Typology of smart city knowledge generation institutes

Authors

DOI:

https://doi.org/10.17072/1994-9960-2019-2-218-231

Abstract

Acceleration of scientific and technical progress and the subsequent widespread use of digital technologies in management and development of social and economic systems have become the basis for a huge number of new theoretical concepts and trends of modeling and assessment of territory development trends. The concept of a smart city is believed to be the most sustainable trend of digitization of the relations in social and economic systems. The object of the study is to develop and substantiate the typology of smart city knowledge generation institutes using the revealed correlations between the results of new knowledge generation processes and digital resources in terms of the digital economy. The index “Digital speed of knowledge generation” has been used for quantitative assessment of the efficiency of the processes of new knowledge generation. The index characterizes the increase of knowledge generation efficiency with the growth of the use of digital resources by 1%. The methodological tool for quantitative assessment of the efficiency of the process of the new knowledge generation has been tested on the sampling for average and large manufacturing enterprises with more than 100 employees and that are located in Ekaterinburg during 2014–2018. During the research the factors for the digital economy development that effect the new product development institutes and new technology development institutes of a smart city have been revealed. We have empirically proved that such types of digital resources as personal computers and servers are significantly related to such types of new knowledge generation process results as new technologies and new products. And the use of the Internet and broadband access to the Internet in enterprises are not associated and do not influence the process of knowledge generation in industrial enterprises in smart cities. Using correlation analysis the institutes of new knowledge generation in smart cities have been divided into advanced efficient institutes, developing inefficient institutes, emerging inefficient institutes, and institutional trap. Geographical interpretation of the distribution of knowledge generation institutes has been suggested when using digital technologies in efficiency/sustainability coordinates. The research results have demonstrated that the use of the principles and ideas of institutional modeling of smart city knowledge generation processes allows everyone to form complete predictive models of using social and technological drivers of smart city development in the digital economy. Further development in the field of methodological support for the analysis of the effectiveness and efficiency of management of knowledge generation processes in the digital economy may be based on the method we have suggested to the assessment and classification of smart city institutes.

Keywords

smart city, digital economy, institutes of knowledge generation, institutional theory, modeling, innovations, typology, efficiency, forecast, economic development

For citation

Popov E.V., Vlasov M.V. Typology of smart city knowledge generation institutes. Perm University Herald. Economy, 2019, vol. 14, no. 2, pp. 218–231. DOI 10.17072/1994-9960-2019-2-218-231

Acknowledgements

The research being a part of scientific project No. 18-00-00665 was financially supported with the Russian Foundation for Basic Research.

References

1. Herscovici A. New development: Lean thinking in smart cities. Public Money and Management, 2018, vol. 38, iss. 4, pp. 320–324.
2. Dameri R.P., Benevolo C., Veglianti E., Li Y. Understanding smart cities as a glocal strategy: A comparison between Italy and China. Technological Forecasting and Social Change, 2019, vol. 142, iss. C, pp. 26–41.
3. Appio F.P., Lima M., Paroutis S. Understanding smart cities: Innovation ecosystems, technological advancements, and societal challenges. Technological Forecasting and Social Change, 2019, vol. 142, iss. C, pp. 1–14.
4. Baradaran V., Farokhi S., Ahamdi Z. A model for evaluation and development of citizens’ electronic readiness for deployment of an E-city using structural equation modeling. Journal of Global Information Management, 2018, vol. 26, iss. 4, pp. 135–157.
5. Anttila J., Jussila K. Universities and smart cities: The challenges to high quality. Total Quality Management and Business Excellence, 2018, vol. 29, iss. 9–10, pp. 1058–1073.
6. Macke J., Casagrande R.M., Sarate J.A.R., Silva K.A. Smart city and quality of life: Citizens’ perception in a Brazilian case study. Journal of Cleaner Production, 2018, vol. 182, pp. 717–726.
7. Nilssen M. To the smart city and beyond? Developing a typology of smart urban innovation. Technological Forecasting and Social Change, 2019, vol. 142, pp. 98–104.
8. Hefnawy A., Bouras A., Cherifi C. Relevance of lifecycle management to smart city development. International Journal of Product Development, 2018, vol. 22, no. 5, pp. 351–376.
9. Novikov D., Belov M. Methodological foundations of the digital economy. Studies in Systems, Decision and Control, 2019, vol. 181, pp. 3–14.
10. Watanabe C., Tou Y., Neittaanmäki P. A new paradox of the digital economy – Structural sources of the limitation of GDP statistics. Technology in Society, 2018, vol. 55, pp. 9–23.
11. Ansong E., Boateng R. Surviving in the digital era – business models of digital enterprises in a developing economy. Digital Policy, Regulation and Governance, 2019, vol. 21, iss. 2, pp. 164–178.
12. Negrea A., Ciobanu G., Dobrea C., Burcea S. Priority aspects in the evolution of the digital economy for building new development policies. Calitatea. 2019, vol. 20, iss. S2, pp. 416–421.
13. Todoruţ A.V., Tselentis V. Digital technologies and the modernization of public administration. Quality – Access to Success, 2018, vol. 19, iss. 165, pp. 73–78.
14. Evtyanova D.V., Tiranova M.V. Tsifrovaya ekonomika kak mekhanizm effektivnoi ekologicheskoi i ekonomicheskoi politiki [Digital economy as a mechanism for effective environmental and economic policy]. Internet-zhurnal “Naukovedenie” [Internet Journal “Science Study”], 2017, vol. 9, no. 6. (In Russian) Available at: https://naukovedenie.ru/PDF/79EVN617.pdf (accessed 14.12.2018).
15. Sudarushkina I.V., Stefanova H. A. Tsifrovaya ekonomika [Digital economy]. ANI: ekonomika i upravlenie [ASR: Economics and Management], 2017, vol. 6, no. 1 (18), pp. 182–184. (In Russian).
16. Nepelski D. How to facilitate digital innovation in Europe. Intereconomics, 2019, vol. 54, iss. 1, pp. 47–52. doi: 10.1007/s10272-019-0791-6.
17. Geliskhanov I.Z., Yudina T.N. Digital platform: A new economic institution. Quality – Access to Success, 2018, vol. 19, iss. S2, pp. 20–26.
18. Raven R., Sengers F., Spaeth P., Xie L., Cheshmehzangi A., de Jong M. Urban experimentation and institutional arrangements. European Planning Studies, 2019, vol. 27, no. 2, pp. 258–281.
19. Dudzeviciute G., Simelyte A., Liucvaitiene A. The application of smart cities concept for citizens of Lithuania and Sweden: Comparative analysis. Independent Journal of Management and Production, 2017, vol. 8, no. 4, pp. 1433–1450. doi: 10.14807/ijmp.v8i4.659.
20. Kobayashi A.R., Kniess C.T., Serra F.A., Ferraz R.R., Ruiz M.S. Smart sustainable cities: Bibliometric study and patent information. International Journal of Innovation, 2017, vol. 5, no. 1, pp. 77–96.
21. Allam Z., Dhunny Z.A. On big data, artificial intelligence and smart cities. Cities, 2019, vol. 89, pp. 80–91
22. Sepasgozar S.M.E., Hawken S., Sargolzaei S., Foroozanfa M. Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance of urban technologies. Technological Forecasting and Social Change, 2019, vol. 142, pp. 105–116.
23. Camboim G.F., Zawislak P.A. Pufal N.A. Driving elements to make cities smarter: Evidences from European projects. Technological Forecasting and Social Change, 2019, vol. 142, pp. 154–167.
24. Ismagilova E., Hughes L., Dwivedi Y.K., Raman K.R. Smart cities: Advances in research – an information systems perspective. International Journal of Information Management, 2019, vol. 47, pp. 88–100.

Show full text

Information about the Authors

  • Evgeniy V. Popov, Institute of Economics, the Ural Branch of Russian Academy of Sciences

    Corresponding Member of the Russian Academy of Sciences, Doctor of Economic Sciences, Professor, the Head of the Center of the Economic Theory, Institute of Economics, the Ural branch of the Russian Academy of Sciences; Professor at the Department of Regional Economics, Innovation Enterprise and Security, Ural Federal University named after the first President of Russia B.N. Yeltsin

  • Maxim V. Vlasov, Institute of Economics, the Ural Branch of Russian Academy of Sciences

    Candidate of Economic Sciences, Associate Professor, Senior Researcher at the Center of the Economic Theory, Institute of Economics, the Ural branch of the Russian Academy of Sciences; Associate Professor at the Department of Regional Economics, Innovation Enterprise and Security, Ural Federal University named after the first President of Russia B.N. Yeltsin

Downloads

Published

2019-06-29

Issue

Section

Economic theory