SIG DITE - Digital Innovation, Transformation and Entrepreneurship
Paper Type
ERF
Paper Number
1651
Description
A platform ecosystem allows developers to leverage the software codebase innovations of others. Codebase (re)combination extends the range of opportunities for product innovation aimed at satisfying the functional needs of users. Despite facilitating extraordinary innovation gains among developers, prior research on how outside developers can more efficiently develop digital ecosystem resident products has not been addressed. We emphasize two layered properties of design momentum that may increase digital product innovation: 1) the dependencies of functions and 2) the dependencies of genres expressed in product descriptions. We analyze the source codes of R platform ecosystem packages using deep learning algorithms (i.e., Struc2Vec and Doc2Vec) to capture how layered software properties change as the dynamics of the R platform ecosystem increases in each month. We found a positive relationship between software codebase usage and genre dependencies and digital product design success.
Recommended Citation
Um, Sungyong; Templeton, Gary; and Kang, Martin, "Product Design And Success in A Platform Ecosystem" (2022). AMCIS 2022 Proceedings. 8.
https://aisel.aisnet.org/amcis2022/sig_dite/sig_dite/8
Product Design And Success in A Platform Ecosystem
A platform ecosystem allows developers to leverage the software codebase innovations of others. Codebase (re)combination extends the range of opportunities for product innovation aimed at satisfying the functional needs of users. Despite facilitating extraordinary innovation gains among developers, prior research on how outside developers can more efficiently develop digital ecosystem resident products has not been addressed. We emphasize two layered properties of design momentum that may increase digital product innovation: 1) the dependencies of functions and 2) the dependencies of genres expressed in product descriptions. We analyze the source codes of R platform ecosystem packages using deep learning algorithms (i.e., Struc2Vec and Doc2Vec) to capture how layered software properties change as the dynamics of the R platform ecosystem increases in each month. We found a positive relationship between software codebase usage and genre dependencies and digital product design success.
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SIG DITE