Digital social portrait on the example of crowdsourcing projects

The Fourth Industrial Revolution, the rapid development of the Internet, social networks and artificial intelligence capabilities, and the growing competition in these areas require the search for new approaches to the study of individual behavior in numerous virtual communities. Our research experience has shown that traditional indicators are not enough to understand human behavior in the digital environment: human behavior in the physical and virtual world is significantly different, moreover, the “online environment” has a significant impact on it. In this regard, it is extremely relevant to supplement the existing traditional socio-demographic indicators with new ones, the importance of which is due to the peculiarities of human behavior in the Internet environment and Internet communities in the digital age. This will allow identifying involved, interested, active individuals as potential participants in future crowdsourcing projects. The results of our research show that interest and common values can form strong creative communities to solve significant social problems, whose participants will not be passive consumers of information, on the contrary, “collective intelligence” and “the power of diversity” (social, cultural, ethnic, etc.) can contribute to the generation of unique ideas, the quality of which may exceed even the decisions of expert groups. The main objective of our research is to develop an effective methodology to increase the “inclusion” of interested parties, to assess the level of involvement of community members by compiling an average digital social portrait of a community member and predicting the crowdsourcing potential of the Internet community with its help. 

1. Bayus B.L. Crowdsourcing New Product Ideas Over Time: An Analysis of the Dell IdeaStorm Community // Management Science. 2013. Vol. 59, Is. 1. P. 226–244.

2. Boudreau K.J., Lakhani K.R. Using the Crowd as an Innovation Partner // Harvard Business Review. 2013. Vol. 91. P. 60–69.

3. Djelassi S., Cambier F. Creative crowdsourcing: understanding participation barriers and levers from a heterogeneous crowd perspective // Journal of Marketing Management. 2022. DOI: 10.1080/0267257X.2022.2131884.

4. Estelles-Arolas E., Gonzalez-Ladron‐de-Guevara F. Towards an Integrated Crowdsourcing Definition // Journal of Information Science. 2012. Vol. XX, Is. X. P. 1–14.

5. Flores C., Rezende D. Crowdsourcing framework applied to strategic digital city projects // Journal of Urban Management. 2022. DOI: https://doi.org/10.1016/j.jum.2022.08.004.

6. Füller J., Hutter K., Kröger N. Crowdsourcing as a service – from pilot projects to sustainable innovation routines // International Journal of Project Management. 2021. Vol. 39, Is. 2. P. 183–195. DOI: https://doi.org/10.1016/j.ijproman.2021.01.005.

7. Krasnov A., Chargaziya G., Griffith R., Draganov M. Dynamic and static elements of a consumer’s digital portrait and methods of their studying // IOP Conference Series: Materials Science and Engineering. 2019. Vol. 497. Art. 012123. DOI: 10.1088/1757-899X/497/1/012123.

8. Piazza M., Mazzola E., Perrone G. How can I signal my quality to emerge from the crowd? A study in the crowdsourcing context // Technological Forecasting and Social Change. 2022. Vol. 176. Art. 121473. DOI: https://doi.org/10.1016/j.techfore.2022.121473.

9. Palacios-Marques D., Gallego-Nicholls J.F., Guijarro-Garcia M. A recipe for success: Crowdsourcing, online social networks, and their impact on organizational performance // Technological Forecasting and Social Change. 2021. Vol. 165. Art. 120566. DOI: https://doi.org/10.1016/j.techfore.2020.120566.

10. Tan L., Xiao H., Yu K., Aloqaily M., Jararweh Y. A blockchain-empowered crowdsourcing system for 5G-enabled smart cities // Computer Standards & Interfaces. 2021. Vol. 76. Art. 103517. DOI: https://doi.org/10.1016/j.csi.2021.103517.

11. Yin X., Zhu K., Wang H., Zhang J., Wang W., Zhang H. Motivating participation in crowdsourcing contests: The role of instruction-writing strategy // Information & Management. 2022. Vol. 59, Is. 3. Art. 103616. DOI: https://doi.org/10.1016/j.im.2022.103616.

12. Digital 2022: Global Overview Report // DataReportal. URL: https://datareportal.com/reports/digital2022-global-overview-report.

13. Burganov R.T., Mavlyautdinova G.S., Gafarov M.R. Inclusive growth model as a mechanism for sustainable development of regional and national economic systems // Kazan Economic Bulletin. 2020. No. 4. P. 33–42.

14. Ermolaeva P.O., Noskova E.P., Zainullina M.R., Kuptsova A.I., Nagimova A.M. Social portrait of the population: methodology, main characteristics. Kazan: Artifact, 2014. 92 p.

15. Safiullin M.R., Burganov R.T., Burganova A.R. Crowdsourcing as a new driver of the digital economy and a tool for harmonizing the interests of participants // Bulletin of Saint Petersburg University. Economy. 2022. Vol. 38, Is. 1. P. 85–112. DOI: https://doi.org/10.21638/spbu05.2022.104.

16. Safiullin M.R., Burganova A.R. Stakeholders of a social crowdsourcing project (using the example of sports) and their expectations // Electronic Economic Bulletin. 2020. No. 4. P. 53–62.

17. Safiullin M.R., Burganova A.R. On the measurement of motivation in crowdsourcing // Kazan Economic Bulletin. 2022. No. 4 (60). P. 103–109.