An Empirical Analysis On iOS App Popularity: On App-specific Characteristics of App Crossing the ...

49 Pages·2016·9.77 MB·English

by  國立中山大學 國際經營管理碩士學程 蔡旻學

際
國
經 立 ，
營 中

山
管
大

理
學

碩 管
理
士
學 國立中山大學管理學院國際經營管理碩士學程
學
院
程 國
碩士論文

Master of Business Administration Program in
碩
士
論 International Business
文
National Sun Yat-Sen University

行
Master Thesis
動
應

用
程
行動應用程式市場普及程度之實證分析研究：
式
市
場 論排行前25之app特性
普
及
An Empirical Analysis On iOS App Popularity:
程
度
On App-specific Characteristics of App
之
實
Crossing the Top 25 Threshold
證
分

析
研 研究生：蔡旻學
究
Min-Hsueh Tsai

指導教授﹕佘健源 博士
研
究
Dr. Chien-Yuan Sher
生
：

蔡
旻
學 中華民國105年1月

January 2016

學
年
度
國立中山大學管理學院國際經營管理碩士學程
碩士論文
Master of Business Administration Program in
International Business
National Sun Yat-Sen University
Master Thesis

行動應用程式市場普及程度之實證分析研究：
論排行前25之app特性
An Empirical Analysis On iOS App Popularity:
On App-specific Characteristics of App
Crossing the Top 25 Threshold

研究生：蔡旻學
Min-Hsueh Tsai
指導教授﹕佘健源 博士
Dr. Chien-Yuan Sher

中華民國105年1月
January 2016
iii
Acknowledgements
My sincere gratitude goes to those who companied me through this path of
research, especially to my thesis advisor Dr. Chien-Yuan Sher, for his insightful
guidance, patience, and immense knowledge on quantitative methodologies, which
changed my perspective of business administration and work ethics.
Still, I am not satisfied with my research. I know there is always something
more I could work on. However, my time has run out, and I‘m leaving the school. I
guess the most precious gift of education the NSYSU offered is that I eventually
realized how ignorant I’ve always been and the universe of knowledge is such a huge,
boundaryless space. The term graduation to me is not an issue anymore, as for me
I’ve found the greatest jewel, which is the journey itself––what matters the most is the
process––just like the answer to the ultimate question is not 42 but real value of
seeking truth lies in the journey.
Last but not the least, I am deeply thankful to my family: my respectful
parents and my dearest wife––Anna, for supporting me spiritually throughout the
writing of my master thesis and in general, my life as well.

i
論文摘要
本篇論文的研究對象為蘋果電腦公司的行動應用程式App。回顧研究文獻
，學者發現蘋果商店的App前25排行榜是個重要的門檻指標；跨過此門檻的
App留在榜單的機率有顯著提升。因此我們的研究問題將參照過去學者的發現
，並著重在App特性：如App容量、App名稱長度、系統相容性及有無中文版
本等因素，觀察其對跨越前25排行榜的勝算比與機率。
透過分層隨機抽樣的方式，我們從App Store的資料庫按照各個類別對整
體的比例抽出1,998個App，然後將抽得樣本綜合成七大概類，為主要分析方式
——羅吉斯回歸的虛擬變數。從敘述統計來觀察資料，接著將App特性如容量
、系統相容性、平台等等連續與類別變數帶入主要研究模型，以觀察各變數的
顯著狀況、平均邊際效果及主要研究問題的交互作用。我們的研究呼應過去學
者的研究結果，發現高評價、評論數與更新頻率均會增加App跨過前25排行榜
的機率。除此之外，也發現App容量、較長的App名稱及某些類別相對基礎類
別，對提升跨越排行榜的機率皆有正面顯著影響。中文版本在主要研究模型不
顯著，然而對於購物類別與休閒類別有顯著交互作用，讓這些類別的跨越門檻
機率顯著提升。
我們相信研究結果除了有學術層面意義，對App開發者也有實務意涵；讓
開發者能透過了解排行榜，來更妥善地運用開發資源。

關鍵字：App Store 行銷、App Store 排行榜、羅吉斯回歸、羅吉斯回歸的交互
作用
ii
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.

Keywords: App Store marketing, App Store ranking, mobile application markets,
logistic regression analysis, App ranking analysis, stratified random sampling,
interaction term in logistic regression.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ........................................................................................................ i
論文摘要 .................................................................................................................................... ii
ABSTRACT .............................................................................................................................. iii
LIST OF TABLES .................................................................................................................... iv
TABLE OF FIGURES ............................................................................................................... v
ABBREVIATIONS .................................................................................................................. vi
1. INTRODUCTION ................................................................................................................. 1
2. LITERATURE REVIEW ............................................................................................................. 4
2.1 THE LONG TAIL PHENOMENON IN DIGITAL ECONOMY ......................................................... 4
2.2 THE EFFECTS OF PUBLIC RANKING LIST ................................................................................ 5
2.3 FACTORS RELATED TO APP STORE’S PUBLIC RANKING ....................................................... 6
3. RESEARCH METHODOLOGY ................................................................................................... 7
3.1 APP STORE DATA DESCRIPTION, DATA COLLECTION, AND SAMPLING METHOD .................. 7
3.2 LOGISTIC REGRESSION ANALYSIS ......................................................................................... 9
3.3 DESIGN VARIABLES AND EXPLANATORY VARIABLES ........................................................ 11
3.3.1 DESIGN VARIABLES ......................................................................................................... 11
3.3.2 EXPLANATORY VARIABLES ............................................................................................. 12
4. Empirical results .................................................................................................................. 18
4.1 Discussion on summary statistics ...................................................................................... 18
4.2 Basic model of logistic regression ..................................................................................... 19
4.3 Predicting the probability of crossing the top 25 threshold ............................................... 24
4.4 Estimation of the marginal effect in logistic regression ..................................................... 26
4.5 Interpretation of the interaction term ................................................................................. 28
4.6 Other relevant results ......................................................................................................... 32
5. Conclusions, research restriction, and implication for future researchers ........................... 35
5.1 Research restriction ............................................................................................................ 36
5.2 Implication to future researchers ........................................................................................ 37
References: ............................................................................................................................... 38
List of Tables
Table 1. All available categories while we conduct our research ......................................... 9
Table 2. The generalized groups and their corresponding percentages on the App
Store. ............................................................................................................................................. 11
Table 3. Summary statistics of app-specific characteristics under generalized
categories. .................................................................................................................................... 12
Table 4. Descriptive statistics of variables included in the logistic regression model 17
Table 5. Summary statistics of the 1,998 randomly selected apps. .................................... 19
Table 6. Coefficients or logistic regression models of crossing the thresholds ............. 20
Table 7. Odds ratio table of variables of interests ................................................................... 23
Table 8. Classification table based on model 5 using cutpoint of 0.11 ............................ 24
Table 9. Interaction terms with main effects of model 3 ....................................................... 29
Table 10. Summary of interaction terms––Chinese and app size with design variables
in model 5 .................................................................................................................................... 31
Table 11. Summary of other interaction terms in model 5 ................................................... 34
iv
Table of Figures
Figure 1. Frequency charts of appsize and osage. ................................................................... 13
Figure 2. Frequency chart of app name length. ........................................................................ 14
Figure 3. Frequency chart of languages. ..................................................................................... 15
Figure 4. Frequency chart of rating. ............................................................................................. 16
Figure 5. The histogram on probability of threshold-crossing distribution .................... 25
Figure 6. The plot of sensitivity and specificity versus cutpoints chart of model 3 ..... 25
Figure 7. ROC curve of all cutpoints for sensitivity versus specificity of model 3 ..... 26
Figure 8. The average marginal effects of generalized categories for different app
sizes ............................................................................................................................................... 27
Figure 9. The average marginal effects of generalized categories for different app
name length ................................................................................................................................. 28
Figure 10. The average marginal effects of shopping apps with and without Chinese
version .......................................................................................................................................... 30
Figure 11. The average marginal effects of relaxing apps with and without Chinese
version .......................................................................................................................................... 31
Figure 12. The average marginal effects of generalized categories in different ratings
......................................................................................................................................................... 32
Figure 13. The average marginal effects of generalized categories in different updates
......................................................................................................................................................... 32
Figure 14. The average marginal effects of news apps with and without 50 rating
counts ............................................................................................................................................ 33
Figure 15. The average marginal effects of non-gaming apps with and without 50
rating counts .............................................................................................................................. 34
v
Abbreviations
App(s) – Mobile Platform Application(s)
API – Application Program Interface
ASO – App Store Optimization
EPF – Enterprise Partner Feed Relational
IAP – In-app-purchase
iOS – Apple Inc.’s mobile operating system
MAM – Mobile Application Market
OS – Operating System
SEO – Search Engine Optimization

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