News big data analysis of international start-up innovation discourses through topic modelling and network analysis: comparing East Asia and North America

References

• Ahn, S., Lee, K. H., & Kim, K. (2018). Assessing the relationship between technological innovation regulation and performance of small and medium enterprises in Korea: Focusing on the effects of individual regulations in a firm’s business life cycle. Journal of Social Science, 57(2), 159–195. https://doi.org/10.22418/JSS.2018.12.57.2.159
   Google Scholar
• Avermaete, T., Viaene, J., Morgan, E. J., Pitts, E., Crawford, N., & Mahon, D. (2004). Determinants of product and process innovation in small food manufacturing firms. Trends in Food Science & Technology, 15(10), 474–483. https://doi.org/10.1016/j.tifs.2004.04.005
   Web of Science ®Google Scholar
• Bae, J. H., Son, J. E., & Song, M. (2013). Analysis of twitter for 2012 South Korea presidential election by text mining techniques. Journal of Intelligence and Information Systems, 19(3), 141–156. https://doi.org/10.13088/jiis.2013.19.3.141
   Google Scholar
• Blei, D. M., & Jordan, M. I. (2003, July). Modeling annotated data. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval (pp. 127–134).
   Google Scholar
• Bogers, M., Chesbrough, H., & Moedas, C. (2018). Open innovation: Research, practices, and policies. California Management Review, 60(2), 5–16. https://doi.org/10.1177/0008125617745086
   Web of Science ®Google Scholar
• Boston Consulting Group. (2020). The most innovative companies 2020 the serial innovation imperative. BCG.
   Google Scholar
• Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press.
   Google Scholar
• Chong, D., & Druckman, J. N. (2007). A theory of framing and opinion formation in competitive elite environments. Journal of Communication, 57(1), 99–118. https://doi.org/10.1111/j.1460-2466.2006.00331_3.x.
   Web of Science ®Google Scholar
• Cohen, B. (1963). The press and foreign polcy. Harcourt.
   Google Scholar
• Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 128–152. https://doi.org/10.2307/2393553
   Web of Science ®Google Scholar
• Colombelli, A., Grilli, L., Minola, T., & Mrkajic, B. (2020). To what extent do young innovative companies take advantage of policy support to enact innovation appropriation mechanisms? Research Policy, 49(10), 103797. https://doi.org/10.1016/j.respol.2019.05.006
   Web of Science ®Google Scholar
• Cooke, P., & Leydesdorff, L. (2006). Regional development in the knowledge-based economy: The construction of advantage. The Journal of Technology Transfer, 31(1), 5–15. https://doi.org/10.1007/s10961-005-5009-3
   Google Scholar
• Cooke, P., Uranga, M. G., & Etxebarria, G. (1997). Regional innovation systems: Institutional and organisational dimensions. Research Policy, 26(4–5), 475–491. https://doi.org/10.1016/S0048-7333(97)00025-5
   Web of Science ®Google Scholar
• Feezell, J. T. (2018). Agenda setting through social media: The importance of incidental news exposure and social filtering in the digital era. Political Research Quarterly, 71(2), 482–494. https://doi.org/10.1177/1065912917744895
   Web of Science ®Google Scholar
• Feld, B. (2020). Startup communities: Building an entrepreneurial ecosystem in your city. John Wiley & Sons.
   Google Scholar
• Freeman, J., & Engel, J. S. (2007). Models of innovation: Startups and mature corporations. California Management Review, 50(1), 94–120. https://doi.org/10.2307/41166418
   Web of Science ®Google Scholar
• Guckenbiehl, P., de Zubielqui, G. C., & Lindsay, N. (2021). Knowledge and innovation in start-up ventures: A systematic literature review and research agenda. Technological Forecasting and Social Change, 172, 121026. https://doi.org/10.1016/j.techfore.2021.121026
   Web of Science ®Google Scholar
• Hinings, B., Gegenhuber, T., & Greenwood, R. (2018). Digital innovation and transformation: An institutional perspective. Information and Organization, 28(1), 52–61. https://doi.org/10.1016/j.infoandorg.2018.02.004
   Web of Science ®Google Scholar
• Huang, H. C., Lai, M. C., & Lo, K. W. (2012). Do founders’ own resources matter? The influence of business networks on start-up innovation and performance. Technovation, 32(5), 316–327. https://doi.org/10.1016/j.technovation.2011.12.004
   Web of Science ®Google Scholar
• Hussain, S., Muhammad, L. J., & Yakubu, A. (2018). Mining social media and DBpedia data using gephi and R. Journal of Applied Computer Science & Mathematics, 12(1), 14–20. https://doi.org/10.4316/JACSM.201801002
   Google Scholar
• Iammarino, S. (2005). An evolutionary integrated view of regional systems of innovation: Concepts, measures and historical perspectives. European Planning Studies, 13(4), 497–519. https://doi.org/10.1080/09654310500107084
   Web of Science ®Google Scholar
• Jeong, Y., Park, I., & Yoon, B. (2019). Identifying emerging research and business development (R&BD) areas based on topic modeling and visualization with intellectual property right data. Technological Forecasting and Social Change, 146, 655–672. https://doi.org/10.1016/j.techfore.2018.05.010
   Web of Science ®Google Scholar
• Jiao, H., Yang, J., Zhou, J., & Li, J. (2019). Commercial partnerships and collaborative innovation in China: The moderating effect of technological uncertainty and dynamic capabilities. Journal of Knowledge Management, 23(7), 1429–1454. https://doi.org/10.1108/JKM-10-2017-0499.
   Web of Science ®Google Scholar
• Jugend, D., Fiorini, P. D. C., Armellini, F., & Ferrari, A. G. (2020). Public support for innovation: A systematic review of the literature and implications for open innovation. Technological Forecasting and Social Change, 156(February), 119985. https://doi.org/10.1016/j.techfore.2020.119985
   Google Scholar
• Kang, S. (2019). A study on the structural relationships among R&D resources, R&D collaboration, policy support, and operational performance — Focusing on Korean health industry clusters. Management & Information Systems Review, 38(2), 225–240. https://doi.org/10.29214/damis.2019.38.2.012
   Google Scholar
• Kim, J., & Diesner, J. (2015). The effect of data pre-processing on understanding the evolution of collaboration networks. Journal of Informetrics, 9(1), 226–236. https://doi.org/10.1016/j.joi.2015.01.002
   Web of Science ®Google Scholar
• Kim, J. H., & Barnett, G. A. (2007). A structural analysis of international conflict: From a communication perspective. International Interactions, 33(2), 135–165. https://doi.org/10.1080/03050620701277764
   Web of Science ®Google Scholar
• Kim, J. H., Jung, H. S., Park, M. H., Lee, S. H., Lee, H., Kim, Y., & Nan, D. (2022). Exploring cultural differences of public perception of artificial intelligence via big data approach. In J. H. Kim, H. S. Jung, M. H. Park, S. H. Lee, H. Lee, Y. Kim, & D. Nan (Eds.), International conference on human-computer interaction (pp. 427–432). Springer.
   Google Scholar
• Kim, J. H., Su, T. Y., & Hong, J. (2007). The influence of geopolitics and foreign policy on the US and Canadian media: An analysis of newspaper coverage of Sudan's Darfur conflict. Harvard International Journal of Press/Politics, 12(3), 87–95. https://doi.org/10.1177/1081180X07302972
   Web of Science ®Google Scholar
• Kim, Y. (2005). Technological collaboration linkages and the innovation output in small and medium-sized firms: A study on the moderating effects of absorptive capacity. Korean Management Review, 34(5), 1365–1390.
   Google Scholar
• Kim, Y., Kim, S., Jaimes, A., & Oh, A. (2014). A computational analysis of agenda setting. Proceedings of the 23rd International Conference on World Wide Web – WWW ‘14 Companion. doi:10.1145/2567948.2577379.
   Google Scholar
• Kok, Y. H., Goh, A., & Holaday, D. (1999). Agenda: A tool for agenda setting research. Information Services & use, 19(4), 265–276. https://doi.org/10.3233/ISU-1999-19403
   Google Scholar
• Lau, A. K., & Lo, W. (2015). Regional innovation system, absorptive capacity and innovation performance: An empirical study. Technological Forecasting and Social Change, 92, 99–114. https://doi.org/10.1016/j.techfore.2014.11.005
   Web of Science ®Google Scholar
• Lee, M., & Song, M. (2020). Incorporating citation impact into analysis of research trends. Scientometrics.
   Google Scholar
• Lew, Y. K., & Park, J. Y. (2021). The evolution of N-helix of the regional innovation system: Implications for sustainability. Sustainable Development, 29(2), 453–464. https://doi.org/10.1002/sd.2143
   Web of Science ®Google Scholar
• Majamaa, W. (2008). The 4th P-People-in urban development based on Public-Private-People Partnership. Teknillinen korkeakoulu.
   Google Scholar
• Miller, M. M. (1997). Frame mapping and analysis of news coverage of contentious issues. Social Science Computer Review, 15(4), 367–378. https://doi.org/10.1177/089443939701500403
   Web of Science ®Google Scholar
• Mimno, D., & McCallum, A. (2012). Topic models conditioned on arbitrary features with dirichlet-multinomial regression. arXiv:1206.3278.
   Google Scholar
• Nah, S., McNealy, J., Kim, J. H., & Joo, J. (2020). Communicating artificial intelligence (AI): Theory, research, and practice. Communication Studies, 71(3), 369–372. https://doi.org/10.1080/10510974.2020.1788909.
   Google Scholar
• Nguyen, V. (2015). Guided Probabilistic Topic Models for Agenda-setting and Framing. (Ph.D. dissertation) University of Maryland, College Park, USA.
   Google Scholar
• Park, S. J., & Lim, Y. S. (2014). Information networks and social media use in public diplomacy: A comparative analysis of South Korea and Japan. Asian Journal of Communication, 24(1), 79–98. https://doi.org/10.1080/01292986.2013.851724
   Web of Science ®Google Scholar
• Son, S. H., & Hong, C. Y. (2019). Potential conflict resolution methods by conflict types in interisland marine transportation planning: Udo-island ferry operation case study. Korean Association Of Cadastre Information, 21(2), 120–139. https://doi.org/10.46416/JKCIA.2019.08.21.2.120
   Google Scholar
• Spender, J. C., Corvello, V., Grimaldi, M., & Rippa, P. (2017). Startups and open innovation: A review of the literature. European Journal of Innovation Management, 20(1), 4–30. https://doi.org/10.1108/EJIM-12-2015-0131
   Web of Science ®Google Scholar
• Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–305. https://doi.org/10.1016/0048-7333(86)90027-2
   Web of Science ®Google Scholar
• Tripathi, N., Oivo, M., Liukkunen, K., & Markkula, J. (2019). Startup ecosystem effect on minimum viable product development in software startups. Information and Software Technology, 114, 77–91. https://doi.org/10.1016/j.infsof.2019.06.008
   Web of Science ®Google Scholar
• Yang, J., Cheng, C., Shen, S., & Yang, S. (2017). Comparison of complex network analysis software: Citespace, SCI 2 and gephi. In J. Yang, C. Cheng, & S. Yang (Eds.), 2017 IEEE 2nd international conference on big data analysis (ICBDA) (pp. 169–172). IEEE.
   Google Scholar
• Yoon, H., Yun, S., Lee, J., & Phillips, F. (2015). Entrepreneurship in East Asian regional innovation systems: Role of social capital. Technological Forecasting and Social Change, 100, 83–95. https://doi.org/10.1016/j.techfore.2015.06.028
   Web of Science ®Google Scholar
• Ziakis, C., Vlachopoulou, M., & Petridis, K. (2022). Start-up ecosystem (StUpEco): A conceptual framework and empirical research. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 35. https://doi.org/10.3390/joitmc8010035
   Google Scholar