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Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance

The current study set out to understand the factors that explain working adults' microlearning usage intentions using the Decomposed Theory of Planned Behaviour (DTPB). Specifically, the authors were interested in differences, if any, in the factors that explained microlearning acceptance acros...

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Autores principales: Puah, Shermain, Bin Mohmad Khalid, Muhammad Iskandar Shah, Looi, Chee Kit, Khor, Ean Teng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666600/
https://www.ncbi.nlm.nih.gov/pubmed/34912272
http://dx.doi.org/10.3389/fpsyg.2021.759181
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author Puah, Shermain
Bin Mohmad Khalid, Muhammad Iskandar Shah
Looi, Chee Kit
Khor, Ean Teng
author_facet Puah, Shermain
Bin Mohmad Khalid, Muhammad Iskandar Shah
Looi, Chee Kit
Khor, Ean Teng
author_sort Puah, Shermain
collection PubMed
description The current study set out to understand the factors that explain working adults' microlearning usage intentions using the Decomposed Theory of Planned Behaviour (DTPB). Specifically, the authors were interested in differences, if any, in the factors that explained microlearning acceptance across gender, age and proficiency in technology. 628 working adults gave their responses to a 46-item, self-rated, 5-point Likert scale developed to measure 12 constructs of the DTPB model. Results of this study revealed that a 12-factor model was valid in explaining microlearning usage intentions of all working adults, regardless of demographic differences. Tests for measurement invariance showed support for invariance in model structure (configural invariance), factor loadings (metric invariance), item intercepts (scalar invariance), and item residuals (strict invariance) between males and females, between working adults below 40 years and above 40 years, and between working adults with lower technology proficiency and higher technology proficiency levels. While measurement invariance existed in the data, structural invariance was only found across gender, not age and technology proficiency. We then assessed latent mean differences and structural path differences across groups. Our findings suggest that a tailored approach to encourage the use of microlearning is needed to suit different demographics of working adults. The current study discusses the implications of the findings on the use and adoption of microlearning and proposes future research possibilities.
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spelling pubmed-86666002021-12-14 Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance Puah, Shermain Bin Mohmad Khalid, Muhammad Iskandar Shah Looi, Chee Kit Khor, Ean Teng Front Psychol Psychology The current study set out to understand the factors that explain working adults' microlearning usage intentions using the Decomposed Theory of Planned Behaviour (DTPB). Specifically, the authors were interested in differences, if any, in the factors that explained microlearning acceptance across gender, age and proficiency in technology. 628 working adults gave their responses to a 46-item, self-rated, 5-point Likert scale developed to measure 12 constructs of the DTPB model. Results of this study revealed that a 12-factor model was valid in explaining microlearning usage intentions of all working adults, regardless of demographic differences. Tests for measurement invariance showed support for invariance in model structure (configural invariance), factor loadings (metric invariance), item intercepts (scalar invariance), and item residuals (strict invariance) between males and females, between working adults below 40 years and above 40 years, and between working adults with lower technology proficiency and higher technology proficiency levels. While measurement invariance existed in the data, structural invariance was only found across gender, not age and technology proficiency. We then assessed latent mean differences and structural path differences across groups. Our findings suggest that a tailored approach to encourage the use of microlearning is needed to suit different demographics of working adults. The current study discusses the implications of the findings on the use and adoption of microlearning and proposes future research possibilities. Frontiers Media S.A. 2021-11-29 /pmc/articles/PMC8666600/ /pubmed/34912272 http://dx.doi.org/10.3389/fpsyg.2021.759181 Text en Copyright © 2021 Puah, Bin Mohmad Khalid, Looi and Khor. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Puah, Shermain
Bin Mohmad Khalid, Muhammad Iskandar Shah
Looi, Chee Kit
Khor, Ean Teng
Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance
title Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance
title_full Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance
title_fullStr Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance
title_full_unstemmed Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance
title_short Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance
title_sort working adults' intentions to participate in microlearning: assessing for measurement invariance and structural invariance
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666600/
https://www.ncbi.nlm.nih.gov/pubmed/34912272
http://dx.doi.org/10.3389/fpsyg.2021.759181
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