English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The authors study the interdependent diffusion of an open source software(OSS) platform and its software complements. They quantify the role of OSSgovernance, quality signals such as product ratings, observational learning,and user actions upon adoption. To do so they extend the Bass Diffusion Modeland apply it to a unique data set of 6 years of daily downloads of the Firefoxbrowser and 52 of its add-ons. The study then re-casts the resultingdifferential equations into non-linear, discrete-time, state space forms; andestimate them using an MCMC approach to the Extended Kalman Filtern (EKF-MCMC).Unlike continuous-time filters, the EKF-MCMC approach avoids numericalintegration, and so is more computational efficient, given the length of ourtime-series, high dimension of our state space and need to model heterogeneity.Results show, for example, that observational learning and add-on ratingsincrease the demand for Firefox add-ons; add-ons can increase the marketpotential of the Firefox platform; a slow add-on review process can diminishplatform success; and OSS platforms (i.e. Chrome and Firefox) compete ratherthan complement each other.

The Joint Diffusion of a Digital Platform and its Complementary Goods: The Effects of Product Ratings and Observational Learning