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Studying mobile learning – the use of electronic devices (i.e. cellphone and tablets) to engage in learning across multiple contexts via connection to peers, media, experts and the larger world is a relatively new academic enterprise. This study analyzes the influencing factors of mobile learning (M-learning) motivation based on the time continuum model of motivation (TCMM).

The study uses structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to verify relationships between mobile learning motivation, attitude, need, stimulation, emotion, ability and reinforcement. Justification for the use of both methods lies in the complementarity relationships that existed between the variables and research methodologies. The sample contains 560 mobile learners' feedback.

Results show that attitude, need, emotion, ability and reinforcement are important factors to enhance mobile learning motivation, while stimulation is not.

This work highlights the importance of training for app designers on how to design an M-learning App with high learning motivation by paying prior attention to learning content, teaching team and online learning communities.

This study proposes three precise solutions (scholars, managers and practitioners) to improve learning motivation based on the categorization of mobile learners.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0226.

The purpose of this paper is to examine how user-perceived video quality, measured by computer assisted web survey, interacts with content richness (CR) and interactivity (IA). It also analyzes how those internet protocol television (IPTV) idiosyncrasies impacts audience perceptions (perceived usefulness (PU) and perceived resistance (PR)) and actual subscriptions.

A computer assisted web survey was administered in Korea to collect data. A structural equation model and Z-test analyses were conducted to address the research hypotheses.

User-perceived video quality influences audience perceptions (PU and PR) through interaction with CR and IA. Subscriber perceptions have a causal relationship with IPTV subscriptions.

Balanced improvement of the IPTV value chain, from content to technical (platform, network, and terminal) perspectives, are necessary for IPTV diffusion. Also, IPTV providers should establish the diffusion strategies, minimizing the user-perceived restrictiveness as well as maximizing the PU.

This study shows computer assisted assessment of video quality can be applied to behavior science. The research model suggests PR, which has been relatively unnoticed is included in existing technology acceptance theories. The introduced antecedents of IPTV subscriptions can be referred to as key performance indicators regarding new media adoption studies.