Analysis of Log File Data to Understand Mobile Service Context and Usage Patterns
DOI:
https://doi.org/10.9781/ijimai.2013.232Keywords:
Analysis, Mobile Services, Human-Computer Interaction (HCI)Abstract
Several mobile acceptance models exist today that focus on user interface handling and usage frequency evaluation. Since mobile applications reach much deeper into everyday life, it is however important to better consider user behaviour for the service evaluation. In this paper we introduce the Behaviour Assessment Model (BAM), which is designed to gaining insights about how well services enable, enhance and replace human activities. More specifically, the basic columns of the evaluation framework concentrate on (1) service actuation in relation to the current user context, (2) the balance between service usage effort and benefit, and (3) the degree to which community knowledge can be exploited. The evaluation is guided by a process model that specifies individual steps of data capturing, aggregation, and final assessment. The BAM helps to gain stronger insights regarding characteristic usage hotspots, frequent usage patterns, and leveraging of networking effects showing more realistically the strengths and weaknesses of mobile servicesDownloads
References
[1] U.-D. Reips, S. Stieger: Scientific LogAnalyzer: A Web-based tool for analyses of server log files in psychological research. Behavior Research Methods, Instruments, & Computers, 36, pp. 304-311 (2004)
[2] J.C. Cassady, R.E. Johnson: Cognitive Test Anxiety and Academic Performance. In: Contemporary Educational Psychology 27, pp. 270-- 295 (2002)
[3] UserTesting.com - Low Cost Usability Testing, http://www.usertesting.com
[4] Morae usability testing tools from TechSmith, http://www.techsmith.com
[5] M. Raento, A. Oulasvirta, R. Petit, H. Toivonen: ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications. In: IEEE Pervasive Computing 4, 2, pp. 51-59 (2005)
[6] K.L. Jensen: RECON: Capturing Mobile and Ubiquitous Interaction in Real Contexts. In: Proceedings of MobileHCI 2009, Bonn, Germany. (2009).
[7] J. Froehlich, M.Y. Chen, S. Consolvo, B. Harrison, J.A. Landay: MyExperience: A System for In situ Tracing and Capturing of User Feedback on Mobile Phones. In: MobiSys’07, June 11–14, 2007, pp. 57--70. ACM, San Juan, Puerto Rico, USA (2007)
[8] H. ter Hofte, R. Otte, A. Peddemors, I. Mulder, What's Your Lab Doing in My Pocket? Supporting Mobile Field Studies with SocioXensor. In: CSCW2006 Conference Supplement, Nov. 4-8, Banff, Alberta, Canada (2006)
[9] I. Pretel, A.B. Lago, Capturing Mobile Devices Interactions Minimizing the External Influence. In Proc. of UBICOMM, pp. 200-205 (2011).
[10] Ajzen, Icek (1 December 1991). "The theory of planned behavior". Organizational Behavior and Human Decision Processes 50 (2): 179–211.
[11] M. Colbert, A diary study on rendezvousing: Implications for positionaware computing and communications for the general public, In Proc. of Group Conference (2001)
[12] B. Klein, J. Perez, C. Guggenmos, O. Pihlajamaa, I. Heino, & J. Ser (2012). Social acceptance and usage experiences from a mobile location-aware service environment. In J. Ser et al. (Eds.), Mobile lightweight wireless systems (Vol. 81, p. 186-197). Springer Berlin
[13] E. M. Rogers, Diffusion of Innovations, Glencoe: Free Press (1962).
Downloads
Published
-
Abstract36
-
PDF30
Submit Article
Information
