Title: Does self-management of learning matter? Exploring mobile learning continuance from a valence framework perspective

Authors: Yuangao Chen; Shuiqing Yang; Shuai Zhang; Jianrong Yao

Addresses: School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, No. 18, XueYuan Street, XiaSha Higher Education Zone, Hangzhou City 310018, China ' School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, No. 18, XueYuan Street, XiaSha Higher Education Zone, Hangzhou City 310018, China ' School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, No. 18, XueYuan Street, XiaSha Higher Education Zone, Hangzhou City 310018, China ' School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, No. 18, XueYuan Street, XiaSha Higher Education Zone, Hangzhou City 310018, China

Abstract: Drawing on the valence framework, the present study intends to explore factors that influence college students' mobile learning (m-learning) continuance behaviours by focusing students' self-management of learning and learning utilities. The structural equation modelling (SEM) method was employed to analysis the proposed research model by utilising data collected from 379 m-learning users of a leading online education platform in China. The results indicated that negative learning utilities including mobile techno-exhausting and anxiety negatively affect m-learning continuance, while positive learning utilities including hedonic learning and fragmented learning positively affect m-learning continuance. More importantly, self-management of learning affects mobile learning continuance through a dual mechanism by decreasing the negative utilities (e.g., mobile techno-exhausting) and increasing the positive utilities (e.g., hedonic learning and fragmented learning). The theoretical and practical implications for m-learning instructors and platform service providers are discussed.

Keywords: mobile learning; m-learning; self-management of learning; fragmented learning; valence framework; mobile techno-exhaustion; continuance.

DOI: 10.1504/IJMC.2023.134028

International Journal of Mobile Communications, 2023 Vol.22 No.4, pp.429 - 448

Received: 05 Jan 2021
Accepted: 19 Jan 2022
Published online: 10 Oct 2023 *

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