Platform-Based Strategy and Ecosystem Competition in the Digital Economy
DOI:
https://doi.org/10.68337/cpsm.v1.i2.2026-032Keywords:
Platform strategy, ecosystem competition, digital economy, content diversity, streaming platforms, machine learning, clustering, content analysis, Netflix, Amazon Prime VideoAbstract
The rapid growth in the number of digital platforms has made competition in the streaming services industry intense, and firms now differentiate their business practices through content and integration strategies. This paper analyzes the differences in the content structures of two major firms, Netflix and Amazon Prime Video. Using public datasets, it examines the types of content offered, the genres offered, the geography covered, and growth over time. Machine learning models for clustering and classification were used to segment the content of both firms and to relate genres to content types. The results show that Netflix follows a content-led strategy with greater diversification and a fast-growing content portfolio, while Prime Video follows an ecosystem-integration strategy with less diversification and stable growth.
References
[1] Michael E. Porter, Competitive Advantage: Creating and Sustaining Superior Performance. New York, NY, USA: Free Press, 1985, ISBN 0-02-925090-0.
[2] Jay Barney, "Firm resources and sustained competitive advantage," J. Manage., vol. 17, no. 1, pp. 99-120, 1991, doi: 10.1177/014920639101700108.
[3] Christoph Zott and Raphael Amit, "Business model design and the performance of entrepreneurial firms," Organ. Sci., vol. 18, no. 2, pp. 181-199, 2007, doi: 10.1287/orsc.1060.0232.
[4] David J. Teece, "Business models, business strategy and innovation," Long Range Plann., vol. 43, no. 2-3, pp. 172-194, 2010, doi: 10.1016/j.lrp.2009.07.003.
[5] Michael G. Jacobides, Carmelo Cennamo, and Annabelle Gawer, "Towards a theory of ecosystems," Strateg. Manag. J., vol. 39, no. 8, pp. 2255-2276, 2018, doi: 10.1002/smj.2904.
[6] Michael L. Wayne, "Netflix, Amazon, and branded television content in subscription video on-demand portals," Media Cult. Soc., vol. 40, no. 5, pp. 725-741, 2018, doi: 10.1177/0163443717736118.
[7] Vanessa Rahe, Christopher Buschow, and Daniela Schlütz, "How users approach novel media products: Brand perception of Netflix and Amazon Prime Video as signposts within the German subscription-based video-on-demand market," J. Media Bus. Stud., vol. 18, no. 1, pp. 45-58, 2021, doi: 10.1080/16522354.2020.1780067.
[8] Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2009, doi: 10.1007/978-0-387-84858-7.
[9] C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006, doi: 10.1007/978-0-387-45528-0.
[10] Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay, "Scikit-learn: Machine learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825-2830, 2011. [Online]. Available: https://jmlr.org/papers/v12/pedregosa11a.html
[11] Fakhitah Ridzuan and Wan Mohd Nazmee Wan Zainon, "A review on data cleansing methods for big data," Procedia Comput. Sci., vol. 161, pp. 731-738, 2019, doi: 10.1016/j.procs.2019.11.177.
[12] D. R. Cox, "The regression analysis of binary sequences," J. R. Stat. Soc. Ser. B (Methodol.), vol. 20, no. 2, pp. 215-232, 1958, doi: 10.1111/j.2517-6161.1958.tb00292.x.
[13] S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves, "Reshaping business with artificial intelligence: Closing the gap between ambition and action," MIT Sloan Manage. Rev., vol. 59, no. 1, pp. 1-20, 2017.
[14] Iqbal H. Sarker, "Machine learning: Algorithms, real-world applications and research directions," SN Comput. Sci., vol. 2, no. 3, Art. no. 160, 2021, doi: 10.1007/s42979-021-00592-x.
Downloads
Published
Data Availability Statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request.Conference Proceedings Volume
Section
Categories
License
Copyright (c) 2026 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles published in Conference Proceedings in Science and Management are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain copyright in their work and grant the journal first publication rights. Readers are free to share and adapt the published material for any purpose, including commercial use, provided appropriate credit is given to the author, a link to the license is provided, and any changes are indicated.
