Skip to main content
SpringerLink
Log in

A Study of Crucial Factors for In-App Purchase of Game Software

  • Conference paper
  • First Online:
New Frontiers in Artificial Intelligence (JSAI-isAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10247))

Included in the following conference series:

  • 1684 Accesses

Abstract

Google Play and App Store registered 17.2 billion downloads of game software worldwide in the first quarter of 2016, according to a report published by Sensor Tower, a platform that supports apps for iOS and Android. Related researchers too predicted tremendous growth in gaming applications. Not only the game App developers need to know how to design products that match gamer’s needs, and will continue to use it, but also allure gamers to decide in-app purchase (IAP) which is the final goal. In particular, IAP is the major revenue model. Hence, this study attempts to define the potential factors influencing IAP for gamer. We collect data for many possible features from which, using Least Absolute Shrinkage and Selection Operator (LASSO) feature selection method, we identify important factors that affect gamer IAP behavior. The extracted factors can help game developers to improve their design for increasing revenue.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. App Annie (2016). http://blog.appannie.com/app-annie-releases-inaugural-mobile-app-forecast/

  2. Business of Apps (2015). http://www.businessofapps.com/app-usage-statistics-2015/

  3. Hsiao, C.-H., Chang, J.-J., Tang, K.-Y.: Exploring the influential factors in continuance usage of mobile social apps: satisfaction, habit, and customer value perspectives. Telemat. Inform. 33, 342–355 (2016)

    Article  Google Scholar 

  4. Hsu, C.-L., Lu, H.-P., Hsu, H.-H.: Adoption of the mobile Internet: an empirical study of multimedia message service (MMS). Omega 35, 715–726 (2007)

    Article  Google Scholar 

  5. Hsu, C.-L., Lin, J.C.-C.: What drives purchase intention for paid mobile apps?-an expectation confirmation model with perceived value. Electron. Commer. Res. Appl. 14, 46–57 (2014)

    Article  Google Scholar 

  6. Hsu, C.-L., Lin, J.C.-C.: Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention. Technol. Forecast. Soc. Chang. 108, 42–53 (2016)

    Article  Google Scholar 

  7. Rogers, E.M.: Diffusion of innovations, 4th edn. Simon and Schuster, New York City (2010)

    Google Scholar 

  8. Park, E., Baek, S., Ohm, J., Joon Chang, H.: Determinants of player acceptance of mobile social network games: an application of extended technology acceptance model. Telemat. Inform. 31, 3–15 (2014)

    Article  Google Scholar 

  9. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2, 192–222 (1991)

    Article  Google Scholar 

  10. D’Angelo, G.M., Rao, D., Gu, C.C.: Combining least absolute shrinkage and selection operator (LASSO) and principal-components analysis for detection of gene-gene interactions in genome-wide association studies. In: BMC Proceedings, vol. 3 (2009)

    Google Scholar 

  11. Gamasutra (2015). http://www.gamasutra.com/view/news/256997/The_top_10_games_take_25_of_global_mobile_app_revenue.php

  12. Games Industry (2015). http://www.gamesindustry.biz/articles/2015-07-10-ios-game-revenues-show-top-20-dominate-newzoo

  13. Park, H.J., Kim, S.-H.: A bayesian network approach to examining key success factors of mobile games. J. Bus. Res. 66, 1353–1359 (2013)

    Article  Google Scholar 

  14. Koekkoek, H.: How the most successful apps monetize their user base. Distimo Publication (2013). http://www.bbvaopen4u.com/sites/default/files/document/document/how_the_most_successful_apps_monetize_their_user_base.pdf

  15. Lin, H.-H., Wang, Y.-S.: An examination of the determinants of customer loyalty in mobile commerce contexts. Inf. Manag. 43, 271–282 (2006)

    Article  Google Scholar 

  16. Kim, H.-W., Kankanhalli, A., Lee, H.-L.: Investigating decision factors in mobile application purchase: a mixed-methods approach. Inf. Manag. (2016, in press). Corrected proof

    Google Scholar 

  17. Al-Jabri, I.M., Sohail, M.S.: Mobile banking adoption: application of diffusion of innovation theory. J. Electron. Commer. Res. 13, 379–391 (2012)

    Google Scholar 

  18. Sweeney, J.C., Soutar, G.N.: Consumer perceived value: the development of a multiple item scale. J. Retail. 77, 203–220 (2001)

    Article  Google Scholar 

  19. Duan, J., Soussen, C., Brie, D., Idier, J., Wan, M., Wang, Y.-P.: Generalized lasso with under-determined regularization matrices. Signal Process. 127, 239–246 (2016)

    Article  Google Scholar 

  20. Lee, J., Lee, J., Lee, H., Lee, J.: An exploratory study of factors influencing repurchase behaviors toward game items: a field study. Comput. Hum. Behav. 43, 13–23 (2015)

    Article  Google Scholar 

  21. Hsu, J.S.-C., Lin, T.-C., Fu, T.-W., Hung, Y.-W.: The effect of unexpected features on app users’ continuance intention. Electron. Commer. Res. Appl. 14, 418–430 (2015)

    Article  Google Scholar 

  22. Kang, J.-Y.M., Mun, J.M., Johnson, K.K.P.: In-store mobile usage: downloading and usage intention toward mobile location-based retail apps. Comput. Hum. Behav. 46, 210–217 (2015)

    Article  Google Scholar 

  23. Gao, L., Waechter, K.A., Bai, X.: Understanding consumer’s continuance intention towards mobile purchase: a theoretical framework and empirical study- a case of China. Comput. Hum. Behav. 53, 249–262 (2015)

    Article  Google Scholar 

  24. Chen, L.-S., Lin, M.-R.: Key factors in-app purchase for game applications. In: International Conference of Emerging Trends in Engineering and Technology, Kobe, pp. 91–95 (2015)

    Google Scholar 

  25. Kleijnen, M., Ruyter, K.D., Wetzels, M.: Consumer adoption of wireless services: discovering the rules, while playing the game. J. Interact. Mark. 18, 51–61 (2004)

    Article  Google Scholar 

  26. Mollaee, M., Moattar, M.H.: A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification. Biocybern. Biomed. Eng. (2016, in press). Uncorrected proof

    Google Scholar 

  27. Mic (2015). http://mic.iii.org.tw/aisp/pressroom/press01_pop.asp?sno=400&type1=2

  28. Connor, P., Hollensen, P., Krigolson, O., Trappenberg, T.: A biological mechanism for bayesian feature selection: weight decay and raising the LASSO. Neural Netw. 67, 121–130 (2015)

    Article  Google Scholar 

  29. Roma, P., Ragaglia, D.: Revenue models, in-app purchase, and the app performance: evidence from apple’s app store and google play. Electron. Commer. Res. Appl. 17, 173–190 (2016)

    Article  Google Scholar 

  30. Zhou, Q., Song, S., Huang, G., Wu, C.: Efficient lasso training from a geometrical perspective. Neurocomputing 168, 234–239 (2015)

    Article  Google Scholar 

  31. Davis, R., Lang, B.: Modeling game usage, purchase behavior and ease of use. Entertain. Comput. 3, 27–36 (2012)

    Article  Google Scholar 

  32. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. 58, 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

  33. Sayed, S.A.-F., Nabil, E., Badr, A.: A binary clonal flower pollination algorithm for feature selection. Pattern Recogn. Lett. 77, 21–27 (2016)

    Article  Google Scholar 

  34. Basuroy, S., Chatterjee, S., Ravid, S.A.: How critical are critical reviews? The box office effects of film critics. J. Mark. 67, 103–117 (2003)

    Article  Google Scholar 

  35. Gunasundari, S., Janakiraman, S., Meenambal, S.: Velocity bounded boolean particle swarm optimization for improved feature selection in liver and kidney disease diagnosis. Expert Syst. Appl. 56, 28–47 (2016)

    Article  Google Scholar 

  36. Kwon, S., Lee, S., Kim, Y.: Moderately clipped LASSO. Comput. Stat. Data Anal. 92, 53–67 (2015)

    Article  MathSciNet  Google Scholar 

  37. Sensor Tower (2016). https://sensortower.com/blog/top-ios-games-december-2015

  38. Techco (2015) http://tech.co/6-app-monetization-models-make-money-2015-08

  39. The Statistics Portal (2016). http://www.statista.com/statistics/536433/mobile-games-revenue-worldwide/

  40. Bardsley, W.E., Vetrova, V., Liu, S.: Toward creating simpler hydrological models: a lasso subset selection approach. Environ. Model. Softw. 72, 33–43 (2015)

    Article  Google Scholar 

  41. Whatls (2015). http://whatis.techtarget.com/definition/in-app-purchase-IAP

  42. Zhang, X., Mei, C., Chen, D., Li, J.: Feature selection in mixed data: a method using a novel fuzzy rough set-based information entropy. Pattern Recognit. 56, 1–15 (2016)

    Article  Google Scholar 

  43. Cong, Y., Wang, S., Fan, B., Yang, Y., Yu, H.: UDSF: unsupervised deep sparse feature selection. Neurocomputing 196, 150–158 (2016)

    Article  Google Scholar 

  44. Liu, Y., Li, H.: Exploring the impact of use context on mobile hedonic services adoption: an empirical study on mobile gaming in China. Comput. Hum. Behav. 27, 890–898 (2011)

    Article  Google Scholar 

  45. Yamada, M., et al.: High-dimensional feature selection by feature-wise non-linear lasso. Neural Comput. 26(1), 185–207 (2014)

    Article  MathSciNet  Google Scholar 

  46. Zu, Z., et al.: Gradient boosted feature selection. In: Proceedings of Knowledge Discovery and Data Mining (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate-Ken, Japan

    Meng-Ru Lin & Goutam Chakraborty

Authors
  1. Meng-Ru Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Goutam Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Goutam Chakraborty .

Editor information

Editors and Affiliations

  1. Graduate School of Business Sciences, University of Tsukuba, Tokyo, Japan

    Setsuya Kurahashi

  2. Fujitsu Laboratories Ltd., Kanagawa, Japan

    Yuiko Ohta

  3. Chiba University, Chiba, Japan

    Sachiyo Arai

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  5. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Lin, MR., Chakraborty, G. (2017). A Study of Crucial Factors for In-App Purchase of Game Software. In: Kurahashi, S., Ohta, Y., Arai, S., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2016. Lecture Notes in Computer Science(), vol 10247. Springer, Cham. https://doi.org/10.1007/978-3-319-61572-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61572-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61571-4

  • Online ISBN: 978-3-319-61572-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation