Artificial Intelligence, Data-Driven Learning, and the Decentralized Structure of Platform Ecosystems
Abstract
Gregory, Henfridsson, Kaganer, and Kyriakou (2020) highlight the important role of data and AI as strategic resources that platforms may use to enhance user value. However, their article overlooks a significant conceptual distinction: the installed base of decentralized users who connect with a platform lie outside the boundaries of the platform-owning firm, whereas the accumulated data derived from that installed base exists internal to the boundaries of the firm and under firm control. Accounting for this distinction brings forth two key departures from their theory. First, the decentralized structure of a platform ecosystem makes value capture by the platform an essential consideration when analyzing the implications of data-driven learning for users. Because AI and data allow a platform to increase the share of value the platform owner captures from the users, the value perceived by users can often decline. Second, as an internal asset of the platform firm, data from users and complementors exhibits different dynamics compared with the dynamics that govern the installed base itself. As a result, the quantity and quality of the platform’s stock of data are only loosely coupled with the size of the platform’s installed base. We highlight the strategic implications of this distinction for a manager launching a new multi-sided platform.
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Data network effects emerge from faster and more precise insights into consumer data by deploying AI capabilities in platform ecosystems, which may affect perceived consumer quality.
“Accordingly, data network effects emerge from faster and more precise insights into consumer data by deploying AI capabilities in platform ecosystems, which may affect perceived consumer quality (Clough & Wu, 2022;Gregory et al., 2021).”
From What role do data network effects play for multihoming complements? An empirical analysis of platform complementors’ strategies for consumer value creation (2025) · cites this paper as Clough & Wu, 2022
The monetization of complementors' products might affect consumer satisfaction.
“Complementors aim for high-quality outputs to meet users' expectations; however, the monetization of their products might affect consumer satisfaction (Cennamo et al., 2018;Clough & Wu, 2022).”
From What role do data network effects play for multihoming complements? An empirical analysis of platform complementors’ strategies for consumer value creation (2025) · cites this paper as Clough & Wu, 2022
The study investigates variance in complement quality using cross-sectional data, so it cannot capture data network effects that occur over time as feedback loops.
“First, while we investigate variance in complement quality using cross-sectional data, data network effects presumably occur over time as feedback loops (Clough & Wu, 2022;Karhu et al., 2024).”
From What role do data network effects play for multihoming complements? An empirical analysis of platform complementors’ strategies for consumer value creation (2025) · cites this paper as Clough & Wu, 2022
Research indicates that overall consumer satisfaction decreases when gaming users need to purchase additional capabilities to increase their chances of winning, with the purchase request triggered by user-specific activities.
“For instance, research indicates that overall consumer satisfaction decreases when gaming users need to purchase additional capabilities to increase their chances of winning, with the purchase request triggered by user-specific activities (Clough & Wu, 2022).”
From What role do data network effects play for multihoming complements? An empirical analysis of platform complementors’ strategies for consumer value creation (2025) · cites this paper as Clough & Wu, 2022
A platform's AI capability leads to greater value as the more a platform learns from the data it collects on users, the more valuable it becomes to each user.
“A platform's AI capability leads to greater value as the more a platform learns from the data it collects on users, the more valuable it becomes to each user (Clough & Wu, 2022;Gregory et al., 2021)”
From What role do data network effects play for multihoming complements? An empirical analysis of platform complementors’ strategies for consumer value creation (2025) · cites this paper as Clough & Wu, 2022
If complementors translate data-driven insights into functionalities that exploit consumers rather than add value with product improvements, consumers might reduce or discontinue their use.
“However, if complementors translate data-driven insights into functionalities that exploit consumers rather than add value with product improvements, consumers might reduce or discontinue their use (Clough & Wu, 2022).”
From What role do data network effects play for multihoming complements? An empirical analysis of platform complementors’ strategies for consumer value creation (2025) · cites this paper as Clough & Wu, 2022
Cites
(2013) · American Economic Review · doi:10.1257/aer.103.2.i · Cited by 8,221 · see citing works
Ries, E. (2011) · Cited by 3,379 · see citing works
Adner, R., & Kapoor, R. (2009) · Strategic Management Journal · doi:10.1002/smj.821 · Cited by 2,831 · see citing works
Gamification by Design: Implementing Game Mechanics in Web and Mobile Apps
Zichermann, G., & Cunningham, C. J. L. (2011) · Cited by 2,390 · see citing works
Bridging differing perspectives on technological platforms: Toward an integrative framework
Gawer, A. (2014) · Research Policy · doi:10.1016/j.respol.2014.03.006 · Cited by 1,869 · cited 1× · see citing works
“…and behavioral insights that platforms derive from users and complementors accumulate over time, whereas direct and indirect network effects depend on the potential interactions that could take place between users and complementors at any given moment (Gawer, 2014; McIntyre & Srinivasan, 2017).”
BackgroundAcademy of Management Proceedings
Epitropaki, O., Kark, R., Day, D., Lord, R. G., Murphy, S. E., & Stevens, N. (2016) · The Academy of Management · Cited by 1,609 · see citing works
Handbook of Industrial Organization
Schmalensee, R. L. (1989) · RePEc: Research Papers in Economics · Cited by 1,598 · see citing works
Networks, platforms, and strategy: Emerging views and next steps
McIntyre, D. P., & Srinivasan, A. (2017) · Strategic Management Journal · doi:10.1002/smj.2596 · Cited by 1,109 · see citing works
Internet Business Models and Strategies: Text and Cases
Afuah, A. N., & Tucci, C. L. (2002) · Cited by 811 · see citing works
An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets
Ghose, A., & Yang, S. (2009) · Management Science · doi:10.1287/mnsc.1090.1054 · Cited by 684 · see citing works
(2019) · Cited by 627 · see citing works
Competing with complementors: An empirical look at Amazon.com
Zhu, F., & Liu, Q. (2018) · Strategic Management Journal · doi:10.1002/smj.2932 · Cited by 546 · see citing works
The Role of Artificial Intelligence and Data Network Effects for Creating User Value
Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, H. (2021) · Academy of Management Review · doi:10.5465/amr.2019.0178 · Cited by 540 · see citing works
Video game loot boxes are psychologically akin to gambling
Drummond, A., & Sauer, J. D. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0360-1 · Cited by 361 · see citing works
Video game loot boxes are linked to problem gambling: Results of a large-scale survey
Zendle, D. I., & Cairns, P. A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206767 · Cited by 352 · see citing works
Are network effects really all about size? The role of structure and conduct
Afuah, A. N. (2012) · Strategic Management Journal · doi:10.1002/smj.2013 · Cited by 267 · see citing works
Predatory monetization schemes in video games (e.g. ‘loot boxes’) and internet gaming disorder
King, D. L., & Delfabbro, P. H. (2018) · Addiction · doi:10.1111/add.14286 · Cited by 250 · see citing works
Dynamic pricing in internet retail: Effects on consumer trust
Garbarino, E., & Lee, O. F. (2003) · Psychology & Marketing · doi:10.1002/mar.10084 · Cited by 242 · see citing works
Redefining Firm Boundaries in the Face of the Internet: Are Firms Really Shrinking?
Afuah, A. N. (2003) · Academy of Management Review · doi:10.5465/amr.2003.8925207 · Cited by 227 · see citing works
IS THE BUYING OF LOOT BOXES IN VIDEO GAMES A FORM OF GAMBLING OR GAMING?
Griffiths, M. D. (2018) · Gaming Law Review · doi:10.1089/glr2.2018.2216 · Cited by 162 · see citing works
Eisenmann, T. R. (2006) · Strategic Management Journal · doi:10.1002/smj.567 · Cited by 149 · see citing works
Platform diffusion at temporary gatherings: Social coordination and ecosystem emergence
Fang, T. P., Wu, A., & Clough, D. R. (2021) · Strategic Management Journal · doi:10.1002/smj.3230 · Cited by 85 · see citing works
Self-citationAirline Pricing, Price Dispersion, and Ticket Characteristics On and Off the Internet
Sengupta, A., & Wiggins, S. N. (2014) · American Economic Journal Economic Policy · doi:10.1257/pol.6.1.272 · Cited by 68 · see citing works
APPROXIMATING PURCHASE PROPENSITIES AND RESERVATION PRICES FROM BROAD CONSUMER TRACKING
Shiller, B. R. (2020) · International Economic Review · doi:10.1111/iere.12442 · Cited by 65 · see citing works
The Impact of Consumer Multi-homing on Advertising Markets and Media Competition
Athey, S. C., Calvano, E., & Gans, J. S. (2016) · Management Science · doi:10.1287/mnsc.2016.2675 · Cited by 39 · see citing works
Ad Networks and Consumer Tracking
D’Annunzio, A., & Russo, A. (2019) · Management Science · doi:10.1287/mnsc.2019.3481 · Cited by 22 · see citing works
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Jacobides, M. G., Brusoni, S., & Candelon, F. (2021) · Strategy Science · doi:10.1287/stsc.2021.0148 · Cited by 220 · see citing works
Culot, G., Podrecca, M., & Nassimbeni, G. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104132 · Cited by 286 · see citing works
Brouthers, K. D., Chen, L., Li, S., & Shaheer, N. A. (2022) · Journal of International Business Studies · doi:10.1057/s41267-022-00521-x · Cited by 166 · see citing works
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Secundo, G., Spilotro, C., Gast, J., & Corvello, V. (2024) · Review of Managerial Science · doi:10.1007/s11846-024-00828-z · Cited by 115 · see citing works
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Luo, Y., & Zahra, S. A. (2023) · Journal of International Business Studies · doi:10.1057/s41267-022-00577-9 · Cited by 99 · see citing works
Chhillar, D., & Aguilera, R. V. (2022) · Business & Society · doi:10.1177/00076503221080959 · Cited by 85 · see citing works
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Climent, R. C., Haftor, D., & Staniewski, M. (2024) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2024.100532 · Cited by 80 · see citing works
Haftor, D. M., Costa Climent, R., & Lundström, J. E. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.04.015 · Cited by 60 · see citing works
Pujadas, R., Valderrama, E., & Venters, W. (2023) · Research Policy · doi:10.1016/j.respol.2023.104931 · Cited by 62 · see citing works
From platform growth to platform scaling: The role of decision rules and network effects over time
Varga, S., Cholakova, M., Jansen, J. J. P., Mom, T. J. M., & Kok, G. J. M. (2023) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2023.106346 · Cited by 51 · see citing works
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Xu, T., Shi, H., Shi, Y., & You, J. (2023) · Asia Pacific Journal of Innovation and Entrepreneurship · doi:10.1108/apjie-10-2023-0195 · Cited by 46 · see citing works
Ramaul, L., Ritala, P., Kostis, A., & Aaltonen, P. (2026) · Journal of Management Studies · doi:10.1111/joms.13246 · Cited by 41 · see citing works
Comparing Business, Innovation, and Platform Ecosystems: A Systematic Review of the Literature
Liu, Z., Li, Z., Zhang, Y., Mutukumira, A. N., Feng, Y., Cui, Y., Wang, S., Wang, J., & Wang, S. (2024) · Biomimetics · doi:10.3390/biomimetics9040216 · Cited by 46 · see citing works
Wang, K., Lu, L., Fang, J., Xing, Y., Tong, Z., & Wang, L. (2023) · Managerial and Decision Economics · doi:10.1002/mde.3882 · Cited by 35 · see citing works
Zhang, H., Song, M., & Wang, Y. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122517 · Cited by 33 · see citing works
Filosa, C., Jovanovic, M., Agostini, L., & Nosella, A. (2024) · International Journal of Production Economics · doi:10.1016/j.ijpe.2024.109466 · Cited by 35 · see citing works
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Vomberg, A., Wiegand, N., Krakowski, S. M., Bogusz, C. I., Gijsenberg, M. J., & Bleier, A. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114236 · Cited by 30 · see citing works
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Tidhar, R., Hallen, B. L., & Eisenhardt, K. M. (2024) · Organization Science · doi:10.1287/orsc.2021.15970 · Cited by 30 · see citing works
Sun, T. Q. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182312682 · Cited by 28 · see citing works
Climent, R. C., Haftor, D., & Staniewski, M. W. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102637 · Cited by 27 · see citing works
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Xing, X., Zhang, Z., & He, W. (2025) · Technovation · doi:10.1016/j.technovation.2025.103349 · Cited by 29 · see citing works
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Schmidt, R., Alt, R., & Zimmermann, A. (2023) · Electronic Markets · doi:10.1007/s12525-023-00671-2 · Cited by 24 · see citing works
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Xiong, L., & Sun, Y. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-11918-2
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Ren, J., & Li, X.-B. (2026) · Long range planning · doi:10.1016/j.lrp.2026.102659
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Li, Y., Zeng, M., & Li, A. (2026) · Strategic Entrepreneurship Journal · doi:10.2139/ssrn.6508698
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Zhang, X., Bao, S., & Li, H. (2026) · Applied Operations and Analytics · doi:10.1080/29966892.2026.2717820
Computer Vision from Tea Cultivation to Quality Evaluation
Chen, Z., Wang, J., Sun, Y., Pang, J., Xin, W., Zhang, Q., & Zhou, J. (2026) · Foods · doi:10.3390/foods15162864
Ivanov, N., Volkov, S., & Morozova, E. (2025) · Journal of Digital Business and Management Studies · doi:10.68159/t204712759
Morales, S., Rojas, L., & Vega, C. (2025) · Journal of Digital Business and Management Studies · doi:10.68159/y023259577
Brown, G., Taylor, M., Wilson, S., & Harris, O. (2025) · Journal of Digital Business and Management Studies · doi:10.68159/j867173316
Martin, C., Robert, J., Bernard, S., & Girard, A. (2022) · Journal of Digital Business and Management Studies · doi:10.68159/u944636665
Wang, F., Jiang, J., & Chen, W. (2026) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2026.101095
Li, R., & Rui, Y. A. N. G. (2026) · Research Square · doi:10.21203/rs.3.rs-10980625/v1
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Shaheer, N. A., Kim, K., Li, F., Yang, R., & Xie, E. (2026) · Journal of International Business Studies · doi:10.1057/s41267-026-00901-7
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Wu, Y., & Ma, W. (2026) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2026.105299
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- Authors
- Clough, D. R., & Wu, A.
- Affiliations
- University of British Columbia (CA)
- Harvard University (US)
- Venue
- Academy of Management Review
- Publisher
- Academy of Management
- Published
- 2022-01
- Volume
- 47
- Issue
- 1
- Pages
- 184-189
- ISSN
- 0363-7425 (ISSN-L)
- 1930-3807
- OpenAlex
- W3096595992
- Cited by
- 84
- References
- 26
- Citations by year
- 2026: 23 · 2025: 14 · 2024: 19 · 2023: 20 · 2022: 5 · 2021: 3
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academy of Management Review (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology's Impact on Media
- Social Sciences › Business, Management and Accounting › Strategy and Management › Innovation and Knowledge Management
- Keywords
- multi-sided platforms, platform ecosystems, artificial intelligence, data-driven learning, value capture, installed base, user value, data as strategic resource, complementors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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