Identifying the effect of Success Factors on Predicting Android Mobile App Success on Google Play Store
DOI:
https://doi.org/10.22232/stj.2025.13.01.13Keywords:
Mobile Applications, Prediction, Android, Success, Failure, Survey, Machine learningAbstract
At the present time, mobile apps play an imperative role in everyone's life. Thousands of apps are being put on the Play Store market for users to install every day. But the life of the app on the Play Store depends on its success parameters. As there is substantial growth in the mobile app market, the competition among software app developers has also increased. Several attributes of an app, attributes that are either internal or external to it, can perform a substantial role in deciding whether the app will be successful or not. In this research paper, an effort has been made to find the success factors that may affect the success of the app. For this, a literature survey is first conducted to find these success factors, and then a survey is conducted through an online questionnaire from mobile app users. Findings from the survey corroborate that out of 14 identified success factors, the security of on app plays a pivotal role. After that, design quality and content quality equally play another major role in the success of an app. The mobile app reviews/input collected via the survey are further utilized as influencers/predictors in predicting the overall success of an app using Machine learning algorithms. To achieve this, various Machine Learning algorithms are applied using “Python web-based interactive Jupiter Notebook 7.0.8". The accuracy of the final selected model is 96%, which is the highest accuracy among many existing models in the literature.
References
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.
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.
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.
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.
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/
S. Amjad et al., “Calculating Completeness of Agile Scope in Scaled Agile Development," IEEE Access, vol. 6, pp. 5822-5847, 2018, doi: 10.1109/ACCESS.2017.2765351.
A. Kaur and K. Kaur, “A COSMIC function points based test effort estimation model for mobile applications,” J. King Saud Univ. - Comput. Inf. Sci., 2019, doi: 10.1016/j.jksuci.2019.03.001.
G. Grela, "Does Weighted average really work,” in In Active Citizenship by Knowledge Management & Innovation: Proceedings of the Management, Knowledge and Learning International Conference, ToKnowPress, 2013, pp. 1219-1227.
J. T. Hancock, R. A. Bauder, H. Wang, and T. M. Khoshgoftaar, "Explainable machine learning models for Medicare fraud detection," J. Big Data, vol. 10, no. 1, Dec. 2023, doi: 10.1186/ s40537-023-00821-5.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2025 Anureet Kaur
This work is licensed under a Creative Commons Attribution 4.0 International License.
© The Author(s) 2025. Published by the Science & Technology Journal (STJ), Mizoram University.
Articles published in this journal are open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited.
Authors retain copyright and grant the journal the right of first publication, with the work simultaneously licensed under the CC BY 4.0 license.
License link: Creative Commons Attribution 4.0 International License (CC BY 4.0)
ORCID – Author IDs for global recognition
Crossref DOIs – Persistent identifiers
PKP PN – Preservation via PKP Network
LOCKSS – Library archiving for permanence
OpenAIRE – Open Access compliance
RSS 
Facebook 
LinkedIn 