Research Paper

How Users Search the Mobile Web: A Model for Understanding the Impact of Motivation and Context on Search Behaviors

  • Dan Wu ,
  • Man Zhu & Aihua Ran
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  • School of Information Management, Wuhan University, Wuhan 430072, China

Received date: 2016-01-03

  Revised date: 2016-03-02

  Online published: 2016-03-03

Supported by

This work is jointly supported by the Wuhan International Science and Technology Cooperation Fund (Grant No.: 2015030809020371) and the Wuhan University Youth Fund of Humanities and Social Sciences.

Abstract

Purpose: This study explores how search motivation and context influence mobile Web search behaviors.

Design/methodology/approach: We studied 30 experienced mobile Web users via questionnaires, semi-structured interviews, and an online diary tool that participants used to record their daily search activities. SQLite Developer was used to extract data from the users' phone logs for correlation analysis in Statistical Product and Service Solutions (SPSS).

Findings: One quarter of mobile search sessions were driven by two or more search motivations. It was especially difficult to distinguish curiosity from time killing in particular user reporting. Multi-dimensional contexts and motivations influenced mobile search behaviors, and among the context dimensions, gender, place, activities they engaged in while searching, task importance, portal, and interpersonal relations (whether accompanied or alone when searching) correlated with each other.

Research limitations: The sample was comprised entirely of college students, so our findings may not generalize to other populations. More participants and longer experimental duration will improve the accuracy and objectivity of the research.

Practical implications: Motivation analysis and search context recognition can help mobile service providers design applications and services for particular mobile contexts and usages.

Originality/value: Most current research focuses on specific contexts, such as studies on place, or other contextual influences on mobile search, and lacks a systematic analysis of mobile search context. Based on analysis of the impact of mobile search motivations and search context on search behaviors, we built a multi-dimensional model of mobile search behaviors.


http://ir.las.ac.cn/handle/12502/8481

Cite this article

Dan Wu , Man Zhu & Aihua Ran . How Users Search the Mobile Web: A Model for Understanding the Impact of Motivation and Context on Search Behaviors[J]. Journal of Data and Information Science, 2016 , 1(1) : 98 -122 . DOI: 10.20309/jdis.201608

References

Bilandzic, M., & Foth, M. (2013). Libraries as coworking spaces understanding user motivations and perceived barriers to social learning. Library Hi Tech, 31(2): 254-273.
Church, K., & Oliver, N. (2011).Understanding mobile web and mobile search use in today's dynamic mobile landscape. In Proceedings of Mobile HCI 2011 Stockholm, 13th International Conference on Human Computer Interaction with Mobile Devices & Services (pp. 67-76). Stockholm, Sweden: Swedish Institute of Computer Science.
Ciampa, K. (2014). Learning in a mobile age: An investigation of student motivation. Journal of Computer Assisted Learning, 30(1): 82-96.
Chang, J., & Yang, J.M. (2009). An empirical study on the relationship among baidupedia users'participating behavior and motivations (in Chinese). Studies in Science of Science, 27(8): 1213-1219.
Gasimov, A., Magagna, F., & Sutanto, J. (2010). CAMB: Context-aware mobile browser. Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (pp. 22:1- 22:5). Limassol, Cyprus: Cyprus University of Technology and Frederick University.
Huizenga, J., Admiraal, W., Akkerman, S., & ten Dam, G. (2009). Mobile game-based learning in secondary education: Engagement, motivation, and learning in a mobile city game. Journal of Computer Assisted Learning, 25(4): 332-344.
Kamvar, M., &Baluja, S. (2006). A large scale study of wireless search behavior: Google mobile search. In Proceedings of CHI 2006 Montréal, Special Interest Group on Computer-Human Interaction Conference (pp. 701-709). Montréal, Canada: Convention Center.
Kim, Y., & Adler, M. (2015). Social scientists' data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories. International Journal of Information Management, 35(4): 408-418.
Kiseleva, J. (2015). Using contextual information to understand searching and browsing behavior. In Proceedings of SIGIR'15 Santiago, 38th Special Interest Group on Information Retrieval Conference (pp. 1059). Santiago, Chile: Association for Computing Machinery.
Kong, W. Z., Li, R., Jie, L., Zhang, A., Chang, Y., & Allan J. (2015). Predicting search intent based on pre-search context. In Proceedings of SIGIR'15 Santiago, 38th Special Interest Group on Information Retrieval Conference (pp. 503-512). Santiago, Chile: Association for Computing Machinery.
Liu, X., & Wu, W. (2015). Learning context-aware latent representations for context-aware collaborative filtering. In Proceedings of SIGIR'15 Santiago, 38th Special Interest Group on Information Retrieval Conference (pp. 887-890). Santiago, Chile: Association for Computing Machinery.
Malhotra, Y., Galletta, D.F., & Kirsch, L.J. (2008). How endogenous motivations influence user intentions: beyond the dichotomy of extrinsic and intrinsic user motivations. Journal of Management Information Systems, 25(1): 267-300.
Park, E., & Ohm, J. (2014). Factors influencing users' employment of mobile map services. Telematics and Informatics, 31(2): 253.
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