Monetization of a Multi-Sided Innovative Platform – A Case Study of the STEDDY Project

Sanja Dalton1 [0009-0004-2163-232X] and Jefto Dzino2 [0009-0007-8913-752X]
1,2 Belgrade Metropolitan University, 63 Tadeuša Košćuška St, Belgrade, Serbia
sanja.dalton@metropolitan.ac.rs, jefto.dzino@metropolitan.ac.rs
DOI: 10.46793/eLearning2025.092D

 

Abstract. This paper analyzes the commercialization potential of an innovative multi-sided platform for personalized learning using a qualitative research approach based on both primary and secondary data. The data was collected through surveys and semi-structured interviews with high school students, university students, and employed individuals in need of personalized upskilling. Through case study methodology and the analysis and synthesis of the collected data, the results indicate that the monetization of the innovative platform is feasible and sustainable.

Keywords: Innovative platform; platform business model; personalized learning; monetization of platforms

References

[1] Adewumni, O. (2024). Monetization strategies for content creators. IOSR Journal of Economics and Finance (IOSR-JEF), 15(6, Ser. 5), 57–66. https://www.iosrjournals.org

[2] Afuah, A., & Tucci, C.I. (2000). Internet Business Models and Strategies: Tet and Cases. McGraw-Hill Higher Education, New York.

[3] Ardolino, M., Saccani, N., Adrodegari, F., & Perona, M. (2020). A business model framework to characterize digital multisided platforms. Journal of Open Innovation: Technology, Market, and Complexity, 6(1), 10. https://doi.org/10.3390/joitmc6010010

[4] Baden-Fuller, C., & Heafliger, S. (2013). Business models and technological innovations. Long Range Planning, 46(6), 419–426.

[5] Baden-Fuller, C., & Mangematin, V. (2013). Business models: A challenging agenda. Strategic Organization, 11(4), 418–427.

[6] Baltas, H., Sirin, M., Gökbayrak, E., & Ozcelik, A. E. (2020). A case study on pollution and a human health risk assessment of heavy metals in agricultural soils around Sinop Province, Turkey. Chemosphere, 241, 125015. https://doi.org/10.1016/j.chemosphere.2019.125015

[7] Cennamo, C., & Santaló, J. (2015). How to avoid platform traps. MIT Sloan Management Review, 57(1), 12–15.

[8] Chatti, M. A., & Muslim, A. (2019). The PERLA framework: Blending personalization and learning analytics. International Review of Research in Open and Distance Learning, 20(1), 244 261. https://doi.org/10.19173/irrodl.v20i1.3936

[9] Cheng, K.-H., & Tsai, C.-C. (2019). A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors. Computers & Education, 140, 103600. https://doi.org/10.1016/j.compedu.2019.103600

[10] Da Silva, E. K. N., Dos Santos, V. B., Resque, I. S., Neves, C. A., Moreira, S. G. C., Franco, M. D. O. K., & Suarez, W. T. (2020). A fluorescence digital image-based method using a 3D printed platform and a UV-LED chamber made of polyacid lactic for quinine quantification in beverages. Microchemical Journal, 157, 104986. https://doi.org/10.1016/j.microc.2020.104986

[11] Debutify. (2021). How content creators are making a living in the creator economy. https://debutify.com/blog/what-is-creator-economy

[12] Feng, W., Zhang, Q., Ji, H., Wang, R., Zhou, N., Ye, Q., Hao, B., Li, Y., Luo, D., & Lau, S. S. Y. (2019). A review of net zero energy buildings in hot and humid climates: Experience learned from 34 case study buildings. Renewable and Sustainable Energy Reviews, 114, 109303. https://doi.org/10.1016/j.rser.2019.109303

[13] W. Gao, P. Veeresha, H.M. Baskonus, D.G. Prakasha, P. Kumar, A new study of unreported cases of 2019-nCOV epidemic outbreaks, Chaos, Solitons & Fractals 138 (2020) 109929, https://doi.org/10.1016/j.chaos.2020.109929

[14] Gómez, S., Zerva, S. P., Sampson, D. G., & Fabregat, R. (2014). Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2013.10.008 26(1), 47–61.

[15] Graham, C. R., Borup, J., Short, C. R., & Archambault, L. (2019). K-12 blended teaching: A guide to personalized learning and online integration. Provo, UT: EdTech Books. http://edtechbooks.org/K12blended

[16] Hagiu, A. (2007). Merchant or two-sided platforms? Review of Network Economics, 6(2), 115–133.

[17] Hagiu, A., & Wright, J. (2015). Marketplace or reseller? Management Science, 61(1), 184 203.

[18] Harvey Arce, N. P., & Cuadros Valdivia, A. M. (2020). Adapting competitiveness and gamification to a digital platform for foreign language learning. International Journal of Emerging Technologies in Learning (iJET), https://doi.org/10.3991/ijet.v15i20.16135

[19] Horn, M. B., & Staker, H. (2014). Blended: Using disruptive innovation to improve schools. Jossey-Bass.

[20] Johnson, M. W., Christensen, C. M., & Kagermann, H. (2008). Reinventing your business ecosystems: Evidence from application software developers in the iOS and Android smartphone ecosystems. Organization Science, 28(3), 531–551.

[21] Kadeni, K., Santoso, E., & Jing, W. (2025). Creative content monetization: Case studies on digital platforms. Journal of Social Entrepreneurship and Creative Technology, 2(2), 81–91. https://doi.org/10.70177/jseact.v2i2.2059

[22] Kannan, K., & Arunachalam, N. (2019). A digital twin for grinding wheel: An information sharing platform for sustainable grinding process. Journal of Manufacturing Science and Engineering, 141(2), 021015. https://doi.org/10.1115/1.4042076

[23] Kohtamäki, M., Parida, V., Patel, P. C., & Gabauer, H. (2020). The relationship between digitalization and servitization: The role of servitization in capturing the financial potential of digitalization. Technological Forecasting and Social Change, 151, 119804.

[24] McIntyre, D. P., & Srinivasan, A. (2017). Networks, platforms and strategy: Emerging views and next steps. Strategic Management Journal, 38(1), 141–160.

[25] Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2021). A survey of digital B2B platforms and marketplace for purchasing industrial product service systems: A conceptual framework. Procedia CIRP, 97, 331–336. https://doi.org/10.1016/j.procir.2020.05.246

[26] Nam, K., Hwangbo, S., & Yoo, C. (2020). A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea. Renewable and Sustainable https://doi.org/10.1016/j.rser.2020.109725 Energy Reviews, 122, 109725.

[27] Niknam, M., & Thulasiraman, P. (2020). LPR: A bio-inspired intelligent learning path recommendation system on meaningful learning theory. Education and Information Technologies, 25, 3797–3919. https://doi.org/10.1007/s10639-020-10133-3

[28] Osipov, I. V., Nikulchev, E., Plokhou, D., & Volinsky, A. A. (2015). Study of monetization as a way of motivating freemium service users. Contemporary Engineering Sciences, 8(20), 911 918. https://doi.org/10.12988/ces2015.57212

[29] Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544. https://doi.org/10.1007/s10488-013-0528-y

[30] Pontual Falcão, T., Peres, F. M. A. e., Sales de Morais, D. C., & da Silva Oliveira, G. (2018). Participatory methodologies to promote student engagement in the development of educational digital games. Computers & https://doi.org/10.1016/j.compedu.2017.09.006 Education, 116, 161–175.

[31] Porter, B., & Grippa, F. (2020). A platform for AI-enabled real-time feedback to promote digital collaboration. Sustainability, 12(24), 10243. https://doi.org/10.3390/su122410243

[32] Sakamiya, M., Fang, Y., Mo, X., Shen, J., & Zhang, T. (2020). A heart-on-a-chip platform for online monitoring of contractile behavior via digital image processing and piezoelectric sensing technique. Medical Engineering & Physics, 75, 36–44.https://doi.org/10.1016/j.medengphy.2019.10.001

[33] Samui, P., Mondal, J., & Khajanchi, S. (2020). A mathematical model for COVID-19 transmission dynamics with a case study of India. Chaos, Solitons & Fractals, 140, 110173. https://doi.org/10.1016/j.chaos.2020.110173

[34] Schamshack, A., & Spector, J. M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments, 7(33).

[35] Schmid, R., & Petko, D. (2019). Does the use of educational technology in personalized learning environments correlate with self-reported digital skills and beliefs of secondary-school students? Computers & Education, 136, 75–86. https://doi.org/10.1016/j.compedu.2019.03.006

[36] Shewshack, A., Kinshuk, & Spector, J. M. (2021). A comprehensive analysis of personalized learning components. Journal of Computers in Education, 1(19). https://doi.org/10.1007/s40692 021-00188-7

[37] Short, L. C., & Shemshack, A. (n.d.). Personalized learning. EDTECHNICA. https://doi.org/10.59668/371.11067

[38] Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43(2–3), 172–194.

[39] Tien Bui, D., Hoang, N.-D., Martínez-Álvarez, F., Ngo, P.-T. T., Hoa, P. V., Pham, T. D., Samui, P., & Costache, R. (2020). A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Science of The Total Environment, 701, 134413. https://doi.org/10.1016/j.scitotenv.2019.134413

[40] Wang, Q., & Su, M. (2020). A preliminary assessment of the impact of COVID-19 on environment – A case study of China. Science of The Total Environment, 728, 138915. https://doi.org/10.1016/j.scitotenv.2020.138915

[41] Watters, A. (2023). Teaching machines: The history of personalized learning. The MIT Press.

[42] Wu, F., Wang, Y., Leung, J. Y. S., Huang, W., Zeng, J., Tang, Y., Chen, J., Shi, A., Yu, X., Xu, X., Zhang, H., & Cao, L. (2020). Accumulation of microplastics in typical commercial aquatic species: A case study at a productive aquaculture site in China. Science of The Total Environment, 708, 135432. https://doi.org/10.1016/j.scitotenv.2019.135432

[43] Xiao, R., Guo, D., Ali, A., Mi, S., Liu, T., Ren, C., Li, R., & Zhang, Z. (2019). Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils: A case study in Hanzhong, Shaanxi, China. Environmental Pollution, 248, 349–357. https://doi.org/10.1016/j.envpol.2019.02.045

[44] Xu, X., Zhang, Q., Song, J., Ruan, Q., Ruan, W., Chen, Y., Yang, J., Zhang, X., Song, Y., Zhu, Z., & Yang, C. (2020). A highly sensitive, accurate, and automated single-cell RNA sequencing platform with digital microfluidics. Analytical Chemistry, 92(12), 8599–8606. https://doi.org/10.1021/acs.analchem.0c01613

[45] Zhang, L., Basham, J. D., & Yng, S. (2020). Understanding the implementation of personalized learning: A research synthesis. Educational Research Review, 31, 100339. https://doi.org/10.1016/j.edurev.2020.100339

 

Izvor: Proceedings of the 16th International Conference on e-Learning (ELEARNING2025)