UDC: 331.5.024.54
https://doi.org/10.25198/2077-7175-2024-2-33
EDN: FZGXKX

GIGONOMY IN PRIORITY INDUSTRIES AND FIELDS OF THE ECONOMY: DEMAND FOR FREELANCERS AND CROWDSOURCERS

Yu. M. Polyakova
Lomonosov Moscow State University, Moscow, Russia
e-mail: flaeeee@gmail.com

Abstract. Remote forms of employment are beginning to play an increasingly important role in the context of systemic digital transformation of industries and sectors of the economy, including the growing importance of platform solutions. Therefore, gigonomics as temporary employment is of particular interest in the development of modern organizations. The purpose of the study is to assess the level of industrial use of gig-workers, identify the least popular areas of their work and propose a mechanism to expand the areas of activity and increase the work of freelancers’ and crowdsourcers’ demand. The work uses desk research methods to study the state of the gigonomics in the world and in Russia, collect statistical data on the number of implemented crowdsourcing projects on 27 Russian platforms and distribute them by industry segments. A comparative analysis of the use of gig-workers in various industries and sectors of the Russian economy was carried out, and based on the modeling method, the development of a national network of e-platforms based on Russian universities was proposed in order to coordinate the joint work of business and science to solve industry problems facing modern organizations. The study identified sectors of the economy with the lowest rates of gig-worker use, including healthcare, transportation, hotel business and tourism, consumer goods and manufacturing. At the same time, the most popular areas include the financial sector, IT, marketing, design, heavy industry and insurance. It was also possible to classify domestic crowd-platforms into three groups: public, private/ commercial and corporate. The sectoral specificity of the tasks, the low level of availability and shortage of highly specialized personnel, the lack of effective mechanisms for searching and selecting personnel are the main reasons for the low growth rates of the listed economic sectors. The proposed model of a national network of e-platforms will reduce the time spent searching for specialists to solve labor-intensive business problems, integrating representatives of the scientific community into the processes of enterprises industrial functioning.

Key words: gigonomics, crowdsourcing, freelancing, gig-worker, electronic platform, industrial economic development.

Cite as: Polyakova, Yu. M. (2024) [Gigonomy in priority industries and fields of the economy: demand for freelancers and crowdsourcers]. Intellekt. Innovacii. Investicii [Intellect. Innovations. Investments]. Vol. 2, pp. 33–42. – https://doi.org/10.25198/2077-7175-2024-2-33.


References

  1. Arhipova, M. Yu., Sobolev, M. A. (2022) [Study of the dynamics of development of the national innovation system of Russia (part 1)]. Gosudarstvennoe upravlenie [State Administration. Electronic newsletter]. Vol. 90, pp. 90– 107. – https://doi.org/10.24412/2070-1381-2022-90-90-107. – EDN: QYSMFF. (In Russ.).
  2. Borisyuk, N. K., Smotrina, O. S. (2022) [On the issue of enterprise functioning in an unstable external environment. Intellekt. Innovacii. Investicii [Intellect. Innovations. Investments]. Vol. 2, pp. 24–30. – https://doi.org/10.25198/2077-7175-2022-2-24. – EDN: XLJDGS. (In Russ.).
  3. Denisenkov, A. N., Polyakova, Yu. M. (2020) [Crowdsourcing and platform solutions in transport: opportunities for the development of the «Digital Metro» in Russia]. Mir transporta [World of Transport]. Vol. 18, No. 1 (86), pp. 6–20. – https://doi.org/10.30932/1992-3252-2020-18-06-20. – EDN: VYUFTI. (In Russ.).
  4. Konkurentnye preimushchestva cifrovoj kooperacii [Competitive advantages of digital cooperation]. Under edit. of V. A. Cvetkova. M.: IPR RAN, 2018. 380 p. – https://doi.org/10.33051/978-5-6041039-1-3-2018-1-380. – EDN: XQOYCB.
  5. Lapidus, L. V. (2020) [Barometer of digital environment turbulence and digital transformation strategies in education]. Teoriya i praktika proektnogo obrazovaniya [Theory and practice of project education]. Vol. 3 (15), pp. 7–10. – EDN: NIQLPA. (In Russ.).
  6. Lapidus, L. V., Polyakova, Yu. M. (2022) [Gigonomics: new opportunities for digital transformation of business in conditions of high turbulence of the digital environment]. Vestnik Instituta ekonomiki Rossijskoj akademii nauk [Bulletin of the Institute of Economics of the Russian Academy of Sciences]. Vol. 5, pp. 23–46. – https://doi.org/10.52180/2073-6487_2022_5_23_46. – EDN: LXIBID. (In Russ.).
  7. Podvojskij, G. L. (2019) [The future of the sphere of labor: the agenda]. Razvitie sfery truda v Rossii: istoki problem, sovremennye trendy i vyzovy globalizacii: Sbornik [Development of the sphere of labor in Russia: the origins of problems, modern trends and challenges of globalization: Collection]. M.: IE RAN, pp. 82–100. (In Russ.).
  8. Sautkina, V. A. (2020) [Virtual employment: new opportunities and risks]. Social’no-trudovye issledovaniya [Social and Labor Research].Vol. 2 (39), pp. 57–68. – https://doi.org/10.34022/2658-3712-2020-39-2-57-68. – EDN: EFGHJM. (In Russ.).
  9. Sautkina, V. A. (2020) [International freelance market: development prospects]. Analiz i prognoz. Zhurnal IMEMO RAN [Analysis and forecast. Journal of IMEMO RAS]. Vol. 3, pp. 35–43. – https://doi.org/10.20542/afij-2020-3-35-43. – EDN: HAIVZZ. (In Russ.).
  10. Cvetkov, V. A. (2019) [Reality and prospects of the Russian economy]. Problemy rynochnoj ekonomiki [Problems of the market economy]. Vol. 1, pp. 5–16. – https://doi.org/10.33051/2500-2325-2019-1-05-16. – EDN: ZEHOIX. (In Russ.).
  11. Bao, Y. et al. (2018) Online job scheduling in distributed machine learning clusters. In IEEE Conference on Computer Communications, Jun 2018. Available at: https://arxiv.org/abs/1801.00936. (In Eng.).
  12. Jin, H. et al (2018) Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems, IEEE/ACM Transactions on Networking, Vol. 26, Is. 5, pp. 2019–2032. – https://doi.org/10.1109/TNET.2018.2840098. (In Eng.).
  13. Johnson, D. (2001) What is Innovation and Entrepreneurship? Lessons for larger Organizations, Industrial and Commercial Training. Vol. 33, No 4, pp. 135–140. – https://doi.org/10.1108/00197850110395245. – EDN: EBLUKB. (In Eng.).
  14. Kim, H. et al (2018) On-device federated learning via blockchain and its latency analysis. Available at: https://arxiv.org/pdf/1808.03949v1.pdf (In Eng.).
  15. Leontieva, L. S., Proskurnova, K. Yu. (2022) Spatial planning levels for territory development. E-journal public administration. No 94. – pp. 108–120. – https://doi.org/10.24412/2070-1381-2022-94-108-120. – EDN: PQXISE. (In Eng.).
  16. Pandey, S. R. et al. (2020) Incentivize to build: A crowdsourcing framework for federated learning, In IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, Dec. 2019. – https://doi.org/10.1109/GLOBECOM38437.2019.9014329. (In Eng.).
  17. Podvoisky, G. L. (2019) The Future of the Sphere of Labor: Agenda. Development of the Sphere of Labor in Russia: Origins of Problems, Modern Trends and Challenges of Globalization: Collection. Ed. I. V. Soboleva and A. P. Sedlov. M.: IE RAN, 306 p.
  18. Wang, Z. et al. (2018) Beyond inferring class representatives: User-level privacy leakage from federated learning, In IEEE Conference on Computer Communications (INFOCOM 2019), pp. 2512–2520. (In Eng.).