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Mobile business ecosystems are based on product innovations and complements created on platforms facilitating transactions between groups of users in a multi-sided market. The purpose of this research is to present a model of success factors (SF) of mobile ecosystems. This research establishes an empirical framework based on the Android ecosystem, which has been analyzed in-depth on firm and ecosystem level, identifying 16 success factors. The main theoretical contribution is a model that identifies SF of platforms, which are related to the identification of the role of users and complementors in increasing innovation success. The model advances research in innovation platforms.

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Purpose The aim of this paper is to identify promoting and restraining factors of a novel mobile service in the pedestrian safety area. Design/methodology/approach This paper uses the case study methodology that focus on analyzing a specific case of mobile safety services in depth. A case study is especially suitable for an emerging case, such as pedestrian safety, where the aim is to identify relevant influencing factors of the particular case and not to generalize the findings. To gather data for case study analysis, several expert interviews were performed. Because they provided a large volume of data, the Service, Technology, Organization, and Finance business model framework was used as a way of structuring the analysis. Findings The main restraining factors are end-user value proposition, battery life, accuracy of GPS positioning and the revenue model. However, the service could improve traffic safety considerably and it should be introduced first locally in places, where many accidents take place. There is a great interest on driver data, which could be the main advantage for this service in the future. Integration to navigation products would complement the service significantly. Originality/value Current traffic safety-related literature covers mainly technical issues, and there are only few papers related to business model issues on that particular service. Observations of the various factors affecting the related evolution at an early phase of the life-cycle support further service design process.

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