과제정보
이 논문은 2021년 대한민국 교육부와 한국연구재단의 인문사회분야 신진연구자지원사업의 지원을 받아 수행된 연구임(NRF-2021S1A5A8061237)
참고문헌
- 김경민, 장하영, 장병탁, "불균형 데이터 처리를 위한 과표본화 기반 앙상블 학습 기법," 정보과학회 컴퓨팅의 실제 논문지, 제20권, 제10호, 2014, pp. 549-554. https://doi.org/10.5626/KTCP.2014.20.10.549
- 김주영, 유승경, "신경망모델(Neural Network Model)을 활용한 CSR 활동의 영향력 분석," 경영학연구, 제49권, 제1호, 2020, pp. 51-74.
- 안경민, "통계적 매칭과 머신러닝 앙상블 기법을 활용한 기업혁신 및 경영성과 예측모형 개발," 동국대학교 박사학위논문, 2021.
- 양울민, 장군, 김성훈, "R&D 활동과 기술혁신이 경영성과에 미치는 영향: 국내 제조업과 서비스업의 비교연구," 대한경영학회지, 제30권, 제7호, 2017, pp. 1139-1157.
- 우종필, "구조방정식모델에서 다차원성 개념의 항목묶음 편향에 대한 연구," 경영학연구, 제44권, 제4호, 2015, pp. 1131-1147.
- 이도명, 임성준, "활용적 혁신활동과 탐색적 혁신활동의 영향요인과 혁신성과 및 인지적 기업성과에 미치는 영향에 관한 연구," 전략경영연구, 제15권, 제1호, 2012, pp. 1-31.
- 조가원, 조용래, 강희종, 김민재, "2018년 한국기업혁신조사: 제조업 부문," 조사연구, 2018, pp. 1-336.
- 조보근, 박경배, 하성호, "기계학습 알고리즘을 활용한 지역 별 아파트 실거래가격지수 예측모델 비교: LIME 해석력 검증," 정보시스템연구, 제29권, 제3호, 2020, pp. 119-144.
- 최은영, "정부지원제도 및 내부 R&D 투자와 R&D 협력이 기술혁신성과에 미치는 영향," 산업경제연구, 제28권, 제4호, 2015, pp. 1473-1492.
- 최진용, 김상유, "ICT 인프라와 투자 환경이 혁신에 미치는 영향: 세계혁신지수를 중심으로," 정보시스템연구, 제29권, 제3호, 2020, pp. 159-178.
- 황정재, 김재영, 박재민, "빅데이터 분석방법을 활용한 제조업 혁신성과예측 방법에 대한 연구: 딥 러닝 알고리즘을 중심으로," 기술혁신학회지, 제21권, 제2호, 2018, pp.818-837.
- Adams, R., Bessant, J., and Phelps, R. "Innovation Management Measurement: A Review," International Journal of Management Reviews, Vol. 8, No. 1, 2006, pp. 21-47. https://doi.org/10.1111/j.1468-2370.2006.00119.x
- Armbruster, H., Bikfalvi, A., Kinkel, S., and Lay, G., "Organizational Innovation: The Challenge of Measuring Non-technical Innovation in Large-scale Surveys," Technovation, Vol. 28, No. 10, 2008, pp. 644-657. https://doi.org/10.1016/j.technovation.2008.03.003
- Barrena-Martinez, J., Cricelli, L., Ferrandiz, E., Greco, M., and Grimaldi, M., "Joint Forces: Towards an Integration of Intellectual Capital Theory and the Open Innovation Paradigm," Journal of Business Research, Vol. 112, 2020, pp. 261-270. https://doi.org/10.1016/j.jbusres.2019.10.029
- Becheikh, N., Landry, R., and Amara, N., "Lessons from Innovation Empirical Studies in The Manufacturing Sector: A Systematic Review of the Literature from 1993-2003," Technovation, Vol. 26, No. 5-6, 2006, pp. 644-664. https://doi.org/10.1016/j.technovation.2005.06.016
- Benner, M. J., and Tushman, M., "Process Management and Technological Innovation: A Longitudinal Study of the Photography and Paint Industries," Administrative Science Quarterly, Vol. 47, No. 4, 2002, pp. 676-707. https://doi.org/10.2307/3094913
- Berger, E. S., von Briel, F., Davidsson, P., and Kuckertz, A., "Digital or Not-The Future of Entrepreneurship and Innovation: Introduction to the Special Issue," Journal of Business Research, Vol. 125, 2021, pp. 436-442. https://doi.org/10.1016/j.jbusres.2019.12.020
- Breiman, L., "Random forests," Machine Learning, Vol. 45, No. 1, 2001, pp. 5-32. https://doi.org/10.1023/A:1010933404324
- Chen, T., and Guestrin, C., "XGboost: A Scalable Tree Boosting System," In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785-794.
- Creamer, G., and Freund, Y., "Automated Trading with Boosting and Expert Weighting," Quantitative Finance, Vol. 10, No. 4, 2010, pp. 401-420. https://doi.org/10.1080/14697680903104113
- Cooper, J. R., "A Multidimensional Approach to the Adoption of Innovation," Management Decision, Vol. 36, No.8, 1998, pp. 493-502. https://doi.org/10.1108/00251749810232565
- Damanpour, F., and Aravind, D., "Managerial Innovation: Conceptions, Processes and Antecedents," Management and Organization Review, Vol. 8, No. 2, 2011, pp. 423-454. https://doi.org/10.1111/j.1740-8784.2011.00233.x
- Damanpour, F., and Evan, W. M., "Organizational Innovation and Performance: the Problem of "Organizational Lag"," Administrative Science Quarterly, Vol. 29, No. 3, 1984, pp. 392-409. https://doi.org/10.2307/2393031
- Dewangan, V., and Godse, M., "Towards a Holistic Enterprise Innovation Performance Measurement System," Technovation, Vol. 34, No. 9, 2014, pp. 536-545. https://doi.org/10.1016/j.technovation.2014.04.002
- Dewar, R. D., and Dutton, J. E., "The Adoption of Radical and Incremental Innovations: An Empirical Analysis," Management Science, Vol. 32, No. 11, 1986, pp. 1422-1433. https://doi.org/10.1287/mnsc.32.11.1422
- Dorogush, A. V., Ershov, V., and Gulin, A., "CatBoost: Gradient Boosting with Categorical Features Support," arXiv preprint arXiv:1810.11363, 2018.
- Edison, H., Bin Ali, N., and Torkar, R. "Towards Innovation Measurement in the Software Industry," Journal of Systems and Software, Vol. 86, No. 5, 2013, pp. 1390-1407. https://doi.org/10.1016/j.jss.2013.01.013
- Ferreira, J., Coelho, A., and Moutinho, L., "Dynamic Capabilities, Creativity and Innovation Capability and Their Impact on Competitive Advantage and Firm Performance: The Moderating Role of Entrepreneurial Orientation," Technovation, Vol. 92-93, 2020, pp. 1-18.
- Fores, B., and Camison, C., "Does Incremental and Radical Innovation Performance Depend on Different Types of Knowledge Accumulation Capabilities and Organizational Size?," Journal of Business Research, Vol. 69, No. 2, 2016, pp. 831-848. https://doi.org/10.1016/j.jbusres.2015.07.006
- Frank, A. G., Cortimiglia, M. N., Ribeiro, J. L. D., and de Oliveira, L. S., "The Effect of Innovation Activities on Innovation Outputs in the Brazilian Industry: Market-Drientation vs. Technology-Acquisition Strategies," Research Policy, Vol. 45, No. 3, 2016, pp. 577-592. https://doi.org/10.1016/j.respol.2015.11.011
- Geldes, C., Felzensztein, C., and Palacios-Fenech, J., "Technological and Non-technological Innovations, Performance and Propensity to Innovate Across Industries: The Case of an Emerging Economy," Industrial Marketing Management, Vol. 61, 2017, pp. 5-66.
- Hatzichronoglou, T., "Revision of the High-technology Sector and Product Classification," Technology and Industry Working Papers, OECD Science, 1997.
- Ho, L. A., "Meditation, Learning, Organizational Innovation and Performance," Industrial Management & Data Systems, Vol. 111, No. 1, 2011, pp. 113-131. https://doi.org/10.1108/02635571111099758
- ISO, "ISO 56002: 2019 Innovation Management-Innovation Management System-Guidance," 2019.
- Jonash, R., and Sommerlatte, T., "The Innovation Premium: How Next Generation Companies Are Achieving Peak Performance and Profitability," Basic Books, 2001.
- Kafetzopoulos, D., and Psomas, E., "The Impact of Innovation Capability on the Performance of Manufacturing Companies: The Greek Case," Journal of Manufacturing Technology Management, Vol. 26, No. 1, 2015, pp. 104-132. https://doi.org/10.1108/JMTM-12-2012-0117
- Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W and Liu, T.Y., "Lightgbm: A Highly Efficient Gradient Boosting Decision Tree," Advances in Neural Information Processing Systems, Vol. 30, 2017, pp. 3146-3154.
- Kianto, A., Saenz, J., and Aramburu, N., "Knowledge-based Human Resource Management Practices, Intellectual Capital and Innovation," Journal of Business Research, Vol. 81, 2017, pp. 11-20. https://doi.org/10.1016/j.jbusres.2017.07.018
- Knight, K. E., "A Descriptive Model of the Intra-Firm Innovation Process," The Journal of Business, Vol. 40, No. 4, 1967, pp. 478-496. https://doi.org/10.1086/295013
- Lee, R., Lee, J. H. and Garrett, T. C. "Synergy Effects of Innovation on Firm Performance," Journal of Business Research, Vol. 99, 2019, pp. 507-515. https://doi.org/10.1016/j.jbusres.2017.08.032
- Natekin, A., and Knoll, A., "Gradient Boosting Machines, A Tutorial," Frontiers in Neurorobotics, Vol. 7, No. 21, 2013, pp. 1-21.
- OECD, "Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3rd Edition," OECD publishing, 2005, pp. 47-111.
- OECD, "Oslo Manual 2018: Guidelines for Collecting, Reporting and Using Data on Innovation," OECD publishing, 2018.
- Rowley, J., Baregheh, A., and Sambrook, S., "Towards an Innovation-type Mapping Tool," Management Decision, Vol. 49, No. 1, 2011, pp. 73-86. https://doi.org/10.1108/00251741111094446
- Saunila, M., "Innovation Capability in SMEs: A Systematic Review of the Literature," Journal of Innovation & Knowledge, Vol. 5, No. 4, 2019, pp. 260-265. https://doi.org/10.1016/j.jik.2019.11.002
- Schumpeter, J. A., "The Theory of Economic Development," New York: Oxford University Press, 1934.
- Skare, M., and PORADA-ROCHON, M., "The Role of Innovation in Sustainable Growth: A Dynamic Panel Dtudy on Micro and Macro Levels 1990-2019," Technological Forecasting and Social Change, Vol. 24, 2021, pp. 1-12. https://doi.org/10.1016/0040-1625(83)90059-8
- Siroky, D. S., "Navigating Random Forests and Related Advances in Algorithmic Modeling," Statistics Surveys, Vol. 3, 2009, pp. 147-163. https://doi.org/10.1214/07-SS033
- Teece, D. J.. "Business Models, Business Strategy and Innovation," Long Range Planning, Vol. 43, No. 2-3, 2010, pp. 172-194. https://doi.org/10.1016/j.lrp.2009.07.003
- Thompson, V. A., "Bureaucracy and Innovation," Administrative Science Quarterly, Vol. 10, No. 1, 1965, pp. 1-20. https://doi.org/10.2307/2391646
- Tidd, J., and Bessant, J., "Innovation Management Challenges: From Fads to Fundamentals," International Journal of Innovation Management, Vol. 22, No. 5, 2018, pp. 1-14.
- Utterback, J. M., "The Process of Technological Innovation within the Firm," Academy of Management Journal, Vol. 14, No. 1, 1971, pp. 75-88. https://doi.org/10.2307/254712
- Utterback, J. M., and Abernathy, W. J., "A Dynamic Model of Process and Product Innovation," Omega, Vol. 3, No. 6, 1975, pp. 639-656. https://doi.org/10.1016/0305-0483(75)90068-7
- Yam, R. C., Lo, W., Tang, E.P., and Lau, A.K., "Analysis of Sources of Innovation, Technological Innovation Capabilities, and Performance: An Empirical Study of Hong Kong Manufacturing Industries," Research Policy, Vol. 40, No. 3, 2011, pp. 391-402. https://doi.org/10.1016/j.respol.2010.10.013
- Yang, L., and Shami, A., "On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice," Neurocomputing, Vol. 415, 2020, pp. 295-316. https://doi.org/10.1016/j.neucom.2020.07.061
- Zhou, B., Yang, C., Guo, H., and Hu, J., "A Quasi-linear SVM Combined with Assembled SMOTE for Imbalanced Data Classification," In 2013 International Joint Conference on Neural Networks (IJCNN), IEEE, 2013, pp. 1-7.