Title page for etd-0101116-111802


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URN etd-0101116-111802
Author Min-Hsueh Tsai
Author's Email Address kai.farwind@gmail.com
Statistics This thesis had been viewed 5340 times. Download 234 times.
Department Master of Business Administration Program in International Business
Year 2014
Semester 1
Degree Master
Type of Document
Language English
Title An Empirical Analysis On iOS App Popularity: On App-specific Characteristics of App Crossing the Top 25 Threshold
Date of Defense 2016-01-29
Page Count 49
Keyword
  • mobile application markets
  • interaction term in logistic regression
  • stratified random sampling
  • logistic regression analysis
  • App Store marketing
  • App Store ranking
  • App ranking analysis
  • Abstract Now the 7 years old App Store already became a huge digital platform where hundreds of million users download and use mobile applications (apps) every day for various practices. This paper focuses on Apple App Store market and examines how app size, Chinese version, app name length and other app-specific characteristics affect the probability of apps crossing the top 25 ranking threshold.
    By stratified random sampling approach, 1,998 apps were semi-manually selected from the App Store database. We defined 7 generalized categories from original 23 categories on App Store as our design variables. Data gathered based on the proportional number of each app category respectively to total apps––along with other sources of public data of these selected app as covariates––were used for logistic regression analysis to determine the relationship between these app-specific predictors and the odds ratios of crossing the top 25 ranking thresholds versus base category. 
    Our results complement previous pieces of literature about factors affecting the App Store ranking such as higher rating and update frequency; in addition, we indicate that app size and app name length are significant to the probability of crossing the threshold versus base category. Though Chinese version is not significant in our base model, its interaction with shopping and relaxing apps appear to be positively associated. Such insights could potentially benefit app developer’s planning in regards to their priority of app development and corporate strategic decision.
    Advisory Committee
  • Yu-Chen Yang - chair
  • Keng-Pei Lin - co-chair
  • Chien-Yuan Sher - advisor
  • Files
  • etd-0101116-111802.pdf
  • Indicate in-campus at 0 year and off-campus access at 1 year.
    Date of Submission 2016-02-01

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