GOETHE UNIVERSITY Information Systems Engineering A thesis presented for the Master of Science Degree App ecosystem out of balance: An empirical analysis of update interdependence between operating system and application software March 31, 2020 Author: Supervisor: Phillip Schneider Daniel Franzmann Examiners: Roland Holten Wolfgang König Abstract Software updates are a critical success factor in mobile app ecosystems. Through pub- lishing regular updates, platform providers enhance their operating systems for the benefit of both end users and third-party developers. It is also a way of attracting new customers. However, this platform evolution poses the risk of inadvertently introducing software problems, which can severely disturb the ecosystem’s balance by compromising its foun- dational technologies. So far, little to no research has addressed this issue from a user- centered perspective. The thesis at hand draws on IS post-adoption literature to investi- gate the potential negative influences of operating system updates on mobile app users. The release of Apple’s iOS 13 update serves as research object. Based on over half a million user reviews from the AppStore, data mining techniques are applied to study the impact of the new platform version. The results show that iOS 13 caused complications with a large number of popular apps, leading to a significant decline in user ratings and an uptrend in negative sentiment. Feature requests, functional complaints, and device compatibility are identified as the three major issue categories. These issue types are com- pared in terms of their quantifiable negative effect on users’ continuance intention. In essence, the findings contribute to IS research on post-adoption behavior and provide guidance to ecosystem participants in dealing with update-induced platform issues. Keywords: App ecosystem, mobile platforms, software updates, IS continuance, IS post-adoption - I - List of contents 1 Introduction ................................................................................................................... 1 2 Theoretical background and literature review .............................................................. 4 2.1 Mobile app ecosystems .......................................................................................... 4 2.1.1 App Store and iOS platform ......................................................................... 7 2.1.2 Studies related to mobile app ecosystems .................................................... 9 2.2 Software updates .................................................................................................. 10 2.2.1 Overview of different update types ............................................................ 11 2.2.2 Studies related to software updates ............................................................ 12 2.3 Information systems continuance model ............................................................. 14 3 Development of hypotheses ........................................................................................ 17 3.1 Adverse effects of update-induced issues on continuance intention ................... 17 3.2 Manifestation of distinct update issue types in user reviews ............................... 17 3.3 Magnitude of influence of issue types on continuance intention ........................ 18 4 Research methodology ................................................................................................ 19 4.1 Study design ......................................................................................................... 19 4.2 Data collection ..................................................................................................... 21 4.2.1 Sample definition ....................................................................................... 21 4.2.2 Data scraping process ................................................................................. 23 4.3 Data preparation ................................................................................................... 25 4.3.1 Data set construction .................................................................................. 25 4.3.2 Text preprocessing ..................................................................................... 26 4.4 Data processing .................................................................................................... 28 4.4.1 Sentiment detection .................................................................................... 28 4.4.2 Review classification .................................................................................. 29 4.4.3 Topic modeling .......................................................................................... 33 4.5 Data evaluation .................................................................................................... 35 5 Empirical analysis and results ..................................................................................... 37 5.1 Assessment of classification models ................................................................... 37 5.2 Statistical summary of classified reviews ............................................................ 40 5.3 Rating and sentiment analysis ............................................................................. 43 5.4 Evaluation of topic model and update issue types ............................................... 46 6 Discussion ................................................................................................................... 52 7 Conclusion .................................................................................................................. 57 7.1 Summary of key findings ..................................................................................... 57 7.2 Theoretical and practical implications ................................................................. 58 7.3 Limitations and future research ........................................................................... 60 Literature ......................................................................................................................... 62 Appendix ......................................................................................................................... 70 - II - List of figures Figure 1: Conceptual model of mobile app ecosystems ................................................. 6 Figure 2: Information systems continuance model ...................................................... 15 Figure 3: Schematic diagram of study design .............................................................. 20 Figure 4: Plate notation for latent Dirichlet allocation ................................................. 34 Figure 5: Boxplots of cross validation accuracy scores ............................................... 37 Figure 6: Extract from decision tree of random forest classifier .................................. 39 Figure 7: Distribution of iOS 13 reviews across app categories .................................. 41 Figure 8: Linear relationship between rating and sentiment scores ............................. 43 Figure 9: Rank-biserial correlation analysis results ..................................................... 44 Figure 10: Weekly rating and sentiment scores of Gmail app ....................................... 45 Figure 11: Topic model coherence and perplexity ......................................................... 46 Figure 12: Intertopic distance map and topic grouping.................................................. 48 Figure 13: Rating histograms for update issue types ..................................................... 49 - III - List of tables Table 1: Extract from mobile app sample ................................................................... 22 Table 2: Comparison of raw and preprocessed review text ........................................ 27 Table 3: Performance metrics for classification model assessment............................ 38 Table 4: False positive predictions of random forest classifier .................................. 38 Table 5: Descriptive statistics of classified reviews ................................................... 40 Table 6: Breakdown of reviews across country and ranking dimensions ................... 40 Table 7: Top 20 apps with highest percentage of iOS 13 reviews.............................. 42 Table 8: Temporal changes in rating and sentiment scores of iOS 13 reviews .......... 45 Table 9: Top 15 most relevant terms per modeled topic ............................................ 47 Table 10: Kruskal-Wallis test results ............................................................................ 50 - IV - List of abbreviations ANOVA Analysis of variance API Application programming interface CSV Comma-separated values ECT Expectation-confirmation theory ETL Extract-transform-load GB Great Britain H Hypothesis HTTP Hypertext transfer protocol IDC International Data Corporation ISCM Information systems continuance model JSON JavaScript object notation KDD Knowledge discovery in databases LDA Latent Dirichlet allocation NB Naïve Bayes NLP Natural language processing NLTK Natural language toolkit OTA Over the air PC1 Principal component 1 PCA Principal component analysis RQ Research question RSS Rich site summary SDK Software development kit TF-IDF Term frequency-inverse document frequency URL Uniform resource locator US United States VADER Valence aware dictionary and sentiment reasoner