Domino Effects of AI: Spillovers from New App Launches on Developer Portfolio

Disruptive innovations often transform digital platforms by reshaping how value is created and consumed, altering dynamics across the entire ecosystem. Among these, AI has emerged as a fast-moving and transformative force. Mobile applications serve as a key channel for delivering AI-powered services, making them crucial for studying AI’s broader impacts. This study explores how launching a new AI app affects the demand for a developer’s existing apps and how this relationship depends on the AI composition within the developer’s portfolio. Leveraging data mining, we constructed a unique six-month biweekly panel dataset from Apple’s App Store. Two-way fixed effects regression models reveal that new AI app launches increase demand for existing apps, especially when the developer’s portfolio is primarily non-AI. However, this effect weakens as the portfolio becomes more AI-heavy. These findings contribute to the demand-spillover and disruption literature and offer practical insights for managing AI integration in digital ecosystems.

Authors

Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
Digital Platforms and Economics
Type
article
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article

Domino Effects of AI: Spillovers from New App Launches on Developer Portfolio

Pankush Kalgotra, Niyanta Sookraj, Yalda Hojjati
ScholarSpace (University of Hawaii at Manoa)
Digital Platforms and Economics
article

Domino Effects of AI: Spillovers from New App Launches on Developer Portfolio

Pankush Kalgotra, Niyanta Sookraj, Yalda Hojjati
article en

Abstract

Disruptive innovations often transform digital platforms by reshaping how value is created and consumed, altering dynamics across the entire ecosystem. Among these, AI has emerged as a fast-moving and transformative force. Mobile applications serve as a key channel for delivering AI-powered services, making them crucial for studying AI’s broader impacts. This study explores how launching a new AI app affects the demand for a developer’s existing apps and how this relationship depends on the AI composition within the developer’s portfolio. Leveraging data mining, we constructed a unique six-month biweekly panel dataset from Apple’s App Store. Two-way fixed effects regression models reveal that new AI app launches increase demand for existing apps, especially when the developer’s portfolio is primarily non-AI. However, this effect weakens as the portfolio becomes more AI-heavy. These findings contribute to the demand-spillover and disruption literature and offer practical insights for managing AI integration in digital ecosystems.

ScholarSpace (University of Hawaii at Manoa)
Openalex Percentile: Top 25%
Digital Platforms and Economics
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