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Modelling searching on academic social networking sites: a focus on learning outcomes

Dan Wu (School of Information Management, Wuhan University, Wuhan, China and Human-Computer Interaction and User Behavior Research Center, Wuhan University, Wuhan, China)
Liuxing Lu (School of Economics and Management, South China Normal University, Guangzhou, China and Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Foshan, China)
Lei Cheng (Skyworth Group Limited, Shenzhen, China)

The Electronic Library

ISSN: 0264-0473

Article publication date: 10 May 2022

Issue publication date: 13 May 2022




This paper aims to establish a theoretical search model on academic social networking sites (ASNSs).


Based on the characteristics of ASNSs and a previous extended sense-making model, this paper first presented an initial model of searching on ASNSs. Next, an online survey was conducted on ResearchGate to understand the search processes and outcomes with the help of a survey questionnaire. In total, 359 participants from 70 countries participated in this online survey. The survey results provided a basis for modifying the initial model.


Results showed that the theoretical model of searching on ASNSs included motives for searching on ASNSs, identification of needs, search triggered by information needs, search triggered by social needs and outcomes. The search triggered by information needs was significantly positively correlated with learning outcomes. Besides learning outcomes, searching on ASNSs could help user amplify their social networks and promote research dissemination.

Practical implications

Understanding users’ search habits and knowledge acquisition can provide insights for ASNSs to design an interface to support searching and enhance learning. Moreover, the proposed model can help users recognize their knowledge status and learning effects and improve their learning efficiency.


This paper contributes to establishing a theoretical model to understand users’ search process and outcomes on ASNSs.



This work is supported by the National Natural Science Foundation of China (No. 71673204).


Wu, D., Lu, L. and Cheng, L. (2022), "Modelling searching on academic social networking sites: a focus on learning outcomes", The Electronic Library, Vol. 40 No. 3, pp. 291-310.



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