ارائه مدل تراکم صنعتی برای ارتقای نوآوری فناورانه در صنایع ایران (با رویکرد سرمایه گذاری) و ضرورت بکارگیری آن

محورهای موضوعی : دانش سرمایه‌گذاری

علی حسن زاده ¹ (دانشجوی دکترای مدیریت فناوری دانشگاه علامه طباطبایی.)
سیدرضا سلامی ² (عضو هیات علمی دانشگاه علامه طباطبایی.)
مقصود امیری ³ (عضو هیات علمی دانشگاه علامه طباطبایی.)
جهانیار بامداد صوفی ⁴ (عضو هیات علمی دانشگاه علامه طباطبایی.)

تاریخ دریافت : 1399/12/13 تاریخ پذیرش : 1399/12/19 تاریخ انتشار : 1400/10/01

کلید واژه: دلفی فازی, گراندد تئوری, ارتقای نوآوری فناورانه, تراکم صنعتی,

چکیده مقاله :

پیچیدگی که ویژگی اصلی محیط بسیاری از سازمانهاست، ‌آنها را مجبور کرده تا علاوه بر همکاری با یکدیگر، به بهره‌برداری از دانش بیرونی پرداخته و آن را با منابع داخلی دانش ترکیب نمایند. در همین راستا ارائه مدل تراکم صنعتی برای ارتقای نوآوری فناورانه در صنایع ایران را ضروری می‌داند. این پژوهش با انتخاب روش تحقیق گراندد تئوری به تبیین و توسعه مدل مفهومی در حوزه مدل تراکم صنعتی برای ارتقای نوآوری فناورانه در صنایع ایران می‌پردازد. روش پژوهش آیخته است و در بخش کیفی مصاحبه‌هایی با 14 نفر از خبرگان مرتبط شکل گرفت. کلیه اطلاعات مستخرج مصاحبه‌ها در قالب مفاهیم و مقولات مطرح‌شده و ارتباطات بین آن‌ها آشکار گردید، درنهایت بر پایه مفاهیم و مقولات تدوین شده، مدل پژوهش تدوین شد. در روش کمی مدل نیز، با استفاده از روش دلفی فازی و توزیع پرسشنامه در سه مرحله نظرات خبرگان را در زمینة، با اهمیت بودن شاخص‌های تراکم صنعتی و نوآوری فناورانه و اجماع خبرگان، و اولویت بندی شاخص-های تحقیق بررسی شد و با توجه به نتایج حاصل از نظر خبرگان و اجماع توافق شده آنها، در توسعه شهرک صنعتی و شکل دهی تراکم صنعتی برای ارتقای نوآوری فناورانه در شهرک های صنعتی ایران، مولفه ‌های حمایت دولت ازصنایع کوچک و متوسط، سیاستهای حمایتی مالیاتی دولت (برای مثال اعطای معافیت مالیاتی) و مزایای طبیعی منطقه مشترکاً در رتبه اول و مابقی مولفه‌ها در رتبه‌های بعدی قرار گرفته‌اند.

چکیده انگلیسی:

Complexity, which is a key feature of many organizations' environments, has forced them to use external knowledge in addition to collaborating with each other and combining it with internal sources of knowledge. In this regard, it is necessary to provide an industrial agglomeration model to promote technological innovation in Iranian industries. By selecting grounded theory research method, this research explains and develops a conceptual model in the field of industrial agglomeration model to promote technological innovation in Iranian industries. The research method is mixed and in the qualitative part, interviews were conducted with 14 related experts. All the information extracted from the interviews was revealed in the form of concepts and categories and the connections between them. Finally, based on the concepts and categories developed, the research model was developed. In the quantitative method of the model, using the fuzzy Delphi method and distributing the questionnaire in three stages, the opinions of experts in the field, the importance of industrial density indicators and technological innovation and expert consensus, and prioritization of research indicators were examined. Considering the results obtained by experts and their agreed consensus in the development of industrial estates and the formation of industrial density to promote technological innovation in Iran's industrial estates, the components of government support for small and medium industries, government tax protection policies (for example, granting Tax exemption) and the natural benefits of the region are jointly ranked first and the rest of the components are ranked in the following.

منابع و مأخذ:

• دانایی فرد حسن، امامی سیدمجتبی (1386). استراتژی­های پژوهش کیفی- نظریه پردازی داده بنیاد، نشریه اندیشه مدیریت راهبردی (اندیشه مدیریت)، پاییز و زمستان 1386، دوره 1، شماره 2.


• فوکردی، رحیم. 1390،"مدلی برای تبیین روابط قدرت در لایه خرده فروشی زنجیره تأمین موارد غذائی، مورد مطالعه: بخش محصولات غذائی شرکت خدماتی کالای شهروند"، رساله دکتری در مدیریت تولید و عملیات، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی.


• Balland, P.A., R. Boschma and K. Frenken (2015) Proximity and innovation: From statics to dynamics, Regional Studies 49 (6), 907-920.


• Bao, Z. (2010), Construction of Industrial Technology Innovation Capability, International Journal of Business and Management, 5(12), pp. 220-224.


• Beckman, C. M., & Haunschild, P.R. (‌2002), Network Learning: The Effects of Partners' Heterogeneity of Experience on Corporate Acquisitions, Administrative Science Quarterly, 47: 2-124.


• Beugelsdijk, S. (2007). The regional environment and a firm´s innovative performance: a plea for a multilevel interactionist approach, Economic Geography, 83(2), 181-199.


• Bi, K.X., Sun, D.H., Zheng R.F., & Li, B.Z. (2006). The construction of synergetic development system   of   product   innovation   and   process   innovation   in manufacturing enterprises, Proceedings of the 13th International Conference on Management   Science   and Engineering   (ICMSE),   Lille,   France,   5-7 October 2006, 628–636.


• Boschma, R. (2005). Proximity and Innovation: A Critical Assessment, Regional Studies, Vol.39, nº 1, February 2005, p. 61-74.


• H. & Fulei. W., & Shuying. Z. (2015). The Impact of Agglomeration Structure on Technology Innovation, IEEE. No. 10552183, pp 768 – 772.


• H.H. (2013).Technological Learning and Technological Innovation Creation: An Empirical Analysis of Biotechnology R&D Teams, Journal of Engineering Science and Technology Review v.6, p.120-124.


• Chesbrough, H. W. (2003). The logic of open innovation: managing intellectual property, California Management Review, 45(3), 33-58.


• Chu H, Hwang GJ. A (2008), Delphi-based approach to developing expert systems with the cooperation of multiple experts. Expert Systems with Applications; vol. 34(4), 2826- 40.


• Claver-Cortés, E. & Marco-Lajara, B. & Manresa-Marhuenda, E. (2015), Types of agglomeration economies: effects on business innovation, CONTEMPORARY ECONOMICS journal, Vol. 10 (3): 217-232.


• R. & Rodríguez-Pose. A. & Michael Storper. M., (2007). The territorial dynamics of innovation: a Europe–United States comparative analysis, Journal of Economic Geography, vol. 7, no. 6, pp. 673-709.


• Diaz-Diaz, N.L., Aguiar-Diaz, I., and De Saa-Perez, P. (2008), the effect of technological knowledge assets on performance: The innovative choice in Spanish firms, Research Policy, 37, pp. 1515-1529.


• Ellison, Glenn, Edward L. Glaeser, and William R. Kerr. (2010). What Causes Industry Agglomeration? Evidence from Co-agglomeration Patterns, American Economic Review, 100(3): 1195-1213.


• L. (2015), Do Clusters Encourage Innovation? A Meta-analysis, Journal of Planning Literature, Vol. 30, Issue 3, pp. 239 - 260


• Ferris, S.P., R. Houston, and D. Javakhadze. (2016). Friends in the right places: The effect of political connections on corporate merger activity. Journal of Corporate Finance 41(1): 81-102


• R. M. (2012), SHIPPING COSTS, INFORMATION COSTS, AND THE SOURCES OF INDUSTRIAL COAGGLOMERATION, JOURNAL OF REGIONAL SCIENCE, VOL. 00, NO. 0, 2012, pp. 1–28.


• Groot, H. L. F., & Nijkamp, P. & Zoltan A. (2001), Knowledge spillovers, innovation and regional development, Regional Science, no.80: 249–253.


• D. (2013), Innovation, proximity and knowledge gatekeepers –Is proximity a necessity for learning and innovation? , International Journal of Innovation and Learning, Vol. 14, No. 2, pp 177-196.


• S. J. (2014). Inside China's \Growth Miracle:" A Structural Framework of Firm Concentration, Innovation and Performance with Policy Distortions, Doctor of Philosophy in Geography, University of California, and Los Angeles.


• X. (2016), improve the Innovation Resources Agglomeration Capacity of Shanghai, Master thesis in Industrial management and business administration, Halmstad University.


• T. (2012), CREATIVE CLUSTERING: AGGLOMERATION EFFECTS IN INNOVATION, the Honors Tutorial College and the Department of Political Science. June 2012.


• Jiménez-Jiménez, D., & Valle, R.S. (2011). Innovation, organizational learning, and performance, Journal of Business Research, 64(4), 408-417.


• Jung Won. S. and Storper, M. (2008), the increasing importance of geographical proximity in knowledge production: an analysis of US patent citations, 1975–1997 Environment and Planning A, 40 (5). 1020-1038.


• M. & wahyuni. S. (2015), FDI impacts on industrial agglomeration: The case of Java, Indonesia, Journal of Asia Business Studies, Vol. 3 ISS 2 pp. 65 – 77.


• Levinthal, D. A., & March, J. G. (1993). The Myopia of Learning, Strategic Management Journal, 14: 95-112.


• G. (2009), Disentangling Clusters Agglomeration and Proximity Effects, Dissertation for the Degree of Ph.D. Stockholm School of Economics, pp. 13-41.


• G. & Lamin. A. (2016), Knowledge, Proximity and R&D Exodus, Research Policy 45 (2016), pp. 8–26.


• S. (2016), Spillovers, absorptive capacity and agglomeration, Journal of Urban Economics, 96(2016), pp.17–35.


• N. A. (2013), the determinants of agglomeration in Brazil: Input-Output, Labor and Knowledge Externalities, Ph. D. Degree Thesis, University of Illinois at Urbana-Champaign.


• Marshall, A. (1920). Principles of Economics (8). London, UK: MacMillan.


• Mukerji, B. & Fantazy, K., Kumar, U., and Kumar, V. (2010), the Impact of Various Dimensions of Manufacturing Capability on Commercialization Performance: Evidence from Canadian Manufacturing Sector, Global Journal of Flexible Systems Management, 11 (3), 1-10.


• M. & Caroline. M. (2009), Proximity and Innovation through an “Accessibility to Knowledge” Lens, Regional Studies 43, 1, pp. 77-88.


• (2003, June 2003). Local partnerships, clusters and SME globalization, Paper presented at the The OECD Bologna Ministerial Conference. Enhancing the Competitiveness of SMEs in the Global Economy: Strategies and Policies, Bologna, Italy.


• OECD/Eurostat. (2005).Oslo Manual: Guidelines for collecting and interpreting innovation data (3rded.). Paris: OECD Publishing. Available from http://www.oecd-ilibrary.org/science-and-technology/oslo-manual_9789264013100-en.


• O'Hara, P. A. (2008). Principle of circular and cumulative causation: fusing Myrdalian and Kaldorian growth and development dynamics, Journal of Economic Issues, 42(2), 375-387.


• Real, J. C, Leal, A., and Roldan, 1. L. (2006), Information Technology as a determinant of Organizational Learning and Technological Distinctive Competencies, Industrial Marketing Management, 35, pp. 505-521.


• Sara Santos Cruz & Aurora A.C. Teixeira (2015) The magnitude of creative industries in Portugal: what do the distinct industry-based approaches tell us?, Creative Industries Journal, 8:1, 85-102, DOI: 10.1080/17510694.2015.1050298


• M. J. (2010), Agglomeration and Innovation: Evidence from DUTCH Micro data, 483th of the Tinbergen Institute Research Series.


• Srivastava, M. K. (2007), Friends or Neighbors? The Effects of Inter-firm Networks and Clusters on Technological Innovations in the U.S. Semiconductor Industry, PhD Thesis in business management Virginia,  Polytechnic Institute and State University, pp 12-44.


• Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management, Strategic Management Journal, 18(7), 509-533.


• Teece, D., and Pisano, G. (1994), the Dynamic Capabilities of Firms: an Introduction, Industrial and Corporate Change, 3(3), pp, 537-556.


• Thornhill, S., (2010). Knowledge, innovation and firm performance in high and low technology regimes, Journal of Business Venturing 21 (1), 687–703.


• M. (2015), A study of industrial agglomeration and co-location in China, Department of Economics, degree of Doctor of Philosophy, University of Birmingham.


• M. (2015), Dimensions of Proximity: Clusters, Intellectual Capital and Knowledge Spillovers, European Scientific Journal February 2015 edition vol.11, No.4, pp. 211-221.


• S. (2012), Agglomeration Patterns in Turkish Manufacturing Industries, University of Sheffield for the Degree of Doctor of Philosophy in the Department of Economics.


• M.(2015), Proximity to innovation: Effect of proximities on cross-border innovation cooperation within Öresund Food Cluster actors, Master program in Economic Growth, Innovations and Spatial Dynamics, Lund University.


• Vásquez-Urriago. A. R. & Barge-Gil. A. & Modrego Rico. A.(2016), Science and  Technology  Parks  and  cooperation  for  innovation: Empirical  evidence  from  Spain, Research  Policy  45  (2016), pp. 137–147.


• WalkerHanlon. W. W. & Misciob. A (2017), Agglomeration: Along-run panel data approach, Journal of Urban Economics ISS99 pp.1–14.


• Winter, S. (2003), Understanding Dynamic Capabilities, Strategic Management Journal, 24, pp. 991-995.


• Yam, R.C.M., Guan, J.e., Pun, K.F., and Tang, P.Y. (2004), an audit of technological innovation capabilities in Chinese firms: some empirical findings in Beijing, China, Research Policy, 33 (8), pp. 1123 1250.


• Z. & Yanhua. N., & Likai, (2009). An Empirical Analysis on the Impact of Knowledge Spillovers on Regional Innovative Output—A Case of High Technology Industry, Soft Science, vol.23, no. 7, pp. 99-102.


• Yu-wen. P. (2015), Study On Dynamical Mechanism Of Industrial Agglomeration Innovation System, IEEE, 978-1-4244-6581-1.