Lee, K. Determinants of industry-academy linkages and, their impact on firm performance: The case of Korea as a latecomer in knowledge industrialization. Research Policy, 39(5): 625-639. 2010.
Etzkowitz, H. The norms of entrepreneurial science: cognitive effects of the new university-industry linkages. Research Policy, 27(8): 823-833. 1998.
Evans, W. Computer-supported content analysis - trends, tools, and techniques. Social Science Computer Review, 14(3): 269-279. 1996.
Fayyad, U., PiatetskyShapiro, G., & Smyth, P. The KDD process for extracting useful knowledge from volumes of data. Communications of the Acm, 39(11): 27-34. 1996.
Ferrier, W. J. Navigating the competitive landscape: The drivers and consequences of competitive aggressiveness. Academy of Management Journal, 44(4): 858-877. 2001.
Fini, R., Grimaldi, R., & Sobrero, M. Factors fostering academics to start up new ventures: an assessment of Italian founders' incentives. Journal of Technology Transfer, 34(4): 380-402. 2009.
Geisler, E. & Rubenstein, A. University-industry relationship: a review of major issues', in A. Link and G. Tassey (Eds.) Boston: Kluwer Co-operative Research and Development: The Industry-University-Government 1989.
Graneheim, U. H. & Lundman, B. Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2): 105-112. 2004.
Harwood, T. G. & Garry, T. An overview of content analysis. The Marketing Review, 3: 479-498. 2003.
Hawthorne, M. The computer in literary analysis - using tact with students. Computers and the Humanities, 28(1): 19-27. 1994.
Holsti, O. R. Content analysis for the social sciences and humanities. MA: Addison-Wesley. 1969.
Hsieh, H. F. & Shannon, S. E. Three approaches to qualitative content analysis. Qualitative Health Research, 15(9): 1277-1288. 2005.
Hui, S. C. & Jha, G. Data mining for customer service support. Information & Management, 38(1): 1-13. 2000.
Hwang, S. Utilizing qualitative data analysis software a review of Atlas.ti. Social Science Computer Review, 26(4): 519-527. 2008.
Kassarjian, H. H. Content-analysis in consumer research. Journal of Consumer Research, 4(1): 8-18. 1977.
Kearns, M. & Mansour, Y. On the boosting ability of top-down decision tree learning algorithms. Journal of Computer and System Sciences, 58(1): 109-128. 1999.
Kim, H. & Koehler, G. J. Theory and practice of decision tree induction. Omega-International Journal of Management Science, 23(6): 637-652. 1995.
Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. Machine learning: a review of classification and combining techniques. Artificial Intelligence Review, 26(3): 159-190. 2006.
Krippendorff, K. Content analysis: An introduction to its methodology. London: Sage. 2004.
Langley, P. & Simon, H. A. Applications of machine learning and rule induction. Communications of the Acm, 38(11): 55-64. 1995.
Lewis, R. B. & Maas, S. M. QDA miner 2.0: Mixed-model qualitative data analysis software. Field Methods, 19(1): 87-108. 2007.
Martindale, C. & McKenzie, D. On the utility of content-analysis in author attribution - the Federalist. Computers and the Humanities, 29(4): 259-270. 1995.
Meyer-Krahmer, F. Science-based technologies: university-industry interactions in four fields. Research Policy, 27(8): 835-851. 1998.
Min, H. Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics-Research and Applications, 13(1): 13-39. 2010.
Minowa, Y. Classification rules discovery from selected trees for thinning with the C4.5 machine learning system. Journal of Forest Research, 10(3): 221-231. 2005.
Mitra, S., Pal, S. K., & Mitra, P. Data mining in soft computing framework: A survey. Ieee Transactions on Neural Networks, 13(1): 3-14. 2002.
Mustar, P., Renault, M., Colombo, M. G., Piva, E., Fontes, M., Lockett, A., Wright, M., Clarysse, B., & Moray, N. Conceptualising the heterogeneity of research-based spin-offs: A multi-dimensional taxonomy. Research Policy, 35(2): 289-308. 2006.
Nacos, B. L., Shapiro, R. Y., Young, J. T., Fan, D. P., Kjellstrand, T., & McCaa, C. Content-analysis of news reports - comparing human coding and a computer - assisted method. Communication, 12(2): 111-128. 1991.
Nastase, V., Koeszegi, S., & Szpakowicz, S. Content analysis through the machine learning mill. Group Decision and Negotiation, 16(4): 335-346. 2007.
Neuendorf, K. A. The content analysis guidebook. Thousand Oaks, CA: Sage. 2002.
O'Shea, R. P., Allen, T. J., Chevalier, A., & Roche, F. Entrepreneurial orientation, technology transfer and spinoff performance of US universities. Research Policy, 34(7): 994-1009. 2005.
Olsen, M. Textpack-V - Text analysis utilities for the personal-computer. Computers and the Humanities, 23(2): 155-160. 1989.
Peter, L. & Fusfeld, H. University industry research relationships USA: National Science Foundation 1982.
Pirnay, F., Surlemont, B., & Nlemvo, F. Toward a typology of university spin-offs. Small Business Economics, 21(4): 355-369. 2003.
Qian, B. & Rasheed, K. Foreign exchange market prediction with multiple classifiers. Journal of Forecasting, 29(3): 271-284. 2010.
Quinlan, J. R. Induction of decision trees. Machine Learning, 1: 81-106. 1986.
Quinlan, J. R. C4.5: Programs for machine learning. Morgan Kaufman. 1993.
Rasmussen, E., Moen, O., & Gulbrandsen, M. Initiatives to promote commercialization of university knowledge. Technovation, 26(4): 518-533. 2006.
Rastogi, R. & Shim, K. PUBLIC: A decision tree classifier that integrates building and pruning. Data Mining and Knowledge Discovery, 4(4): 315-344. 2000.
Ruggieri, S. Efficient C4.5. Ieee Transactions on Knowledge and Data Engineering, 14(2): 438-444. 2002.
Sebeok, T. A. & Zeps, V. J. An analysis of structured content with application of electronic computer research in psycholinguistics. Language and Speech, 1: 181-193. 1958.
Siradeghyan, Y., Zakarian, A., & Mohanty, P. Entropy-based associative classification algorithm for mining manufacturing data. International Journal of Computer Integrated Manufacturing, 21(7): 825-838. 2008.
Sorensen, K. & Janssens, G. K. Data mining with genetic algorithms on binary trees. European Journal of Operational Research, 151(2): 253-264. 2003.
Srnka, K. J. & Koeszegi, S. T. From words to numbers: how to transform qualitative data into meaningful quantitative results. Schmalenbach Business Review. 2007.
Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D. M. The General Inquirer : A computer approach to content analysis. Cambridge, MA: MIT Press. 1966.
Sun, J. & Li, H. Data mining method for listed companies' financial distress prediction. Knowledge-Based Systems, 21(1): 1-5. 2008.
van de Vrande, V., Lemmens, C., & Vanhaverbeke, W. Choosing governance modes for external technology sourcing. R & D Management, 36(3): 347-363. 2006.
Vedovello, C. Firms' R & D activity and intensity and the university-enterprise partnerships. Technological Forecasting and Social Change, 58(3): 215-226. 1998.
Webb, C. Analysing qualitative data: computerized and other approaches. Journal of Advanced Nursing, 29(2): 323-330. 1999.
Weber, R. Basic content analysis. CA: Sage. 1985.
Weber, R. Basis content analysis (2nd ed.). Thousand Oaks, CA: Sage. 1990.
Weka. http://www.cs.waikato.ac.nz/ml/weka/.
Witten, I. H. & Frank, E. Data mining: practical machine learning tools and techniques. Elsevier. 2005.
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