Mobile Application Usage Concentration in a Multidevice World

Benjamin Finley, Tapio Soikkeli, Kalevi Kilkki

Abstract

Mobile applications are a ubiquitous part of modern mobile devices. However the concentration of mobile application usage has been primarily studied only in the smartphone context and only at an aggregate level. In this work we examine the app usage concentration of a detailed multidevice panel of US users that includes smartphones, tablets, and personal computers. Thus we study app usage concentration at both an aggregate and individual device level and we compare the app usage concentration of different device types. We detail a variety of novel results. For example we show that the level of app usage concentration is not correlated between smartphones and tablets of the same user. Thus extrapolation between a user’s devices might be difficult. Overall, the study results emphasize the importance of a multidevice and multilevel approach.

References

  1. Alstott, J., Bullmore, E., and Plenz, D. (2014). powerlaw: A python package for analysis of heavy-tailed distributions. PLoS ONE, 9(1):e85777.
  2. Böhmer, M., Hecht, B., Schöning, J., Krüger, A., and Bauer, G. (2011). Falling asleep with angry birds, facebook and kindle: A large scale study on mobile application usage. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, MobileHCI 7811, pages 47-56, New York, NY, USA. ACM.
  3. Church, K., Ferreira, D., Banovic, N., and Lyons, K. (2015). Understanding the challenges of mobile phone usage data. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 7815, pages 504- 514, New York, NY, USA. ACM.
  4. Clauset, A., Shalizi, C. R., and Newman, M. E. J. (2009). Power-law distributions in empirical data. SIAM Review, 51(4):661-703.
  5. Cowell, F. A. and Flachaire, E. (2007). Income distribution and inequality measurement: The problem of extreme values. Journal of Econometrics, 141(2):1044-1072.
  6. Cowell, F. A. and Flachaire, E. (2014). Statistical methods for distributional analysis. In Atkinson, A. and Bourguignon, F., editors, Handbook of Income Distribution SET vols. 2A-2B, Handbooks in economics. Elsevier Science.
  7. Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., and Estrin, D. (2010). Diversity in smartphone usage. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, MobiSys 7810, pages 179-194, New York, NY, USA. ACM.
  8. Goldstein, D. G., McAfee, R. P., and Suri, S. (2011). The effects of exposure time on memory of display advertisements. In Proceedings of the 12th ACM Conference on Electronic Commerce, EC 7811, pages 49-58.
  9. Google (2012). The new multi-screen world: Understanding cross-platform consumer behavior. https://think.withgoogle.com/databoard/media/pdfs/thenew-multi-screen-world-study research-studies.pdf.
  10. Hays, R. D., Liu, H., and Kapteyn, A. (2015). Use of internet panels to conduct surveys. Behavior Research Methods, 47(3):685-690.
  11. Hintze, D., Findling, R. D., Scholz, S., and Mayrhofer, R. (2014). Mobile device usage characteristics: The effect of context and form factor on locked and unlocked usage. In Proceedings of the 12th International Conference on Advances in Mobile Computing and Multimedia, MoMM 7814, pages 105-114. ACM.
  12. Jung, J., Kim, Y., and Chan-Olmsted, S. (2014). Measuring usage concentration of smartphone applications: Selective repertoire in a marketplace of choices. Mobile Media & Communication, 2(3):352-368.
  13. Microsoft (2013). Cross-screen engagement. http://advertising.microsoft.com/es-xl/WWDocs/ User/display/cl/researchreport/1932/global/Cross Sc reenWhitepaper.pdf.
  14. Montanez, G. D., White, R. W., and Huang, X. (2014). Cross-device search. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 7814, pages 1669-1678, New York, NY, USA. ACM.
  15. Pew Internet and American Life Project (2015). June 10-july 12, 2015 gaming, jobs and broadband. http://www.pewinternet.org/datasets/june-10- july-12-2015-gaming-jobs-and-broadband/.
  16. Pew Research Center (2016). Our survey methodology in detail. http://www.pewresearch.org/methodology/u-ssurvey-research/our-survey-methodology-in-detail/.
  17. Roberto, E. (2015). The Boundaries of Spatial Inequality: Three Essays on the Measurement and Analysis of Residential Segregation. PhD thesis, Yale University.
  18. Rula, J. P., Jun, B., and Bustamante, F. (2015). Mobile ad(d): Estimating mobile app session times for better ads. In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, HotMobile 7815, pages 123-128.
  19. Soikkeli, T., Karikoski, J., and Hammainen, H. (2013). Characterizing smartphone usage: Diversity and end user context. International Journal of Handheld Computing Research, 4(1):15-36.
  20. Theil, H. (1967). Economics and information theory. Studies in mathematical and managerial economics. North-Holland Pub. Co.
  21. Vlachos, M., Meek, C., Vagena, Z., and Gunopulos, D. (2004). Identifying similarities, periodicities and bursts for online search queries. In Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, SIGMOD 7804, pages 131-142.
  22. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2):307-333.
  23. Wagner, D. T., Rice, A., and Beresford, A. R. (2014). Device analyzer: Understanding smartphone usage. In Mobile and Ubiquitous Systems: Computing, Networking, and Services, pages 195-208. Springer.
  24. Wang, Y., Huang, X., and White, R. W. (2013). Characterizing and supporting cross-device search tasks. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM 7813, pages 707-716, New York, NY, USA. ACM.
  25. Xu, K., Chan, J., Ghose, A., and Han, S. P. (2015). Battle of the channels: The impact of tablets on digital commerce. Management Science.
  26. Xu, Q., Erman, J., Gerber, A., Mao, Z., Pang, J., and Venkataraman, S. (2011). Identifying diverse usage behaviors of smartphone apps. In Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, IMC 7811, pages 329-344.
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Paper Citation


in Harvard Style

Finley B., Soikkeli T. and Kilkki K. (2016). Mobile Application Usage Concentration in a Multidevice World . In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 6: WINSYS, (ICETE 2016) ISBN 978-989-758-196-0, pages 40-51. DOI: 10.5220/0005964000400051


in Bibtex Style

@conference{winsys16,
author={Benjamin Finley and Tapio Soikkeli and Kalevi Kilkki},
title={Mobile Application Usage Concentration in a Multidevice World},
booktitle={Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 6: WINSYS, (ICETE 2016)},
year={2016},
pages={40-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005964000400051},
isbn={978-989-758-196-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications - Volume 6: WINSYS, (ICETE 2016)
TI - Mobile Application Usage Concentration in a Multidevice World
SN - 978-989-758-196-0
AU - Finley B.
AU - Soikkeli T.
AU - Kilkki K.
PY - 2016
SP - 40
EP - 51
DO - 10.5220/0005964000400051