A look at the possible impact of Apple's new policy on third-party apps and the data food chain of iOS users
DOI:
https://doi.org/10.54691/0vawyj54Keywords:
Apple, smartphones, big data.Abstract
The research presents and discusses that Apple's new policy of applying the privacy application framework "App Tracking Transparency" to Apple's IOS14 system has implications for third-party companies in the data food chain as well as users. Users has paid attention to private information including consumption habits, spending power, product preferences, price sensitivity. We found that Apple's new Privacy Application Framework gives Apple absolute dominance in the data food chain of smart media users, Apple and the Apple ecosystem, third-party application platforms, and downstream marketing companies that provide data mining analysis for third-party applications, changing the traditional equal relationship between third-party application platforms and their direct access to user data.
Downloads
References
[1] E.A. Marwick, D. Boyd: Understanding privacy at the margins: Introduction, International Journal of Communication,vol. 12(2018), 1157-1165.
[2] Z. Muhammad, Z. Anwar, B. Saleem, et al. Emerging cybersecurity and privacy threats to electric vehicles and their impact on human and environmental sustainability, Energies, vol.16(2023), 1113.
[3] 2021 Smartphone growth to reach its highest level since 2015, according to IDC, International Data Corporation(2021).
[4] D. Smith: Apple macOS and iOS System Administration, Apress(2020).
[5] M. Juang: Apple’s IOS update puts publishers and platform relationships on thinner ice: App Tracking Transparency update might be a big win for Apple but could leave rest of industry fighting for scraps, AdAge(2021).
[6] E. Aguirre, D. Mahr, D. Grewal, et al. Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness, Journal of retailing, vol.91(2015), 34-49.
[7] C. Fox, A. Levitin, T. Redman: The notion of data and its quality dimensions, Information Processing & Management, vol.30(1994),9-19.
[8] A. Shollo, R.D. Galliers: Towards an understanding of the role of business intelligence systems in organisational knowing, Information Systems Journal, vol. 26(2016), 339-367.
[9] Q. V. Viet, B. Behdani, J. Bloemhof, et al. Value of data in multi-level supply chain decisions: A case study in the Dutch floriculture sector, International Journal of Production Research, vol. 59(2021), 1368-1385.
[10] S. A. Gawankar, A. Gunasekaran, S. Kamble: A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context, International Journal of Production Research, vol.58(2020), 1574-1593.
[11] D. Arunachalam, N. Kumar, J.P. Kawalek: Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice, Transportation Research Part E: Logistics and Transportation Review, vol.114(2018), 416-436.
[12] M. Brinch: Understanding the value of big data in supply chain management and its business processes: Towards a conceptual framework, International Journal of Operations & Production Management, vol.38(2018), 1589-1614.
[13] F. Pasquale: The black box society: The secret algorithms that control money and information, Harvard University Press(2015).
[14] N. Just, M. Latzer: Governance by algorithms: Reality construction by algorithmic selection on the Internet, Media, Culture & Society, vol.39(2017), 238-258.
[15] T. Hoppner, P. Westerhoff: Privacy by default, abuse by design: EU competition concerns about Apple’s new app tracking policy, Hausfeld(2021).
[16] H. Nissenbaum: Contextual integrity up and down the data food chain, Theoretical Inquiries in Law, vol.20(2019), 221-256.
Downloads
- Views: 1 | Downloads: 0 PDF
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.