Журнал Университетское управление: практика и анализ
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» Архив » 2017 » №6 » Типология институтов генерации научных знаний университетами

Е. В. Попова, b, Д. Г. Сандлера, Н. Г. Поповас, Д. М. Кочеткова,b, В. И. Баженоваd

aУральский федеральный университет им. первого Президента России Б. Н. Ельцина 
Россия, 620002, г. Екатеринбург, ул. Мира, 19; kochetkovdm@hotmail.com

bИнститут экономики УрО РАН 
Россия, 620014, г. Екатеринбург, ул. Московская, 29

cИнститут философии и права УрО РАН 
Россия, 620990, г. Екатеринбург, ул. Софьи Ковалевской, 16

dМинистерство образования и науки Российской Федерации 
Россия, 125993, г. Москва, ул. Тверская, д. 11

ТИПОЛОГИЯ ИНСТИТУТОВ ГЕНЕРАЦИИ НАУЧНЫХ ЗНАНИЙ УНИВЕРСИТЕТАМИ

Ключевые слова: высшее образование, региональная экономика, генерация знаний, институт, институциональная конфигурация, явное знание, неявное знание, типология.

Эффективность генерации научных знаний в условиях новой экономики приобретает критическое значение для функционирования экономической системы. Университет является основным «производителем» научных знаний и ключевым актором экономической трансформации на каждом уровне анализа (страна, регион, город). Существует множество моделей анализа влияния университетов на региональную экономику. Цель работы состоит в выработке единого инструментария анализа влияния университетов на региональные экономические системы на основе достижений институциональной экономической теории. Методологической основой работы служит модель институциональной конфигурации генерации знаний. Чтобы избежать искусственного разрыва субъектно-объектной связи, проблема анализируется на основе методологического единства субъектов, стейкхолдеров и институтов, а также их взаимосвязей с окружающей средой (географической, политической, социальной, экономической, культурной). Авторы предлагают оригинальную типологию институтов генерации знаний университетами на основе популярной модели корпоративного менеджмента знаний SECI. С помощью данной модели были идентифицированы институты социализации, экстернализации, комбинации и интернализации знания. Был сформулирован ряд показателей, которые могут использоваться для целей индикативного планирования и статистического анализа. Результаты работы могут применяться для проведения дальнейших теоретических и прикладных исследований в области анализа региональных систем высшего образования. Кроме того, модель представляет потенциальный интерес для чиновников в части разработки региональной и национальной образовательной политики.

Список литературы

  1. Sirotin V., Arkhipova M., Egorov A. Capitalization of Professional Knowledge in Economies with Different Level of Innovation Development. In: Loue C., Ben Slimane S. (eds), Proceedings of the 12th European Conference on Innovation and Entrepreneurship, Paris, 2017, pp. 628-637.
  2. Egorov A., Leshukov O., Gromov A. The Role of Universities in Economic Development of Russian Regions [Электронный ресурс]. URL: https://www.hse.ru/ mirror/pubs/lib/data/access/ram/ticket/85/15154278010613b 16966e7cdc51974e30fe8f19baa/41EDU 2017.pdf (дата обращения: 10.10.2017).
  3. Отдельные аспекты развития российских университетов: региональная специфика, рейтинги, формирование образовательных траекторий / Г. А. Агарков, И. А. Гурбан, В. А. Кокшаров [и др.]. Екатеринбург: Изд-во Урал. ун-та, 2017. 172 с.
  4. Карпов А. О. Современный университет как драйвер экономического роста: модели и миссии // Вопросы экономики. 2017. № 3. С. 58-76.
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  7. Rogers E. M. Diffusion of Innovations. N. Y.: Free Press, 2003. 576 p.
  8. Dissanayake W. Understanding the Role of Environment in Knowledge Generation and Use: a Plea for Hermeneutical Approach, Social Science Information, 1985, vol. 24, no. 3, pp. 625-649.
  9. Gadamer H.-G. Hermeneutics and Social Science, Cultural Hermeneutics, 1975, no. 2, pp. 307-316.
  10. North D. C. Institutions, Institutional Change, and Economic Performance. Cambridge University Press, 1990. 159 p.
  11. Nelson R. R. Routines. In: Hodgson G., Samuels W., Tool M. (eds), The Elgar companion to institutional and evolutionary economics. Vol. 2, Aldershot, 1994, pp. 249-253.
  12. Searle J. The Construction of Social Reality. N. Y.: Free Press, 1995. 241 p.
  13. Scott W. R. Institutions and Organizations. Ideas, Interests and Identities. Thousand Oaks: Sage, 1995. 360 p.
  14. Denzau A. T., North D. C. Shared Mental Models: Ideologies and Institutions, Kyklos, 1994, vol. 47, iss. 1, pp. 3-31.
  15. Nonaka I., Takeuchi H. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, 1995. 304 p.
  16. Alavi M., Leidner D. E. Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues, MIS Quarterly, 2001, vol. 25, no. 1, pp. 107-136.
  17. Sawhney M., Prandelli E. Communities of Creation: Managing Distributed Innovation in Turbulent Markets, California Management Review, 2000, vol. 42, no. 4, pp. 25-54.
  18. Prichard C., Hull R., Chumer M., Willmott H. Managing knowledge: Critical investigations of work and learning. London: Macmillan Press, 2000. 255 p.
  19. Johnson A. M. A Guide for Early Career Researchers. Amsterdam: Elsevier B. V., 2011. 118 p.
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  22. Kozlowski S. W. J., Bell B. S. Work Groups and Teams in Organizations. In: Borman W. C., Ilgen D. R., Klimoski R. J. (eds) Handbook of Psychology: Industrial and Organizational Psychology, London, 2003, pp. 333-375.
  23. Davis B. 97 Things Every Project Manager Should Know: Collective Wisdom from the Experts. Sebastopol: O’Reilly Media, 2009. 252 p.
  24. Khatri N., Brown G. D., Hicks L. L. From a Blame Culture to a Just Culture in Health Care, Health Care Management Review, 2009, vol. 34, iss. 4, pp. 312-322.
  25. ChiregiM., Navimipour N. J. A New Method for Trust and Reputation Evaluation in the Cloud Environments Using the Recommendations of Opinion Leaders’ Entities and Removing the Effect of Troll Entities, Computers in Human Behavior, 2016, no. 60, pp. 280-292.
  26. Park J.-G., Lee J. Knowledge Sharing in Information Systems Development Projects: Explicating the Role of Dependence and Trust, International Journal of Project Management, 2014, vol. 32, iss. 1, pp. 153-165.
  27. Aghdam S. M., Jafari Navimipour N. Opinion Leaders Selection in the Social Networks Based on Trust Relationships Propagation, Karbala International Journal of Modern Science, 2016, vol. 2, iss. 2, pp. 88-97.
  28. Yuan M., Zhang X., Chen Z., Vogel D. R., Chu X. Antecedents of Coordination Effectiveness of Software Developer Dyads from Interacting Teams: An Empirical Investigation, IEEE Transactions on Engineering Management, 2009, vol. 56, iss. 3, pp. 494-507.
  29. Huang C. H., Huang I. C. The Moderating Effect of Co-Workers’ Reactions on Social Ties and Knowledge Sharing in Work Teams, International Journal of Learning and Intellectual Capital, 2008, vol. 6, iss. 1-2, pp. 156-169.
  30. Chuang C.-H., Jackson S. E., Jiang Y. Can KnowledgeIntensive Teamwork Be Managed? Examining the Roles of HRM Systems, Leadership, and Tacit Knowledge, Journal of Management, 2016, vol. 42, iss. 2, pp. 524-554.
  31. Austin A. E. Faculty Cultures, Faculty Values, New Directions for Institutional Research, 1990, no. 68 (Winter), pp. 61-74.
  32. Kim S., Ju B. An Analysis of Faculty Perceptions: Attitudes towards Knowledge Sharing and Collaboration in an Academic Institution, Library & Information Science Research, 2008, vol. 30, iss. 4, pp. 282-290.
  33. Grigsby R. K., Kirch D. G. Faculty and Staff Teams: a Tool for Unifying the Academic Health Center and Improving Mission Performance, Academic Medicine: Journal of the Association of American Medical Colleges, 2006, vol. 81, no. 8, pp. 688-695.
  34. Sanfilippo F., Bendapudi N., Rucci A., Schlesinger L. Strong Leadership and Teamwork Drive Culture and Performance Change: Ohio State University Medical Center 2000-2006, Academic Medicine: Journal of the Association of American Medical Colleges, 2008, vol. 83, no. 9, pp. 845-854.
  35. Anderson K. M., Aydin A. A., Barrenechea M., Cardenas A., Hakeem M., Jambi S. Design Challenges/ Solutions for Environments Supporting the Analysis of Social Media Data in Crisis Informatics Research. In: 2015 48th Hawaii International Conference on System Sciences, IEEE, 2015, pp. 163-172.
  36. Schreck T., Keim D. Visual Analysis of Social Media Data, Computer, 2013, vol. 46, iss. 5, pp. 68-75.
  37. Nicholls J. Everyday, Everywhere: Alcohol Marketing and Social Media - Current Trends, Alcohol and Alcoholism, 2012, vol. 47, iss. 4, pp. 486-493.
  38. Ceron A., Curini L., Iacus S. M., Porro G. Every Tweet Counts? How Sentiment Analysis of Social Media Can Improve our Knowledge of Citizens’ Political Preferences with an Application to Italy and France, New Media & Society. 2014, vol. 16, no. 2, pp. 1-19.
  39. Sobkowicz P., Kaschesky M., Bouchard G. Opinion Mining in Social Media: Modeling, Simulating, and Forecasting Political Opinions in the Web, Government Information Quarterly, 2012, vol. 29, iss. 4, pp. 470-479.
  40. Kochetkov D., Larionova V. The Changing Role of Universities in Economic Growth. In: Proceedings of the 11th European Conference on Innovation and Entrepreneurship, The JAMK University of Applied Science, 2016, pp. 389-397.
  41. Dessibourg O. La Suisse, carrefour de la circulation des cerveaux, Le Temps, 2012, Thursday 15 November, p. 14.
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E. V. Popova, b, D. G. Sandlera, N. G. Popovac, D. M. Kochetkova, b, V. I. Bazhenovad

aUral Federal University named after the first President of Russia B. N. Yeltsin 
19 Mira str., Yekaterinburg, 620002, Russian Federation; kochetkovdm@hotmail.com

bInstitute of Economics of the Ural Branch of the Russian Academy of Sciences 
29 Moskovskaya str., Yekaterinburg, 620014, Russian Federation

cInstitute of Philosophy and Law of the Ural Branch of the Russian Academy of Sciences 
16 Sof’i Kovalevskoy str., Yekaterinburg, 620990, Russian Federation

dMinistry of Education and Science of the Russian Federation
11 Tverskaya str., Moscow, 125993, Russia

TYPOLOGY OF INSTITUTIONS OF SCIENTIFIC KNOWLEDGE GENERATION BY UNIVERSITIES

Keywords: higher education, regional economy, knowledge generation, institution, institutional configuration, explicit knowledge, tacit knowledge, typology.

The effectiveness of scientific knowledge generation in the new economy becomes critical for the functioning of the economic system. University is the main «producer» of scientific knowledge and a key actor of economic transformation at each level of analysis (country, region, city). There are many models for evaluating universities’ impact on the regional economies. The aim of the work is the development of a common tool for analysis based on the institutional economic theory. The methodological basis of the work is the model of institutional configuration of knowledge generation. To bridge the artificial gap in the subject-object relationship, the problem has been analyzed on the basis of the methodological unity of the actors, stakeholders and institutions, as well as their interrelations with the environment (natural, political, social, economic, cultural). The authors have proposed an original institutional typology of knowledge generation by universities based on the popular model of corporate knowledge management SECI. By means of this model, the institutions of socialization, externalization, combination and internalization of knowledge were identified. A number of indicators has been formulated that can be used for the purposes of indicative planning and statistical analysis. The results of the work can be also used to conduct further theoretical and applied research in the field of analysis of regional higher education systems. In addition, the model is of potential interest for politicians in the development of regional and national educational policies.

References

  1. Sirotin V., Arkhipova M., Egorov A. Capitalization of Professional Knowledge in Economies with Different Level of Innovation Development, in: Loue C., Ben Slimane S. (eds), Proceedings of the 12th European Conference on Innovation and Entrepreneurship, Paris, 2017, pp. 628-637.
  2. Egorov A., Leshukov O., Gromov A. The Role of Universities in Economic Development of Russian Regions, available at: https://www.hse.ru/mirror/pubs/lib/data/access/ ram/ticket/85/15154278010613b16966e7cdc51974e30fe8f19ba a/41EDU 2017.pdf (accessed 10.10.2017).
  3. Agarkov G. A, Gurban I. A., Koksharov V. A. [et al.] Otdel’nye aspekty razvitiya rossiiskikh universitetov: regional’naya spetsifika, reitingi, formirovanie obrazovatel’nykh traektorii [Some Aspects of the Development of Russian Universities: Regional Specificity, Ratings, the Formation of Educational Trajectories], Ekaterinburg, Ural University Press, 2017. 172 p.
  4. Karpov A. O. Sovremennyi universitet kak draiver eko-nomicheskogo rosta: modeli i missii [Modern University as an Economic Growth Driver: Models & Missions]. Voprosy ekonomiki, 2017, no. 3, pp. 58-76.
  5. Frolov D. P. Metodologicheskii institutsional-izm 2.0: ot institutov k institutsional’nym konfiguratsiy-am [Methodological Institutionalism 2.0: from Institutions to Institutional Configurations], Voprosy ekonomiki, 2016, no. 7, pp. 147-160.
  6. Froumin I., Kuzminov Y., Semyonov D. Institutional Diversity in Russian Higher Education: Revolutions and Evolution, European Journal of Higher Education, 2014, vol. 4, iss. 3, pp. 209-234.
  7. Rogers E. M. Diffusion of Innovations. N. Y.: Free Press, 2003. 576 p.
  8. Dissanayake W. Understanding the Role of Environment in Knowledge Generation and Use: a Plea for Hermeneutical Approach, Social Science Information, 1985, vol. 24, no. 3, pp. 625-649.
  9. Gadamer H.-G. Hermeneutics and Social Science, Cultural Hermeneutics, 1975, no. 2, pp. 307-316.
  10. North D. C. Institutions, Institutional Change, and Economic Performance. Cambridge University Press, 1990. 159 p.
  11. Nelson R. R. Routines. In: Hodgson G., Samuels W., Tool M. (eds), The Elgar companion to institutional and evolutionary economics. Vol. 2, Aldershot, 1994, pp. 249-253.
  12. Searle J. The Construction of Social Reality. N. Y.: Free Press, 1995. 241 p.
  13. Scott W. R. Institutions and Organizations. Ideas, Interests and Identities. Thousand Oaks: Sage, 1995. 360 p.
  14. Denzau A. T., North D. C. Shared Mental Models: Ideologies and Institutions, Kyklos, 1994, vol. 47, iss. 1, pp. 3-31.
  15. Nonaka I., Takeuchi H. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, 1995. 304 p.
  16. Alavi M., Leidner D. E. Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues, MIS Quarterly, 2001, vol. 25, no. 1, pp. 107-136.
  17. Sawhney M., Prandelli E. Communities of Creation: Managing Distributed Innovation in Turbulent Markets, California Management Review, 2000, vol. 42, no. 4, pp. 25-54.
  18. Prichard C., Hull R., Chumer M., Willmott H. Managing knowledge: Critical investigations of work and learning. London: Macmillan Press, 2000. 255 p.
  19. Johnson A. M. A Guide for Early Career Researchers. Amsterdam: Elsevier B. V., 2011. 118 p.
  20. Korzhuev A. V., Popkov V. A. Kadry nauki, kul’tury, obrazovaniya. Podgotovka prepodavatelya vysshei shkoly [Personnel of Science, Culture, Education. Training a Teacher of Higher Education]. Pedagogika [Pedagogics], 2000, no. 7, pp. 53-58.
  21. Reznik S. D. Formirovanie kompetentsii prepodavatel’skogo i upravlencheskogo korpusa vu-zov Rossii: opyt, problemy, metodicheskoe obespech-enie [Forming the competencies of the teaching and management corps of Russian universities: experience, problems, methodological support], Alma Mater, 2010, no. 6, pp. 70-76.
  22. Kozlowski S. W. J., Bell B. S. Work Groups and Teams in Organizations. In: Borman W. C., Ilgen D. R., Klimoski R. J. (eds) Handbook of Psychology: Industrial and Organizational Psychology, London, 2003, pp. 333-375.
  23. Davis B. 97 Things Every Project Manager Should Know: Collective Wisdom from the Experts. Sebastopol: O’Reilly Media, 2009. 252 p.
  24. Khatri N., Brown G. D., Hicks L. L. From a Blame Culture to a Just Culture in Health Care, Health Care Management Review, 2009, vol. 34, iss. 4, pp. 312-322.
  25. Chiregi M., Navimipour N. J. A New Method for Trust and Reputation Evaluation in the Cloud Environments Using the Recommendations of Opinion Leaders’ Entities and Removing the Effect of Troll Entities, Computers in Human Behavior, 2016, no. 60, pp. 280-292.
  26. Park J.-G., Lee J. Knowledge Sharing in Information Systems Development Projects: Explicating the Role of Dependence and Trust, International Journal of Project Management, 2014, vol. 32, iss. 1, pp. 153-165.
  27. Aghdam S. M., Jafari Navimipour N. Opinion Leaders Selection in the Social Networks Based on Trust Relationships Propagation, Karbala International Journal of Modern Science, 2016, vol. 2, iss. 2, pp. 88-97.
  28. Yuan M., Zhang X., Chen Z., Vogel D. R., Chu X. Antecedents of Coordination Effectiveness of Software Developer Dyads from Interacting Teams: An Empirical Investigation, IEEE Transactions on Engineering Management, 2009, vol. 56, iss. 3, pp. 494-507.
  29. Huang C. H., Huang I. C. The Moderating Effect of CoWorkers’ Reactions on Social Ties and Knowledge Sharing in Work Teams, International Journal of Learning and Intellectual Capital, 2008, vol. 6, iss. 1-2, pp. 156-169.
  30. Chuang C.-H., Jackson S. E., Jiang Y. Can KnowledgeIntensive Teamwork Be Managed? Examining the Roles of HRM Systems, Leadership, and Tacit Knowledge, Journal of Management, 2016, vol. 42, iss. 2, pp. 524-554.
  31. Austin A. E. Faculty Cultures, Faculty Values, New Directions for Institutional Research, 1990, no. 68 (Winter), pp. 61-74.
  32. Kim S., Ju B. An Analysis of Faculty Perceptions: Attitudes towards Knowledge Sharing and Collaboration in an Academic Institution, Library & Information Science Research, 2008, vol. 30, iss. 4, pp. 282-290.
  33. Grigsby R. K., Kirch D. G. Faculty and Staff Teams: a Tool for Unifying the Academic Health Center and Improving Mission Performance, Academic Medicine: Journal of the Association of American Medical Colleges, 2006, vol. 81, no. 8, pp. 688-695.
  34. Sanfilippo F., Bendapudi N., Rucci A., Schlesinger L. Strong Leadership and Teamwork Drive Culture and Performance Change: Ohio State University Medical Center 2000-2006, Academic Medicine: Journal of the Association of American Medical Colleges, 2008, vol. 83, no. 9, pp. 845-854.
  35. Anderson K. M., Aydin A. A., Barrenechea M., Cardenas A., Hakeem M., Jambi S. Design Challenges/ Solutions for Environments Supporting the Analysis of Social Media Data in Crisis Informatics Research. In: 2015 48th Hawaii International Conference on System Sciences, IEEE, 2015, pp. 163-172.
  36. Schreck T., Keim D. Visual Analysis of Social Media Data, Computer, 2013, vol. 46, iss. 5, pp. 68-75.
  37. Nicholls J. Everyday, Everywhere: Alcohol Marketing and Social Media - Current Trends, Alcohol and Alcoholism, 2012, vol. 47, iss. 4, pp. 486-493.
  38. Ceron A., Curini L., Iacus S. M., Porro G. Every Tweet Counts? How Sentiment Analysis of Social Media Can Improve our Knowledge of Citizens’ Political Preferences with an Application to Italy and France, New Media & Society. 2014, vol. 16, no. 2, pp. 1-19.
  39. Sobkowicz P., Kaschesky M., Bouchard G. Opinion Mining in Social Media: Modeling, Simulating, and Forecasting Political Opinions in the Web, Government Information Quarterly, 2012, vol. 29, iss. 4, pp. 470-479.
  40. Kochetkov D., Larionova V. The Changing Role of Universities in Economic Growth. In: Proceedings of the 11th European Conference on Innovation and Entrepreneurship, The JAMK University of Applied Science, 2016, pp. 389-397.
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DOI 10.15826/umpa.2017.06.072

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» Архив » 2017 » №6 » Типология институтов генерации научных знаний университетами

Е. В. Попова, b, Д. Г. Сандлера, Н. Г. Поповас, Д. М. Кочеткова,b, В. И. Баженоваd

aУральский федеральный университет им. первого Президента России Б. Н. Ельцина 
Россия, 620002, г. Екатеринбург, ул. Мира, 19; kochetkovdm@hotmail.com

bИнститут экономики УрО РАН 
Россия, 620014, г. Екатеринбург, ул. Московская, 29

cИнститут философии и права УрО РАН 
Россия, 620990, г. Екатеринбург, ул. Софьи Ковалевской, 16

dМинистерство образования и науки Российской Федерации 
Россия, 125993, г. Москва, ул. Тверская, д. 11

ТИПОЛОГИЯ ИНСТИТУТОВ ГЕНЕРАЦИИ НАУЧНЫХ ЗНАНИЙ УНИВЕРСИТЕТАМИ

Ключевые слова: высшее образование, региональная экономика, генерация знаний, институт, институциональная конфигурация, явное знание, неявное знание, типология.

Эффективность генерации научных знаний в условиях новой экономики приобретает критическое значение для функционирования экономической системы. Университет является основным «производителем» научных знаний и ключевым актором экономической трансформации на каждом уровне анализа (страна, регион, город). Существует множество моделей анализа влияния университетов на региональную экономику. Цель работы состоит в выработке единого инструментария анализа влияния университетов на региональные экономические системы на основе достижений институциональной экономической теории. Методологической основой работы служит модель институциональной конфигурации генерации знаний. Чтобы избежать искусственного разрыва субъектно-объектной связи, проблема анализируется на основе методологического единства субъектов, стейкхолдеров и институтов, а также их взаимосвязей с окружающей средой (географической, политической, социальной, экономической, культурной). Авторы предлагают оригинальную типологию институтов генерации знаний университетами на основе популярной модели корпоративного менеджмента знаний SECI. С помощью данной модели были идентифицированы институты социализации, экстернализации, комбинации и интернализации знания. Был сформулирован ряд показателей, которые могут использоваться для целей индикативного планирования и статистического анализа. Результаты работы могут применяться для проведения дальнейших теоретических и прикладных исследований в области анализа региональных систем высшего образования. Кроме того, модель представляет потенциальный интерес для чиновников в части разработки региональной и национальной образовательной политики.

Список литературы

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E. V. Popova, b, D. G. Sandlera, N. G. Popovac, D. M. Kochetkova, b, V. I. Bazhenovad

aUral Federal University named after the first President of Russia B. N. Yeltsin 
19 Mira str., Yekaterinburg, 620002, Russian Federation; kochetkovdm@hotmail.com

bInstitute of Economics of the Ural Branch of the Russian Academy of Sciences 
29 Moskovskaya str., Yekaterinburg, 620014, Russian Federation

cInstitute of Philosophy and Law of the Ural Branch of the Russian Academy of Sciences 
16 Sof’i Kovalevskoy str., Yekaterinburg, 620990, Russian Federation

dMinistry of Education and Science of the Russian Federation
11 Tverskaya str., Moscow, 125993, Russia

TYPOLOGY OF INSTITUTIONS OF SCIENTIFIC KNOWLEDGE GENERATION BY UNIVERSITIES

Keywords: higher education, regional economy, knowledge generation, institution, institutional configuration, explicit knowledge, tacit knowledge, typology.

The effectiveness of scientific knowledge generation in the new economy becomes critical for the functioning of the economic system. University is the main «producer» of scientific knowledge and a key actor of economic transformation at each level of analysis (country, region, city). There are many models for evaluating universities’ impact on the regional economies. The aim of the work is the development of a common tool for analysis based on the institutional economic theory. The methodological basis of the work is the model of institutional configuration of knowledge generation. To bridge the artificial gap in the subject-object relationship, the problem has been analyzed on the basis of the methodological unity of the actors, stakeholders and institutions, as well as their interrelations with the environment (natural, political, social, economic, cultural). The authors have proposed an original institutional typology of knowledge generation by universities based on the popular model of corporate knowledge management SECI. By means of this model, the institutions of socialization, externalization, combination and internalization of knowledge were identified. A number of indicators has been formulated that can be used for the purposes of indicative planning and statistical analysis. The results of the work can be also used to conduct further theoretical and applied research in the field of analysis of regional higher education systems. In addition, the model is of potential interest for politicians in the development of regional and national educational policies.

References

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DOI 10.15826/umpa.2017.06.072

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