factors influencing students blended learning adoption a case study of western sydney international partnership program at ueh isb in ho chi minh city

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 STUDY

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 of this technological advancement, blended learning has emerged as a 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 as part of the digital transformation efforts to modernize institutions This approach fosters an interactive learning environment through diverse delivery methods, enhancing student engagement and skill development Notably, it reduces barriers to interaction in online settings, offering flexibility, depth of learning, and cost-effectiveness By restructuring curriculum design, blended learning encourages student initiative in online participation Researchers predict it will become the "new normal" in higher education, a trend further accelerated by the COVID-19 pandemic as universities increasingly adopt this preferred learning mode.

Blended learning is gaining popularity in education worldwide, yet it faces significant challenges in Vietnam due to a lack of familiarity with technology and inadequate access to necessary devices among students Many learners are used to traditional teaching methods that rely heavily on instructor guidance, which complicates the transition to self-directed learning required in blended environments This lack of support can lead to anxiety and decreased academic performance, as student engagement in online components often falls short of expectations Additionally, despite basic training in information technology for instructors, many lack the advanced skills needed for effective blended learning implementation The scarcity of quality learning resources and the high costs associated with technology further hinder the development of successful blended learning models in educational institutions.

Prior empirical studies have shown that cultural and contextual factors can significantly influence results, particularly between developed and developing nations Notably, there is a lack of research on the determinants of blended learning adoption among students in Vietnam, especially in Ho Chi Minh City, creating challenges for local research and development To achieve accurate results, it is crucial to investigate the unique characteristics and target audiences of various educational programs within institutions With the rise of blended learning, 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 meet 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 This study will assist school management in positively shaping students’ perceptions of blended learning, enhancing their adaptability and collaboration skills Ultimately, these improvements are expected to elevate students' learning performance and satisfaction with blended learning methods.

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 with three experienced school management executives were conducted to identify additional factors influencing students' adoption of blended learning and to refine any potentially confusing measurement items Additionally, a focus group discussion with ten undergraduate students was organized to evaluate the alignment between scholarly definitions of constructs and the perceptions of the target respondents.

Primary data was gathered via an online survey using a Google Forms questionnaire distributed to students' emails A five-point Likert scale was utilized to assess students' attitudes towards the factors influencing their adoption of blended learning.

The data collected was analyzed using SPSS version 26.0, employing 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 in 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 exploring 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 investigations.

The research develops a comprehensive scale for the topic by integrating insights from prior studies and introducing new metrics to enhance the existing scale system related to students' adoption of blended learning.

In highly competitive markets, institutions must effectively deliver high-quality education This study offers valuable insights for educators and administrators on students' adoption of blended learning The findings will aid school management in enhancing strategies within the quality assurance framework to support blended learning Ensuring the quality of blended education is essential, as satisfied students tend to be more motivated and committed, leading to improved learning outcomes compared to their dissatisfied peers.

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 the definitions of key terms and the theoretical framework related to the research topic It also reviews prior empirical studies in relevant fields, highlighting the gaps in existing research that this study aims to address.

This research presents a conceptual model alongside hypotheses designed to evaluate the impact of various factors 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, outlining the reasons for choosing the topic and defining the objectives and research questions Additionally, it specifies the subject matter and scope of the research.

The chapter outlines the statistical methodologies for sampling and data analysis, detailing the analytical approach that will be employed 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, and an online component that requires independent completion of tasks using supportive technology and internet resources (Hung and Chou, 2015; Padilla Meléndez et al., 2013).

Blended learning is defined as a student-centered approach that enhances learning experiences through the integration of face-to-face interaction and information technology (2011) It addresses individual learning needs by combining the advantages of online education with the engagement of traditional learning methods (Thorne, 2003).

Blended learning is an educational approach that merges online resources and interactive opportunities with conventional classroom teaching, as defined by Lawless (2019) Heinze et al (2004) further characterize blended learning as a multifaceted method that integrates various delivery techniques, teaching models, and learning styles, emphasizing open communication among all course participants.

Blended learning, as defined by Driscoll (2002), encompasses four key categories: the integration of web-based technology to achieve educational objectives, the combination of diverse pedagogical methods to enhance learning outcomes, the fusion of instructional technology with traditional face-to-face training, and the blending of technology with practical training tasks This multifaceted approach to education maximizes the effectiveness of learning experiences.

Blended learning, as defined by No.12/2016/TT-BGDDT, is an educational approach that integrates e-Learning with traditional face-to-face teaching methods This combination aims to improve training effectiveness and enhance the overall quality of education.

Blended learning merges traditional face-to-face instruction with online learning, offering a flexible program design This educational approach features a dynamic and active learning process, where part of the instruction occurs online and the rest in the classroom The integration of online and offline elements enhances the overall learning experience, making it more cohesive and effective.

Blended learning empowers students to go beyond the resources provided by their teachers, allowing them to explore a wide range of materials through various methods These include library searches, online discussions with peers, website and search engine inquiries, as well as utilizing educational portals, blogs, and tutorial software (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 promotes a shift from teacher-centered to student-centered learning, encouraging educators to focus more on the needs and engagement of students.

2.1.1.2 Different approaches to blended learning

Blended learning is a complex educational model that integrates in-person and online learning, as noted by Garrison and Vaughan (2008) According to Carman (2005), effective blended learning in corporate environments relies on five essential components: live events that engage and motivate learners, online content for independent study, collaborative opportunities for peer learning, assessments for tracking understanding, and timely reference materials to enhance knowledge retention and transfer.

Rovai and Jordan (2004) define blended learning as a method that prioritizes active learning through collaboration and the social construction of knowledge This perspective is further supported by Dzuiban, Hartman, and Moskal (2004), who characterize blended learning as a re-design of the instructional model.

- 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 well e-learning replicates the advantages of in-person lectures, the quality of the produced e-content, and the purpose of the e-course Blended learning requires sophisticated teaching strategies that involve not only face-to-face presentations but also regular monitoring of students' work, contributions, and activities within the e-course.

Adoption refers to the process of accepting or rejecting a decision, followed by its implementation, discontinuance, or modification by individuals or organizations (Kee, 2017) This process unfolds at both individual and organizational levels before spreading throughout the entire system.

Adoption refers to the ongoing acceptance and use of a product, service, or idea Customers typically undergo a process of knowledge acquisition, persuasion, decision-making, and confirmation before purchasing This process, as outlined by Rogers and Shoemaker (1971), is crucial for understanding how consumers embrace new offerings.

The adoption decision and its implementation are often separate processes that can occur at different times According to Reed et al (1996), these two phases of adoption are distinct and may not happen simultaneously.

In conclusion, adoption refers to the process through which individuals or organizations integrate new concepts, methods, ideas, services, goods, or products into their routines This process generally unfolds in a sequence of stages, starting with awareness or understanding of the innovation, leading to a decision to adopt or reject it, and culminating in the actual implementation and utilization 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 well-defined strategy that includes clear institutional guidelines and the formation of advisory groups Institutions must develop a coherent plan and ensure the availability of essential resources and time to effectively implement blended learning for targeted objectives By prioritizing strategy, educational institutions can make informed decisions about adopting and executing blended learning initiatives.

The structure component of blended learning encompasses the organizational elements that enhance and support this educational approach It involves crucial considerations regarding technology integration, pedagogical strategies, and administrative frameworks Essential aspects include governance structures, effective models for merging technology with teaching methods, the scheduling of blended learning activities, and evaluation techniques to assess the success of blended learning initiatives.

Support in blended learning involves institutions enhancing faculty performance through both technical and pedagogical assistance This includes offering training and resources to improve faculty members' technological skills and knowledge Furthermore, providing incentives can foster faculty engagement and commitment to blended learning practices.

To successfully implement blended learning, institutions should focus on the key elements of strategy, structure, and support By leveraging these frameworks, educational organizations can effectively integrate blended learning into their practices, ensuring a solid foundation for adoption and long-term success.

In addition to the framework proposed by Graham et al (2013), Porter et al

In 2016, a phased approach was proposed to help organizations transition towards a mature institutionalization of 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 and understanding blended learning approaches Individual educators are encouraged to experiment with these strategies in their classrooms, despite the absence of formal institutional policies This stage reflects a growing acknowledgment of the potential of blended learning, accompanied by limited support for faculty to explore its implementation.

In the adoption and early implementation stage, institutions have embraced blended learning as a key policy and begun to roll out related initiatives This phase is marked by the introduction of innovative programs that integrate blended learning into the curriculum, emphasizing initial implementation and experimentation with various blended learning practices.

In the mature implementation stage of blended learning, institutions have developed a robust framework that includes governance structures, scheduling, and evaluation mechanisms to support ongoing growth This integration of blended learning practices into the overall educational ecosystem ensures a strong foundation for sustained implementation and expansion, fostering the continuous development of innovative educational initiatives.

Social Cognitive Theory (SCT), introduced by Albert Bandura in 1986, explores human behavior through the interplay of three key elements: behavior, personal factors, and the environment This theory highlights the bi-directional interactions among these factors, which influence both individual and group behavior, enabling the prediction and modification of actions.

Social Cognitive Theory (SCT) emphasizes the interconnectedness of three key factors: behavior, personal attributes, and environmental influences The behavior factor examines the usage, performance, and 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 This triadic structure is fundamental to understanding how these factors continuously interact and shape one another within SCT.

Social Cognitive Theory (SCT) evaluates information technology usage through various constructs, including self-efficacy, which is the belief in one's ability to successfully perform specific behaviors Additionally, outcome expectations pertain to the anticipated results of these behaviors Other important constructs in the SCT model include performance, anxiety, affect, and personal outcome expectations, all of which contribute to a comprehensive assessment of information technology usage.

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 complete volitional control TPB introduces an important element, Perceived Behavioral Control, enhancing the understanding of how behaviors are influenced by both intention and perceived ability to perform them.

Perceived Behavioral Control (PBC) significantly affects both the intention to use a product and actual usage behavior According to the Theory of Planned Behavior (TPB), an individual's confidence in their ability to perform a behavior greatly influences their actions, with PBC serving as a key factor in this relationship.

According to Ajzen (1991), 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 The theory emphasizes that greater perceived behavioral control leads to stronger intentions to act, which in turn increases the probability of actual behavior Additionally, TPB suggests that attitudes, subjective norms, and perceived behavioral control play crucial roles in shaping these intentions.

Figure 2.3 Theory of Planned Behavior

From an information systems perspective, identifying specific causal factors that can enhance system acceptance is crucial by understanding the antecedents of user attitudes Unlike the Theory of Reasoned Action (TRA), which posits that attitudes are formed 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 asserts that users' acceptance of technology is primarily influenced by their perceptions of its usefulness and ease of use, as defined by Davis (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) explored the impact of personal characteristics, including self-efficacy, technology experience, and personal innovativeness, on learners' adoption of a Learning Management System (LMS) in blended learning and their intention to pursue full e-learning Data was gathered from 512 learners in Oman through a questionnaire The results revealed that personal innovativeness, perceived usefulness (PU), and satisfaction with the LMS significantly affect learners' intentions to engage in full e-learning.

Adopting a Learning Management System (LMS) in blended learning significantly enhances learners' intention to fully engage in e-learning This finding offers essential insights for both practitioners and researchers, aiding in the effective planning and strategy formulation for successful e-learning implementation.

A study by Nguyen Van Than (2014) utilized linear regression analysis to examine the acceptance of over-the-top technology services 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 a significant role In a related investigation, Yan Dang et al (2016) explored the influences on student learning in blended learning environments, considering factors from students, instructors, and institutional support Their research highlighted the importance of computer self-efficacy, instructor characteristics, and facilitating conditions on students' perceived accomplishment, enjoyment, and satisfaction Notably, the study found that these factors significantly impacted female students' learning experiences, whereas for male students, only instructor characteristics and facilitating conditions showed a significant effect, with computer self-efficacy having no substantial influence.

A 2020 study by Seyyed Mohsen Azizi, Nasrin Roozbahani, and Alireza Khatony explored the factors affecting the acceptance of blended learning in medical education using the UTAUT2 model The research, involving 225 Iranian medical students, aimed to identify variables influencing students' intentions to adopt blended learning Data analysis was conducted with SPSS-18 and AMOS-23 software, employing structural equation modeling to validate the hypotheses The study confirmed that constructs such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit significantly impacted students' adoption of blended learning Additionally, the findings indicated that students' behavioral intentions significantly influenced their actual engagement in blended learning, highlighting the UTAUT2 framework's effectiveness in identifying key factors affecting students' intentions.

A study by Zhaoli Zhang et al (2020) investigates the key factors influencing college students' adoption of e-learning systems in mandatory blended learning environments, highlighting the importance of effective implementation and management for continuous improvement in higher education The researchers introduce the Unified Technology Acceptance and System Success (UTASS) model, which combines self-reported questionnaire data and system log data to analyze students' online behavior, while also considering gender and major as moderator variables A total of 287 valid responses were collected through the e-learning system starC, and the data was analyzed using structural equation modeling The findings indicate that system quality (SQ), social influence (SI), and facilitating conditions (FC) positively influence students' behavioral intention (BI) to use the e-learning system, whereas information quality (IQ) does not significantly affect BI Additionally, no significant relationship was found between FC, BI, and use behavior (UB), with gender moderating the effects, revealing that male students are more influenced by system quality and social influence.

A study by Mahboubeh Taghizadeh and Fatemeh Hajhosseini (2020) explored graduate students' attitudes, interaction patterns, and satisfaction with blended learning technology, focusing on 140 TEFL graduate students at Iran University of Science and Technology Utilizing four questionnaires, the research assessed learner satisfaction, attitudes, interaction types, and teaching quality Findings revealed that participants had positive attitudes towards blended learning, with effective instructors successfully teaching TEFL concepts and fostering engaging online discussions Learner-instructor interaction was the most prevalent, and multiple regression analysis indicated that teaching quality significantly influenced student satisfaction more than interaction and attitudes This underscores the need for training online educators to improve their teaching effectiveness in digital environments.

Kurniawan et al (2021) conducted a study to identify the factors influencing the adoption of blended learning in non-formal education, particularly in developing countries like Indonesia, where research on this topic is limited The study emerged from the increased necessity of blended learning during the Covid-19 pandemic, which highlighted constraints in physical educational spaces Data was collected through a Google Forms questionnaire distributed to 566 users in Indonesian non-formal education institutions, utilizing existing scales to measure variables in the theoretical model Structural Equation Model (SEM) analysis was performed using SPSS and Amos software, revealing that out of thirteen hypotheses, nine were significant The most impactful factors included Social Influence affecting Perceived Usefulness, Compatibility with Existing Environment influencing Perceived Ease of Use, and Perceived Usefulness leading to Behavioral Intention, with Social Influence being the most significant factor in adopting blended learning in non-formal education This research enhances both theoretical and practical understanding of blended learning adoption, providing valuable insights for its successful implementation.

A study by Arumugam Raman and Raamani Thannimalai (2021) examined the factors influencing higher education students' intention to use e-learning during the Covid-19 pandemic, utilizing the UTAUT2 model The research aimed to evaluate students' behavioral intentions towards e-learning, as few studies have applied UTAUT2 in this context during the pandemic Employing snowball sampling, the study surveyed 159 students who utilized online learning platforms Data were gathered through a UTAUT2-adapted questionnaire and analyzed using PLS-SEM Findings indicated that social influence and habit significantly impacted students' behavioral intention to engage with e-learning, while performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, and price value showed no significant effect 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 suggests directions for future studies.

A study by Norman Rudhumbu (2022) examined the effectiveness of the UTAUT2 model in predicting university students' acceptance of blended learning Utilizing a quantitative approach, the research gathered data from 432 postgraduate students through a structured questionnaire The findings, validated by Confirmatory Factor Analysis (CFA) and analyzed via Structural Equation Modeling (SEM), indicated that factors such as performance expectancy, effort expectancy, social influences, facilitating conditions, and hedonic motivation positively impacted students' intentions to use blended learning However, habit and price value did not significantly affect these intentions Ultimately, the study confirmed that students' behavioral intentions significantly influenced their acceptance of blended learning, demonstrating the UTAUT2 model's suitability for measuring such intentions in a university context.

A study by Jueliang Huang and Thanawan Phongsatha (2022) examined the factors influencing blended learning acceptance among early childhood undergraduate students in China, utilizing a mixed-method research approach The research, grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) and the College and University Classroom Environment Inventory (CUCEI), surveyed 363 students and employed SEM analysis to assess significant factors affecting blended learning acceptance Findings indicated that social influence and classroom environment significantly impacted acceptance, while ease of use and convenience correlated with higher acceptance rates However, no 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 era, employing the UTAUT2 model with the addition of Learning Value An online survey of 314 students from various Greek universities identified key influences on students’ intentions, including performance expectancy, social influence, hedonic motivation, learning value, and habit The research also found that facilitating conditions and learning value directly impacted the actual use of eLearning platforms These insights contribute to a refined understanding of the post-Covid-19 eLearning landscape by integrating Learning Value into the analysis of students' engagement with eLearning platforms.

Jashwini Narayan and Samantha Naidu's 2023 study utilized a modified UTAUT2 model to investigate 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, employing covariance-based structural equation modeling to analyze the data The 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 did not significantly influence behavioral intentions The study introduced the novel construct of "COVID-19 fear," but it did not moderate the relationship between behavioral intentions and use behavior Additionally, the inclusion of trust, commitment, and comfortability as new independent variables improved the model's predictive efficacy.

A study by Tran Trong Duc et al (2022) explored the factors influencing students' intention to use IoT services in retail stores in Ha Noi City, gathering 355 survey responses The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) for hypothesis testing The findings revealed that performance expectancy and social influence significantly impacted students' intention, while hedonic motivation and facilitating conditions also contributed positively to their willingness to adopt IoT services.

A study by Ardvin Kester Ong and Michael Young (2023) examined the factors influencing students' continuous intention to enroll in the ubiquitous online experience (UOX) learning modality in the Philippines, particularly towards the end of the Covid-19 pandemic Utilizing the DeLone and McLean IS Success Model alongside the UTUAT2 framework, the researchers employed deep learning neural networks for analysis Their findings indicated that facilitating conditions significantly impact students' intentions to engage in UOX learning, 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 analysis reveals that different authors have identified various independent variables to assess their effects on adoption, with the significance of these variables differing among studies Certain variables consistently emerge as influential in the adoption process After thorough evaluation of prior research and its relevance to the current study, five key factors from the UTAUT2 model have been chosen to form the conceptual framework for this research, aiming to further investigate their impact on adoption.

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, including Facilitating Conditions (FC), Social Influence (SI), Performance Expectancy (PE), Hedonic Motivation (HM), and Effort Expectancy (EE) Previous adoption studies have shown that these factors play a crucial role in shaping outcomes and experiences within the program.

Several key factors influencing students' adoption of blended learning in the program were examined, with a focus on Self Efficacy (SE) and Instructor Characteristics (IC), which were integrated into the model to enhance understanding of this educational approach.

Despite SE and IC being less frequently selected in prior research and showing limited influence, the author has opted to incorporate these factors into the model This approach allows for a re-evaluation of their impacts and the potential for new insights The absence of strong influence in earlier studies does not rule out positive findings in this specific context By including SE and IC, the author aims to rigorously assess their significance and uncover valuable contributions to the research.

To enhance the understanding of blended learning adoption, a new variable, Course Flexibility (CF), has been introduced, highlighting the flexible transition between online and offline learning modes This variable sets blended learning apart from purely online or offline methods and has not been previously addressed in related studies, except for a 2019 study by Mehmet Kokoỗ 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 extent to which individuals believe that using a system will enhance their job performance In the realm of blended learning, it assesses students' likelihood of achieving their desired academic outcomes through this approach This concept reflects the students' perception of the benefits of blended learning and its potential to enhance their academic performance, aligning with the perceived usefulness aspect of the Technology Acceptance Model (TAM) (Venkatesh et al., 2003).

According to a study by Williams, Rana, and Dwived (2014), performance expectancy was examined in 116 out of 174 studies regarding its impact on behavioral adoption Notably, 93 of these studies found that performance expectancy significantly predicted behavioral adoption, establishing it as the most influential predictor in this context.

Research in higher education indicates that performance expectancy significantly influences the adoption of e-learning information services, highlighting its crucial role in shaping user behavior (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 the system to facilitate their tasks with minimal effort (Venkatesh et al., 2003; Huang and Kao, 2015) In the context of blended learning, this concept reflects university students' beliefs about the simplicity of integrating blended learning into their studies Students who view blended learning as easy to use are more likely to adopt it in their academic pursuits This construct is foundational for understanding related concepts such as perceived ease of use, complexity, and ease of use, as supported by various studies (Asare et al., 2016; Pardamean and Susanto, 2012; Venkatesh et al., 2003).

Research indicates that effort expectancy plays a crucial role in the behavioral adoption of blended learning and learning management systems (Azizi et al., 2020; Widjaja et al., 2020) While its predictive power may be less than other factors (Morosan and Defranco, 2016), numerous studies highlight its significant influence on the adoption of various digital services, including 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 response systems among students.

H2: Effort expectancy has a positive influence on students’ blended learning adoption

Social influence, as defined by Venkatesh et al (2003), is the extent to which individuals feel that others believe they should use a system In the realm of blended learning, this concept highlights how perceptions from social groups impact an individual's decision to engage with this learning method Research by Asare et al (2016), Pardamean and Susanto (2012), and Venkatesh et al (2003) links social influence to behavioral change through various theoretical frameworks, including subjective norms in the Theory of Planned Behavior and Theory of Reasoned Action, 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 support from peers, lecturers, or parents Research indicates that social influence significantly impacts the acceptance of various educational technologies, including blended learning (Azizi et al., 2020), e-learning (Tarhini et al., 2017), learning management systems (Ain et al., 2016; Widjaja et al., 2020), MOOCs (Tseng et al., 2019), and online teaching by school teachers (Tandon, 2020) Venkatesh et al (2012) emphasize the direct relationship between social influence and behavioral adoption, while Fidani and Idrizi (2012) confirm a strong correlation between social influence and the adoption of learning management systems.

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 organization's provision of technological resources to support e-learning In the context of blended learning, these conditions encompass a student's expectations regarding the necessary infrastructure and technical support during implementation Key examples include reliable internet access, data availability, internet-enabled devices, effective online pedagogy, and instructional strategies, as well as the creation of modules that ensure content accessibility for all students This concept aligns with earlier theories of perceived behavioral control and compatibility (Asare et al., 2016; Lakhal et al., 2013; Venkatesh et al., 2003).

Students who believe that their institution possesses the essential technological and organizational resources for blended learning are more likely to adopt this learning mode in their academic pursuits.

Research by Oh and Yoon (2014) highlights 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 Further supporting these findings, Tran (2013) established that facilitating conditions significantly impact the adoption of English language e-learning websites, while Jong and Wang (2016) noted its importance for web-based learning systems.

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 the integration of the author's observations This foundational work informed the conceptual model In the subsequent stage, the development of the research scale took place, which included conducting in-depth interviews to guarantee the quality and appropriateness of the questionnaire for the official survey and reliability testing.

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 study involved three experienced school management executives to propose managerial implications for blended learning operations Additionally, a focus group of ten undergraduate students was organized to assess the alignment between the author's proposed definitions of concepts and observed variables with the students' perceptions.

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 practical implications for administration, while also recognizing its limitations and outlining potential directions for future research.

The research process involved creating a theoretical framework, refining the questionnaire, collecting and analyzing data through statistical methods, drawing conclusions, providing administrative implications, and identifying limitations along with future research directions.

Scale development

Developing valid and reliable measurement scales requires extensive research and time investment, but well-constructed scales provide greater accuracy and dependability in evaluating 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 reduce measurement effort and enhance validity This collaborative process involves engaging potential survey respondents and subject matter experts, outlined in five systematic steps.

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 as a scale, it is essential to conduct various assessments These should encompass an analysis of the mean and variability for each item, calculation of item-total correlations, and reliability testing Additionally, it is crucial to verify that the measures align with the established conceptualization of the construct (Gehlbach & Brinkworth, 2011).

Qualitative research was carried out through in-depth interviews with three experts from the WSU international partnership program at UEH-ISB, aiming to explore their perceptions of students' adoption of blended learning The insights obtained will be used to refine and enhance the initial measurement scale, ultimately finalizing 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 draft scale was shared with the experts beforehand, and during the discussion, any content that garnered approval from at least two out of three participants was either retained or modified as needed.

The measurement scale outlined in Table 3.1 consists of 46 observed variables, developed based on methodologies from 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 held 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 in the research model, which examines factors influencing students' blended learning adoption in the WSU international partnership program, remains unchanged These variables include performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, self-efficacy, instructor characteristics, and course flexibility However, the content and number of observed variables (measurement items) in the scale have been revised.

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 method in educational research, to collect factual data Primary data was gathered through an online survey via Google Forms, distributed to students' emails To enhance the clarity and interpretation of the survey questions, in-depth interviews were conducted with three school management executives, who provided feedback on the understandability of the proposed questions and identified potential areas for misinterpretation.

The questionnaire included two main sections: a demographic profile with four questions and a 49-question segment focused on factors influencing students' adoption of blended learning in the Western Sydney international partnership program The demographic section utilized a nominal scale to categorize respondents by gender, age, academic major, and family income, aligning with the classification methods suggested by Sekaran and Bougie (2019) To evaluate the factors affecting blended learning adoption, a 5-point Likert Scale was implemented, measuring respondents' levels of agreement or satisfaction, with responses ranging from "Strongly Disagree" to "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 employed convenience sampling, a non-probability sampling method that selects samples based on specific characteristics and research requirements Hair et al (1998) suggest that for exploratory factor analysis (EFA), the ideal sample size should be five times the number of scale observations, resulting in a 5:1 ratio Given this study has 49 observations, the necessary sample size is calculated to be 245 This approach is crucial for ensuring the validity of subsequent regression analysis.

According to Tabachnick and Fidell (1996), the required sample size can be calculated using the formula n = 50 + 8 * var, where "var" denotes the number of independent variables In this study, with eight independent variables in the questionnaire, the necessary sample size is determined to be 114 To fulfill the criteria for both 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 systematically gathered, organized, and refined data from the questionnaire, categorizing all variables into a coding sheet This data was then analyzed using the Statistical Package for Social Science (SPSS 26.0) software.

Descriptive statistics serve as concise informational coefficients that offer an overview of a data set, whether it represents a sample or the entire population By summarizing key measures, descriptive statistics enhance the understanding and description of the characteristics inherent in the data set.

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 Central tendency identifies the data's central point through the mean, median, and mode, while frequency distribution reveals how often each data point occurs.

Measures of central tendency, including the 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 the data set and dividing by the total number of values These descriptive statistics simplify complex quantitative insights from large data sets into easily understandable 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 set of items is, making it particularly useful for researchers utilizing Likert scales to gauge scale 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 generally leads to a higher Cronbach’s Alpha (α), as indicated by its formula A low average inter-item correlation results in a low Cronbach’s Alpha, while an increase in this correlation (with a constant number of items) causes the Alpha to rise Ranging from 0 to 1, Cronbach’s Alpha assesses the reliability of a measure; a value of 0 indicates that scale items are independent, whereas a value close to 1 suggests high covariance among items, reflecting that they measure the same concept Thus, a higher Cronbach’s Alpha coefficient indicates greater covariation among items, allowing them to be evaluated under a unified concept.

The recommended standards for Cronbach's alpha, which measures the reliability of questionnaires, typically range from 0.65 to 0.80, with a minimum acceptable value of 0.60 Nunnally and Bernstein (1994) suggest that a corrected item-total correlation of 0.30 or higher indicates satisfactory variables DeVellis (2003) recommends a minimum alpha value of 0.70, while values as low as 0.63 may still be usable However, coefficients below 0.50 are deemed unacceptable, especially for unidimensional scales.

Exploratory Factor Analysis (EFA) is a multivariate statistical technique used to identify latent variables that explain the correlations among observed variables, particularly in psychology and education This method simplifies data by reducing numerous measured variables into a more manageable set while uncovering the underlying dimensions that link observed variables to latent constructs Additionally, EFA aids in theory development and refinement and offers validation for participants' self-reports.

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 According to Hair et al (2006), KMO values below 0.5 suggest that factor analysis is inappropriate, while 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 For factor analysis to be valid, these observed variables must exhibit correlations, reflecting various dimensions of the same underlying factor A significant result from Bartlett’s Test indicates that factor analysis is appropriate for the data.

‘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 is 1 or higher, and the Total Variance Explained should reach a minimum of 50% This threshold signifies that the model effectively captures a significant portion of the observed variance In this context, with 100% representing the total variance, the resulting value reflects how well the extracted factors summarize the data, while also indicating the percentage of observed variables that remain unaccounted for by the factor analysis.

Factor loading quantifies the correlation between observed variables and underlying factors in factor analysis, with higher values indicating a stronger relationship Hair (2009) provides 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 sample size, as varying sample sizes necessitate different weighting factors to assess the significance of factor loading accurately.

Pearson’s correlation coefficient, denoted as r, is a key statistical tool for measuring the strength and direction of the relationship between two continuous variables By utilizing covariance, it effectively evaluates associations, and its values are confined within a specific range, ensuring reliable 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 utilized to forecast the value of an outcome variable (Y) based on one or more predictor variables (X) Its primary goal is to identify a linear relationship between the predictor variables and the response variable, allowing for the estimation of the response (Y) when the predictor values (Xs) are available.

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, UEH - 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 collaboration will lead to the implementation of a doctoral program, six new undergraduate programs, and various postgraduate offerings, ultimately elevating the quality of education at UEH-ISB The partnership is designed to provide Vietnamese students with greater opportunities to study in an international learning environment, while advancing expertise across multiple fields for application in the global labor market.

The collaboration between UEH-ISB and WSU has established them as a premier higher education provider in the region, driving social and economic development WSU's offshore campus in Vietnam aims to become a leading institution within the ASEAN Hub, offering diverse educational programs This strategic partnership enhances the skills of Vietnamese students, fostering regional advancement on a global scale.

WSU Australia is a prestigious institution recognized for its academic excellence and applied research, recently advancing from 58th to 33rd 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 the quality of its international partnership program with UEH-ISB The university offers a modern educational experience that equips students for successful careers while addressing critical societal challenges through its interdisciplinary research agenda, focusing on climate change, public health, and infrastructure engineering WSU is committed to fostering a society that values social, cultural, economic, and political fairness.

WSU is enhancing its teaching methods by integrating blended learning models, responding to students' demand for flexibility through innovative technologies This approach combines the strengths of in-person and online interactions across various subjects, positioning WSU as a forward-thinking institution dedicated to empowering students and promoting innovation 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 enhance their education through the Global Pathways transfer program at Western Sydney University, which boasts a high-quality intake, with 60% of students achieving an IELTS score above 7.0 from top high schools and gifted classes nationwide 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 adherence to the highest Australian education standards Completing the program in just 2 years and 7 months, students have the flexibility to study entirely in Vietnam or transfer abroad With 100% of faculty holding AACSB accreditation, students gain insights from leading experts Additionally, the program provides up to 30 billion VND in scholarships and connects students to a vast alumni network in over 100 multinational companies, alleviating financial burdens and opening numerous career opportunities.

Descriptive statistics

A study involving 400 undergraduate students in the WSU international partnership program gathered data through an online survey via Google Forms After data cleaning and validation, 384 responses were deemed valid, 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 aged 18 to 20 This age distribution indicates differing levels of technological proficiency, learning preferences, and responsibilities among students, potentially affecting their willingness to embrace blended learning.

The gender distribution among participants is almost equal, with females making up 52.1% and males 47.9% This balance suggests that analyses of blended learning adoption should consider gender perspectives Exploring potential differences in engagement and perception of the blended learning environment between genders could provide valuable insights.

The study reveals that the majority of participants are Marketing majors, making up 31.8% of the sample, followed by International Business students at 27.1% Advertising majors account for 22.9% of the participants, while Applied Finance students represent the smallest group at 18.2% Each major offers distinct perspectives on technology use, which may affect how students perceive and engage with blended learning For instance, Marketing and Advertising students may favor digital platforms, reflecting the digital focus of their fields, whereas Finance students may emphasize the importance of data security and platform reliability in blended learning environments.

The analysis of family income reveals that 59.1% of the sample belongs to the middle-income category, indicating that most students come from middle to upper-middle-class backgrounds This socioeconomic status is significant as it suggests that these students are likely to have the financial resources needed to access the technology essential for blended learning Therefore, financial limitations may pose less of a challenge in this demographic compared to those from lower-income backgrounds.

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 high reliability, with a coefficient of 0.832, exceeding the minimum threshold of 0.6 All six observed variables show a Corrected Item-Total Correlation above 0.3, reflecting a strong relationship between each item Moreover, none of the observed variables have a Cronbach’s Alpha If Item Deleted greater than the overall coefficient, confirming the scale's suitability for this study.

The Effort Expectancy (EE) factor exhibits a strong reliability, with a Cronbach’s Alpha coefficient of 0.734, 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 Removing any variable would lead to a decline in the overall Cronbach’s Alpha, and the Cronbach’s Alpha If Item Deleted values for each variable remain lower than 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 All Corrected Item-Total Correlations are above 0.3, and no individual item shows 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 All observed variables show Corrected Item-Total Correlations ranging from 0.497 to 0.544, indicating a robust relationship with the overall factor Additionally, none of the observed variables present a Cronbach’s Alpha If Item Deleted value exceeding 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, the variable HM5 shows 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 recalculation of Cronbach’s Alpha yields an improved coefficient of 0.841, reinforcing the factor's reliability All Corrected Item-Total Correlations exceed 0.3, and no variables present a Cronbach’s Alpha If Item Deleted value greater than the overall coefficient, confirming that the HM factor is appropriate 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 above the minimum threshold of 0.3 Removing any variable would lower the overall Cronbach’s Alpha, and the Cronbach’s Alpha If Item Deleted values for each variable do not surpass the overall coefficient Consequently, all variables are retained, affirming their contribution to the scale's reliability.

The Instructor Characteristics (IC) factor demonstrates a strong reliability with a 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 their significant contribution to the overall construct.

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, evidenced by a Cronbach’s Alpha coefficient of 0.784, exceeding the acceptable threshold of 0.6 However, CF6's Corrected Item-Total Correlation of 0.042 falls short of the 0.3 benchmark, leading to its exclusion from the scale before conducting exploratory factor analysis (EFA) After removing CF6, the Cronbach’s Alpha coefficient rises to 0.865, reinforcing the scale's reliability All remaining Corrected Item-Total Correlations exceed 0.3, and no observed variables show a Cronbach’s Alpha If Item Deleted value greater than the overall coefficient, confirming the factor's suitability for further analysis.

The Blended Learning Adoption (BLA) factor exhibits a strong Cronbach’s Alpha reliability coefficient of 0.865, significantly above the acceptable threshold of 0.6 The Corrected Item-Total Correlation coefficients for all variables within this factor range from 0.621 to 0.699, exceeding the minimum requirement of 0.3 Furthermore, the Cronbach’s Alpha If Item Deleted values for each variable do not surpass the overall Cronbach’s Alpha coefficient, indicating 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, assessed using the Cronbach’s Alpha coefficient, is 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 identified 41 independent variables categorized into eight factor groups, demonstrating a strong correlation with a KMO coefficient of 0.852, well above the 0.5 threshold Bartlett’s test of sphericity confirmed this correlation with a chi-square value of 6089.185 and a significance level of 0.000 The total variance extracted was 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 threshold of 1, confirming the suitability of the data for factor analysis.

The Varimax procedure, an orthogonal rotation technique, is utilized 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 significant measurements are retained The exploratory factor analysis (EFA) will categorize observed variables with factor loadings greater than 0.5 into major groups As shown in Table 4.4, all factor loadings exceed the 0.5 threshold, confirming their acceptability for convergence in this study.

The Blended Learning Adoption factor is validated by six observations (BLA1 to BLA6) and exhibits a KMO coefficient of 0.879, indicating strong data suitability for exploratory factor analysis With an Eigenvalue of 3.584, which exceeds 1, the analysis further confirms the factor's significance Additionally, Bartlett’s Test shows a statistically significant result (Sig < 0.05), reflecting a correlation among the observed variables.

The total variance extracted is 59.736%, surpassing the 50% threshold, indicating that the six identified factors account for a significant portion of the data variation The Varimax rotation applied to the component matrix shows that all Factor Loadings exceed 0.5, confirming their importance and appropriateness for integration (refer to Appendix 4.3) 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 correlations among the variables Pearson’s correlation coefficient effectively measures both the strength and direction of the relationship between two quantitative variables.

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 between the dependent variable and independent variables are as follows: 0.565, 0.459, 0.533, 0.306, 0.599, 0.541, 0.208, and 0.293 All coefficients are statistically significant, with a significance level (Sig.) of less than 0.05, indicating a meaningful correlation exists among the 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 analyzing the relationships among independent variables, pairs with a significance value (Sig.) greater than 0.05 indicate no correlational relationship and eliminate the risk of multicollinearity Conversely, pairs with a significance value (Sig.) less than 0.05 and an absolute correlation coefficient below 0.4 exhibit a weak to moderate correlational relationship, also suggesting that multicollinearity is unlikely to occur between these variables.

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 outcomes, all independent variables will be incorporated into the regression model using the Enter method, with a significance level set at < 0.05 for variable inclusion The results of 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 is a crucial hypothesis test used to evaluate the suitability of the overall linear regression model As indicated in table 4.6, the F test produced a value of 93.598 with a significance level (Sig.) of 0.000, which is less than 0.05 This result demonstrates that the multiple linear regression model is a good fit for the dataset and is reliable for generalization.

The analysis of Table 4.6 reveals a strong positive correlation coefficient (R) of 0.666 between the model's variables The Adjusted R-Square value of 0.659 indicates the linear regression model's adequacy, with 8 independent variables accounting for 65.9% of the variation in the dependent variable This leaves 34.1% of the variation attributed to unmeasured factors, underscoring the model's overall relevance.

Table 4.7 Multiple linear regression results

Source: Author’s data processing results from SPSS 26.0

The VIF (Variance Inflation Factor) values presented in table 4.7 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 factors impacting the dependent variable, ranked by their beta values, are as follows: (HM) at 0.243, (PE) at 0.208, (SI) at 0.200, (CF) at 0.195, (EE) at 0.170, (FC) at 0.160, (IC) at 0.158, and the least impactful factor, (SE), at 0.149.

H1: Performance expectancy has a positive influence on students’ blended learning adoption

Linear regression analysis revealed a statistically significant positive correlation between performance expectancy and students' adoption of blended learning, indicated by a standardized regression coefficient (β) of 0.208 and a significance level (Sig.) of less than 0.05.

H2: Effort expectancy has a positive influence on students’ blended learning adoption

The linear regression analysis revealed a significant positive correlation between effort expectancy and students' adoption of blended learning, evidenced by 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

The linear regression analysis revealed a significant positive correlation between social influence and students' adoption of blended learning, with 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

The linear regression analysis reveals 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

Linear regression analysis revealed a significant positive correlation between hedonic motivation and students' adoption of blended learning, evidenced by 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

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

A linear regression analysis revealed a statistically significant positive correlation between instructor characteristics and students' adoption of blended learning, with a standardized regression coefficient (β) of 0.158 and a significance level (Sig.) of less than 0.05.

H8: Course flexibility has a positive influence on students’ blended learning adoption

The linear regression analysis revealed a standardized regression coefficient (β) of 0.195 for the "course flexibility" factor, with a significance level (Sig.) of less than 0.05 This indicates a statistically significant positive correlation between course flexibility and the adoption of blended learning by students.

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 graph (Figure 4.2) indicates that data points are closely aligned with the diagonal line, showing random scatter around the 0 coordinate These observations confirm that the residuals approximate a normal distribution, validating the assumption of normality for the residuals.

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 tendency to align in a straight line This pattern indicates that the predicted values and residuals are independent, confirming that the assumption of linearity remains intact.

One-way ANOVA

The Independent Sample T-test is suitable for comparing means when the categorical variable has two values, but it becomes less practical with more than two values due to the complexity of pairwise comparisons One-way ANOVA effectively overcomes these limitations by allowing the comparison of mean values across two or more groups Utilizing SPSS 26.0, One-way ANOVA can perform the same function as the Independent Sample T-test, yielding equivalent results when the categorical variable has two values Consequently, the author has opted to employ 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 male and female students, 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 (3.1294) compared to male students (2.9820), indicating that, on average, female students demonstrate a greater capacity for adopting blended learning methodologies 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 in the ANOVA table indicates that the significance level (Sig 0.162) is greater than 0.05, leading to the acceptance of the null hypothesis (Ho) This suggests that there is no statistically significant difference in the means of blended learning adoption among different age groups of students.

The analysis of blended learning adoption (BLA) indicates that students above the age of 20 (Mean = 3.0991) show a similar capacity for adopting blended learning as those aged 18 to 20 (Mean = 3.0097) This similarity in mean values suggests that both age groups possess 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 indicate a statistically significant difference in means among the groups of majors regarding students' adoption of blended learning, as evidenced by a significance value of 0.000, which is less than the 0.05 threshold, leading to the rejection of the null hypothesis.

The analysis of blended learning adoption (BLA) reveals a decreasing trend in mean values across 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 are the most proficient in adopting blended learning, while Marketing students are the least capable Additionally, the International Business group demonstrates a stronger ability to adopt blended learning compared to the Applied Finance group.

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 between 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, leading to the rejection of the null hypothesis.

The analysis of blended learning adoption (BLA) reveals a decreasing trend in mean values based on family income levels, as outlined in the Descriptives table (refer to Appendix 4.6) Specifically, students from families with an income range of 10 to [insert specific income range] exhibit lower BLA scores.

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 These factors include 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 the reliability of all independent factors, although measurement items HM5 and CF6 were excluded due to statistical insignificance The remaining observed variables were statistically significant and retained for further analysis In the exploratory factor analysis (EFA), 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 indicated significant relationships among the independent variables.

The study found significant correlations between blended learning adoption (BLA) and the independent 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 student groups based on gender, major, and income However, there is no statistically significant difference in blended learning adoption when comparing groups based on age.

After conducting linear regression analysis, the standardized linear regression equation is described as follows:

The study revealed that "hedonic motivation" significantly influences students' adoption of blended learning (β = 0.243, Sig 0.000), highlighting the importance of enjoyment and satisfaction in their decision-making This aligns with previous research by Masitah Musa et al (2022) and Ardvin Kester S Ong and Michael N Young (2023) Additionally, "performance expectancy" emerged as the second most impactful factor (β = 0.208, Sig = 0.000), indicating that students are 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 (β = 0.200, Sig = 0.000), indicating that their decisions are heavily influenced by the views of their social circles, including parents, instructors, and peers, alongside anticipated academic benefits and enjoyment of the learning experience This aligns with previous research findings.

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 highlights that "course flexibility" (β = 0.195, Sig = 0.000) significantly impacts students' adoption of blended learning, aligning with findings from Mehmet Kokoc (2019) Blended learning merges in-person instruction with online elements, allowing for adjustments based on learning objectives and course content This adaptable approach provides students with optimal scheduling flexibility and access to diverse learning materials anytime, anywhere, and on various devices.

The study revealed a significant positive correlation between "effort expectancy" and students' adoption of blended learning (β = 0.170, Sig = 0.000), supporting findings from Silvana Dakduk et al (2018) and Yeop et al (2019) The ease of use plays a crucial role, as students are more inclined to adopt blended learning when the platforms and software are user-friendly and easy to navigate A seamless technology interface facilitates a smooth transition for students adapting to this innovative learning method.

Facilitating conditions, such as adequate physical and technical infrastructure, significantly influence students’ adoption of blended learning, ranking as the sixth most important factor (β = 0.160, Sig = 0.000) This aligns with findings from previous studies by Yan Dang et al (2016) and Niraj Mishra et al (2022) For successful implementation of blended learning, institutions must provide essential resources, including physical classroom facilities and reliable digital platforms, bandwidth, and network connectivity to support online learning components.

Instructor characteristics significantly impact students' adoption of blended learning, with a positive correlation (β = 0.158, Sig = 0.000) Research aligns with findings from Mahmoud Abou Naaj et al (2012) and Al-Busaidi and Kamla Ali (2012), emphasizing the critical role instructors play in student engagement Timely support and guidance from instructors enhance the blended learning experience, as they provide essential feedback and evaluation 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 further promotes student acceptance of this instructional model.

Self-efficacy, defined as students' belief in their ability to effectively use blended learning systems, showed a minimal impact (β = 0.149, Sig = 0.000) on their adoption of this instructional format Many students struggle to navigate blended learning, particularly if they lack prior experience, making support from instructors, helpdesks, and peers essential for enhancing their initial experiences Access to guidance and assistance can significantly boost students' confidence and self-efficacy, facilitating their adaptation to blended learning tools This aligns with findings from Yan Dang et al (2016) and Kurniawan et al (2021).

This research expands on the UTAUT2 model by incorporating three additional factors: self-efficacy (SE), instructor characteristics (IC), and course flexibility (CF), which have not been studied together in previous adoption research While SE and IC have been explored individually in some studies, CF is a novel factor, highlighting the inherent flexibility of blended learning The findings reveal that all three factors positively impact the adoption of blended learning, with course flexibility ranked 4th, instructor characteristics 7th, and self-efficacy 8th, underscoring their significance in enhancing blended learning environments.

To enhance the effectiveness of blended learning in schools, it is crucial to emphasize the roles of instructional communication (IC) and student engagement (SE) These factors present valuable opportunities for further research on blended learning adoption and its impacts Additionally, the newly introduced variable, collaboration flexibility (CF), ranks fourth in influence, highlighting its significant role in promoting blended learning adoption This underscores the importance of flexibility as a key factor in increasing student participation in international partnership programs CF also shows potential for future studies in this area.

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 by students in WSU's international partnership program has identified key factors influencing their acceptance of this approach, which integrates online and in-person learning While blended learning has gained traction globally, its popularity in Vietnam surged recently due to the COVID-19 pandemic However, many institutions reverted to traditional methods post-pandemic, raising concerns about the effective application of blended learning Transitioning to a blended learning model necessitates a shift from teacher-centered to student-centered pedagogy, emphasizing the importance of student engagement in its success Understanding the factors affecting students' adoption of blended learning is crucial for modern educational institutions, yet there remains a lack of research specifically addressing this issue within international partnership contexts.

The proposed model identifies students’ blended learning adoption as a dependent variable influenced by eight key factors: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, self-efficacy, instructor characteristics, and course flexibility By evaluating the impact of these factors, the research aims to improve the effectiveness of blended learning implementation, ensuring optimal outcomes for student performance This enhancement will strengthen the distinction and competitiveness of WSU's international partnership program in the education market The measurement scale for the research was developed based on established studies, including those 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

A recent data 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 influence students' adoption of blended learning Among these, hedonic motivation has the strongest impact (β = 0.243), 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 as the sixth and seventh most influential factors The least impactful factor is self-efficacy, with a β value of 0.149 These findings corroborate previous research indicating a positive relationship between these factors and blended learning adoption, as noted in studies by Arumugam Raman and Yahya Don (2013), Cao Hao Thi et al (2014), Talai Osmonbekov et al (2016), Alireza Khatony et al (2020), Kurniawan et al (2021), and Norman Rudhumbu (2022) However, the ranking of these factors differs from previous studies, likely due to the unique 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 course objectives and assignments, ensuring clarity and conciseness, with a rating of 3.33 Additionally, they provide clear guidelines for engaging in course activities, achieving a rating of 3.22.

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, with a beta value of 0.243 The mean score of 3.29 indicates that students generally support the hedonic aspects of blended learning; however, a slightly lower average of 3.25 for the specific statement HM3 suggests that some students may have reservations about these methods.

Positive learning is essential for enhancing the effectiveness of the educational process, shaped by various factors including students' needs, motivations, interests, and personal backgrounds To promote positive learning, instructors should create engaging content, employ active teaching methods, and utilize diverse instructional formats alongside modern technologies to spark interest and foster creativity In blended learning programs, maintaining student motivation and engagement is vital; institutions should communicate clear expectations and recognize students' achievements to sustain motivation Additionally, fostering a welcoming and inclusive environment encourages participation and interaction, while promoting feedback, peer interaction, and teamwork helps build a strong sense of community among students.

Empowering students is crucial for enhancing their motivation and engagement in learning, as suggested by Kelly (2014) Providing students with choices fosters a sense of autonomy over their education Institutions can enhance this empowerment by offering discussion tools, contextual commenting features, and collaboration options, allowing students to contribute their own materials and resources Additionally, instructors should employ a variety of teaching techniques, including group projects, gamification, discussions, interactive multimedia, real-world applications, and cooperative learning exercises, to maintain student engagement Learning games, often used as supplementary activities, can be found online and are typically quiz-style reviews that align with educational objectives.

According to Andrew Miller (2012), meaningful activities are crucial for student engagement, requiring them to be purposeful, enjoyable, and relevant to effectively maintain student participation Learning activities should align with defined objectives and balance online and in-person elements, as well as individual and collaborative tasks within a blended learning framework It's important to consider the distribution of individual versus group assignments and the ratio of synchronous to asynchronous activities, ensuring adequate instructor guidance while promoting student autonomy and collaboration.

Tanner (2012) emphasizes the importance of flexible activities, such as reflective journals and guiding questions, to enhance students' cognitive processes Active learning is essential in effective blended course design, requiring students to engage with the material through discussion, writing, and real-world application When creating assignments, it is vital to tailor them to students' unique needs, ensuring they are engaging and challenging while connecting to relevant life experiences.

Students, regardless of their achievements, deserve to feel proud of their efforts Providing praise and encouragement fosters a positive learning environment, enhancing their self-confidence and self-esteem Recognizing their progress with rewards can further motivate students and promote positive learning attitudes.

Performance expectancy plays a crucial role in the adoption of blended learning, with a significant influence score of β = 0.208 Students generally agree on its impact, as indicated by a mean value of 3.44 However, the lower mean score of 3.39 for the statement PE2 suggests that students feel blended learning has yet to significantly enhance their academic performance.

Transitioning from traditional high school teaching methods to a self-directed learning approach can be challenging for students While they may grasp the provided instructions, they must take initiative to explore and identify the strategies that work best for them to maximize their learning outcomes.

In addition to promoting student autonomy, key factors such as course objectives, tasks, structure, activities, assignments, and assessment methods play a crucial role in enhancing academic performance To maximize results, these elements must be organized and structured effectively, allowing students to fully integrate the course's operations into their 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 align with students' needs and preferences while integrating with curriculum standards and desired competencies in a blended learning environment By effectively communicating these objectives, instructors can create a structured plan that empowers students to acquire the necessary knowledge and skills for success Additionally, every activity and assignment must include clear instructions and expectations that align with the established learning outcomes, fostering collaboration and engagement among students.

A well-structured course outline is essential for both students and instructors in a blended learning environment It helps students stay organized and informed about the course objectives, assignments, and deadlines Instructors can use the outline to ensure the course progresses at the right pace The outline should clearly communicate expectations regarding student participation and attendance, as well as detail the teaching resources and materials that will be used throughout the course.

A well-crafted curriculum should incorporate a blend of teaching methods, such as online modules, videos, discussions, and hands-on activities, to create a cohesive learning experience It is essential to integrate online and offline components that complement each other effectively Schools must identify the subjects that will utilize a blended learning approach and specify which content will be delivered online, as well as the blended learning model to be employed Maintaining consistency throughout the program is vital for its success.

When designing a blended learning course, it is essential to ensure that students grasp the course material and have a clear path through the content According to Kelly (2014), students need to understand the relevance of tasks within blended learning Instructors can enhance engagement by explicitly connecting the skills learned in the course to real-world applications Properly labeling and organizing course materials, as highlighted by Shea and Bidjerano (2003), creates a clear structure that aids in student comprehension and time management Additionally, instructors should regularly solicit summaries, clarifications, and open-ended reflections from students to reinforce understanding throughout the course.

Effective online learning design involves dividing course content into logical, modular sections, each centered around a key idea and accompanied by estimated completion times, relevant readings, activities, and objectives Presenting information in manageable, bite-sized chunks enhances student engagement and understanding Utilize web-friendly formatting tools such as headings, bullet points, and images to facilitate

Limitations and recommendations

The research focused exclusively on the adoption of blended learning among students in the WSU international partnership program, primarily due to time and resource limitations This limited scope prevents any comparative analysis with other international partnership programs at UEH-ISB or in a wider context A significant drawback of this study is the inability to access students from other institutions, which confines the findings to the specific institution being examined.

The study focused exclusively on full-time undergraduate students, which may restrict the applicability of the findings to other groups, such as postgraduate students, who could have different experiences and views on blended learning Additionally, the use of convenience sampling implies that the participants might exhibit similar learning behaviors, potentially missing out on the diverse teaching methods employed by instructors.

The study recognizes a limitation in its focus on only eight key constructs affecting blended learning adoption, potentially overlooking relevant independent variables Although 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 its convenience rather than through random sampling, which may limit its representativeness of the target population Consequently, this approach could jeopardize the accuracy of the collected data, as it only includes participants who were readily accessible.

The use of self-reported survey data, while a prevalent method, is subject to inherent biases such as social desirability and inaccuracies in participants' responses Despite the author's efforts to encourage honest reporting, these measures may not completely address the limitations associated with self-reported data.

To enhance the applicability of research findings, future studies should include a diverse participant pool from various programs and institutions, allowing for a better understanding of how factors influencing blended learning adoption differ across educational contexts Additionally, incorporating the perspectives of both postgraduate and undergraduate students can reveal whether the determinants of 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 This could include intermediate factors like age, gender, and academic major, which may serve as moderating influences on students' adoption of blended learning Investigating the impact of these demographic and academic characteristics can yield valuable insights into how individual attributes affect the uptake of blended learning.

The researchers emphasize the need for future studies on blended learning in higher education to include interviews with key stakeholders, such as instructors and parents, in addition to self-reported data from students This approach would provide a more comprehensive evaluation of students' experiences and perceptions 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 By incorporating both student and instructor viewpoints, a deeper understanding of the dynamics influencing blended learning adoption can be achieved.

Chapter 5 synthesizes the findings from the previous chapter, highlighting the relationships among eight key independent variables—performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, self-efficacy, instructor characteristics, and course flexibility—and their impact on students' adoption of blended learning This analysis aims to offer valuable insights for enhancing blended learning adoption within the WSU international partnership program.

The chapter highlights the study's limitations and proposes future research directions, offering a roadmap 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 the context of electronic banking services and virtual travel communities Their research, published in the Online Information Review, highlights the significant role that personal investment plays in shaping user experiences and engagement The findings underscore the importance of optimizing both aesthetic appeal and usability to foster stronger user commitment in digital platforms.

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 regarding 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.