An empirical study of behavioral intention model: Using learning and teaching styles as individual differences

Lu, Hsin-Ke; Lin, Peng-Chun; Chen, Alexander N.

doi:10.1080/09720529.2016.1177968

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…Another example is a study that focused on cognitive-oriented individual differences in order to integrate relevant features between users and systems. The results show that both the learning styles and teaching styles on using behaviors of e-Learning systems affected the intention [11].…”

Section: Research Frameworkmentioning

confidence: 82%

A TAM-Based Study of the Attitude towards Use Intention of Multimedia among School Teachers

Weng

Yang

et al. 2018

ASI

141

105

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Multimedia teaching materials are widely applied in various disciplines. More resources are provided by authorities to encourage elementary school teachers to use them. The resources provide an opportunity for teachers to share teaching resources for students. In this study, the technology acceptance model (TAM) was used as the basic model to explore the effects of the information technology (IT) environment on the perceived usefulness, perceived ease of use, and attitude towards using multimedia, and the relevance and influence of these attitudes on behavioral intention. There are 2317 teachers in Chiayi county, and 460 participants were selected by stratified random sampling. The results showed that the ease of use of the multimedia material would enhance the intention to use. The attitude toward use also influences the intention to use.

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Section: Research Frameworkmentioning

confidence: 82%

A TAM-Based Study of the Attitude towards Use Intention of Multimedia among School Teachers

Weng

Yang

et al. 2018

ASI

141

105

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“…The authors found that learners showed better learning performance by choosing courses that aligned with their learning preferences. Lu et al (2017) investigated the impact of learning preference on learning satisfaction as a dimension in the Technology Acceptance Model. The authors found that different types of learning preferences had an impact on learners’ usability, usefulness, willingness to use.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results show that different dimensions in FSLSM have different effects on the relationship between every two of five factors. Lu et al (2017) conducted a similar study, using TAM (Technology Acceptance Model). Their major research question focused on the influence of learning preference types on TAM.…”

Section: The Interaction Between Learning Preference and Other Factors In A Learning Contextmentioning

confidence: 99%

“…Students with different learning preference types feel different experiences (Lu et al , 2017; Ramirez-Correa et al , 2017) and adopting learning preference in recommendation advance the acceptance (Dascalu et al , 2015). However, the above studies adopted surveys to judge the students’ satisfaction at the end of the course (Chang et al , 2015).…”

Section: The Interaction Between Learning Preference and Other Factors In A Learning Contextmentioning

confidence: 99%

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Learning preference: development in smart learning environments

Zhang

Hung

et al. 2021

IDD

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Purpose The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods. Design/methodology/approach This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research. Findings After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments. Research limitations/implications This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account. Originality/value The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

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“…Intention to use is derived from behavioral intention and is defined as "the strength of one's intention to perform a specified behavior" (Fishbein & Ajzen, 1975). Research studies on TAM have shown that behavioral intention has a positive effect on behavior (Lu, Lin, & Chen, 2017). noted that usage is significantly correlated to behavioral intention to use, and that behavioral intention is a significant determinant of user behavior in technology acceptance.…”

Section: Technology Acceptance -Behavioral Intention To Usementioning

confidence: 99%

A Proposed Framework to Understand the Intrinsic Motivation Factors on University Students’ Behavioral Intention to Use a Mobile Application for Learning

Shroff

Keyes

2017

JITE:Research

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Aim/Purpose: By integrating a motivational perspective into the Technology Acceptance Model, the goal of this study is to empirically test the causal relationship of intrinsic motivational factors on students’ behavioral intention to use (BIU) a mobile application for learning. Background: Although the Technology Acceptance Model is a significant model, it largely remains incomplete as it does not take into consideration the motivation factors and/or outside influences in the adoption of new technology. Methodology: A Mobile Application Motivation Instrument (MAMI) was developed from a comprehensive review of literature on intrinsic motivation and verified using a formalized card sorting procedure. Four intrinsic motivation scales were developed: perceived competence (COM), perceived challenge (CHA), perceived choice (CHO), and perceived interest (INT). Consequently, a scale to assess students’ behavioral intention (BIU) to use mobile applications was developed using existing scales from prior TAM instruments. Contribution: Incorporating the motivational factors into TAM may provide better explanation and prediction of student acceptance and usage of mobile applications. A potential contribution of this study is the development of a reliable and valid instrument that could be further used by a growing community of researchers, instructional designers, and instructors. Findings: Data were collected from 193 participants to test the causal relationship of perceived competence (COM), perceived challenge (CHA), perceived choice (CHO), and perceived interest (INT) on students’ behavioral intention to use (BIU) a mobile application, using a structural equation modeling approach. The structural path model indicated that perceived competence (COM), perceived challenge (CHA), perceived choice (CHO), and perceived interest (INT) had a significant influence on students’ behavioral intention to use (BIU) a mobile application for learning. Implications of this study are important for researchers and educational practitioners. Future Research: One environmental dimension, understudied but with likely implications for intrinsic motivation, is the social environment.

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An empirical study of behavioral intention model: Using learning and teaching styles as individual differences

Cited by 9 publications

References 20 publications

A TAM-Based Study of the Attitude towards Use Intention of Multimedia among School Teachers

A TAM-Based Study of the Attitude towards Use Intention of Multimedia among School Teachers

Learning preference: development in smart learning environments

A Proposed Framework to Understand the Intrinsic Motivation Factors on University Students’ Behavioral Intention to Use a Mobile Application for Learning

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