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Investigating Adoption of Free Beta Applications in a Platform‐Based Business Ecosystem

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Journal of Product Innovation Management

Published online on

Abstract

Planning new product development (NPD) activities is becoming increasingly difficult, as contemporary businesses compete at the level of business ecosystems in addition to the firm‐level product‐market competition. These business ecosystems are built around platforms interlinking suppliers, complementors, distributors, developers, etc. together. The competitiveness of these ecosystems relies on members utilizing the shared platform for their own performance improvement, especially in terms of developing new valuable offerings for end users. Therefore, managing the development of the platform‐based applications and gaining timely end‐user input for NPD are of vital importance both to the ecosystem as a whole and to the developers. Subsequently, to succeed in NPD planning developers utilizing beta testing need a thorough understanding of the adoption dynamics of beta products. Developers need to plan for example resource allocation; development costs; and timing of commercial, end‐product launches. Therefore, the anticipation of the adoption dynamics of beta products emerges as an important antecedent in planning NPD activities when beta testing is used for gaining end‐user input to the NPD process. Consequently, we investigate how free beta software products that are built upon software platforms diffuse among their end users in a cocreation community. We specifically study whether the adoption of these beta products follows Bass or Gompertz model dynamics used in the previous literature when modeling the adoption of stand‐alone products. Further, we also investigate the forecasting abilities of these two models. Our results show that the adoption dynamics of free beta products in a cocreation community follow Gompertz's model rather than the Bass model. Additionally, we find that the Gompertz model performs better than the Bass model in forecasting both short and long out‐of‐sample time periods. We further discuss the managerial and research implications of our study.