AI AS AN APPROACH TO ANALYZING UBER'S BUSINESS ENVIRONMENT IN THE TRANSPORTATION SERVICES SECTOR
Djilali Mezouaghi
E-mail: djilali.mezouaghi@univ-relizane.dz
Associate Professor "B", PhD University of Relizane
Relizane, Algeria
https://orcid.org/0009-0003-5827-4338
Adda Mellah
E-mail: adda.mellah@univ-relizane.dz
Associate Professor "A", PhD University of Relizane
Relizane, Algeria
https://orcid.org/0000-0002-6417-0418
Abstract: This study aimed to analyze the business environment of organizations that use artificial intelligence in the service industry through a SWOT analysis of Uber's global environment. The findings concluded that the company relies on artificial intelligence techniques to develop its services, such as the smart positioning system and the pricing algorithms that consider various price-determining factors, alongside a demand forecasting system to manage its customers. This reliance on AI has contributed to Uber's strengths, including its popularity, low costs, and user convenience. It has also created future opportunities for investment in self-driving cars, a broad geographical scope for expansion, and a promising outlook with the increasing number of internet users. However, these advantages do not overshadow the weaknesses, which include ease of use, traditional practices, limited customer profitability, biased pricing, and driver distribution systems. Additionally, the reliance on artificial intelligence exposes Uber to various threats, particularly in legal, regulatory, ethical, and security domains.
Keywords: artificial intelligence, SWOT analysis, transport services, Uber.
JEL classification: L25; L86; M15; O32
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References
https://doi.org/10.1257/app.20190655
Bishnoi, V. K., & Reetika, B. (2019). Cab Aggregators in India: A Case Study of Ola and Ube. International Journal of Research in Social Sciences , 9 (4), pp. 1029-1040.
Chang, H.-H. (2017). The economic effects of Uber on taxi drivers in Taiwan. Journal of Competition Law & Economics , 13 (3), pp. 475-500. https://doi.org/10.1093/joclec/nhx017
Dinisco, C., & Schachtebeck, C. (2018). Patent No. Proceedings ISBN Number: 978-1-920017-89-7. Pretoria, South Africa.
Dudovskiy, J. (2021). Uber SWOT Analysis. Retrieved 08 17, 2023, from Business Research Methodology AT:https://research-methodology.net: https://research-methodology.net/uber-swot-analysis-damaged-brand-image-considerable-weakness/
Guido, L. D. (2016). Uber law and awareness by design. An empirical study on online platforms and dehumanised negotiations. european consumer law , pp. 383-413.
Gwak, J., Jung, J., Oh, R., Park, M., Rakhimov, M. A., & Ahn, J. (2019). A review of intelligent self-driving vehicle software research. KSII Transactions on Internet and Information Systems (TIIS) , 13 (11), pp. 5299-5320. https://doi.org/ 10.3837/tiis.2019.11.002
Hu, X., Binaykiya, T., Frank, E., & Olcay, C. (2022). DeeprETA: An ETA post-processing system at scale. :, 2022. arXiv preprint arXiv , 2206.02127.
Kashyap, R., & Anjali, B. (2018). Taxi drivers and taxidars: a case study of Uber and Ola in Delhi. Journal of Developing Societies , 34 (2), pp. 169-194. https://doi.org/10.1177/0169796X18757144
Nayyar, G., Hallward-Driemeier, M., & Davies, E. (2021). At Your Service? The Promise of Services-Led Development. Washington: World Bank Group.
Nowag, J. (2016). UBER between Labour and Competition Law. (LSEU) , 03, pp. 95-104.
Pandey, A., & Aylin, C. (2021). Disparate impact of artificial intelligence bias in ridehailing economy's price discrimination algorithms. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 822-833). USA: AIES ’21, May 19–21, 2021, https://doi.org/10.48550/arXiv.2006.04599
Rihan, I. (2023, JULY 15). SWOT analysis. Retrieved from www.academia.edu: https://www.academia.edu/29435689/SWOT_Analysis_Definition_Application_and_Limitation
Sawhney, M., Shah, B., Yu, R., & Rubtsov, E. (2019). Uber: Applying Machine Learning to Improve the Customer Pickup Experience. Kellogg School of Management Cases , pp. 1-21. https://doi.org/10.1108/case.kellogg.2021.000090
Shah, S. H. (2024). Monitoring and Evaluation of Practice and Methods in Applied Social Research. United Kingdom: Taylor & Francis.
Sun, C., Nader, A., & Chintan, T. (2020). Gallery: A Machine Learning Model Management System at Uber. In EDBT, Industry and Applications Paper , 20, pp. 474-485. https://doi.org/10.5441/002/edbt.2020.59
Ternullo, F. (2019, 02 08). AI Implications in GIG Economy (Uber Scenario and not only). Retrieved 08 18, 2023, from Commission européenne: https://futurium.ec.europa.eu/en/european-ai-alliance/forum/ai-implications-gig-economy-uber-scenario-and-not-only?language=fr&%3Bfile=2023-01/avatar-2-el-camino-del-agua-2022-pelicula-gratis.pdf&page=2
UBER. (2023, 05 08). 2023 ANNUAL MEETING OF STOCKHOLDERS Q&A. USA, USA.
UBER. (2019). Annual Report. Washington: UBER TECHNOLOGIES, INC.
UBER. (2022). Annual Report. Washington: UBER TECHNOLOGIES, INC.
UBER. (2019, 12 18). Uber AI in 2019: Advancing Mobility with Artificial Intelligence. Retrieved 08 17, 2023, from https://www.uber.com: https://www.uber.com/en-ES/blog/uber-ai-blog-2019/
UBER. (2023). Uber Announces Results for Second Quarter 2023. USA: UBER.
Vidhury, B., Kandwal, R., Aggarwal, H., Danishwaran, S., & Pande, A. (2023). Uber Price Prediction System. International Journal of Science and Research (IJSR) , 12 (5), pp. 341-343. https://doi.org/10.21275/SR23504095514
Vultur, M., & Enel, L. (2020). Les Plateformes De Travail Numériques : Uber Et La Dérèglementation De L’industrie Du Taxi Au Québec. Québec: Institut national de la recherche scientifique.
Yunhan, L., & Dohoon, K. (2022). Why did Uber China fail? Lessons from business model analysis. Journal of Open Innovation: Technology, Market, and Complexity , 8 (2), p. 90. https://doi.org/ 10.3390/joitmc8020090
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