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Exploring the Factors That Influence Students Acceptance and Use of Online Learning Technology in Higher Education Institutes of Khyber Pakhtunkhwa Pakistan

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Title: Exploring the Factors That Influence Students Acceptance and Use of Online Learning Technology in Higher Education Institutes of Khyber Pakhtunkhwa Pakistan
Language: English
Authors: Abdus Saboor, Muhammad Zahid Khan, Muhammad Nawaz Khan, Tariq Hussain (ORCID 0000-0002-4761-0346), Razaz Waheeb Attar, Mrim M. Alnfiai, Nabil Sharaf Almalki
Source: Education and Information Technologies. 2025 30(13):18433-18460.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 28
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Online Courses, Technology Uses in Education, Educational Technology, College Students, Expectation, Social Influences, Affordances, Intention, Student Behavior, Foreign Countries
Geographic Terms: Pakistan
DOI: 10.1007/s10639-025-13543-3
ISSN: 1360-2357
1573-7608
Abstract: Online learning technologies have turned into an essential part of education, particularly in this cutting-edge era of ICT. These technologies are becoming more and more popular in increasing educational access in impoverished nations. The ongoing investigation looks into the variables impacting students' use of online learning technologies in universities, particularly considering the circumstances of Khyber Pakhtunkhwa (KP), Pakistan. We use the Structural Equation Modeling (SEM) and Unified Theory of Acceptance and Use of Technology (UTAUT) paradigm model, which provided a strong analytical background for understanding the dynamics of technology acceptance and usage within this situation. This study examined the UTAUT model among 410 students from KP's higher education institutions. A strong correlation was found between constructs, supporting 10 of 16 hypotheses. Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC) and Attitude (At) explained 73% of Behavioral Intention (BI) variance. These variables also accounted for 63% of At variance. BI and At jointly explained 73% of Use Behavior (UB) variance. The significant relationships in the study include PE having a significant effect on BI ([beta] = 0.347, p-value = ***), SI affecting BI ([beta] = 0.130, p-value = 0.006) and At significantly influencing BI ([beta] = 0.312, p-value = ***). Additionally, PE and EE both significantly impact At, with [beta] values of 0.339 and 0.367, respectively (both p-values = ***). At also significantly predicts UB ([beta] = 0.229, p-value = 0.002), while BI has a strong effect on UB ([beta] = 0.668, p-value = ***). The analysis reveals PE has a weak effect on At (f[superscript 2] = 0.13) and a moderate effect on BI (f[superscript 2] = 0.17), while both At (f[superscript 2] = 0.19) and BI (f[superscript 2] = 0.15) have moderate effects on UB. The study's conclusion shows that the success of online learning technologies positively impacts the students' actual use behavior in higher education institutions. This suggests a positive correlation between the usage of online learning software tools and the achievement of education goals. By recognizing and understanding these perceptions, we recommend that facilitators and institutes make up-to-date assessments to improve student online learning involvement. The study adds valuable knowledge regarding the elements impacting students' adoption and acceptance of online learning technologies at universities. The study emphasizes the importance of fostering a supportive environment for integrating online learning tools in educational settings.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1480842
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Abdus+Saboor%22">Abdus Saboor</searchLink><br /><searchLink fieldCode="AR" term="%22Muhammad+Zahid+Khan%22">Muhammad Zahid Khan</searchLink><br /><searchLink fieldCode="AR" term="%22Muhammad+Nawaz+Khan%22">Muhammad Nawaz Khan</searchLink><br /><searchLink fieldCode="AR" term="%22Tariq+Hussain%22">Tariq Hussain</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-4761-0346">0000-0002-4761-0346</externalLink>)<br /><searchLink fieldCode="AR" term="%22Razaz+Waheeb+Attar%22">Razaz Waheeb Attar</searchLink><br /><searchLink fieldCode="AR" term="%22Mrim+M%2E+Alnfiai%22">Mrim M. Alnfiai</searchLink><br /><searchLink fieldCode="AR" term="%22Nabil+Sharaf+Almalki%22">Nabil Sharaf Almalki</searchLink>
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 10.1007/s10639-025-13543-3
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  Data: Online learning technologies have turned into an essential part of education, particularly in this cutting-edge era of ICT. These technologies are becoming more and more popular in increasing educational access in impoverished nations. The ongoing investigation looks into the variables impacting students' use of online learning technologies in universities, particularly considering the circumstances of Khyber Pakhtunkhwa (KP), Pakistan. We use the Structural Equation Modeling (SEM) and Unified Theory of Acceptance and Use of Technology (UTAUT) paradigm model, which provided a strong analytical background for understanding the dynamics of technology acceptance and usage within this situation. This study examined the UTAUT model among 410 students from KP's higher education institutions. A strong correlation was found between constructs, supporting 10 of 16 hypotheses. Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC) and Attitude (At) explained 73% of Behavioral Intention (BI) variance. These variables also accounted for 63% of At variance. BI and At jointly explained 73% of Use Behavior (UB) variance. The significant relationships in the study include PE having a significant effect on BI ([beta] = 0.347, p-value = ***), SI affecting BI ([beta] = 0.130, p-value = 0.006) and At significantly influencing BI ([beta] = 0.312, p-value = ***). Additionally, PE and EE both significantly impact At, with [beta] values of 0.339 and 0.367, respectively (both p-values = ***). At also significantly predicts UB ([beta] = 0.229, p-value = 0.002), while BI has a strong effect on UB ([beta] = 0.668, p-value = ***). The analysis reveals PE has a weak effect on At (f[superscript 2] = 0.13) and a moderate effect on BI (f[superscript 2] = 0.17), while both At (f[superscript 2] = 0.19) and BI (f[superscript 2] = 0.15) have moderate effects on UB. The study's conclusion shows that the success of online learning technologies positively impacts the students' actual use behavior in higher education institutions. This suggests a positive correlation between the usage of online learning software tools and the achievement of education goals. By recognizing and understanding these perceptions, we recommend that facilitators and institutes make up-to-date assessments to improve student online learning involvement. The study adds valuable knowledge regarding the elements impacting students' adoption and acceptance of online learning technologies at universities. The study emphasizes the importance of fostering a supportive environment for integrating online learning tools in educational settings.
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