Effect of Release Timing of App Innovations based on Mobile Platform Innovations

ABSTRACT

This study focuses on app innovations based on mobile platform innovations (MPIs), examining how app developers can time the app innovations release to best leverage MPIs and increase app financial performance. We suggest that the performance is contingent on the adoption curve of mobile platform generations and the level of backward compatibility of the MPIs. We find support for our hypotheses after analyzing 1,213 MPI-based app innovations on the iOS mobile platform ecosystem. The main theoretical contribution of this study, supported empirically, is to better understand the role of the platform generation adoption curve and MPIs’ level of backward compatibility in the assessment of the effect of MPI-based app innovation release timing on complementor’s performance. We encourage third-party developers to create MPI-based app innovations more prominently and release them early during the growth stage of the adoption curve while prioritizing MPIs with no backward compatibility.

KEYWORDS:

• Mobile platforms
• innovation
• release timing
• backward compatibility
• adoption curve
• app releases
• online innovations
• platform ecosystems

Supplemental Material

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Notes

1. ARKit, SiriKit, and CoreML are technologies developed by Apple. ARKit and CoreML were introduced in 2017 with iOS 11, while SiriKit became part of iOS 6 in 2012. ARKit is a technology that enables augmented reality experiences for app users. SiriKit facilitates the creation of voice commands that can work with the Apple voice assistant Siri. Finally, CoreML allows the creation of machine learning models for apps.

2. We randomly selected some third-party apps from the sample to ensure no other MPI-based app innovations are released during the time period [-7; +21]. Our observations indicate that mainly minor improvements (e.g., bug fixes, performance improvements, or stability improvements) are released. Moreover, we conducted several robustness checks using different time periods. The results are consistent across these time periods (See Robustness Checks Section)

3. No other MPI is released during the growth stage.

4. Adapted from [53]

Additional information

Notes on contributors

Franck Soh

Franck Soh ([email protected]; corresponding author) is an Assistant Professor of Information Systems at Bryan School of Business and Economics, The University of North Carolina at Greensboro. He received his Ph.D. degree in Information Systems from University of Arkansas. His research interests include business value of IT, competition in mobile platform ecosystems, healthcare IT, customer service performance, open source, and social media. Dr. Soh has published in Journal of Computer Information Systems and has presented his research in several conferences and symposiums, including International Conference on Information Systems.

Varun Grover

Varun Grover ([email protected]) is the David D. Glass Endowed Chair and Distinguished Professor of Information Systems at the Sam M. Walton College of Business, University of Arkansas. His work focuses on the impacts of digitalization on individuals and organizations. He has published extensively in the information systems field, with over 400 publications, 250 of which are in major refereed journals. He has served as senior editor of several major journals, is a recipient of numerous awards and a Fellow of the Association for Information Systems. He has been invited to present numerous keynote addresses at various forums around the world.