THE STATE BANK OF VIETNAM MINISTRY OF EDUCATION AND TRAINING HO CHI MINH UNIVERSITY OF BANKING PHÙNG NGỌC VÂN ANH FACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITYFACTORS INFLUENCING STUDENTS’ BLENDED LEARNING ADOPTION: A CASE STUDY OF WESTERN SYDNEY INTERNATIONAL PARTNERSHIP PROGRAM AT UEH-ISB IN HO CHI MINH CITY
INTRODUCTION
Research problem statement
The information and communications technology sector is rapidly evolving, providing numerous opportunities for communication and multimedia delivery in universities (Wu et al., 2010) As a result, blended learning has emerged as the predominant teaching method in contemporary education.
Blended learning, introduced by universities in the late 1990s in the US and Canada, marks a significant advancement in higher education, representing a key aspect of digital transformation This model fosters an interactive learning environment through diverse delivery methods, enhancing student engagement and skill development Notably, blended learning mitigates barriers to interaction between professors and students in online sessions (Jusoff and Khodabandelou, 2009), while offering flexibility, depth of learning, and cost-effectiveness (Graham, 2006) It requires a restructured curriculum that promotes student initiative in online participation (Yin and Yuan, 2021) Researchers anticipate that blended learning will become the "new normal" in higher education (Norberg, Moskal, and Dziuban, 2011), a trend further accelerated by the COVID-19 pandemic (UNESCO).
Blended learning is gaining popularity globally but remains a relatively new concept in Vietnam, facing several challenges during implementation Many students struggle with this model due to limited prior experience with technology and a reliance on traditional teaching methods Without adequate support for self-directed learning, students may experience anxiety and poorer academic performance Even in structured programs, student engagement and online learning quality often fall short of expectations, leading to decreased teaching satisfaction and motivation Instructors, while possessing basic IT skills, often lack advanced capabilities necessary for effective blended learning Additionally, quality learning resources are scarce, and many institutions face barriers such as high technology costs, poor decision-making, and a lack of comprehensive strategies, hindering the development of successful blended learning models.
Prior empirical studies indicate that cultural and contextual factors can significantly influence research outcomes, particularly between developed and developing countries Notably, there is a lack of studies focusing on the determinants of blended learning adoption among students in Vietnam, especially in Ho Chi Minh City, creating challenges for local research and development It is crucial to examine the unique characteristics and target audiences of various educational programs to achieve accurate results As blended learning has become increasingly important, particularly following the COVID-19 pandemic, understanding the factors that affect its adoption in international partnership programs is vital for the effective implementation and advancement of this instructional approach to better serve student needs.
The research titled “Factors Influencing Students’ Blended Learning Adoption: A Case Study of Western Sydney International Partnership Program at UEH-ISB in Ho Chi Minh City” aims to provide valuable insights for future studies on blended learning It seeks to assist school management in positively shifting students’ perceptions of blended learning methods, enhancing their skills and adaptability, and promoting greater collaboration among students Ultimately, this research is expected to improve students' learning performance and satisfaction with the blended learning approach.
Research objectives
To examine and verify factors influencing students’ blended learning adoption at Western Sydney international partnership program
The first is to identify factors influencing students’ blended learning adoption at Western Sydney international partnership program
The second is to evaluate the influence of these factors on students’ blended learning adoption at Western Sydney international partnership program
The third is to provide the implications to improve the students’ blended learning adoption at Western Sydney international partnership program.
Research questions
Question 1: Which factors can influence students’ blended learning adoption at Western Sydney international partnership program?
Question 2: What are the impact levels of these factors on students’ blended learning adoption at Western Sydney international partnership program?
Question 3: What are the implications to improve students’ learning outcome in blended learning adoption at Western Sydney international partnership program?
Research subject and scope
Factors influencing students’ blended learning adoption: A case study of Western Sydney international partnership program at UEH-ISB in Ho Chi Minh City
Research space: Western Sydney international partnership program at UEH- ISB in Ho Chi Minh City
Research duration: The research will be conducted from November 2023 to June 2024, while the survey taking place from November 2023 to January 2024 The secondary data was collected from 2018 to 2023
Research population: 400 undergraduate students of Western Sydney international partnership program at UEH-ISB in Ho Chi Minh City.
Research methodology
This research employed qualitative methods to develop a conceptual model and measurement scale, utilizing secondary data from relevant literature In-depth interviews were conducted with three school management executives experienced in blended learning to identify additional factors influencing student adoption of blended learning and to refine any measurement items that could cause confusion Additionally, a focus group discussion with ten undergraduate students was organized to ensure that the definitions of constructs and observed variables aligned with the perceptions of the target audience.
Primary data was gathered via an online survey using a Google Forms questionnaire distributed to students' emails The survey employed a five-point Likert scale to assess students' attitudes regarding the factors that affect their adoption of blended learning.
The collected data was analyzed using SPSS version 26.0 through various methods, including descriptive statistics, Cronbach’s Alpha reliability test, exploratory factor analysis, Pearson’s correlation coefficient, multiple linear regression, and One-way ANOVA.
Research contribution
The study organizes existing theories related to the research topic to pinpoint the key factors that may affect students' adoption of blended learning within the Western Sydney International Partnership Program.
The research seeks to re-evaluate the conceptual model proposed by previous studies and the author's recommendations Given the absence of studies examining the factors affecting students' adoption of blended learning in international partnership programs in Vietnam, this research is anticipated to serve as a valuable reference for future related studies.
The research develops a comprehensive scale for assessing students' blended learning adoption, integrating insights from previous studies while introducing new metrics to enhance the existing scale system in this area.
In competitive markets, institutions must effectively deliver high-quality education This study offers valuable insights for educators, administrators, and stakeholders on students' adoption of blended learning The findings will aid school management in enhancing quality assurance strategies to support blended learning Ensuring the quality of blended education is essential, as satisfied students tend to be more motivated, committed, and ultimately more successful learners compared to those who are dissatisfied.
Thesis structure
Chapter 1 offers an overview of the topic (the remaining problem and the necessity of conducting the research), research objectives and questions, research subject and scope, research methodology, research contribution, and research structure
Chapter 2 outlines key definitions and the theoretical framework related to the research topic, followed by a review of prior empirical studies in relevant fields This section aims to identify and address gaps in existing research that this study will explore.
This research presents a conceptual model along with hypotheses designed to evaluate the impact of specific variables on students' adoption of blended learning within the Western Sydney International Partnership Program.
Chapter 3 mentions the process of conducting the research, how to determine the sample size, the design of questionnaire, the scale construction (including the scale development process and the official research scale), and the data analysis methods applied in quantitative research
Chapter 4 presents and discusses the results of the data analysis using the Statistical Package for the Social Sciences (SPSS) statistical software The methods used for analyzing the collected data include descriptive statistics, Cronbach’s Alpha reliability test, exploratory factor analysis, Pearson’s correlation coefficient, multiple linear regression, and One-way ANOVA
Chapter 5 draws conclusions from the findings obtained through the research data analysis, then provides implications to enhance the students’ blended learning adoption, and indicates the limitations as well as the directions for future research
Chapter 1 offers a detailed examination of the research problem, highlighting the rationale behind the chosen topic It clearly defines the objectives and research questions, while also delineating the specific subject matter and scope of the study.
The chapter outlines the statistical methodologies for sampling and data analysis, detailing the analytical approach to be used in the study.
Finally, the chapter highlights the potential contributions of the research, both from a theoretical and practical standpoint Additionally, it introduces the overall structure and organization of the thesis.
LITERATURE REVIEW
Definition
A blended learning environment combines in-person interactions, where students participate in physical activities with teachers and peers, with online components that require independent completion of tasks using technological support and the Internet (Hung and Chou, 2015; Padilla Meléndez et al., 2013).
Blended learning is defined as an enriched, student-centered approach that integrates face-to-face interaction with information and communications technology, creating diverse learning experiences (2011) It addresses individual learning needs by combining the innovative aspects of online education with the interactive elements of traditional learning (Thorne, 2003).
Blended learning is an educational approach that integrates online resources and interactive opportunities with traditional face-to-face teaching methods (Lawless, 2019) According to Heinze et al (2004), this method combines various delivery techniques, teaching models, and learning styles, fostering open communication among all course participants.
Blended learning, as defined by Driscoll (2002), encompasses four key categories: integrating web-based technology to achieve educational objectives, merging diverse pedagogical strategies for optimal learning outcomes, combining instructional technology with traditional face-to-face training, and mixing various training methods and technologies to enhance work tasks This multifaceted approach aims to create a more effective learning environment.
Blended learning, as defined by No.12/2016/TT-BGDDT, integrates e-Learning with traditional teaching methods, where instructors and learners interact in person This approach aims to enhance training effectiveness and improve the overall quality of education.
Blended learning merges traditional face-to-face instruction with online education, offering a flexible program design This approach fosters a dynamic and active learning environment, where students engage in both online and classroom activities The integration of online and offline elements enhances the overall learning experience, making it more comprehensive and effective.
Blended learning empowers students to go beyond their teacher's materials, enabling them to explore a wide range of resources They can conduct library searches, engage in online discussions with peers, and utilize various digital platforms, including websites, search engines, portals, and blogs Additionally, students can access learning and tutorial software to enhance their educational experience (Tayebinik and Puteh, 2013).
Table 2.1 Proportion of Content Delivered Online
A lesson is considered blended or hybrid when 30% to 79% of its content is delivered online This approach to blended learning promotes a transition from teacher-centered to student-centered education, fostering a more engaging and interactive learning environment.
2.1.1.2 Different approaches to blended learning
Blended learning is more than just a combination of in-person and online education; it involves a complex interplay between the two modalities (Garrison and Vaughan, 2008) According to Carman (2005), five essential components are crucial for effective blended learning in corporate settings: motivating live events, independent online content, collaborative opportunities for peer learning, assessments for understanding progress, and timely reference materials to enhance knowledge retention and transfer.
Blended learning is defined as a method that prioritizes active learning through collaboration and the social construction of understanding, as noted by Rovai and Jordan (2004) Dzuiban, Hartman, and Moskal (2004) further elaborate on this by describing blended learning as a re-design of the instructional model, emphasizing its innovative approach to education.
- A transition from lecture-based to more student-oriented approach in which the students become active and interactive learners in both the face-to-face and the online components;
- Increases in interaction between student-teacher, student-student and student-resources;
- An integration in formative and summative assessment mechanisms According to David Nagel (2011), there are six models of blended learning:
- “Face-to-face driver” model, which involves the use of online learning by teachers in traditional classroom settings, as a form of remediation or supplementary instruction;
- “Rotation” model, which allows students to transition between online and traditional classroom instruction;
- “Flex” model, where the curriculum is primarily delivered via an online platform and teachers provide on-site support;
- “Online lab” approach, which involves the delivery of an online course in a physical class or computer laboratory
- “Self-blend”, which encourages students to choose the course they would like to take online by themselves to supplement their schools’ offerings
- “Online driver” model, where the courses are primarily delivered through online platforms, with physical facilities only used for external activities such as check-ins and similar functions
Blended learning is a broad concept that is further expanded upon by Alan Clarke (2004):
- Conventional lectures with instructional materials and visual aids are presented on the college’s intranet, which can be accessed by students
- Digital cameras are utilized to document real-world activities for the purpose of creating a portfolio of evidence
- This course provides students with the opportunity to submit all assignments electronically and receive feedback in the form of annotations
- Students will have the opportunity to communicate with tutors through email and video conferencing instead of in-person tutorials
- Simulations of laboratory experiments will be included as part of the conventional/science course
- A distance learning course will be offered with regular in-person meetings
Carman (2005) identifies five key principles for the implementation of learning through blended learning, including:
- Live Events, direct learning sessions that take place at the same time and location, or at the same time but in a different location
- Self-Paced Learning, a learning form that combines self-paced learning with the ability to learn at any time – from any location via online
- Collaboration, a combination of teacher collaboration and collaboration between study participants
- Assessment, the designer who come up with the blended learning scheme needs to know how to create tests and non-tests in both online and offline formats
- Performance Support Materials, materials are produced in digital format, making them available to learners both offline and online
To effectively implement blended learning in higher education, it is crucial to evaluate how e-learning can replicate the advantages of traditional lectures, assess the quality of e-content, and determine if the e-course serves a greater purpose Blended learning requires sophisticated teaching strategies, where regular monitoring of students' contributions and activities in the e-course, alongside face-to-face presentations, is essential for success.
Adoption refers to the choice made by individuals or organizations, encompassing acceptance, rejection, and the subsequent actions of implementation, discontinuance, or modification (Kee, 2017) This process unfolds at both individual and organizational levels before spreading throughout the broader system.
Adoption refers to the ongoing acceptance and use of a product, service, or idea Customers typically undergo a process of knowledge, persuasion, decision, and confirmation before making a purchase, as outlined by Rogers and Shoemaker (1971).
The adoption decision and its implementation are not always simultaneous; they can occur at different times during the adoption process (Reed et al., 1996).
In summary, adoption refers to how individuals or organizations integrate new concepts, methods, ideas, services, goods, or products into their routines This process generally follows a sequence of steps, starting with awareness of the innovation, moving to a decision on whether to adopt or reject it, and culminating in the implementation and use of the chosen innovation.
The process of adopting an innovation is governed by five fundamental principles:
- The characteristics of the innovation influence the adoption process
- The decision-making process begins when an individual or group considers implementing an innovation
- The likelihood of an innovation being adopted by an individual or group is contingent upon the individual’s or group’s adoptive characteristics
- The speed of adoption depends on whether an innovation is perceived positively or negatively by an individual or a group, as well as the level of acceptance of the innovation
- Individuals typically do not respond to an innovation at the same time or rate
Theoretical framework
2.2.1 Institutional blended learning adoption framework
Graham et al (2013) propose a tripartite framework for understanding the adoption of blended learning This framework consists of three key components: strategy, structure, and support
A successful blended learning approach requires a comprehensive strategy that includes clear institutional guidelines, the formation of advisory groups, and the allocation of essential resources and time By prioritizing these elements, institutions can effectively decide on the adoption and implementation of blended learning tailored to their specific objectives.
The structure component of blended learning encompasses the organizational elements that enhance the learning environment, focusing on technology, pedagogy, and administration Essential aspects include governance frameworks, integration models for combining technology with teaching methods, scheduling of blended learning sessions, and evaluation strategies to assess the success of blended learning initiatives.
The support component focuses on how institutions enhance faculty performance and maintain effective instructional design in blended learning environments It includes both technical and pedagogical support, offering training and resources to improve faculty members' technological skills and knowledge Furthermore, it may involve incentives to foster faculty engagement and commitment to blended learning practices.
To effectively adopt and implement blended learning, institutions must focus on the key elements of strategy, structure, and support These foundational frameworks offer essential guidance for integrating blended learning into educational practices, ensuring a successful transition to this innovative approach.
In addition to the framework proposed by Graham et al (2013), Porter et al
In 2016, a phased approach was proposed to help organizations navigate their transition towards effectively institutionalizing blended learning This approach encompasses progressive stages that focus on the development, implementation, and integration of blended learning practices within educational institutions.
In the awareness and exploration stage of blended learning, institutions lack standardized strategies, yet faculty members are increasingly recognizing the value of blended learning approaches Individual educators are motivated to experiment with these strategies in their classrooms, despite the absence of formal institutional policies This stage reflects a growing understanding of blended learning's potential, alongside minimal support for faculty to explore its implementation.
In the adoption and early implementation stage, institutions recognize blended learning as a key policy and begin to execute related initiatives This phase is marked by the introduction of innovative programs and methods that integrate blended learning into the curriculum The emphasis is on the initial application and exploration of blended learning practices.
In the mature implementation stage of blended learning, institutions have developed a robust structure and support system that includes clear governance, effective models, strategic scheduling, and evaluation mechanisms This comprehensive framework facilitates the continuous growth of blended learning initiatives, seamlessly integrating these practices into the institution's overall educational ecosystem As a result, there is a solid foundation for the sustained implementation and expansion of blended learning programs.
Social Cognitive Theory (SCT), introduced by Albert Bandura in 1986, utilizes concepts from social psychology to analyze human behavior through three key components: behavior, personal factors, and the environment These components interact in a bi-directional manner, shaping both individual and collective behaviors, and serve as a framework for predicting and altering behavior effectively.
Social Cognitive Theory (SCT) emphasizes the interplay between three key factors: behavior, personal attributes, and environmental influences The behavior factor addresses usage, performance, and the adoption of specific behaviors The personal factor includes an individual's personality traits, cognitive processes, and demographic characteristics Meanwhile, the environmental factor encompasses both physical and social elements external to the individual SCT highlights the dynamic interconnection among these three factors, forming the core foundation of the theory.
Social Cognitive Theory (SCT) utilizes various constructs to evaluate information technology usage, including self-efficacy, which is the belief in one’s ability to successfully perform a specific behavior Additionally, outcome expectations pertain to the anticipated results of engaging in a behavior Other important constructs in the SCT model include performance, anxiety, affect, and personal outcome expectations, all of which contribute to assessing how individuals interact with information technology.
By considering these factors and constructs, SCT offers a comprehensive framework for understanding and evaluating human behavior, particularly in the context of information technology usage
The Theory of Planned Behavior (TPB) builds upon The Theory of Reasoned Action (TRA) by addressing criticisms related to behaviors beyond an individual's full volitional control This enhanced model incorporates an additional element known as Perceived Behavioral Control, which plays a crucial role in understanding behavioral intentions.
Perceived Behavioral Control (PBC) is a key factor influencing both the intention to use a product and actual usage behavior The Theory of Planned Behavior (TPB) posits that individuals' actions are significantly shaped by their confidence in their ability to perform those actions, with PBC serving as a crucial component in this process.
The Theory of Planned Behavior (TPB) posits that individuals' perceptions of their ability to perform a behavior significantly influence their likelihood of engaging in that behavior According to Ajzen (1991), greater perceived behavioral control leads to stronger intentions to act, which in turn increases the likelihood of actual behavior Additionally, the theory emphasizes that intentions are shaped by attitudes, subjective norms, and perceived behavioral control, highlighting the interconnectedness of these factors in determining behavioral outcomes.
Figure 2.3 Theory of Planned Behavior
From an information systems perspective, identifying specific causal factors that enhance system acceptance is crucial by examining the antecedents of users' attitudes Unlike the Theory of Reasoned Action (TRA), which posits that attitudes are shaped by a combination of belief-evaluation terms, the Technology Acceptance Model (TAM) developed by Davis et al (1989) focuses on two key beliefs: perceived usefulness and perceived ease of use The core principle of TAM suggests that user acceptance of technology hinges on their perceptions of its utility and user-friendliness, as defined by Davis in 1989.
- Perceived usefulness: the extent to which an individual believes that the utilization of a technology would enhance his/her job performance
- Perceived ease of use: the extent to which an individual believes that the process of using the technology will be effortless
Overview of empirical studies
A study by Kamla Ali Al-Busaidia (2013) investigated how personal characteristics, including self-efficacy, technology experience, and personal innovativeness, affect learners' adoption of a Learning Management System (LMS) in blended learning and their intention to pursue full e-learning Data collected from 512 learners in Oman revealed that personal innovativeness, perceived usefulness (PU), and satisfaction with the LMS significantly impacted learners' intentions to engage in full e-learning.
When learners utilize a Learning Management System (LMS) in blended learning environments, it significantly enhances their willingness to participate in comprehensive e-learning These findings offer essential guidance for both practitioners and researchers in effectively planning and strategizing the implementation of full e-learning programs.
In Nguyen Van Than's 2014 study on the acceptance of over-the-top technology services, linear regression analysis was utilized to evaluate the hypotheses The results indicated that facilitating conditions emerged as the most significant factor influencing technology acceptance, followed by effort expectancy, performance expectancy, and social influence.
A study by Cao Hao Thi et al (2014) revealed that while price value did not affect the acceptance and use of virtual training in cloud computing, factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonistic motivation played significant roles Similarly, Yan Dang et al (2016) investigated the factors influencing student learning in blended learning environments, focusing on students' computer self-efficacy, instructor characteristics, and institutional support The research model assessed how these factors impacted students' perceived accomplishment, enjoyment, and satisfaction, revealing that for female students, all three factors significantly influenced their perceived outcomes, while for male students, only instructor characteristics and facilitating conditions showed a significant impact.
A 2020 study by Seyyed Mohsen Azizi, Nasrin Roozbahani, and Alireza Khatony examined the factors influencing the acceptance of blended learning in medical education using the UTAUT2 model Conducted with a sample of 225 Iranian medical students, the research utilized SPSS-18 and AMOS-23 software for data analysis and structural equation modeling to test hypotheses The study confirmed the validity and reliability of the model constructs, revealing that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit significantly affected students' adoption of blended learning Additionally, the findings indicated that students' behavioral intention to engage in blended learning greatly influenced their actual implementation of it, highlighting the UTAUT2 framework's effectiveness in identifying key factors affecting students' intentions to use blended learning.
A study by Zhaoli Zhang et al (2020) examined the factors affecting college students' adoption of e-learning systems in mandatory blended learning environments, emphasizing the importance of effective e-learning implementation for higher education The researchers introduced the Unified Technology Acceptance and System Success (UTASS) model, utilizing self-reported questionnaire data and system log data to analyze students' online behavior, with responses gathered from 287 participants via the e-learning platform starC The analysis, conducted through structural equation modeling, revealed that system quality (SQ), social influence (SI), and facilitating conditions (FC) positively influence students' behavioral intention (BI) to use the e-learning system, while information quality (IQ) did not show a significant effect on BI Additionally, no significant relationship was found between FC, BI, and use behavior (UB), and a moderator effect of gender indicated that male students were more influenced by SQ and SI.
The study by Mahboubeh Taghizadeh and Fatemeh Hajhosseini (2020) aimed to explore graduate students' attitudes, interaction patterns, and satisfaction with blended learning technology, involving 140 TEFL graduate students from Iran University of Science and Technology Utilizing four types of questionnaires, the research assessed learner satisfaction, attitudes, interaction types, and teaching quality Findings revealed that participants had positive attitudes towards blended learning, with instructors effectively teaching TEFL concepts and fostering online discussions The predominant interaction type was learner-instructor interaction Additionally, multiple regression analysis indicated that teaching quality significantly influenced student satisfaction more than interaction and attitude, highlighting the need for training online educators to improve their teaching effectiveness.
Kurniawan et al (2021) conducted a study to explore the factors influencing the adoption of blended learning in non-formal education, particularly in developing countries like Indonesia, where research in this area has been limited The necessity for blended learning has grown during the Covid-19 pandemic due to constraints on physical space in educational institutions Data was collected through a Google Forms questionnaire distributed to 566 users of blended learning in Indonesian non-formal education settings, utilizing established scales to measure variables in a theoretical model Structural Equation Model (SEM) analysis was performed using SPSS and Amos software The findings revealed that out of thirteen initial hypotheses, nine were significant, with Social Influence (SI) impacting Perceived Usefulness (PU), Compatibility with Existing Environment (CE) affecting Perceived Ease of Use (PEU), and PU influencing Behavioral Intention (BI) Notably, Social Influence emerged as the most critical factor in adopting blended learning in these institutions This research enhances both theoretical and practical insights into blended learning adoption, offering guidance for effective implementation in non-formal educational contexts.
A study by Arumugam Raman and Raamani Thannimalai (2021) examined the factors influencing the behavioral intention to use e-learning in higher education during the Covid-19 pandemic, employing the UTAUT2 model The research aimed to evaluate students' intentions regarding e-learning, with limited prior studies using UTAUT2 in this context Utilizing snowball sampling, the study surveyed 159 higher education students who engaged with online learning platforms during the pandemic Data were gathered through a UTAUT2-adapted questionnaire, and PLS-SEM statistical analysis was performed Findings indicated that social influence and habit significantly impacted the intention to use e-learning, while performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, and price value did not Notably, habit emerged as the strongest predictor of behavioral intention This research offers valuable insights for higher education institutions to enhance e-learning implementation and outlines directions for future studies.
A study by Norman Rudhumbu (2022) examined the predictive power of the UTAUT2 model regarding blended learning acceptance among university students Utilizing a quantitative approach, data was collected from 432 post-graduate students through a structured questionnaire, which was then validated with Confirmatory Factor Analysis (CFA) and analyzed using Structural Equation Modeling (SEM) The findings indicated that performance expectancy, effort expectancy, social influences, facilitating conditions, and hedonic motivation positively influenced students' intentions to use blended learning In contrast, habit and price value did not significantly affect these intentions Additionally, students' behavioral intentions were found to significantly impact their acceptance of blended learning Overall, the study confirmed the UTAUT2 model's effectiveness in measuring students' intentions to adopt blended learning in universities.
A study by Jueliang Huang and Thanawan Phongsatha (2022) examined the factors affecting the acceptance of blended learning among early childhood undergraduate students in China, utilizing a mixed-method approach grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) and the College and University Classroom Environment Inventory (CUCEI) The research involved a survey of 363 students, employing SEM analysis to determine significant influences on blended learning acceptance Findings revealed that social influence and classroom environment notably impacted acceptance rates, while ease of use and convenience, along with an enhanced classroom atmosphere, were linked to higher acceptance of blended learning Conversely, no significant correlations were found between acceptance rates and performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, or price value.
A study by Georgios Zacharis and Kleopatra Nikolopoulou (2022) explored the factors influencing university students' behavioral intention to use eLearning platforms in the post-pandemic period, utilizing the UTAUT2 framework with the addition of Learning Value Conducted through an online questionnaire with 314 students from various Greek universities, the research identified key elements impacting students' intentions, including performance expectancy, social influence, hedonic motivation, learning value, and habit Furthermore, the study found that facilitating conditions and learning value directly affected the actual usage of eLearning platforms, enhancing the understanding of the post-Covid-19 eLearning model by integrating Learning Value to assess students' intentions to engage with these platforms.
Jashwini Narayan and Samantha Naidu's 2023 study applied a modified UTAUT2 model to analyze the behavioral intentions of tertiary students in a developing country during the post-COVID-19 era The research involved 419 students from a regional university, utilizing covariance-based structural equation modeling to confirm the proposed model Findings indicated a strong correlation among social influence, hedonic motivation, facilitating conditions, commitment, behavioral intention, and use behavior However, factors such as performance expectancy, effort expectancy, price value, trust, and comfortability showed no significant impact on behavioral intentions Notably, the study introduced "COVID-19 fear" as a new construct but found it did not moderate the relationship between behavioral intentions and use behavior Additionally, the inclusion of trust, commitment, and comfortability as independent variables improved the model's predictive efficacy.
A study by Tran Trong Duc et al (2022) explored the factors influencing students' intention to utilize IoT services in retail stores in Ha Noi City The research collected 355 responses, which were analyzed using CFA and SEM models for hypothesis testing Findings revealed that performance expectancy and social influence significantly impacted students' intentions, while hemonic motivation and facilitating conditions also contributed positively to their willingness to engage with IoT services.
A study by Ardvin Kester Ong and Michael Young (2023) examined the factors influencing the continuous intention to enroll in the ubiquitous online experience (UOX) learning modality among students in the Philippines, particularly towards the end of the Covid-19 pandemic Utilizing the DeLone and McLean IS Success Model alongside UTUAT2, the researchers employed deep learning neural networks for analysis The findings indicated that facilitating conditions had the most significant impact on students' continuous enrollment intentions, followed by factors such as student satisfaction, performance expectancy, behavioral intentions, hedonic motivation, information quality, effort expectancy, system quality, habit, and price value.
Conceptual model and hypotheses
Based on previous research, the following table summarises the variables used in the above studies as well as their influence level on adoption:
Table 2.3 Summary of supported factors in previous studies
The summary table reveals that different authors have identified various independent variables to assess their impact on adoption, with the level of influence differing across studies Certain variables have consistently been prioritized and demonstrated significant effects on adoption After thorough analysis of prior research and its relevance to the current context, five key factors from the UTAUT2 model have been selected for inclusion in this research's conceptual model to further investigate their influence.
PEU Perceived Ease of Use 0 0 0
The research context of the Western Sydney international partnership program in Vietnam is significantly influenced by several key factors: Facilitating Conditions (FC), Social Influence (SI), Performance Expectancy (PE), Hedonic Motivation (HM), and Effort Expectancy (EE) Each of these elements has shown considerable impact in previous adoption studies, highlighting their importance in shaping the program's effectiveness.
In addition to the primary factors influencing students' adoption of blended learning, two additional elements were examined: Self Efficacy (SE) and Instructor Characteristics (IC) These factors were integrated into the model to better understand their potential impact on student engagement in the program.
Despite being less frequently selected and showing limited influence in prior studies, the author has opted to include social engagement (SE) and interpersonal communication (IC) in the model This choice allows for a re-evaluation of their impact, potentially leading to new insights The absence of strong influence in earlier research does not negate the possibility of positive findings within this specific context By incorporating SE and IC, the author aims to thoroughly investigate their significance, which may reveal valuable contributions to the overall study.
To enhance the understanding of blended learning adoption, a new variable, Course Flexibility (CF), has been introduced, highlighting the unique transition between online and offline modes This distinguishes blended learning from purely online or offline methods Notably, CF has not been featured in prior research on blended learning, appearing only in Mehmet Kokoỗ's 2019 study on e-learning flexibility The author anticipates that incorporating this variable will provide valuable insights into students' adoption of blended learning.
Performance expectancy, as defined by Venkatesh et al (2003), refers to the belief that using a system will enhance job performance In the realm of blended learning, it assesses the likelihood that students will achieve their desired academic outcomes through this method Essentially, performance expectancy reflects students' perceptions of the benefits of blended learning and its potential to improve their academic performance, paralleling the perceived usefulness concept from the Technology Acceptance Model (TAM).
A study by Williams, Rana, and Dwived (2014) analyzed the link between performance expectancy and behavioral adoption across 116 out of 174 researched studies The findings revealed that 93 of these studies indicated a significant predictive relationship, establishing performance expectancy as the leading predictor of behavioral adoption.
Studies in higher education indicate that performance expectancy significantly influences the behavioral adoption of e-learning information services (Hsu, 2012; Oh and Yoon, 2014; Raman and Don).
2013), web-based learning systems (Jong and Wang, 2009; Lwoga and Komba, 2014; Masadeh, Tarhini, Mohammed, and Maqableh, 2016), Moodle (Decman, 2015; Olatubosun, Olusoga, and Samuel, 2015), and social media (Kasaj and Xhindi,
H1: Performance expectancy has a positive influence on students’ blended learning adoption
Effort expectancy refers to the perceived ease of use of a system, highlighting how users expect a system to be effortless in fulfilling their tasks (Venkatesh et al., 2003; Huang and Kao, 2015) In the context of blended learning, it reflects students' beliefs about the simplicity of integrating this approach into their studies University students who view blended learning as easy are more inclined to adopt it in their academic pursuits This concept is foundational to several other constructs in different theories, including perceived ease of use, complexity, and ease of use (Asare et al., 2016; Pardamean and Susanto, 2012; Venkatesh et al., 2003).
Research has shown that effort expectancy significantly influences behavioral adoption in blended learning and learning management systems (Azizi et al., 2020; Widjaja et al., 2020) While its predictive power may be lower compared to other factors (Morosan and Defranco, 2016), numerous studies highlight its positive effect on the adoption of services such as digital libraries (Nov and Ye, 2009), e-learning, and online gaming (Oh and Yoon, 2014) Additionally, Chan et al (2015) demonstrated a strong positive correlation between effort expectancy and the adoption of mobile devices for student response systems.
H2: Effort expectancy has a positive influence on students’ blended learning adoption
Social influence is defined as the extent to which individuals feel it is important for others to believe they should use a system (Venkatesh et al., 2003) In blended learning, social influence pertains to how strongly an individual perceives the encouragement from their social group to adopt this learning approach Research by Asare et al (2016), Pardamean and Susanto (2012), and Venkatesh et al (2003) links social influence to behavioral modification theories, including subjective norms from the Theory of Planned Behavior and Theory of Reasoned Action (Ajzen, 1991; Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975), social factors in Social Learning Theory, and external variables in the Technology Acceptance Model (TAM).
Undergraduate students are more likely to adopt blended learning when they perceive that their peers, lecturers, or parents support its use in their education Research has shown that social influence significantly impacts the acceptance of various online learning contexts, including blended learning, e-learning, learning management systems (LMS), MOOCs, and the integration of new technologies in higher education Studies by Azizi et al (2020), Tarhini et al (2017), and others highlight the importance of social influence in behavioral adoption, with Venkatesh et al (2012) establishing a direct connection between the two Additionally, Fidani and Idrizi (2012) demonstrated a strong correlation between social influence and the adoption of LMS.
H3: Social influence has a positive influence on students’ blended learning adoption
Facilitating conditions, as defined by Venkatesh et al (2003), refer to an individual’s perception of the organizational support for technology that enhances e-learning In the context of blended learning, these conditions encompass a student's expectations regarding the availability of infrastructure and technical assistance Key examples include reliable internet access, appropriate devices, effective online pedagogy, and instructional strategies, along with the development of accessible content modules for all students This concept aligns with earlier theories of perceived behavioral control and compatibility, as discussed by Asare et al (2016), Lakhal et al (2013), and Venkatesh et al (2003).
Students who believe their institution possesses the essential technological and organizational resources are more likely to develop a behavioral intention to integrate blended learning into their academic studies.
Research by Oh and Yoon (2014) indicates that facilitating conditions are a significant predictor of e-learning and online gaming adoption among university students in South Korea, based on a modified UTAUT model Similarly, studies by Asare et al (2016) and Masadeh et al (2016) confirm that facilitating conditions positively influence students' adoption of e-learning Additionally, Tran (2013) and Jong and Wang's findings further support the notion that facilitating conditions are crucial for the adoption of English language e-learning websites and web-based learning systems, respectively.
2009), and (c) video conferencing in a distance course (Lakhal et al., 2013)
H4: Facilitating conditions have a positive influence on students’ blended learning adoption
RESEARCH METHODOLOGY
Research process
The research process unfolded in three key stages, beginning with an introduction to the study and the establishment of its theoretical framework through a thorough literature review and analysis of prior research, alongside the author's insights This foundational work set the stage for the second phase, where the research scale was developed To guarantee the questionnaire's quality and appropriateness for the official survey and reliability testing, detailed interviews were conducted.
Proposing research model & initial measurement scales
Conducting in-depth interview & focus group discussion to refine measurement scales
Conducting online survey of 400 undergraduate students via Google
Assessing the reliability of the measurement scales, item-total correlations to identify and remove unsuitable observed variables
Removing observed variables with Factor Loadings < 0.3
Assessing Total Variance Explained (≥50%), KMO Value (0 ≤ KMO ≤ 1), and Eigenvalue (≥ 1)
Assessing the model fit, analyzing the regression coefficients, and testing the research hypotheses
The article outlines managerial implications derived from the insights of three experienced school management executives in blended learning operations Furthermore, a focus group discussion with ten undergraduate students was conducted to assess the alignment between the author's proposed definitions of concepts and observed variables and the perceptions of the target respondents.
After completing the data collection phase, the gathered information was encoded for analysis using statistical methods to extract meaningful insights Appropriate statistical techniques facilitated the exploration and interpretation of the research findings Ultimately, the study concluded with administrative implications, emphasizing the practical significance of the results Additionally, it recognized its limitations, outlining the study's boundaries, and proposed potential directions for future research.
The research process involved developing a theoretical framework, refining the questionnaire, collecting and analyzing data with statistical methods, drawing conclusions, providing administrative implications, and highlighting limitations along with suggestions for future research.
Scale development
Developing valid and reliable measurement scales requires extensive research and time investment, yet these well-constructed scales significantly enhance accuracy and dependability in assessing the intended constructs (McIver & Carmines, 1981) Gehlbach and Brinkworth (2011) introduced a rigorous methodology for creating reliable survey scales by integrating established survey development techniques, aiming to streamline measurement efforts and improve validity This collaborative process involves engaging both potential respondents and subject matter experts, outlined in a five-step procedure.
Step 1: A thorough review of the relevant literature should be conducted to precisely define the construct in relation to the existing research topic This step also involves identifying how current measures of the construct in the previous research could form the foundation of a new scale, then the initial measure items are built accordingly
Step 2: It is recommended to seek professional validation of the initial items from the experts by conducting an in-depth interview with them and asking them to provide feedback on the items, including suggestions for revisions or deletions This step, in addition to ensuring the items align with the conceptualization of the construct, may yield further input on potential missing indicators When reaching out to experts in the relevant construct, be sure to provide them with the definition of the construct, request they rate the degree to which each developed item is relevant to the construct, and ask them to identify any significant aspects of the construct that the items do not address
Step 3: Conduct a focus group discussion with potential respondents to determine if their perceptions of the construct align with the refined conceptualization To carry out a focus group discussion, a sample of people from the target respondent group will need to be selected This step will help ascertain whether the definition of the constructs and measurement items provided by scholars matches the perceptions of the intended respondents
Step 4: Combine the insights from the interview and focus group with the findings from the literature review to resolve any discrepancies between the academic and public understanding of the relevant construct In this step, when there is conceptual alignment between scholars and respondents but divergent descriptions of the indicators or sub-themes, the language used by the respondents can be adopted This will facilitate the compilation of a list of indicators or sub-themes that can form the foundation for item development
Step 5: Generate official measure items The purpose of this step is to finalize the items used in the official scale that embody the themes or indicators that surfaced from integrating the interview and focus group data with the literature
To evaluate the effectiveness of items functioning as a scale, it is essential to conduct various assessments These analyses should encompass the examination of the mean and variability of each item, calculation of item-total correlations, and assessment of reliability Additionally, it is crucial to test whether the measures align with the established conceptualization of the construct (Gehlbach & Brinkworth, 2011).
Qualitative research involved in-depth interviews with three experts from the WSU international partnership program at UEH-ISB, aiming to understand their perceptions of students' adoption of blended learning The insights gathered will help refine and enhance the initial measurement scale, leading to the finalization of the official scale for data collection.
Before consulting with experts, the author created an initial measurement scale based on theories and studies outlined in Chapter 2 This process included identifying and defining key terms and constructs related to the research topic The preliminary scale was shared with the experts beforehand, and during the discussion, only the content that received approval from at least two out of three participants was retained or modified as needed.
The measurement scale presented in Table 3.1 includes 46 observed variables, developed based on the frameworks established by notable researchers such as Taylor and Todd (1995), Venkatesh et al (2003), Ball and Levy (2008), Venkatesh et al (2012), Al-Busaidi and Kamla Ali (2012), Wu and Liu (2013), Huang and Kao (2015), Brusso (2015), and Sury Ravindran et al (2016).
(2017), Lawless (2019), Chao (2019), White (2019), Moorthy et al (2019), Mehmet Kokoc (2019), Chen et al (2020), Georgakopoulos et al (2020), Lu et al (2020), Lee and Lee (2020), Azizi et al (2020), Abu-Gharrah and Aljaafreh (2021), Amparo
Table 3.1 Research initial measurement scales
PE1 I find blended learning beneficial to my studies Venkatesh et al
(2012), Huang and Kao (2015), Lawless (2019), Chao (2019), White (2019), Chen et al
PE2 By employing blended learning, my ability to achieve good academic performance is significantly enhanced
PE3 Blended learning enables me to complete my learning tasks quickly
PE4 The integration of blended learning components allows me to enhance my learning productivity
PE5 Using blended learning improves my understanding of the course materials
EE1 I find it easy to learn how to use blended learning in my studies
EE2 I find it effortless to become proficient at using blended learning
EE3 My experience with blended learning is smooth and comprehensible
Implementing the blended learning system is not a challenging task for me within the classroom environment
SI1 Important people to me believe that I should adopt blended learning for my studies
(2012), Huang and Kao (2015), Georgakopoulos et al (2020), Abu-Gharrah and Aljaafreh
The individuals who have a significant impact on my behavior think I should utilize blended learning for my studies
SI3 Blended learning has been recommended to me by people whose opinions I highly respect
SI4 I believe that other classmates also use blended learning to support their studies
SI5 It can be observed that my instructors have greatly encouraged the implementation of blended learning
FC1 I have been provided with access to the necessary resources for my implementation of blended learning
(2013), Sattari et al (2017), Moorthy et al
(2019), Georgakopoulos et al (2020), Lu et al (2020), Abu-Gharrah and Aljaafreh
FC2 I am equipped with the essential information to implement blended learning in my studies
FC3 Blended learning aligns well with the information and communication technology tools I use in my studies
FC4 Blended learning is in harmony with other pedagogical methods my instructors employ in class
FC5 I can get assistance from others if I have trouble with blended learning techniques
HM1 Blended learning activities make me feel fun
HM2 I experience a sense of enjoyment when using blended learning for my studies
HM3 I have a favourable attitude toward using the blended learning approach
HM4 Blended learning courses enhance the level of interest within the classroom
HM5 I prefer blended learning as it aligns with my learning style
SE1 I am confident in my ability to use computer and software applications for blended learning courses Taylor and
SE2 I have confidence in my ability to employ different methods within the blended learning courses
SE3 I am able to complete the assignment submissions using
Code Content Source blended learning tools
SE4 Despite having no prior experience with a blended learning system, I am capable of using it effectively
IC1 I am pleased with how readily accessible and available the instructors are to assist me
Al-Busaidi and Kamla Ali
IC2 The instructors provide timely responses to my queries and actively participate in discussions
IC3 The instructors convey the course objectives and assignments to me in a clear and concise manner
IC4 The instructors deliver clear guidelines on how to engage in the course activities
IC5 The instructors effectively foster interaction and engagement among students
IC6 The instructors use blended learning technology appropriately
CF1 Blended learning allows flexible access to lectures and learning activities from anywhere and anytime
CF2 With the help of blended learning, I can easily access the learning materials
CF3 Blended learning courses offer a broad selection of materials for me to choose from
Blended learning courses enable me to concentrate on learning activities that align with my individual needs and preferences
CF5 With blended learning, it is effortless for me to stay updated and keep track of all the learning activities
CF6 Through blended learning, I am able to give priority to specific topics during my learning process
BLA1 I would opt for blended learning whenever I intend to study
(2015), Huang and Kao (2015), Lee and Lee
(2020), Azizi et al (2020), AbuGharrah and Aljaafreh
BLA2 In order to improve my academic skills, I plan to continue to use blended learning on a regular basis
BLA3 In my daily life, I will consistently prioritize the use of blended learning as an effective learning method
BLA4 I am determined to utilize the blended learning system continuously and extensively
BLA5 I would advise others to consider enrolling in blended learning courses
BLA6 I acknowledge that mastering the blended learning method will be crucial for the future
The author conducted in-depth interviews with three school management experts and a focus group discussion with ten students to assess their understanding of the survey questionnaire's content and appropriateness The qualitative research findings reveal that the number of independent variables influencing students' blended learning adoption in the WSU international partnership program remains unchanged, including performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, self-efficacy, instructor characteristics, and course flexibility However, adjustments have been made to the content and number of observed variables (measurement items) in the scale.
Change the content of PE4 from “The integration of blended learning components allows me to enhance my learning productivity” to “Engaging in blended learning boosts my learning productivity”
Add a new measured item PE6 with the content as “Blended learning significantly fulfills my learning and research needs”
Applying the blended learning system in class is not a challenge for me.
My institution possesses the required resources to support my implementation of blended learning in my studies.
The help desk is prepared to provide support for any challenges that may occur during the implementation of blended learning techniques.
Change the content of HM3 from “I have a favourable attitude toward using the blended learning approach” to “I hold a positive view on utilizing the blended learning approach”
Add a new measured item HM6 with the content as “I derive the pleasure from engaging in both e-learning and traditional classes”
I have the capability to fulfill the required tasks by using the tools available in blended learning.
Add a new measured item SE5 with the content as “I am able to use the blended learning system by relying solely on the system manuals as a reference guide”
Change the content of IC6 from “The instructors use blended learning technology appropriately” to “The instructors demonstrate a high level of proficiency in using blended learning technologies”
Questionnaire design
This study utilized a questionnaire, a widely used tool in educational research for collecting factual information Primary data was gathered through an online survey via Google Forms, distributed to students' email addresses To enhance the clarity and interpretation of the survey questions, in-depth interviews were conducted with three school management executives, seeking their feedback on the comprehensibility of the proposed questions and the likelihood of misinterpretation.
The questionnaire for the Western Sydney international partnership program included two main sections: a demographic profile with four questions and a 49-question segment on factors affecting students' adoption of blended learning The demographic section utilized a nominal scale to categorize respondents by gender, age, academic major, and family income, facilitating the classification of individuals with similar traits, as suggested by Sekaran and Bougie (2019) To evaluate the factors influencing blended learning adoption, a 5-point Likert Scale was implemented, measuring respondents' levels of agreement or satisfaction, with responses ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).
- 2 (Disagree) - 3 (Neutral) - 4 (Agree) - 5 (Strongly Agree) Participants were required to select the scale point that best represented their opinions, as suggested by Sekaran and Bougie (2019).
Sample size
The author utilized convenience sampling, a non-probability method that selects samples based on specific characteristics and research needs Hair et al (1998) suggest that for exploratory factor analysis (EFA), the sample size should be five times the number of observations, resulting in a 5:1 ratio Given this study has 49 observations, the necessary sample size would be 245.
According to Tabachnick and Fidell (1996), the sample size formula n = 50 + 8 * var, where "var" denotes the number of independent variables, suggests that with eight independent variables, a total sample size of 114 is necessary To fulfill the criteria for exploratory factor analysis (EFA) and regression analysis, a sample of 400 undergraduate students from the Western Sydney international partnership program at UEH-ISB in Ho Chi Minh City will be utilized.
Data analysis methods
The author meticulously gathered, organized, and categorized data from the questionnaire Subsequently, all variables were encoded into a coding sheet and analyzed using the Statistical Package for Social Science (SPSS 26.0) software.
Descriptive statistics are essential tools that offer concise summaries of data sets, whether they represent a sample or the entire population These statistics facilitate a better understanding of the data's characteristics by providing key insights and measures, ultimately enhancing the analysis and interpretation of the information.
Descriptive statistics encompass three main categories: variability, central tendency, and frequency distribution Variability measures the dispersion within a data set, utilizing metrics such as standard deviation, variance, minimum and maximum values, kurtosis, and skewness Frequency distribution reveals how often data points occur, while central tendency identifies the data's central point through mean, median, and mode.
Measures of central tendency, including mean, median, and mode, are essential components of descriptive statistics that help define and characterize data sets The mean is determined by adding all values in a data set and dividing by the total number of values These descriptive statistics simplify complex quantitative insights from large data sets into clear and manageable summary measures.
Cronbach’s Alpha (α), introduced by Lee Cronbach in 1951, is a key metric for assessing the reliability and internal consistency of a questionnaire It evaluates how closely related a group of items is, making it especially useful for researchers utilizing Likert scales to measure reliability The calculation of Cronbach’s Alpha involves a specific formula that quantifies this internal consistency.
- c̄ = average covariance between item-pairs
Increasing the number of items in a scale typically raises Cronbach’s Alpha (α), as shown by its formula A low average inter-item correlation leads to a lower Cronbach’s Alpha, while an increase in this correlation—given a constant number of items—will enhance the Alpha value Ranging from 0 to 1, Cronbach’s Alpha assesses the reliability of a measure, where a score of 0 indicates independence among scale items and a score close to 1 reflects high covariance, suggesting that the items effectively measure the same concept Thus, a higher Cronbach’s Alpha coefficient indicates stronger covariation among items, allowing for a unified measurement of the underlying concept.
The acceptable standards for Cronbach's alpha can differ based on the questionnaire scale, with many researchers recommending a minimum alpha value between 0.65 and 0.80 Nunnally and Bernstein (1994) indicated that a corrected item-total correlation of 0.30 or higher is satisfactory, while a reliability threshold of 0.60 or above is deemed acceptable DeVellis (2003) suggested a minimum alpha of 0.70, although values as low as 0.63 may still be usable Conversely, alpha coefficients falling below 0.50 are generally regarded as unacceptable, especially for unidimensional scales.
Exploratory Factor Analysis (EFA) is a multivariate statistical technique that helps identify latent variables underlying patterns of correlations among manifest variables, particularly in psychology and education This method simplifies data by reducing numerous measured variables into a more manageable set, uncovers underlying dimensions linking observed variables to latent constructs, and aids in the development and refinement of theories Additionally, EFA provides evidence of validity for self-reported data, enhancing the reliability of participant responses.
The Kaiser-Meyer-Olkin Test (KMO) evaluates the independence of variables from errors caused by others, with a KMO value of 0.5 or higher indicating that factor analysis is suitable for the data Values below 0.5 suggest that factor analysis is inappropriate, while KMO values ranging from 0.5 to 0.7 are deemed mediocre, and those between 0.7 and 0.8 are classified as good.
Bartlett’s Test of Sphericity evaluates the correlation among observed variables within a factor, ensuring that these variables, which reflect various dimensions of the same factor, are interrelated For factor analysis to be valid, a significant level in Bartlett’s Test indicates that the observed variables are suitably correlated.
‘sig.’, is less than 0.05, it indicates that the observed variables are correlated within the same factor group
Eigenvalue is a widely used criterion in EFA to decide the number of factors to retain in the analysis According to this criterion, only factors with an Eigenvalue of
To ensure the effectiveness of the exploratory factor analysis (EFA) model, it is essential that the factor loading values are 1 or higher and that the Total Variance Explained is at least 50% This threshold signifies that the model effectively accounts for a significant portion of the observed variance, illustrating how well the extracted factors summarize the data When considering total variance as 100%, this value reflects both the extent to which the factors encapsulate the data and the percentage of observed variables that remain unaccounted for by the analysis.
Factor loading quantifies the relationship between observed variables and underlying factors in factor analysis, with higher values signifying stronger correlations Hair (2009) provides specific guidelines for interpreting these factor loadings effectively.
- Factor Loading at ± 0.3: This is the minimum threshold for observed variables to be retained in the analysis
- Factor Loading at ± 0.5: Indicating that the observed variables are statistically good in relation to the factor
- Factor Loading at ± 0.7: Indicating that the observed variables are statistically excellent in relation to the factor
The standard value for factor loading is influenced by the sample size, as varying sample sizes necessitate different weighting factors to accurately assess the significance of factor loading.
Pearson’s correlation coefficient, denoted as r, is a statistical measure that evaluates the strength and direction of the relationship between two continuous variables By utilizing covariance, it effectively assesses associations, and its value is confined within a specific range, ensuring consistent interpretation of results.
- Positive values indicate a positive linear correlation
- Negative values indicate a negative linear correlation
- A value of 0 indicates no linear correlation
- Values closer to 1 or -1 indicate a stronger linear correlation
Pearson’s correlation coefficient (r) and the associated p-value are computed together The p-value can be interpreted as follows to determine the statistical significance of the correlation:
- If the P-value is less than 0.05, it indicates the correlation is statistically significant
- If the P-value is greater than 0.05, it indicates the correlation is not statistically significant
Since correlation is an effect size, Evans (1996)’s guide could be used to verbally describe the correlation’s strength based on the absolute value of r:
Linear regression is a statistical method that forecasts the outcome variable (Y) using one or more predictor variables (X) Its primary goal is to create a linear relationship between these predictors and the response variable, allowing for the estimation of Y based solely on known values of X.
FINDINGS AND DISCUSSIONS
Overview of WSU international partnership program
Over 47 years of establishment and development history, the University of Economics Ho Chi Minh City (UEH) has consistently developed innovative training programs to improve the quality of teaching and learning, promote international integration, and cultivate Vietnam’s future talent To provide students with access to an internationally-standard learning environment within Vietnam, UEH has collaborated extensively with leading universities around the world This includes a long history of training and research partnerships with prestigious Australian institutions, including Western Sydney University (WSU) as one of its strategic partners in Australia These global collaborations are part of UEH’s broader strategy to internationalize its education offerings and create more opportunities for Vietnamese students to experience high-caliber learning without having to study abroad Through this multifaceted approach, UEH aims to consistently elevate the quality of teaching and learning, foster international integration, and develop the future leaders of Vietnam
On December 12, 2021, the University of Economics Ho Chi Minh City - International School of Business (UEH-ISB) and WSU Australia signed a cooperation agreement aimed at enhancing internationally-standard training programs and diversifying the curriculum This partnership will facilitate the development of one doctoral program, six new undergraduate programs, and various postgraduate offerings, ultimately improving the quality of education at UEH-ISB The collaboration is designed to provide Vietnamese students with greater opportunities to engage with advanced academic values in an international learning environment, while also advancing expertise across multiple fields for application in the global labor market.
The collaboration between UEH-ISB and WSU has established them as a prominent higher education provider in the region, driving social and economic development WSU's offshore campus in Vietnam aspires to be a leading institution within the ASEAN Hub, offering diverse educational programs This strategic partnership enhances the capabilities of Vietnamese students, aiming to elevate the region's presence on the global stage.
WSU Australia is a prestigious institution recognized for its academic excellence and applied research, having advanced from 58th to 33rd place in the 2023 Ranking of the World’s Top Young Universities Ranked in the top 1% globally and among the top 250 institutions by Times Higher Education, WSU's strong academic foundation enhances its international partnership programs, such as with UEH-ISB The university offers a modern educational experience that empowers students to achieve successful careers, while its research focuses on critical societal issues like climate change, public health, and infrastructure engineering WSU's interdisciplinary and innovative academic approach is grounded in values promoting social, cultural, economic, and political fairness.
WSU is embracing blended learning models to provide students with greater flexibility in their education, leveraging both current and emerging technologies This strategic integration of in-person and online interactions enhances the learning experience across various subjects By adopting this modern approach, WSU demonstrates its commitment to empowering students, fostering innovation, and promoting positive change in alignment with its core values.
The WSU undergraduate program in Vietnam features a variety of majors, including a Bachelor of Business with three specializations—Marketing, Applied Finance, and International Business Additionally, it offers a Bachelor of Communication with a specialization in Advertising, as well as a Bachelor of Applied Data Science, which is a comprehensive four-year double degree program.
Students can participate in the Global Pathways transfer program, which is part of the Western Sydney international partnership, known for its high-quality student intake—60% of students have an IELTS score above 7.0 and come from top high schools with strong GPAs This program offers an international learning environment in Vietnam, following the Australian Bachelor’s degree model, and awards degrees directly from Western Sydney University, ensuring compliance with the highest Australian education standards The program can be completed in just 2 years and 7 months, with options to study entirely in Vietnam or transfer abroad With 100% of the faculty holding AACSB accreditation, students gain knowledge from leading experts Additionally, the program provides up to 30 billion VND in scholarships and connects students with an alumni network at over 100 multinational companies, enhancing career opportunities and alleviating financial burdens.
Descriptive statistics
A study involving 400 undergraduate students from the WSU international partnership program gathered data through an online survey via Google Forms After data cleaning and validation, 384 valid responses were retained, representing a 96% response rate The analysis was conducted using SPSS version 26.0, with Table 4.1 providing an overview of the respondents' characteristics.
Table 4.1 Descriptive statistics results Characteristic Frequency Percent (%)
Above 20 to 40 million VND/month 227 59.1
Source: Author’s data processing results from SPSS 26.0
In a sample of 384 students, 54.9% are over 20 years old, while 45.1% are between 18 and 20 years old This age distribution indicates diverse levels of technological proficiency, learning preferences, and responsibilities among students, which may influence their readiness to embrace blended learning.
The gender distribution among participants is nearly equal, with females making up 52.1% and males 47.9% This balance suggests that analyses of blended learning adoption should incorporate a gender-balanced perspective Additionally, it is important to investigate how each gender engages with and perceives the blended learning environment.
In a recent study, the majority of participants were Marketing majors, making up 31.8% of the sample, followed by International Business students at 27.1% Advertising majors accounted for 22.9%, while the smallest group, Applied Finance, comprised 18.2% of the participants Each major offers distinct perspectives on technology use, which may influence the adoption of blended learning For instance, Marketing and Advertising students are likely to favor digital platforms due to the inherent digital focus of their fields, whereas Finance students may emphasize the importance of data security and the reliability of blended learning platforms.
The analysis of family income, categorized into three groups (10 to 20 million VND/month, 20 to 40 million VND/month, and above 40 million VND/month), reveals that a significant 59.1% of students belong to the middle-income bracket This indicates that most students come from middle to upper-middle-class families, suggesting they likely possess the financial resources to access essential technology for blended learning As a result, financial limitations may pose a lesser challenge in this context compared to areas with lower-income student demographics.
Cronbach’s Alpha reliability test
Table 4.2 Cronbach’s Alpha reliability test results
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach’s Alpha if Item Deleted
“Performance Expectancy” with Cronbach’s Alpha = 0.832
“Effort Expectancy” with Cronbach’s Alpha = 0.734
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach’s Alpha if Item Deleted
“Social Influence” with Cronbach’s Alpha = 0.797
“Facilitating Conditions” with Cronbach’s Alpha = 0.754
“Hedonic Motivation” with Cronbach’s Alpha = 0.751 (Round 1)
“Hedonic Motivation” with Cronbach’s Alpha = 0.841 (Round 2)
“Self Efficacy” with Cronbach’s Alpha = 0.809
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach’s Alpha if Item Deleted
“Instructor Characteristics” with Cronbach’s Alpha = 0.823
“Course Flexibility” with Cronbach’s Alpha = 0.784 (Round 1)
“Course Flexibility” with Cronbach’s Alpha = 0.865 (Round 2)
“Blended Learning Adoption” with Cronbach’s Alpha = 0.865
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach’s Alpha if Item Deleted
Source: Author’s data processing results from SPSS 26.0
The Performance Expectancy (PE) factor in this study exhibits a high reliability coefficient of 0.832, significantly exceeding the minimum threshold of 0.6 All six observed variables show a Corrected Item-Total Correlation above 0.3, reflecting a strong relationship among the items Furthermore, there are no observed variables with a Cronbach’s Alpha If Item Deleted that exceeds the overall Cronbach’s Alpha coefficient, confirming the scale's suitability and appropriateness for this research.
The Effort Expectancy (EE) factor exhibits a strong reliability, with a Cronbach’s Alpha coefficient of 0.734, which is well above the acceptable threshold of 0.6 The Corrected Item-Total Correlation coefficients for the observed variables range from 0.484 to 0.545, all exceeding the minimum requirement of 0.3 Notably, removing any variable would lower the overall Cronbach’s Alpha, and the Cronbach’s Alpha If Item Deleted values for all variables do not exceed the overall coefficient Therefore, all four observed variables will be included in the exploratory factor analysis (EFA).
The Social Influence (SI) factor exhibits strong reliability, with a Cronbach’s Alpha coefficient of 0.797, surpassing the minimum threshold of 0.6 Furthermore, all Corrected Item-Total Correlations exceed 0.3, and none of the observed variables present a higher Cronbach’s Alpha If Item Deleted value than the overall coefficient Therefore, the Social Influence factor is deemed reliable for this study.
The Facilitating Conditions (FC) factor exhibits a strong reliability coefficient of 0.754, surpassing the minimum threshold of 0.6 Additionally, all observed variables show Corrected Item-Total Correlations ranging from 0.497 to 0.544, reflecting a robust relationship with the overall factor Notably, none of the observed variables have a Cronbach’s Alpha If Item Deleted value that exceeds the overall Cronbach’s Alpha coefficient, confirming the scale's reliability and its appropriateness for further analysis.
The Hedonic Motivation (HM) factor exhibits strong reliability with a Cronbach’s Alpha coefficient of 0.751, surpassing the acceptable threshold of 0.6 However, variable HM5 has a Corrected Item-Total Correlation of -0.013, which is below the desired threshold of 0.3, leading to its removal from the scale prior to the Exploratory Factor Analysis (EFA) Following this adjustment, a second calculation of Cronbach’s Alpha reveals an increase to 0.841, further validating the factor's reliability All Corrected Item-Total Correlations exceed 0.3, and no variables show a Cronbach’s Alpha If Item Deleted value higher than the overall coefficient As a result, the Hedonic Motivation factor is deemed suitable for further analysis.
The Self Efficacy (SE) factor exhibits a strong Cronbach’s Alpha coefficient of 0.809, indicating high reliability, as it exceeds the acceptable threshold of 0.6 The Corrected Item-Total Correlation coefficients for the observed variables range from 0.563 to 0.628, all surpassing the minimum requirement of 0.3 Notably, removing any variable would lower the overall Cronbach’s Alpha coefficient, and the Cronbach’s Alpha If Item Deleted values for all variables remain below the overall coefficient Consequently, all variables are retained, ensuring the scale's reliability.
The Instructor Characteristics (IC) factor demonstrates a robust Cronbach’s Alpha coefficient of 0.823, exceeding the acceptable threshold of 0.6 Additionally, all six observed variables exhibit Corrected Item-Total Correlation coefficients above 0.3, with values ranging from 0.550 to 0.622, indicating strong internal consistency among the variables.
Alpha If Item Deleted value higher than the overall Cronbach’s Alpha coefficient
As a result, this scale is appropriate for use in this study
The Course Flexibility (CF) factor demonstrates strong reliability with a Cronbach’s Alpha coefficient of 0.784, exceeding the acceptable threshold of 0.6 However, the variable CF6, with a Corrected Item-Total Correlation of 0.042, is below the desired threshold of 0.3 and will be excluded from the scale before conducting the exploratory factor analysis (EFA) Following this exclusion, the recalculated Cronbach’s Alpha rises to 0.865, reinforcing the scale’s reliability All remaining Corrected Item-Total Correlations are above 0.3, and no observed variables show a Cronbach’s Alpha If Item Deleted value greater than the overall coefficient, indicating that this factor is suitable for further analysis.
The Blended Learning Adoption (BLA) factor exhibits a strong reliability, indicated by a Cronbach’s Alpha coefficient of 0.865, which is well above the acceptable threshold of 0.6 Furthermore, the Corrected Item-Total Correlation coefficients for all associated variables fall between 0.621 and 0.699, exceeding the minimum requirement of 0.3 Importantly, the Cronbach’s Alpha If Item Deleted values for each variable do not surpass the overall reliability coefficient, confirming that all variables are essential for maintaining the scale's reliability.
The Cronbach’s Alpha test indicated that all items, with the exception of HM5 and CF6, were retained for the Exploratory Factor Analysis (EFA) The reliability of the scale was assessed using the Cronbach’s Alpha coefficient, with the results detailed in Table 4.3.
Table 4.3 Reliable observations after Cronbach’s Alpha analysis
Performance Expectancy (PE) PE1, PE2, PE3, PE4, PE5, PE6 0.832
Effort Expectancy (EE) EE1, EE2, EE3, EE4 0.734
Social Influence (SI) SI1, SI2, SI3, SI4, SI5 0.797 Facilitating Conditions (FC) FC1, FC2, FC3, FC4, FC5 0.754 Hedonic Motivation (HM) HM1, HM2, HM3, HM4, HM6 0.841
Self Efficacy (SE) SE1, SE2, SE3, SE4, SE5 0.809
Instructor Characteristics (IC) IC1, IC2, IC3, IC4, IC5, IC6 0.823 Course Flexibility (CF) CF1, CF2, CF3, CF4, CF5 0.865
Blended Learning Adoption (BLA) BLA1, BLA2, BLA3, BLA4,
Source: Author’s data processing results from SPSS 26.0
Exploratory factor analysis
The results of the reliability test led to the conduct of a factor analysis involving
The analysis involved 41 observed variables categorized into eight distinct factor groups With a KMO coefficient of 0.852, well above the 0.5 threshold, the data demonstrates a strong correlation suitable for factor analysis The Bartlett’s test of sphericity produced a chi-square value of 6089.185 and a significance level of 0.000, confirming the consistency among the observations The total variance extracted is 58.124%, indicating that these eight factors explain a significant portion of the dataset's variation Additionally, the Eigenvalue of 1.150 exceeds the minimum requirement of 1, validating the factors' relevance for analysis.
The Varimax procedure, an orthogonal rotation technique, is employed to minimize the number of variables with high loadings on each factor In this analysis, any observations with a factor loading below 0.5 will be excluded, ensuring that only statistically meaningful measurements are retained The exploratory factor analysis (EFA) will focus on observed variables with factor loadings greater than 0.5, organizing them into major groups As shown in Table 4.4, all factor loadings exceed 0.5, indicating acceptable convergence for this study.
The Blended Learning Adoption factor consists of six observations (BLA1 to BLA6) and is validated by a KMO coefficient of 0.879, indicating data suitability for exploratory factor analysis Additionally, an Eigenvalue of 3.584 supports the factor's significance, while Bartlett’s Test reveals a statistically significant correlation among the observed variables (Sig < 0.05).
The total variance extracted is 59.736%, surpassing the 50% threshold, indicating that the six factors account for a significant portion of the data variation The component matrix, rotated using the Varimax method, shows that all Factor Loadings exceed 0.5, confirming their importance and appropriateness for integration Consequently, the scale is deemed suitable for further analysis.
Table 4.4 Exploratory factor analysis results
PE EE SI FC HM SE IC CF BLA
PE EE SI FC HM SE IC CF BLA
Source: Author’s data processing results from SPSS 26.0
Pearson’s correlation coefficient
Before conducting multiple linear regression analysis, it's essential to assess the linear relationships among the variables The strength and direction of the association between two quantitative variables can be measured using Pearson’s correlation coefficient.
The correlation matrix (Table 4.5) displays the correlation coefficients between the dependent variable BLA and the independent variables PE, EE, SI, FC, HM, SE,
The correlation coefficients for the variables IC and CF are 0.565, 0.459, 0.533, 0.306, 0.599, 0.541, 0.208, and 0.293 These values are statistically significant, with a significance level (Sig.) of less than 0.05, indicating a meaningful correlation between the dependent variable and the independent variables.
- BLA has a weak correlation with the variables IC and CF, as the correlation coefficients for these two variables (0.208 and 0.293) are less than 0.3
- BLA has a moderate correlation with the variables EE and FC, as the correlation coefficients for these two variables (0.459 and 0.306) are less than 0.5
- BLA has a strong correlation with the variables PE, SI, HM, and SE, as the correlation coefficients for these four variables (0.565, 0.533, 0.599, 0.541) are greater than 0.5
In examining the relationships between independent variables, pairs with a significance value (Sig.) greater than 0.05 indicate no correlational relationship and no risk of multicollinearity Conversely, pairs with a significance value (Sig.) less than 0.05 and an absolute correlation coefficient below 0.4 demonstrate a weak to moderate correlation, also indicating no likelihood of multicollinearity.
Based on these findings, it is appropriate to conduct multiple linear regression analysis to further examine the relationship between the dependent variable and the independent variables
Table 4.5 Pearson’s correlation coefficient results
BLA PE EE SI FC HM SE IC CF
Source: Author’s data processing results from SPSS 26.0
Multiple linear regression
Based on the theoretical framework and analysis results, all independent variables will be incorporated into the regression model using the Enter method, with a selection criterion of a significance level of less than 0.05 The findings from the subsequent regression analysis are detailed below.
Std Error of the Estimate
1 816 a 0.666 0.659 0.36406 2.037 93.598 0.000 a Predictors: (Constant) CF FC EE IC HM SI PE SE b Dependent Variable: BLA
Source: Author’s data processing results from SPSS 26.0
The Durbin-Watson statistic (d) is calculated to be 2.037 (1 < d < 3), indicating that the model does not exhibit self-correlation among the residuals
The F test serves as a vital hypothesis test for evaluating the suitability of the overall linear regression model As shown in table 4.6, the F test produced a value of 93.598 with a significance level of 0.000, which is below the 0.05 threshold This indicates that the multiple linear regression model is well-fitted to the dataset and is suitable for generalization.
The analysis of Table 4.6 reveals a strong positive correlation coefficient (R) of 0.666 among the variables, with an Adjusted R-Square value of 0.659, indicating a well-constructed linear regression model This model includes 8 independent variables, which together explain 65.9% of the variation in the dependent variable, while the remaining 34.1% is influenced by factors outside the model Overall, these findings highlight a significant relevance for the model.
Table 4.7 Multiple linear regression results
Source: Author’s data processing results from SPSS 26.0
According to the data in Table 4.7, the Variance Inflation Factor (VIF) values range from 1.029 to 1.659, all of which are below the critical threshold of 2 This indicates that the model does not exhibit any multicollinearity issues.
▪ The unstandardized linear regression equation is provided below:
BLA = -2.144 + 0.223*PE + 0.179*EE + 0.219*SI + 0.184*FC + 0.244*HM
Blended learning adoption = -2.144 + 0.223*performance expectancy + 0.179*effort expectancy + 0.219*social influence + 0.184*facilitating conditions + 0.244*hedonic motivation + 0.163*self efficacy + 0.167*instructor characteristics
PE: β = 0.223, meaning that a 1-unit increase in “performance expectancy” leads to a 0.223 unit increase in “blended learning adoption”, assuming all other independent variables remain constant
EE: β = 0.179, meaning that a 1-unit increase in “effort expectancy” leads to a 0.179 unit increase in “blended learning adoption”, assuming all other independent variables remain constant
SI: β = 0.219, meaning that a 1-unit increase in “social influence” leads to a 0.219 unit increase in “blended learning adoption”, assuming all other independent variables remain constant
FC: β = 0.184, meaning that a 1-unit increase in “facilitating conditions” leads to a 0.184 unit increase in “blended learning adoption”, assuming all other independent variables remain constant
HM: β = 0.244, meaning that a 1-unit increase in “hedonic motivation” leads to a 0.244 unit increase in “blended learning adoption”, assuming all other independent variables remain constant
SE: β = 0.163, meaning that a 1-unit increase in “self-efficacy” leads to a 0.163 unit increase in “blended learning adoption”, assuming all other independent variables remain constant
IC: β = 0.167, meaning that a 1-unit increase in “instructor characteristics” leads to a 0.167 unit increase in “blended learning adoption”, assuming all other independent variables remain constant
CF: β = 0.198, meaning that a 1-unit increase in “course flexibility” leads to a 0.198 unit increase in “blended learning adoption”, assuming all other independent variables remain constant
▪ The standardized linear regression equation is provided below:
BLA = 0.243*HM + 0.208*PE + 0.200*SI + 0.195*CF + 0.170*EE +
Standardized coefficients are used to evaluate the influence of independent variables on the dependent variable, revealing the strength of their relationships The impact factors, ranked by their beta values, are as follows: (HM) has the highest influence at 0.243, followed by (PE) at 0.208, (SI) at 0.200, (CF) at 0.195, (EE) at 0.170, (FC) at 0.160, and (IC) at 0.158 The factor with the least impact is (SE), with a beta value of 0.149.
H1: Performance expectancy has a positive influence on students’ blended learning adoption
The linear regression analysis revealed a standardized regression coefficient (β) of 0.208 for the "performance expectancy" factor, with a significance level (Sig.) of less than 0.05 This indicates a statistically significant positive correlation between performance expectancy and the adoption of blended learning by students.
H2: Effort expectancy has a positive influence on students’ blended learning adoption
The linear regression analysis revealed a statistically significant positive correlation between effort expectancy and students' adoption of blended learning, with a standardized regression coefficient (β) of 0.170 and a significance level (Sig.) of less than 0.05.
H3: Social influence has a positive influence on students’ blended learning
Blended learning adoption = 0.243*hedonic motivation + 0.208*performance expectancy + 0.200*social influence + 0.195*course flexibility + 0.170*effort expectancy + 0.160*facilitating conditions + 0.158*instructor characteristics + 0.149*self efficacy adoption
A linear regression analysis revealed a statistically significant positive correlation between social influence and students' adoption of blended learning, indicated by a standardized regression coefficient (β) of 0.200 and a significance level (Sig.) of less than 0.05.
H4: Facilitating conditions have a positive influence on students’ blended learning adoption
Linear regression analysis revealed a statistically significant positive correlation between facilitating conditions and students’ adoption of blended learning, with a standardized regression coefficient (β) of 0.160 and a significance level (Sig.) of less than 0.05.
H5: Hedonic motivation has a positive influence on students’ blended learning adoption
The linear regression analysis revealed a significant positive correlation between hedonic motivation and students' adoption of blended learning, with a standardized regression coefficient (β) of 0.243 and a significance level (Sig.) of less than 0.05.
H6: Self efficacy has a positive influence on students’ blended learning adoption
A linear regression analysis revealed a standardized regression coefficient (β) of 0.149 for the "self efficacy" factor, with a significance level (Sig.) of less than 0.05 This indicates a statistically significant positive correlation between self efficacy and the adoption of blended learning among students.
H7: Instructor characteristics have a positive influence on students’ blended learning adoption
The linear regression analysis revealed a standardized regression coefficient (β) of 0.158 for the "instructor characteristics" factor, with a significance level (Sig.) of less than 0.05 This indicates a statistically significant positive correlation between instructor characteristics and the adoption of blended learning by students.
H8: Course flexibility has a positive influence on students’ blended learning adoption
Linear regression analysis revealed that course flexibility has a standardized regression coefficient (β) of 0.195 and a significance level (Sig.) of less than 0.05, indicating a statistically significant positive correlation with students' adoption of blended learning.
To detect violations of the residuals’ normal distribution assumption, the Histogram chart and Normal P-P Plot chart will be employed
Source: Author’s data processing results from SPSS 26.0
Source: Author’s data processing results from SPSS 26.0
The Histogram chart (Figure 4.1) reveals that the residuals exhibit a normal distribution, with a mean value near 0 (Mean = 6.08E-15) and a standard deviation close to 1 (Std.Dev = 0.990), forming a bell-shaped pattern Additionally, the Normal P-P Plot (Figure 4.2) indicates that the data points are closely aligned with the diagonal line, displaying a random scatter around the zero coordinate These findings suggest that the residuals conform to a normal distribution, confirming that the assumption of normality for the residuals remains intact.
Source: Author’s data processing results from SPSS 26.0
In a linear relationship between the dependent and independent variables, the standardized predicted plot reveals that sample data points cluster around the y-axis at 0, suggesting a linear trend This pattern indicates that the predicted values and residuals are independent, thereby confirming that the assumption of linearity holds true.
One-way ANOVA
The Independent Sample T-test is suitable for comparing means when a categorical variable has two values, but it becomes less efficient with more than two values due to the complexity of pairwise comparisons One-way ANOVA addresses these limitations by allowing for the comparison of mean values across two or more groups, and it can also replicate the functions of the Independent Sample T-test Notably, the results from One-way ANOVA are identical to those obtained from the Independent Sample T-test when the categorical variable is limited to two values Consequently, the author opts to utilize One-way ANOVA for all scenarios involving categorical variables.
Hypothesis HL-0: There is no difference in variance between the groups of genders in students’ blended learning adoption
Hypothesis HL-1: There is a difference in variance between the groups of genders in students’ blended learning adoption
In SPSS 26.0, the Levene’s test statistics are taken from the Based on Mean row of the Test of Homogeneity of Variances table (see Appendix 4.6) As Sig = 0.004
< 0.05, HL-0 is rejected, meaning there is a statistically significant difference in variance between the groups of genders in students’ blended learning adoption
Hypothesis H0: There is no difference in means between the groups of genders in students’ blended learning adoption
Hypothesis H1: There is a difference in means between the groups of genders in students’ blended learning adoption
The Welch test results indicate a statistically significant difference in the means of blended learning adoption between gender groups, with a significance value of 0.019, which is less than the 0.05 threshold, leading to the rejection of the null hypothesis.
The analysis of blended learning adoption (BLA) reveals that female students have a higher mean value (Mean = 3.1294) compared to male students (Mean = 2.9820) This indicates that, on average, female students demonstrate a stronger capability to adopt blended learning than their male counterparts.
Hypothesis HL-0: There is no difference in variance between the groups of ages in students’ blended learning adoption
Hypothesis HL-1: There is a difference in variance between the groups of ages in students’ blended learning adoption
In SPSS 26.0, the Levene’s test statistics are taken from the Based on Mean row of the Test of Homogeneity of Variances table (see Appendix 4.6) As Sig = 0.611
> 0.05, HL-0 is accepted, meaning there is no statistically significant difference in variance between the groups of ages in students’ blended learning adoption
Hypothesis H0: There is no difference in means between the groups of ages in students’ blended learning adoption
Hypothesis H1: There is a difference in means between the groups of ages in students’ blended learning adoption
The F test result from the ANOVA table indicates that there is no statistically significant difference in the means of blended learning adoption among different age groups of students, as evidenced by a significance value of 0.162, which is greater than the 0.05 threshold.
The analysis of blended learning adoption (BLA) indicates that students over 20 years old (Mean = 3.0991) demonstrate a similar capacity for adopting blended learning as students aged 18 to 20 (Mean = 3.0097) This suggests that both age groups exhibit comparable abilities in embracing blended learning methodologies.
Hypothesis HL-0: There is no difference in variance between the groups of majors in students’ blended learning adoption
Hypothesis HL-1: There is a difference in variance between the groups of majors in students’ blended learning adoption
In SPSS 26.0, the Levene’s test statistics are taken from the Based on Mean row of the Test of Homogeneity of Variances table (see Appendix 4.6) As Sig = 0.039
< 0.05, HL-0 is rejected, meaning there is a statistically significant difference in variance between the groups of majors in students’ blended learning adoption
Hypothesis H0: There is no difference in means between the groups of majors in students’ blended learning adoption
Hypothesis H1: There is a difference in means between the groups of majors in students’ blended learning adoption
The Welch test results, as indicated in the Robust Tests of Equality of Means table (see Appendix 4.6), show a significance value of 0.000, which is less than the threshold of 0.05 This leads to the rejection of the null hypothesis (Ho), confirming a statistically significant difference in the means of blended learning adoption among different student major groups.
The analysis of blended learning adoption (BLA) reveals a descending trend in mean values among different student groups: Advertising students lead with a mean of 3.3148, followed by Applied Finance at 3.1146, International Business at 2.9936, and Marketing at 2.8977 This indicates that Advertising students have the highest proficiency in adopting blended learning, while Marketing students show the least capability Additionally, the International Business group outperforms the Applied Finance group in blended learning adoption.
Hypothesis HL-0: There is no difference in variance between the groups of income in students’ blended learning adoption
Hypothesis HL-1: There is a difference in variance between the groups of income in students’ blended learning adoption
In SPSS 26.0, the Levene’s test statistics are taken from the Based on Mean row of the Test of Homogeneity of Variances table (see Appendix 4.6) As Sig = 0.001
< 0.05, HL-0 is rejected, meaning there is a statistically significant difference in variance between the groups of income in students’ blended learning adoption
Hypothesis H0: There is no difference in means between the groups of income in students’ blended learning adoption
Hypothesis H1: There is a difference in means between the groups of income in students’ blended learning adoption
The Welch test results indicate a statistically significant difference in means among the income groups regarding students' adoption of blended learning, as evidenced by a significance value of 0.000, which is less than the 0.05 threshold.
The analysis of blended learning adoption (BLA) reveals a decreasing trend in mean values based on family income levels, as detailed in the Descriptives table (refer to Appendix 4.6) Specifically, students from families earning between 10 to [insert income level] demonstrate lower levels of BLA, indicating a correlation between family income and the adoption of blended learning methodologies.
20 million VND/month (Mean = 3.5821), students with family income of above 20 to 40 million VND/month (Mean = 3.0626), students with family income of above
40 million VND/month (Mean = 2.7173) It can be concluded that the higher the family income level of students, the lower their adoption of blended learning.
Discussions
The proposed research model identifies eight independent factors that significantly impact students' adoption of blended learning: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), self-efficacy (SE), instructor characteristics (IC), and course flexibility (CF).
The Cronbach’s Alpha reliability test confirmed that all independent factors exhibited reliability, although two measurement items, HM5 and CF6, were deemed statistically insignificant and excluded from further analysis The remaining observed variables were statistically significant and retained for subsequent examination Exploratory factor analysis (EFA) indicated that all observed variables demonstrated strong convergent and discriminant validity, leading to the extraction of eight independent factors and one dependent factor, aligning with the research conceptual model Additionally, Pearson’s correlation coefficient analysis revealed significant relationships among the independent variables.
The study found significant correlations between blended learning adoption (BLA) and the variables EE, SI, FC, HM, SE, IC, and CF Additionally, there were no significant correlations among the independent variables, and multicollinearity issues were not detected.
The ANOVA test reveals significant differences in the means of blended learning adoption among students based on gender, major, and income However, no statistical differences were found in blended learning adoption when comparing different age groups.
After conducting linear regression analysis, the standardized linear regression equation is described as follows:
The research revealed that "hedonic motivation" significantly influences students' adoption of blended learning (β = 0.243, Sig 0.000), highlighting that enjoyment and satisfaction are key factors driving this choice This aligns with earlier studies by Masitah Musa et al (2022) and Ardvin Kester S Ong and Michael N Young (2023) Following closely is "performance expectancy" (β = 0.208, Sig = 0.000), indicating that students are also motivated by their belief that blended learning will enhance their academic performance and help them achieve educational goals, consistent with findings from Arumugam Raman and Yahya Don (2013), Cao Hao Thi et al (2014), and NoorUl Ain et al (2015).
The "social influence" factor emerged as the third most significant determinant in students' adoption of blended learning, with a β value of 0.200 and a significance level of 0.000 This indicates that students' decisions are heavily influenced by the views of their social referents, including parents, instructors, and peers, alongside the expected academic benefits and enjoyment of the blended learning experience These findings align with existing research in the field.
BLA = 0.243*HM + 0.208*PE + 0.200*SI + 0.195*CF + 0.170*EE +
0.160*FC + 0.158*IC + 0.149*SE reported in studies conducted by Alireza Khatony et al (2020), Zhaoli Zhang et al
The study identified "course flexibility" (β = 0.195, Sig = 0.000) as a crucial factor in students' adoption of blended learning, aligning with findings from Mehmet Kokoc (2019) Blended learning integrates in-person instruction with online elements, allowing for adjustments based on learning objectives and course content to enhance student scheduling flexibility This adaptable format enables students to access diverse learning materials at any time and from various devices.
The study revealed a significant positive relationship between "effort expectancy" and students' adoption of blended learning (β = 0.170, Sig = 0.000), supporting findings from previous research by Silvana Dakduk et al (2018) and Yeop et al (2019) The ease of use of platforms and software plays a crucial role, as students are more inclined to adopt blended learning when they find the technology user-friendly and easy to navigate A seamless technology interface facilitates a smoother transition for students adapting to this innovative learning method.
Facilitating conditions, including physical and technical infrastructure, significantly impact students' adoption of blended learning, as evidenced by a beta value of 0.160 and a significance level of 0.000, making it the sixth most influential factor This aligns with findings from previous research by Yan Dang et al (2016) and Niraj Mishra et al (2022) For successful blended learning implementation, institutions must provide adequate infrastructure and resources, encompassing both physical classroom facilities and essential digital platforms, bandwidth, and network connectivity to support online learning components.
Instructor characteristics significantly impact students' adoption of blended learning, as evidenced by a positive relationship (β = 0.158, Sig = 0.000) This aligns with findings from Mahmoud Abou Naaj et al (2012) and Al-Busaidi and Kamla Ali (2012), highlighting the crucial role instructors play in student engagement with blended learning Timely support and guidance from instructors enhance the blended learning experience, as they provide essential feedback and advice for student improvement Instructors with strong technical skills and experience in blended course implementation can effectively integrate online and in-person components, fostering regular interaction through digital platforms, which positively influences student acceptance of this instructional model.
Self-efficacy, defined as students' confidence in their ability to effectively utilize blended learning systems, was found to have the smallest impact on their adoption of this instructional format (β = 0.149, Sig = 0.000) Students may struggle to navigate blended learning environments, particularly if they lack prior experience Therefore, support from instructors, helpdesks, and peers is essential in enhancing their initial experiences and boosting their confidence This aligns with findings from Yan Dang et al (2016) and Kurniawan et al (2021), highlighting the importance of assistance in fostering self-efficacy in blended learning contexts.
This research expands upon the UTAUT2 model by introducing three additional factors influencing the adoption of blended learning: self-efficacy (SE), instructor characteristics (IC), and course flexibility (CF) While SE and IC have been explored separately in previous studies, their combined effect, along with the newly introduced CF, had not been examined before The findings indicate that all three factors positively impact the adoption of blended learning, with course flexibility ranked 4th, instructor characteristics ranked 7th, and self-efficacy ranked 8th, highlighting the importance of flexibility in blended learning environments.
To enhance the effectiveness of blended learning in schools, it is crucial to emphasize the roles of IC and SE, which are also potential areas for further research on blended learning adoption The introduction of CF as a new variable, ranked 4th in influence, highlights its significant impact on blended learning adoption This underscores the importance of flexibility in blended learning as a key factor for increasing student engagement, particularly in international partnership programs Additionally, CF presents a promising avenue for future research in this domain.
Hypotheses Independent variables β Sig Results
H1: Performance expectancy has a positive influence on students’ blended learning adoption
H2: Effort expectancy has a positive influence on students’ blended learning adoption
H3: Social influence has a positive influence on students’ blended learning adoption
H4: Facilitating conditions have a positive influence on students’ blended learning adoption
H5: Hedonic motivation has a positive influence on students’ blended learning adoption
H6: Self efficacy has a positive influence on students’ blended learning adoption
H7: Instructor characteristics have a positive influence on students’ blended learning adoption
H8: Course flexibility has a positive influence on students’ blended learning adoption
Source: Concluded by author from data analysis results
Source: Concluded by author from data analysis results
Chapter 4 includes an introduction to the WSU international partnership program and the results of data analysis to test the hypotheses that were previously established
The analysis reveals that all eight independent factors positively and significantly influence students' adoption of blended learning, aligning with previous research findings Notably, this study uniquely emphasizes the blended learning adoption among students participating in an international partnership program.
By utilizing a range of statistical methods, the findings not only demonstrate statistical significance but also provide practical value for improving the implementation of blended learning within the institution.
CONCLUSIONS AND IMPLICATIONS
Conclusions
Research on the adoption of blended learning in WSU's international partnership program highlights key factors influencing students' acceptance of this approach, which integrates online and in-person learning Although blended learning is not a novel concept globally, its significance has surged in Vietnam due to the COVID-19 pandemic Yet, many institutions have since abandoned this method, prompting educators to reassess its effective implementation Transitioning to blended learning requires a shift from a teacher-centered to a student-centered model, emphasizing students' roles in its success Consequently, understanding the factors affecting students' adoption of blended learning has become essential for modern educational institutions, especially given the lack of research in this area, particularly within international partnership contexts.
The proposed model identifies students' blended learning adoption as a dependent variable influenced by eight key independent factors: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, self-efficacy, instructor characteristics, and course flexibility By evaluating these factors, the research aims to improve the effectiveness of blended learning implementation, ensuring optimal outcomes for student performance This approach seeks to enhance the distinction and competitiveness of WSU's international partnership program within the education market The measurement scale for the research is based on established studies, including works by Taylor and Todd (1995), Venkatesh et al (2003), Ball and Levy (2008), Venkatesh et al (2012), Al-Busaidi and Kamla Ali (2012), and Wu and Liu.
(2013), Huang and Kao (2015), Brusso (2015), Sury Ravindran et al (2016), Sattari et al (2017), Lawless (2019), Chao (2019), White (2019), Moorthy et al (2019), Mehmet Kokoc (2019), Chen et al (2020), Georgakopoulos et al (2020), Lu et al
(2020), S M Lee and Lee (2020), Azizi et al (2020), Abu-Gharrah and Aljaafreh
(2021), Amparo (2021), Bordoloi et al (2021) The collected data were then analyzed using a variety of statistical methods facilitated by SPSS 26.0
The analysis reveals that all eight independent factors—performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, self-efficacy, instructor characteristics, and course flexibility—positively impact students' adoption of blended learning Notably, hedonic motivation (β = 0.243) has the strongest influence, followed by performance expectancy (β = 0.208) and social influence (β = 0.200) Course flexibility (β = 0.195) and effort expectancy (β = 0.170) rank fourth and fifth, while facilitating conditions (β = 0.160) and instructor characteristics (β = 0.158) follow Self-efficacy has the weakest influence with a β value of 0.149 These findings are consistent with previous studies, such as those by Arumugam Raman and Yahya Don (2013) and others, which also found a positive correlation between these factors and blended learning adoption However, the ranking of these factors differs in this study, likely due to its focus on students in an international partnership program.
Implications
Table 5.1 Descriptive statistics results of observed variables
PE1 I find blended learning beneficial to my studies 3.40 0.781 PE2
By employing blended learning, my ability to achieve good academic performance is significantly enhanced
PE3 Blended learning enables me to complete my learning tasks quickly 3.48 0.768
PE4 Engaging in blended learning boosts my learning productivity 3.50 0.754
PE5 Using blended learning improves my understanding of the course materials 3.44 0.776
PE6 Blended learning significantly fulfills my learning and research needs 3.41 0.831
EE1 I find it easy to learn how to use blended learning in my studies 2.50 0.785
EE2 I find it effortless to become proficient at using blended learning 2.57 0.815
EE3 My experience with blended learning is smooth and comprehensible 2.56 0.766
EE4 Applying the blended learning system in class is not a challenge for me 2.46 0.813
SI1 Important people to me believe that I should adopt blended learning for my studies 3.75 0.787
The individuals who have a significant impact on my behavior think I should utilize blended learning for my studies
SI3 Blended learning has been recommended to me by people whose opinions I highly respect 3.81 0.830
SI4 I believe that other classmates also use blended learning to support their studies 3.77 0.731 SI5
It can be observed that my instructors have greatly encouraged the implementation of blended learning
My institution possesses the required resources to support my implementation of blended learning in my studies
FC2 I am equipped with the essential information to implement blended learning in my studies 3.18 0.795 FC3
Blended learning aligns well with the information and communication technology tools I use in my studies
Blended learning is in harmony with other pedagogical methods my instructors employ in class
The help desk is ready to assist with any issues that may arise during the blended learning implementation
HM1 Blended learning activities make me feel fun 3.27 0.806 HM2 I experience a sense of enjoyment when using blended learning for my studies 3.30 0.789
HM3 I hold a positive view on utilizing the blended learning approach 3.25 0.818
HM4 Blended learning courses enhance the level of interest within the classroom 3.28 0.800
HM6 I derive the pleasure from engaging in both e- learning and traditional classes 3.33 0.748
SE1 I am confident in my ability to use computer and software applications for blended learning courses 3.73 0.734
SE2 I have confidence in my ability to employ different methods within the blended learning courses 3.74 0.778
SE3 I have the capability to fulfill the required tasks by using the tools available in blended learning 3.80 0.750 SE4
Despite having no prior experience with a blended learning system, I am capable of using it effectively
I am able to use the blended learning system by relying solely on the system manuals as a reference guide
IC1 I am pleased with how readily accessible and available the instructors are to assist me 3.33 0.815
IC2 The instructors provide timely responses to my queries and actively participate in discussions 3.28 0.798
The instructors effectively communicate the course objectives and assignments, ensuring clarity and conciseness, which fosters a better understanding of expectations Additionally, they provide clear guidelines for engaging in course activities, enhancing the overall learning experience.
IC5 The instructors effectively foster interaction and engagement among students 3.30 0.787
IC6 The instructors demonstrate a high level of proficiency in using blended learning technologies 3.34 0.792
CF1 Blended learning allows flexible access to lectures and learning activities from anywhere and anytime 3.07 0.733
CF2 With the help of blended learning, I can easily access the learning materials 3.03 0.773
CF3 Blended learning courses offer a broad selection of materials for me to choose from 3.04 0.791 CF4
Blended learning courses enable me to concentrate on learning activities that align with my individual needs and preferences
CF5 With blended learning, it is effortless for me to stay updated and keep track of all the learning activities 3.01 0.747
Source: Author’s data processing results from SPSS 26.0
Hedonic motivation is the most influential factor in the adoption of blended learning, evidenced by a beta value (β) of 0.243 With a mean score of 3.29, students generally agree on the importance of hedonic motivation in this context However, the specific statement HM3 has a slightly lower average score of 3.25, indicating that some students may have a less favorable perception of blended learning methods.
Positive learning significantly enhances the effectiveness of the learning process, influenced by factors such as needs, motivations, interests, attitudes, and environmental elements To promote this positive disposition, instructors should create engaging content, employ active teaching methods, and utilize modern technologies to stimulate interest and creativity In blended learning programs, maintaining student motivation and engagement is essential; clear learning expectations and recognition of achievements can enhance this Additionally, fostering a welcoming and inclusive environment encourages participation and interaction, while promoting feedback and teamwork helps cultivate a strong sense of community among students.
Empowering students is essential for enhancing their motivation, as suggested by Kelly (2014) Providing choices in their learning fosters a sense of autonomy and control By utilizing discussion tools, contextual commenting features, and collaboration options, educational institutions can encourage students to contribute their own materials, thereby promoting ownership of their education Additionally, instructors should employ a variety of teaching methods, including group projects, gamification, and interactive multimedia, to maintain student engagement Learning games can serve as effective tools in the classroom, particularly for students who finish assignments early or require additional support Educators can find topic-related learning games online, which often align with specific learning objectives and facilitate quiz-style reviews.
Andrew Miller (2012) emphasizes that meaningful activities are crucial for student engagement, highlighting the need for learning tasks to be purposeful, enjoyable, and relevant These activities should align with defined learning objectives and maintain a balance between online and in-person elements, as well as individual and collaborative efforts in a blended learning environment It's important to consider the ratio of individual to group assignments and the mix of synchronous and asynchronous activities, ensuring adequate instructor support while promoting student autonomy and collaboration.
Tanner (2012) emphasizes the importance of flexible activities, such as reflective journals and guiding questions, to enhance student cognition and self-assessment Active learning is essential in effective blended course design, requiring students to engage deeply with the material through discussion, writing, and real-world application When creating assignments, it is vital to address students' unique needs and ensure tasks are both interesting and challenging, connecting the content to their personal experiences and real-life scenarios.
Students, regardless of the size of their achievements, deserve to take pride in their efforts Offering praise and encouragement fosters a positive self-image and enhances happiness in their learning journey This support significantly boosts their self-confidence and self-esteem Additionally, implementing rewards can be effective for instructors aiming to promote students' progress and positive learning attitudes.
Performance expectancy plays a crucial role in the adoption of blended learning, with a significant influence represented by β = 0.208 The average rating of 3.44 indicates that students generally recognize the importance of performance expectancy in this educational approach However, the lower mean score of 3.39 for the specific statement PE2 reveals that students feel blended learning has not yet substantially enhanced their academic performance.
Transitioning from traditional high school teaching methods to a self-directed learning approach can pose challenges for students While they may grasp the new instructions, the shift demands that they independently explore and identify the strategies that work best for them to optimize their learning outcomes.
In addition to fostering student autonomy, various course-related factors—including objectives, tasks, structure, activities, assignments, and assessment methods—play a crucial role in enhancing academic performance To maximize student success, these elements must be organized and structured effectively, allowing for a seamless integration of the course's operations into the learning process.
To ensure a successful learning journey, it is crucial to define clear learning objectives that are specific, measurable, achievable, relevant, and time-bound These objectives should cater to students’ needs and preferences while aligning with curriculum standards and desired competencies in a blended learning environment By effectively communicating these goals, instructors can create a structured plan that empowers students with the necessary knowledge and skills to succeed Each activity and assignment must include clear instructions, expectations, and rubrics that correspond with the established learning outcomes, fostering collaboration and enhancing the overall educational experience.
A well-structured course outline is essential for both students and instructors in blended learning environments It helps students stay on track while ensuring that the course progresses at the intended pace The outline should clearly articulate course goals, outline assignments, papers, tests, and their respective due dates It is also important to communicate expectations regarding student participation and attendance Furthermore, the outline should detail the teaching resources and materials that will be used to deliver the blended learning content effectively.
A well-crafted curriculum should incorporate a variety of blended teaching methods, such as online modules, videos, discussions, and hands-on activities, ensuring a seamless integration of online and offline components that support one another Schools must identify the subjects suitable for blended learning and specify which content will be delivered online, along with the appropriate blended learning model for each subject Maintaining consistency throughout the program is essential for effective learning outcomes.
When designing a blended learning course, it is essential to ensure students grasp the material and have a clear pathway through the content Kelly (2014) highlights the importance of students understanding the relevance of tasks within blended learning Instructors can enhance engagement by explicitly connecting course skills and knowledge to real-world applications Proper labeling and organization of course materials, as noted by Shea and Bidjerano (2003), create a clear structure that aids student comprehension and time management Additionally, instructors should encourage student feedback through summaries, requests for clarification, and open-ended questions at the beginning of each session, reinforcing understanding at multiple points throughout the course.
Effective online learning design involves dividing course content into logical, modular sections, with each module focused on a key idea and accompanied by estimated completion times, relevant readings, activities, and objectives Presenting information in manageable, bite-sized chunks enhances comprehension, while formatting text with concise paragraphs, headings, and bullet points improves readability For audio and video content, brief summaries and segment durations should be provided, with multimedia elements kept to 2-15 minutes to optimize retention Incorporating brief reviews or practice questions after each topic helps reinforce understanding and prevent information overload, as emphasized by Karpicke and Blunt (2011).
Limitations and recommendations
The research focused exclusively on the adoption of blended learning among students in the WSU international partnership program, primarily due to limitations in time and resources This narrow scope prevents any comparative analysis with other international programs at UEH-ISB or within a wider context Consequently, the inability to access students from different institutions is a significant limitation, confining the results to the specific institution being studied.
The study focused exclusively on full-time undergraduate students, which may restrict the applicability of the findings to other groups, like postgraduate students, who might have different experiences and views on blended learning Additionally, the use of convenience sampling may result in participants with similar learning behaviors, possibly missing out on the diverse teaching methods employed by instructors.
The study acknowledges a limitation in its focus on only eight key constructs influencing blended learning adoption, potentially overlooking relevant independent variables While it explored students' adoption of blended learning, it did not consider the moderating effects of demographic factors such as age, gender, education level, and socioeconomic status Including these contextual elements could enhance the understanding of the research topic.
The sample was chosen for convenience instead of random sampling, which may limit its representativeness of the target population Consequently, this approach could undermine the accuracy of the collected data, as it only includes participants who were readily accessible.
The use of self-reported survey data, while a standard methodology, is subject to inherent biases such as social desirability and the accuracy of responses Despite the author's efforts to promote honest reporting, these measures may not completely mitigate the limitations associated with self-reported data.
To enhance the relevance of research findings, the author suggests expanding future studies to include a diverse participant pool from various programs and institutions This cross-institutional approach aims to identify how factors influencing blended learning adoption differ across educational contexts and student demographics Additionally, the author emphasizes the importance of including postgraduate perspectives alongside those of undergraduates By examining both groups, future research can reveal whether the determinants affecting blended learning adoption vary between these educational stages, providing valuable insights into the changing needs and preferences of students throughout their academic journeys.
To enhance the research design, the authors suggest incorporating a broader range of independent variables and measurement items, including demographic factors like age, gender, and academic major This addition could reveal how these characteristics moderate students' adoption of blended learning, offering valuable insights into the relationship between individual attributes and the uptake of this educational approach.
The researchers emphasize that the current study's reliance on self-reported data from students limits its scope They recommend future investigations include interviews with key stakeholders like instructors and parents to provide a more comprehensive evaluation of students' experiences and perceptions of blended learning in higher education Additionally, the authors suggest that future research should explore the factors influencing blended learning adoption from the instructors' perspective, offering valuable insights into the pedagogical, technological, and institutional elements that shape faculty attitudes and approaches By integrating both student and instructor viewpoints, a deeper understanding of the dynamics influencing blended learning adoption can be achieved.
Chapter 5 summarizes the research findings and discusses the implications of the relationships among eight independent variables—performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, self-efficacy, instructor characteristics, and course flexibility—on students’ adoption of blended learning The goal is to offer valuable insights aimed at enhancing the adoption of blended learning within the WSU international partnership program.
The chapter highlights the study's limitations and proposes future research directions, creating a framework for enhancing the understanding of factors that affect blended learning adoption across various educational settings.
1 Ajzen, I (1991) “The theory of planned behavior”, Organizational behavior and human decision processes, 50(2), 179–211
2 Ain, N., Kaur, K., & Waheed, M (2016) “The influence of learning value on learning management system use: An extension of UTAUT2”, Information Development, 32(5), 1306-1321
3 Allen, I E., Seaman, J., & Garrett, R (2007) Blending in: The extent and promise of blended education in the United States, Sloan Consortium, PO Box 1238,
4 Arain, A A., Hussain, Z., Rizvi, W H., & Vighio, M S (2019) “Extending UTAUT2 toward acceptance of mobile learning in the context of higher education”, Universal Access in the Information Society, 18, 659-673
5 Azizi, S M., Roozbahani, N., & Khatony, A (2020) “Factors affecting the acceptance of blended learning in medical education: Application of UTAUT2 model”, BMC Medical Education, 20(1)
6 Ahmed, H M S (2010) “Hybrid E-Learning Acceptance Model: Learner Perceptions”, Decision Sciences Journal of Innovative Education, 8(2), 313-
7 Arbaugh, J.B (2000) “Virtual classroom characteristics and student satisfaction in Internet based MBA courses”, Journal of Management Education, 24(1), 32–54
8 Abou Naaj, M., Nachouki, M., & Ankit, A (2012) “Evaluating student satisfaction with blended learning in a gender-segregated environment”,
Journal of Information Technology Education: Research, 11(1), 185-200
9 Al-Busaidi, K A., & Al-Shihi, H (2012) “Key factors to instructors’ satisfaction of learning management systems in blended learning”, Journal of
10 Abu Seman, S A., Hashim, M J., Mohd Roslin, R., & Mohd Ishar, N I
(2018) “Millennial learners’ acceptance and satisfaction of blended learning environment”
11 Abu Gharrah, A & Aljaafreh, A (2021) “Why students use social networks for education: extension of UTAUT2”, Journal of Technology and Science Education, Vol 11, No 1, 53-66, doi: 10.3926/jotse.1081
12 Amparo, M.M (2021) “Factors affecting learners’ performance on blended learning: a literature review paper”, Global Scientific Journals, Vol 9, No 3, pp 1775-1795
13 Angelino, L M., Williams, F K., & Natvig, D (2007) “Strategies to engage online students and reduce attrition rates”, Journal of Educators Online, 4(2)
14 Balakrishnan, V., & Gan, C L (2016) “Students’ learning styles and their effects on the use of social media technology for learning”, Telematics and Informatics, 33(3), 808-821
15 Bandura, A (1977) “Self-efficacy: toward a unifying theory of behavioral change”, Psychological review, 84(2), 191
16 Bandura, A (1986) “The explanatory and predictive scope of self efficacy theory”, Journal of social and clinical psychology, 4(3), 359-373
17 Brownson, R C., Colditz, G A., & Proctor, E K (2018) Dissemination and implementation research in health: translating science to practice, Oxford
18 Barr, N., & Cary, J (2000) “Influencing improved natural resource management on farms”, Bureau of Rural Sciences, Canberra
19 Boyle, T., Bradley, C., Chalk, P., Jones, R., & Pickard, P (2003) “Using blended learning to improve student success rates in learning to program”,
20 Ball, D M., & Levy, Y (2008) “Emerging educational technology: Assessing the factors that influence instructors’ acceptance in information systems and other classrooms”, Journal of Information Systems Education, 19(4), 431
21 Brusso, R C (2015) Employee behavioral intention and technology use: mediating processes and individual difference moderators Old Dominion
22 Bordoloi, R., Das, P & Das, K (2021) “Perception towards online/blended learning at the time of Covid-19 pandemic: an academic analytics in the Indian context”, Asian Association of Open Universities Journal, doi:
23 Bougie, R., & Sekaran, U (2019) Research methods for business: A skill building approach John Wiley & Sons
24 Chen, W S., & Yao, A Y T (2016) “An empirical evaluation of critical factors influencing learner satisfaction in blended learning: A pilot study”,
Universal Journal of Educational Research, 4(7), 1667-1671
25 Can, G., & Cagiltay, K (2006) “Turkish prospective teachers’ perceptions regarding the use of computer games with educational features” Journal of
26 Carman, J M (2005) “Blended learning design: Five key ingredients”
27 Cheng, P., OuYang, Z., & Liu, Y (2019) “Understanding bike sharing use over time by employing extended technology continuance theory”,
Transportation research part A: policy and practice, 124, 433-443
28 Chigeza, P & Halbert, K (2014) “Navigating e-learning and blended learning for pre-service teachers: Redesigning for engagement, access and efficiency”,
Australian Journal of Teacher Education, 39(11), 133–146
29 Clarke, A (2008) E-learning skills, Palgrave Macmillan
30 Carman, J M (2005) “Blended learning design: Five key ingredients”,
31 Chao, C M (2019) “Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model”,
32 Cronbach, L J (1951) Coefficient alpha and the internal structure of tests psychometrika, 16(3), 297-334
33 Dalsgaard, C (2006) “Social software: E-learning beyond learning management systems” European Journal of Open, Distance and e- learning, 9(2)
34 Dearing, J W., Kee, K F., & Peng, T Q (2012) “Historical roots of dissemination and implementation science”, Dissemination and implementation research in health: Translating science to practice, 55, 71
35 Davis, F D., Bagozzi, R P., & Warshaw, P R (1992) “Extrinsic and intrinsic motivation to use computers in the workplace 1”, Journal of applied social psychology, 22(14), 1111-1132
“Examining student satisfaction and gender differences in technology- supported, blended learning”, Journal of Information Systems Education, 27(2), 119
37 DeLone, W H., & McLean, E R (2003) “The DeLone and McLean model of information systems success: a ten-year update”, Journal of management information systems, 19(4), 9-30
38 Driscoll, M (2010) Web-based training: Creating e-learning experiences,
39 Davis, F.D., Bagozzi, R.P & Warshaw, P.R (1989) “User acceptance of computer technology: A comparison of two theoretical models”, Management
40 Dakduk, S., Santalla-Banderali, Z., & Van Der Woude, D (2018)
“Acceptance of blended learning in executive education”, SAGE Open, 8(3)
41 Delialioğlu, ệ (2012) “Student engagement in blended learning environments with lecture-based and problem-based instructional approaches”, Journal of Educational Technology and Society, 15(3), 310–322
42 Dobbs, L & Moore, C (2002) “Engaging communities in area-based regeneration: the role of participatory evaluation”, Policy Studies, 23(3), 151-
43 Dziuban, C., Hartman, J., Juge, F., Moskal, P., & Sorg, S (2006) “Blended learning enters the mainstream”, The handbook of blended learning: Global perspectives, local designs, 195(3), 206-218
44 Dziuban, C., Graham, C R., Moskal, P D., Norberg, A., & Sicilia, N (2018)
“Blended learning: the new normal and emerging technologies”, International journal of educational technology in higher education, 15(1), 1-16
45 Dziuban, C D., Moskal, P., & Hartman, J (2005) “Higher education, blended learning, and the generations: Knowledge is power-No more”, Elements of quality online education: Engaging communities Needham, MA: Sloan Center for Online Education, 88, 89
46 DeVellis RF (2003) Scale development: theory and applications, SAGE,
47 Evans, R H (1996) “An analysis of criterion variable reliability in conjoint analysis”, Perceptual and motor skills, 82(3), 988-990
48 Foster, K., McCloughen, A., Delgado, C., Kefalas, C., & Harkness, E (2015)
“Emotional intelligence education in pre-registration nursing programmes: An integrative review”, Nurse Education Today, 35(3), 510-517
49 Fishbein, M., & Ajzen, I (1975) “Belief, attitude, intention, and behavior: An introduction to theory and research”, 181-202
50 Gartrell, John W & Gartrell, C David (1979) “Status, knowledge, and innovation”, Rural Sociology, Vol.44
51 Graham, C R., Woodfield, W., & Harrison, J B (2013) “A framework for institutional adoption and implementation of blended learning in higher education”, The internet and higher education, 18, 4-14
52 Gambetta, D (1988) “Can We Trust Trust?”, Trust: Making and Breaking Cooperative Relations, Oxford: Blackwell, 213-237
53 Garrison, D R., & Kanuka, H (2004) “Blended learning: Uncovering its transformative potential in higher education”, The internet and higher education, 7(2), 95-105
54 Garrison, D R., & Vaughan, N D (2008) Blended learning in higher education: Framework, principles, and guidelines, John Wiley & Sons
55 Gefen, D., Karahanna, E., & Straub, D W (2003) “Trust and TAM in online shopping: An integrated model”, MIS Quarterly, 27(1), 51-90
56 Ghazali, R., Soon, C C., Has, Z., Hassan, S N S., & Hanafi, D (2018) “The effectiveness of blended learning approach with Student’s perceptions in control systems engineering course”, International Journal of Human and Technology Interaction, 2(2), 103–108
57 Goto, J., & Munyai, A (2022) “The acceptance and use of online learning by law students in a South African university: An application of the UTAUT2 model”, The African Journal of Information Systems, 14(1)
58 Graham, C R (2006) “Blended learning systems: Definition, current trends and future directions”, Handbook of blended learning: Global perspectives, local designs, San Francisco: Wiley, 3-21
59 Guoyan, S., Khaskheli, A., Raza, S A., Khan, K A., & Hakim, F (2021)
“Teachers’ self-efficacy, mental well-being and continuance commitment of using learning management system during COVID-19 pandemic: A comparative study of Pakistan and Malaysia”, Interactive Learning Environments, 1-23
60 Gehlbach, H., & Brinkworth, M E (2011) “Measure twice, cut down error:
A process for enhancing the validity of survey scales”, Review of general psychology, 15(4), 380-387
“Identifying factors of students’ failure in blended courses by analyzing students’ engagement data”, Education Sciences, Vol 10, No 9, p.242
62 Hooks, G.M., Napier, T.L & Carter, M.V (1983) “Correlates of adoption behaviors: the case of farm technologies”, Rural Social, 48: 308-323
63 Huang, C Y., & Kao, Y S (2015) “UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP”, Mathematical Problems in Engineering, 2015(1), 603747
Multivariate data analysis (5th ed.), Upper Saddle River, NJ: Prentice-Hall
66 Haig, B D (2005) “Exploratory factor analysis, theory generation, and scientific method”, Multivariate Behavioral Research, 40(3), 303-329
67 Hobjilă, A (2012) “Positive politeness and negative politeness in didactic communication–landmarks in teaching methodology”, Procedia-Social and Behavioral Sciences, 63, 213-222
68 Holley, D., & Dobson, C (2008) “Encouraging student engagement in a blended learning environment: The use of contemporary learning spaces”,
69 Hung, M.L., & Chou, C (2015) “Students’ perceptions of instructors’ roles in blended and online learning environments: A comparative study”, Computers and Education, 81, 315-325
70 Heinze, A & Procter, C (2004) “Reflections on the use of blended learning”,
71 Huang, J., & Phongsatha, T (2022) “Factors influencing the acceptance of blended learning by early childhood undergraduate students”, Scholar: Human Sciences, 14(2), 678-678
72 Jusoff, K., & Khodabandelou, R (2009) “Preliminary study on the role of social presence in blended learning environment in higher education”,
74 Kurniawan, R., Pramana, E., & Budianto, H (2021) “The adoption of blended learning in non-formal education using extended technology acceptance model”, Indonesian Journal of Information Systems, 4(1), 27-42
75 Kiener, M., Green, P., & Ahuna, K (2014) “Using the comfortability-in- learning scale to enhance positive classroom learning environments”, InSight:
76 Kokoỗ, M (2019) “Flexibility in e-Learning: Modelling Its Relation to Behavioural Engagement and Academic Performance”, Themes in eLearning, 12(12), 1-16
77 Kelly, H (2014) “A path analysis of educator perceptions of open educational resources using the technology acceptance model”, International Review of
Research in Open and Distance Learning, 15(2)
78 Karpicke, J D., & Blunt, J R (2011) “Retrieval practice produces more learning than elaborative studying with concept mapping”,
79 Lawless, P (2019) “What is blended learning?”
80 López-Pérez, M V., Pérez-López, M C., & Rodríguez-Ariza, L (2011)
“Blended learning in higher education: Students’ perceptions and their relation to outcomes”, Computers and Education, 56(3), 818-826
81 Lin, W S., & Wang, C H (2012) “Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task- technology fit”, Computers & Education, 58(1), 88-99
82 Li, Y., Duan, Y., Fu, Z., & Alford, P (2012) “An empirical study on behavioural intention to reuse e‐learning systems in rural China”, British Journal of Educational Technology, 43(6), 933-948
83 Lu, D.N., Le, H.Q & Vu, T.H (2020) “The factors affecting acceptance of e-learning: a machine learning algorithm approach”
84 Lee, S M., & Lee, D (2020) “Healthcare wearable devices: an analysis of key factors for continuous use intention”, Service Business, 14(4), 503-531
85 Lehman, R M., & Conceiỗóo, S C (2013) “Motivating and retaining online students: Research-based strategies that work”, John Wiley & Sons
86 McQuaid, R.W (1999) “The role of partnerships in urban economic regeneration”, International Journal of Public-Private Partnerships, 3-28
87 McQuaid, R.W (2000) “The theory of partnerships: why have partnerships?”, In: Public-private partnerships, Routledge, 27-53
88 Mellikeche, S., de Fatima Marin, H., Benítez, S E., de Lira, A C O., de Quirós, F G B., & Degoulet, P (2020) “External validation of the unified model of information systems continuance (UMISC): An international comparison”, International Journal of Medical Informatics, 134, 103927
89 Mitchell, A., & Honore, S (2007) “Criteria for successful blended learning”,
90 Musa, M (2022) “Student acceptance towards online learning management system based on UTAUT2 model”, Universiti Tun Hussein Onn Malaysia
91 Mitchell, I & R.W McQuaid (2001) “Developing models of partnership in economic regeneration”, In Public and Private Sector Partnerships – The Enterprise Governance, Sheffield: Sheffield Hallam University Press, 395-
92 Mishra, N (2022) “Student acceptance of social media in higher education:
An application of UTAUT2 model”, Thailand and The World Economy,
93 M Tayebinik & M Puteh (2013) “Blended Learning or E-learning?”
94 Moorthy K, Yee TT, T’ing LC, Kumaran VV (2019) “Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia”, Aust J Educ Technol, 35(4), 174–91
95 Mayer, R E., Heiser, J., & Lonn, S (2001) “Cognitive constraints on multimedia learning: When presenting more material results in less understanding”, Journal of educational psychology, 93(1), 187
96 Miller, A (2012) “Five best practices for the flipped classroom”, Edutopia,
97 McIver, J., & Carmines, E G (1981) Unidimensional scaling, Sage
98 Narayan, J & Naidu, S (2023) “A new contextual and comprehensive application of the UTAUT2 model post‐COVID‐19 pandemic in higher education”, Higher Education Quarterly, 78(1), 47-77
99 Nelson, J & Zadek, S (2000) “Partnership alchemy: New social partnerships in Europe”, Copenhagen Centre
100 Ngan, L (2011) “Effective student project management with peer interaction”, Blended learning: Maximization of teaching and learning effectiveness, 178-180
101 Norberg, A., Moskal, P D., & Dziuban, C D (2011) “A time-based blended learning model”, On the Horizon, 19(3), 207–216
102 Nunnally, J C., & Bernstein, I H (1994) Psychometric theory New York: McGraw-Hill
103 OECD Publishing (2008) “Public-private partnerships: In pursuit of risk sharing and value for money”, Organisation for Economic Co-operation and
104 Ong, A K S., & Young, M N (2023) “Evaluation of Factors Affecting Ubiquitous Online Experience Learning Modality during the Near End of COVID-19: A Case Study in the Philippines”, International Journal of Information and Education Technology, 13(7)
105 Padilla-Meléndez, A., Aguila-Obra, A R., & Garrido-Moreno, A (2013)
“Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario”, Computers and Education, 63, 306-317
106 Park, S Y (2009) “An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning research hypotheses”, Educational Technology and Society, 12, 150-162
107 Poirier, L., & Ally, M (2020) “Considering learning styles when designing for emerging learning technologies”, Emerging technologies and pedagogies in the curriculum, 153-167
108 Presser, H B (1969) “The role of sterilization in controlling Puerto Rican fertility”, Population Studies, 23(3), 343-361
109 Reed, J., J., Erickson, J, Ford, & N P Hall (1996) “The after effect of a residential marketing program Implication for understanding market transformation”, Boca Raton: Association of Energy Service Professionals
110 Rogers, E M., & Singhal, A (2003) “Empowerment and communication: Lessons learned from organizing for social change”, Annals of the International Communication Association, 27(1), 67-85
111 Rogers, E M., Singhal, A., & Quinlan, M M (2014) “Diffusion of innovations”, In: An integrated approach to communication theory and research, Routledge, 432-448
112 Raman, A., & Thannimalai, R (2021) “Factors impacting the behavioural intention to use E- learning at higher education amid the COVID-19 pandemic: UTAUT2 model”, Psychological Science & Education, 26(3)
113 Raman, A., & Don, Y (2013) “Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model”, International
114 Rovai, A.P & Jordan, H M (2004) “Blended learning and sense of community: a comparative analysis with traditional and fully online graduate courses”, International Review of Research in Open and Distributed Learning, 5(2), 1-13
115 Rudhumbu, N (2022) “Applying the UTAUT2 to predict the acceptance of blended learning by university students”, Asian Association of Open Universities Journal, 17(1), 15-36
116 Surry, D W., Ensminger, D C., & Haab, M (2005) “A model for integrating instructional technology into higher education”, British journal of educational technology, 36(2), 327-329
117 Sawang, S., Newton, C., & Jamieson, K (2013) “Increasing learners’ satisfaction/intention to adopt more e‐learning”, Education+ Training, 55(1), 83-105
118 Sabah, N M (2020) “Motivation factors and barriers to the continuous use of blended learning approach using Moodle: students’ perceptions and individual differences”, Behaviour & Information Technology, 39(8), 875-898
119 Selim, H.M (2007) “Critical success factors for e-learning acceptance: confirmatory factor models”, Computers & Education, Vol.49, No.2, 396-
Sánchez-Franco and Martín-Velicia (2011) explore how ego involvement influences the interplay between aesthetics, usability, and user commitment in electronic banking services and virtual travel communities Their research, published in the Online Information Review, highlights the significance of aesthetic appeal and usability in fostering user engagement and loyalty The findings suggest that a deeper understanding of these interaction effects can enhance the design and functionality of online services, ultimately improving user satisfaction and commitment.
121 Sattari, A., Abdekhoda, M., & Zarea Gavgani, V (2017) “Determinant factors affecting the web–based training acceptance by health students, applying UTAUT model”
122 Shea, P., & Bidjerano, T (2013) “Understanding distinctions in learning in hybrid, and online environments: An empirical investigation of the community of inquiry framework”, Interactive Learning Environments, 21(4),
123 Savery, J R (2005) “Characteristics of successful online instructors”, Journal of Interactive Online Learning, 4(2), 141-152
124 Torrisi-Steele, G (2011) “This thing called blended learning–a definition and planning approach”, Research and development in higher education: Reshaping higher education, 34, 360-371
125 Thorne, K (2003) Blended learning: how to integrate online & traditional learning, Kogan Page Publishers
126 Twigg, C A (2003) “Improving learning and reducing costs: Lessons learned from round I of the PEW grant program in course redesign”, Center for Academic Transformation, Rensselaer Polytechnic Institute Toy: NY
127 Tornatzky, L.G & Klein, K.J (1982) “Innovation characteristics and innovation adoptionimplementation: a meta-analysis of findings”, IEEE Transactions on Engineering Management, Vol.29, 28-45
128 Taghizadeh, M., & Hajhosseini, F (2021) “Investigating a blended learning environment: Contribution of attitude, interaction, and quality of teaching to satisfaction of graduate students of TEFL”, The Asia-Pacific Education Researcher, 30(5), 459-469
129 Taylor, S., & Todd, P (1995) “Assessing IT usage: The role of prior experience”, MIS quarterly, 561-570
130 Tabachnick, B G., & Fidell, L S (1996) Using multivariate statistics (3rd ed.) New York: Harper Collins
131 Tanner, K D (2012) “Promoting student metacognition”, Life Sciences Education, 11(2), 113-120
132 UNESCO, U (2020) “COVID-19 impact on education”, UNESCO
133 Venkatesh, V., Morris, M G., Davis, G B., & Davis, F D (2003) “User acceptance of information technology: Toward a unified view”, MIS quarterly, 425-478
134 Venkatesh, V Y L Thong, J., & Xu, X (2012) “Consumer acceptance and use of information Technology: Extending the unified theory of acceptance and use of technology”, MIS Quarterly, 36(1), 157-178
135 Vallerand, R J (1997) “Toward a hierarchical model of intrinsic and extrinsic motivation”, In: Advances in experimental social psychology, Academic Press, Vol.29, 271-360
136 Vygotsky, L S (1968) “Problema soznanjia”, in Psychologija grammatiki, Alexander A Léontiev and TB Riabovọ
137 Wu, J., & Liu, W (2013) “An empirical investigation of the critical factors affecting students’ satisfaction in EFL blended learning”, Journal of Language Teaching and Research, 4(1), 176
138 Wu, J H., Tennyson, R D., & Hsia, T L (2010) “A study of student satisfaction in a blended e-learning system environment”, Computers & education, 55(1), 155-164
139 White, J (2019) “Archive for the ‘blended learning models’ category for mastery-based approaches, consider a disruptive blended-learning model”
140 Yin, B., & Yuan, C H (2021) “Precision teaching and learning performance in a blended learning environment”, Frontiers in psychology, 12, 631125
141 Yeop, M A., Yaakob, M F M., Wong, K T., Don, Y., & Zain, F M (2019)
“Implementation of ICT policy (Blended Learning Approach): Investigating factors of behavioural intention and use behaviour”, International Journal of
142 Yu, T., Dai, J., & Wang, C (2023) “Adoption of blended learning: Chinese university students’ perspectives”, Humanities and Social Sciences Communications, 10(1), 1-16
143 Zacharis, G., & Nikolopoulou, K (2022) “Factors predicting University students’ behavioral intention to use eLearning platforms in the post‑pandemic normal: an UTAUT2 approach with Learning Value”,
144 Zhang, Y., Chen, T., & Wang, C (2020) “Factors influencing students’ willingness to choose blended learning in higher education”, Springer International Publishing
145 Zhang, Z., Cao, T., Shu, J., & Liu, H (2022) “Identifying key factors affecting college students’ adoption of the e-learning system in mandatory blended learning environments”, Interactive Learning Environments, 30(8), 1388-
1 Bộ Giáo dục và Đào tạo (2016) Thông tư số 12/2016/TT-BGDĐT của Bộ Giáo dục và Đào tạo: Quy định Ứng dụng công nghệ thông tin trong quản lý, tổ chức đào tạo qua mạng, ban hành ngày 22/04/2016
2 Cao Hào Thi và cộng sự (2014) “Sự chấp nhận và sử dụng đào tạo trực tuyến trên điện toán đám mây”, Tạp chí phát triển KH&CN, Tập 17, Số Q3-2014
3 Chính phủ (2018) Nghị định số 86/2018/NĐ-CP của Chính phủ: Quy định về hợp tác, đầu tư của nước ngoài trong lĩnh vực giáo dục, ban hành ngày
4 Chính phủ (2022) Nghị định số 24/2022/NĐ-CP của Chính phủ: Sửa đổi, bổ sung các Nghị định quy định về điều kiện đầu tư và hoạt động trong lĩnh vực giáo dục nghề nghiệp, ban hành ngày 06/04/2022
5 Nguyễn Văn Thản (2014) “Nghiên cứu mô hình chấp nhận dịch vụ công nghệ viễn thông OTT (Over-The-Top Content)”, Đại học Đà Nẵng, truy cập ngày 18/12/2023
6 Trần Trọng Đức và cộng sự (2022) “Các nhân tố tác động đến ý định sử dụng dịch vụ IoT tại cửa hàng bán lẻ của sinh viên tại Thành phố Hà Nội”, Tạp chí
Kinh tế&Phát triển, Số 301(2)
APPENDIX APPENDIX 1: INTERVIEW AND GROUP DISCUSSION OUTLINE
Phung Ngoc Van Anh is a master’s student at Ho Chi Minh University of Banking, focusing on research about the factors that influence students' adoption of blended learning Her study specifically examines the Western Sydney International Partnership Program at UEH-ISB in Ho Chi Minh City.