Understanding digital platform evolution using compartmental models

Bibliographic Data

ID21404069
AuthorsGabriel Andy Szalkowski (0000-0001-9062-4551, Norwegian University of Science and Technology, corresponding author), Patrick Mikalef (0000-0002-6788-2277, Norwegian University of Science and Technology)
Year2023
Volume193
Pages122600
Publication date2023-08-01
Open AccessYes
TypeARTICLE
VenueTechnological Forecasting and Social Change (JOURNAL)
Journal identifiersISSN: 0040-1625 • E-ISSN: 1873-5509
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.techfore.2023.122600
OpenAlexW4368617505
LanguageEN
References cited36

Due to the growing impact of digital platforms, it is increasingly important to understand their evolution through mathematical models. As their value is dependent on their user base, we present an improved perspective on modeling the number of users. Modeling digital platforms is frequently constrained by the scarcity of available data. Thus, researchers resort to open access data like Google Trends. We provide a new interpretation of such data, using it as a proxy for the demand, in contrast with the previous method of considering it as the active user-base. This is implemented using compartmental methods, in which the expression of the demand is fitted to the number of Google Searches for a specific keyword. Two cases, the MMO World of Warcraft and Facebook are analyzed in this fashion, using two different compartmental models. The solutions given by both models replicate key features of the real evolution of the services at study, and the parameters of the fit are in accordance with the expected relative values

Base (topology) · Computer security · Data mining · Data science · Key (lock) · Machine learning · Perspective (graphical) · Proxy (statistics) · Replicate · Scarcity · Statistics · Artificial Intelligence · Computer Science · Consumer Market Behavior and Pricing · Digital Platforms and Economics · Innovation Diffusion and Forecasting · Mathematics

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