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Unveiling the types of growth patterns of mobile startups: do business models matter?

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Titel: Unveiling the types of growth patterns of mobile startups: do business models matter?
Autoren: Lee, Saerom1 (AUTHOR), Kim, Jongdae2 (AUTHOR), Lee, Hakyeon3 (AUTHOR) hylee@seoultech.ac.kr
Quelle: Technology Analysis & Strategic Management. Dec2025, Vol. 37 Issue 12, p2379-2393. 15p.
Schlagwörter: *BUSINESS models, *MOBILE businesses, *GROWTH curves (Statistics), *DECISION making in investments
Geografische Kategorien: SOUTH Korea
Abstract: It is important to understand the growth of startups because they are a driver of economic prosperity and wealth. However, due to their short history and high failure rate, research measuring startup growth has been limited and has relied on qualitative data. This study uses monthly active user (MAU) data of mobile-based startups (n = 266) collected by InnoForest, a startup growth analysis platform in South Korea, to classify the patterns of their growth curves using GA-NLS-based Bass model estimation. We also investigate how growth patterns differ depending on several popular business characteristics across mobile apps. The Bass model was used to categorise the pattern of the growth curve, resulting in the four growth patterns: Stealthy Influencer, Rapid Scaler, Late Bloomer, and Niche Dominator. Furthermore, there were differences in growth patterns between the platform and non-platform businesses, and between fixed-fee and pay-per-transaction services. This study reveals that the growth of mobile-based startups, as measured by MAUs using Bass model, follows different patterns depending on the attitudes of the users of startups' apps and the speed at which apps' growth peaks. The results can be useful for developing startup growth strategies, making investment decisions, and formulating policies for startup growth. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
Beschreibung
Abstract:It is important to understand the growth of startups because they are a driver of economic prosperity and wealth. However, due to their short history and high failure rate, research measuring startup growth has been limited and has relied on qualitative data. This study uses monthly active user (MAU) data of mobile-based startups (n = 266) collected by InnoForest, a startup growth analysis platform in South Korea, to classify the patterns of their growth curves using GA-NLS-based Bass model estimation. We also investigate how growth patterns differ depending on several popular business characteristics across mobile apps. The Bass model was used to categorise the pattern of the growth curve, resulting in the four growth patterns: Stealthy Influencer, Rapid Scaler, Late Bloomer, and Niche Dominator. Furthermore, there were differences in growth patterns between the platform and non-platform businesses, and between fixed-fee and pay-per-transaction services. This study reveals that the growth of mobile-based startups, as measured by MAUs using Bass model, follows different patterns depending on the attitudes of the users of startups' apps and the speed at which apps' growth peaks. The results can be useful for developing startup growth strategies, making investment decisions, and formulating policies for startup growth. [ABSTRACT FROM AUTHOR]
ISSN:09537325
DOI:10.1080/09537325.2024.2361420