 Understanding digital platform evolution using compartmental modelsGabriel Andy Szalkowski and Patrick Mikalef Technological Forecasting and Social Change, 2023, vol. 193, issue C Abstract: 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. Keywords: Digital economy; Network effects; Compartmental models; Market dynamics; Business forecasting (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:193:y:2023:i:c:s0040162523002858 DOI: 10.1016/j.techfore.2023.122600 Access Statistics for this article Technological Forecasting and Social Change is currently edited by Fred Phillips More articles in Technological Forecasting and Social Change from Elsevier |
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