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Examining the Factors Related to Learners' Intention and Usage Continuity of Online Learning

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Title: Examining the Factors Related to Learners' Intention and Usage Continuity of Online Learning
Language: English
Authors: Abdulvahap Sönmez (ORCID 0000-0003-4524-8790), Nilgün Özdamar (ORCID 0000-0002-0634-5734)
Source: Open Praxis. 2024 16(2):195-207.
Availability: International Council for Open and Distance Education. Lilleakerveien 23, 0283 Oslo, Norway. Tel: +47-22-06-26-30; Fax: +47-22-06-26-31; e-mail: icde@icde.org; Web site: https://openpraxis.org/
Peer Reviewed: Y
Page Count: 13
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Adult Education
Descriptors: Intention, Electronic Learning, Open Education, Student Behavior, Adjustment (to Environment), Personality Traits, Foreign Countries, Educational Technology, Technology Uses in Education, Use Studies, Higher Education, Continuing Education, Correlation
Geographic Terms: Turkey
ISSN: 1369-9997
2304-070X
Abstract: The aim of this study is to examine the variables that influence the learners' intention of using online learning platforms and usage behavior continuity to evaluate relationships between variables with a model proposal and to determine the predictive power of the model. Accordingly, a sustained technology acceptance model (STAM) was developed to identify the factors that influence learners' intention and usage behavior of online learning. The sample of this study consisted of learners registered in the Anadolu University Open Education System. As a result of the factor analyses, a 42-item measurement tool consisting of eleven factors was developed. Structural equation modeling revealed that the eleven-structured model explained 49.8% of perceived usefulness, 30.3% of perceived ease of use, 39.4% of usage intention, and 52.4% of usage behavior continuity, and all 12 proposed hypotheses were supported. The analysis to determine the predictive power of the model revealed that it has a strong predictive power. The developed model is believed to be efficient in determining learners' expectations, supporting their adaptation to the online learning environment, and ensuring their continued usage.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1427405
Database: ERIC
FullText Text:
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  Data: Examining the Factors Related to Learners' Intention and Usage Continuity of Online Learning
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  Data: <searchLink fieldCode="AR" term="%22Abdulvahap+Sönmez%22">Abdulvahap Sönmez</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4524-8790">0000-0003-4524-8790</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nilgün+Özdamar%22">Nilgün Özdamar</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0634-5734">0000-0002-0634-5734</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Open+Praxis%22"><i>Open Praxis</i></searchLink>. 2024 16(2):195-207.
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  Data: International Council for Open and Distance Education. Lilleakerveien 23, 0283 Oslo, Norway. Tel: +47-22-06-26-30; Fax: +47-22-06-26-31; e-mail: icde@icde.org; Web site: https://openpraxis.org/
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  Data: 13
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  Data: The aim of this study is to examine the variables that influence the learners' intention of using online learning platforms and usage behavior continuity to evaluate relationships between variables with a model proposal and to determine the predictive power of the model. Accordingly, a sustained technology acceptance model (STAM) was developed to identify the factors that influence learners' intention and usage behavior of online learning. The sample of this study consisted of learners registered in the Anadolu University Open Education System. As a result of the factor analyses, a 42-item measurement tool consisting of eleven factors was developed. Structural equation modeling revealed that the eleven-structured model explained 49.8% of perceived usefulness, 30.3% of perceived ease of use, 39.4% of usage intention, and 52.4% of usage behavior continuity, and all 12 proposed hypotheses were supported. The analysis to determine the predictive power of the model revealed that it has a strong predictive power. The developed model is believed to be efficient in determining learners' expectations, supporting their adaptation to the online learning environment, and ensuring their continued usage.
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      – Text: English
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        PageCount: 13
        StartPage: 195
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      – SubjectFull: Intention
        Type: general
      – SubjectFull: Electronic Learning
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      – SubjectFull: Open Education
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      – SubjectFull: Student Behavior
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      – SubjectFull: Personality Traits
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Educational Technology
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Use Studies
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      – SubjectFull: Continuing Education
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