Mobile Search Behavious:   An In-depth Analysis based on Contexts, APPs, and Devices

Wu, Dan; Liang, Shaobo

doi:10.2200/s00831ed1v01y201802icr063

Cited by 5 publications

(3 citation statements)

References 105 publications

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“…Consequently, readers who seek implementation details of individual algorithms and systems should only use this book as a jumping board for diving into specific relevant literature for details. Another unfortunate omission is that Apps are viewed in this book only as document content for searching, but they can be one important context factor because many mobile searches are happening within certain Apps (Wu & Liang, ).…”

Section: Discussionmentioning

confidence: 99%

Mobile Information Retrieval. FabioCrestani, StefanoMizzaro, and IvanScagnetto. Cham, Switzerland: Springer, 2017. 110 pp. $54.99 (softcover). (ISBN 9783319607764)

2018

Asso for Info Science & Tech

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Section: Discussionmentioning

confidence: 99%

Mobile Information Retrieval. FabioCrestani, StefanoMizzaro, and IvanScagnetto. Cham, Switzerland: Springer, 2017. 110 pp. $54.99 (softcover). (ISBN 9783319607764)

2018

Asso for Info Science & Tech

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“…In existing studies using user experiments, 20-30 participants are considered appropriate [5,13,51,52]. Particularly for long-term experiments, this number of participants can produce enough rich research data.…”

Section: Participants' Recruitmentmentioning

confidence: 99%

Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches

Liang

Dong

2022

Sustainability

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Mobile searches have become the main channel for people to search for information, and mobile searches have received attention in the field of information-seeking behavior. Especially as users use various apps to search network information, the app-switching behaviors in mobile searches have also attracted scholars’ attention in recent years. Research on app-switching behaviors in mobile searches can help to further understand users’ search motivations, evaluate search results, and improve users’ mobile search experiences. This study recruited participants (n = 30) and conducted a 15-day user experiment. This study collected all participants’ mobile phone log data during the experiment and identified the app-switching behaviors in mobile searches through a log collection tool. This study aimed to discover the app-switching behavior paths and patterns in mobile searches. Firstly, the basic characteristics of app-switching behaviors in mobile searches were analyzed, as were the app-switching paths in mobile searches from the perspective of switching probability between apps. Then, the different behaviors in mobile search sessions were identified and app-switching behavior patterns were put forward. These behavior patterns summarize user behavior changes in mobile search sessions. This paper focused on analyzing app-switching behavior paths under different patterns and found apparent differences in app-switching behavior paths. This study examined mobile search behavior from the perspective of app-switching. The research of this paper can help to better understand the relationship between users’ mobile search behaviors and app interactions and is an excellent supplement to the analysis of mobile search behaviors.

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“…Through the above research, “cross-app” or “app transition” refers to the transitions from one app to another in an interaction session when people use mobile devices. Wu et al (2018) studied the users' mobile search behavior from an app interaction perspective and found that users would switch from search engine apps to other apps in a mobile search session. They also believed that the cross-app behavior in mobile search is one behavior phenomenon that needs attention.…”

Section: Related Workmentioning

confidence: 99%

Characterizing and predicting the cross-app behavior in mobile search

Liang

2021

AJIM

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PurposeThis paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.Design/methodology/approachThis paper presents a longitudinal user experiment in 15 days. This paper recruited 30 participants and collected their mobile phone log data in the whole experiment. The structured diary method was also used to collect contextual information in mobile search.FindingsThis study focused on the users' cross-app behavior in mobile search and described cross-app behavior's basic characteristics. Usage of communication app and tool apps could trigger more cross-app behavior in mobile search. The method of cross-app behavior prediction in the mobile search was proposed. Collecting users' more contextual information, such as search tasks, search motivation and other environmental information, can effectively improve the prediction accuracy of cross-app behavior in mobile search.Practical implicationsThe future research on cross-app behavior prediction should focus on context information in mobile search. Better prediction of cross-app behavior can reduce the users' interaction burden.Originality/valueThis paper contributes to research into cross-app behavior, especially in the mobile search research domain.

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Mobile Search Behavious: An In-depth Analysis based on Contexts, APPs, and Devices

Cited by 5 publications

References 105 publications

Mobile Information Retrieval. FabioCrestani, StefanoMizzaro, and IvanScagnetto. Cham, Switzerland: Springer, 2017. 110 pp. $54.99 (softcover). (ISBN 9783319607764)

Mobile Information Retrieval. FabioCrestani, StefanoMizzaro, and IvanScagnetto. Cham, Switzerland: Springer, 2017. 110 pp. $54.99 (softcover). (ISBN 9783319607764)

Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches

Characterizing and predicting the cross-app behavior in mobile search

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