Data-Driven Success Prediction of Android Mobile Applications on the Google Play Store: A Systematic Literature Review (SLR): Data-Driven Success Prediction of Android Mobile Applications on the Google Play Store: A Systematic Literature Review (SLR)

Anureet kaur

Authors

Anureet kaur Department of Computer Science and Applications, Khalsa College, Amritsar, India https://orcid.org/0000-0002-8698-1633

Keywords:

Mobile Applications, Prediction, Android, stematic Literature Review, Machine Learning

Abstract

Every day number of mobile apps are added or removed from the Google Play Store platform depending upon app’s popularity in market place. Several attributes of an app, attributes that are either internal to or external to it, can perform a substantial role in deciding whether the app will be successful or not. In this paper, the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) are used for presenting a systematic literature review for success prediction for Android Mobile Apps before they are launched on the Play Store market. With the help of this SLR, developers and researchers can check existing models that can be used for predicting the success of an app. Apart from presenting existing approaches; the author has tried to assist in building a new success prediction model by identifying a few features/factors that can make a mobile soft-ware/application a success or failure. This paper formulates five Research Questions (RQs) and precedes the entire PRISMA process to find the answers to them. The answers to the RQs will help to devise a new success prediction model for mobile apps in the future.

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References

L. Shamseer et al., “Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation,” BMJ, vol. 349, no. jan02 1, pp. g7647–g7647, Jan. 2015, doi: 10.1136/bmj.g7647.

M. Petticrew and H. Roberts, Systematic Reviews in the Social Sciences. Blackwell Publishing, 2006. doi: 10.1002/9780470754887.

B. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” 2007. doi: 10.1145/1134285.1134500.

C. Wohlin, “Guidelines for snowballing in systematic literature studies and a replication in software engineering,” in Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering - EASE ’14, 2014, p. 38. doi: 10.1145/2601248.2601268.

V. N. I. and D. D. K. and T. K. and M. Inukollu, “Factors influencing quality of mobile apps: Role of mobile app development life cycle,” Int. J. Softw. Eng. Appl., vol. 5, no. 2, 2014, doi: https://doi.org/10.48550/arXiv.1410.4537.

F. Agboma and A. Liotta, “Addressing user expectations in mobile content delivery,” Mob. Inf. Syst., 2007, doi: 10.1155/2007/719840.

T. H. Hsu and J. W. Tang, “Development of hierarchical structure and analytical model of key factors for mobile app stickiness,” J. Innov. Knowl., 2020, doi: 10.1016/j.jik.2019.01.006.

O. Al-Shamaileh and A. Sutcliffe, “Why people choose Apps: An evaluation of the ecology and user experience of mobile applications,” Int. J. Hum. Comput. Stud., 2023, doi: 10.1016/j.ijhcs.2022.102965.

I. C. Fang and S. C. Fang, “Factors affecting consumer stickiness to continue using mobile applications,” Int. J. Mob. Commun., 2016, doi: 10.1504/IJMC.2016.078720.

V. Venkatesh and F. D. Davis, “Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies,” Manage. Sci., 2000, doi: 10.1287/mnsc.46.2.186.11926.

C. Su-Chao and T. Feng-Cheng, “An empirical investigation of students’ behavioural intentions to use the online learning course Web sites,” Br. J. Educ. Technol., 2008.

T. P. Liang, Y. L. Ling, Y. H. Yeh, and B. Lin, “Contextual factors and continuance intention of mobile services,” Int. J. Mob. Commun., 2013, doi: 10.1504/IJMC.2013.055746.

O. Al-Shamaileh and A. Sutcliffe, “Investigating a multi-faceted view of user experience,” in Proceedings of the 24th Australian Computer-Human Interaction Conference, OzCHI 2012, 2012. doi: 10.1145/2414536.2414538.

O. Al-Shamaileh, “I Have Issues with Facebook: But I Will Keep Using It,” IEEE Technol. Soc. Mag., 2018, doi: 10.1109/MTS.2018.2826078.

I. L. Wu and J. L. Chen, “An extension of Trust and TAM model with TPB in the initial adoption of on-line tax: An empirical study,” Int. J. Hum. Comput. Stud., 2005, doi: 10.1016/j.ijhcs.2005.03.003.

T. Zhou, “Understanding continuance usage of mobile services,” Int. J. Mob. Commun., 2013, doi: 10.1504/IJMC.2013.050995.

P. A. Paramartha, H. B. Santoso, and P. H. Putra, “FACTORS INFLUENCING THE USER STICKINESS OF A MOBILE NEWS APPLICATION: THE CASE OF ‘LINE TODAY APP,’” JST (Jurnal Sains dan Teknol., 2021, doi: 10.23887/jstundiksha.v10i2.36345.

T. Ahn, S. Ryu, and I. Han, “The impact of Web quality and playfulness on user acceptance of online retailing,” Inf. Manag., 2007, doi: 10.1016/j.im.2006.12.008.

D. Sledgianowski and S. Kulviwat, “Using social network sites: The effects of playfulness, critical mass and trust in a hedonic context,” J. Comput. Inf. Syst., 2009, doi: 10.1080/08874417.2009.11645342.

Y. Zhang, Y. Fang, K. K. Wei, E. Ramsey, P. McCole, and H. Chen, “Repurchase intention in B2C e-commerce - A relationship quality perspective,” Inf. Manag., 2011, doi: 10.1016/j.im.2011.05.003.

A. Bhattacherjee, “UNDERSTANDING INFORMATION SYSTEMS CONTINUANCE: AN EXPECTATION- CONFIRMATION MODEL Motivation for the Study,” Inf. Syst. Contin. MIS Q., 2001.

C. C. Tu, K. Fang, and C. Y. Lin, “Perceived ease of use, trust, and satisfaction as determinants of loyalty in e-auction marketplace,” J. Comput., 2012, doi: 10.4304/jcp.7.3.645-652.

H. Van der Heijden, “Factors influencing the usage of websites: The case of a generic portal in The Netherlands,” Inf. Manag., 2003, doi: 10.1016/S0378-7206(02)00079-4.

F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Q. Manag. Inf. Syst., 1989, doi: 10.2307/249008.

L. Guerrouj and O. Baysal, “Investigating the android apps’ success: An empirical study,” in IEEE International Conference on Program Comprehension, 2016. doi: 10.1109/ICPC.2016.7503724.

M. Linares-Vásquez, G. Bavota, C. Bernal-Cárdenas, M. Di Penta, R. Oliveto, and D. Poshyvanyk, “API change and fault proneness: A threat to the success of android apps,” in 2013 9th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2013 - Proceedings, 2013. doi: 10.1145/2491411.2491428.

J. Y. L. Thong, S. J. Hong, and K. Y. Tam, “The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance,” Int. J. Hum. Comput. Stud., 2006, doi: 10.1016/j.ijhcs.2006.05.001.

G. C. Bruner and A. Kumar, “Explaining consumer acceptance of handheld Internet devices,” J. Bus. Res., 2005, doi: 10.1016/j.jbusres.2003.08.002.

C. I. Wei, P.S., Lee, S.Y., Lu, H.P., Tzou, J.C., Weng, “Why do people abandon mobile social games? Using Candy Crush Saga as an example.,” Int. J. Soc. Educ. Econ. Manag. Eng., vol. 9, no. 1, pp. 13–18., 2015.

J. Hartmann, A. Sutcliffe, and A. De Angeli, “Towards a theory of user judgment of aesthetics and user interface quality,” ACM Trans. Comput. Interact., 2008, doi: 10.1145/1460355.1460357.

M. J. Noh and K. T. Lee, “An analysis of the relationship between quality and user acceptance in smartphone apps,” Inf. Syst. E-bus. Manag., 2016, doi: 10.1007/s10257-015-0283-6.

R. Adhikari, D. Richards, and K. Scott, “Security and privacy issues related to the use of mobile health apps,” in Proceedings of the 25th Australasian Conference on Information Systems, ACIS 2014, 2014.

B. H. Jones and A. G. Chin, “On the efficacy of smartphone security: A critical analysis of modifications in business students’ practices over time,” Int. J. Inf. Manage., 2015, doi: 10.1016/j.ijinfomgt.2015.06.003.

M. A. Harris, S. Furnell, and K. Patten, “Comparing the Mobile Device Security Behavior of College Students and Information Technology Professionals,” J. Inf. Priv. Secur., 2014, doi: 10.1080/15536548.2014.974429.

Y. Ding and K. H. Chai, “Emotions and continued usage of mobile applications,” Ind. Manag. Data Syst., 2015, doi: 10.1108/IMDS-11-2014-0338.

D. W. Gefen, D., Karahanna, E., Straub, “Trust and TAM in online shopping: an integrated model.,” MIS Q., vol. 27, no. 1, pp. 51–90, 2003.

V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology,” MIS Q. Manag. Inf. Syst., 2012, doi: 10.2307/41410412.

A. Malik, S. Suresh, and S. Sharma, “Factors influencing consumers’ attitude towards adoption and continuous use of mobile applications: A conceptual model,” in Procedia Computer Science, 2017. doi: 10.1016/j.procs.2017.11.348.

M. Kapias, “10-key-characteristics-of-a-successful-mobile-app-for-every-business.” [Online]. Available: https://fireart.studio/blog/10-key-characteristics-of-a-successful-mobile-app-for-every-business/

C. J. Tuckerman, “Predicting Mobile Application Success,” Stanford., 2014.

Y. Yao, W. X. Zhao, Y. Wang, H. Tong, F. Xu, and J. Lu, “Version-Aware rating prediction for mobile app recommendation,” ACM Trans. Inf. Syst., 2017, doi: 10.1145/3015458.

A. M. Khushba, A. Tuba, I. Waqqas, I. Supervisor, and A. Chakrabarty, “Exploratory Data Analysis and Success Prediction of Google Play Store Apps,” BRAC Univ., 2018.

S. Suleman, M., Malik, A., & Hussan, “Google play store app ranking prediction using machine learning algorithm,” in In Proceedings of the International Conference on Data Science, 2019, pp. 57–61.

J. Businge, M. Openja, D. Kavaler, E. Bainomugisha, F. Khomh, and V. Filkov, “Studying Android App Popularity by Cross-Linking GitHub and Google Play Store,” in SANER 2019 - Proceedings of the 2019 IEEE 26th International Conference on Software Analysis, Evolution, and Reengineering, 2019. doi: 10.1109/SANER.2019.8667998.

G. M. Muradul Bashir, M. S. Hossen, D. Karmoker, and M. J. Kamal, “Android apps success prediction before uploading on google play store,” in 2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019, 2019. doi: 10.1109/STI47673.2019.9068071.

A. Singh, “Mobile App Success Prediction,” Int. J. Res. Appl. Sci. Eng. Technol., 2020, doi: 10.22214/ijraset.2020.6273.

M. R. Dehkordi, H. Seifzadeh, G. Beydoun, and M. H. Nadimi-Shahraki, “Success prediction of android applications in a novel repository using neural networks,” Complex Intell. Syst., 2020, doi: 10.1007/s40747-020-00154-3.

Z. Zhong, Y. Bao, S. Shen, and E. Zhou, “Predict New App Quality by Using Machine Learning,” in Journal of Physics: Conference Series, 2020. doi: 10.1088/1742-6596/1693/1/012111.

S. Rathod, P. Ugalmugle, R. Waghmare, and T. Maktum, “An Approach To Predict Software Application Success Using Voting Ensemble Method,” ITM Web Conf., 2020, doi: 10.1051/itmconf/20203203020.

S. Shashank and B. Naidu, “Google Play Store Apps-Data Analysis and Ratings Prediction,” Int. Res. J. Eng. Technol., 2020.

P. B. Prakash Reddy and R. Nallabolu, “Machine learning based Descriptive Statistical Analysis on Google Play Store Mobile Applications,” in Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020, 2020. doi: 10.1109/ICIRCA48905.2020.9183271.

B. T. Magar, S. Mali, and E. Abdelfattah, “App Success Classification Using Machine Learning Models,” in 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021, 2021. doi: 10.1109/CCWC51732.2021.9376021.

W. Tafesse, “The effect of app store strategy on app rating: The moderating role of hedonic and utilitarian mobile apps,” Int. J. Inf. Manage., 2021, doi: 10.1016/j.ijinfomgt.2020.102299.

B. Davazdahemami, P. Kalgotra, H. M. Zolbanin, and D. Delen, “A developer-oriented recommender model for the app store: A predictive network analytics approach,” J. Bus. Res., 2023, doi: 10.1016/j.jbusres.2023.113649.

and D. N. Pattanaik, Priyadarshini, “Comparison of machine learning algorithms used to catalog Google Appstore,” J. Med. Artif. Intell., vol. 6, no. 22, 2023, doi: 10.21037/jmai-23-58.

Ruhel, S., “Google Playstore Application Analysis and Prediction,” Jaypee University of Information Technology, 2019.

N. Chen, S. C. H. Hoiy, S. Li, and X. Xiao, “SimApp:A framework for detecting similar mobile applications by online kernel learning,” in WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining, 2015. doi: 10.1145/2684822.2685305.

D. E. Krutz et al., “A dataset of open-source android applications,” in IEEE International Working Conference on Mining Software Repositories, 2015. doi: 10.1109/MSR.2015.79.

D. H. Park, M. Liu, C. Zhai, and H. Wang, “Leveraging user reviews to improve accuracy for mobile app retrieval,” in SIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015. doi: 10.1145/2766462.2767759.

S. GHOSH, “Google Play Store App Details.” [Online]. Available: https://www.kaggle.com/datasets/souravghosh01/google-play-store-app-details