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博碩士論文 etd-0101116-111802 詳細資訊
Title page for etd-0101116-111802
論文名稱
Title
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
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
49
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2016-01-29
繳交日期
Date of Submission
2016-02-01
關鍵字
Keywords
羅吉斯回歸的交互作用、羅吉斯回歸、App Store行銷、App Store排行榜
mobile application markets, interaction term in logistic regression, stratified random sampling, logistic regression analysis, App Store marketing, App Store ranking, App ranking analysis
統計
Statistics
本論文已被瀏覽 5875 次,被下載 294
The thesis/dissertation has been browsed 5875 times, has been downloaded 294 times.
中文摘要
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。
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.
目次 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
參考文獻 References
References:
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