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

The blended learning model has become a challenge for students who lackprior experience with technology or access to the necessary devices.. Even when the online component is mandatory i

Researchproblemstatement

The information and communications technology sector is one of the fastest- evolvingproductsofthemodernera,andaccordingtoWuetal.(2010)“advancesin technology offer a multiplicity of possibilities for communication, interaction and multimedia delivery systems in universities” Due to the prevalence of information technology, blended learning has become the common teaching method in moderneducation.

Blended learning was first introduced by universities in the late 90’s in the US and Canada and is considered to be the third generation of advancement in higher education The application of the blended learning model in higher education represents a key component of the broad digital transformation efforts to build a modern higher education institution By cultivating an interactive environment through diverse delivery modes, the blended approach encourages students to be more actively engagedin their education, which can foster the developmentoftheir knowledge and skills Various authors have highlighted the significance of blended learning in education,especially in businessschools For instance,blended learning reduces the barriers that professors and their students face in online sessions and improves interaction (Jusoff and Khodabandelou, 2009) Blended learning offers flexibility, depth of learning, and cost-effectiveness (Graham, 2006) Blended learninginvolvesrestructuringcurriculumdesignthatencouragesstudents’initiative to participate in online learning (Yin and Yuan, 2021) Researchers predicted that blended learning will become the “new normal” of higher education (Norberg, Moskal, and Dziuban, 2011) The

COVID-19 pandemic has further accelerated the adoptionofblendedlearningasapreferredlearningmodeinuniversities(UNESCO,2020).

Thoughblendedlearningisbecomingmorepopularatalllevelsofeducationin many countries and has been proven to have many advantages, it is still a relatively newconceptinVietnamwithmanyremainingchallengesduringtheimplementation process The blended learning model has become a challenge for students who lack prior experience with technology or access to the necessary devices Moreover, the majority of students are accustomed to the traditional teaching and learning style from their high schools, which relies heavily on the instructor’s guidance. Implementing the blended learning approach without adequately supporting students’ self-directed learning and research skills can lead to anxiety, discouragement,andpooreracademicperformanceastheystruggletoadoptthenew learning methods Even when the online component is mandatory in the blended course, the level of student involvement and the quality of online learning is lower than anticipated, even in well-structured blended learning programs The poor performance ofblended learning has led to a decrease in teaching satisfaction and a lack of motivation to keep using this form of teaching Additionally, though instructors have been provided with fundamental information technology skills to facilitate blended learning, they still lack certain advanced capabilities that are crucial for effective blended learning implementation Furthermore, the availability of quality-assured learning resources to support blended learning models remains severely limited Many institutions are unable to develop a successful blended learningmodelduetothehighcostoftechnology,inadequatedecision-makingskills, and a lack of a comprehensive strategy.

Regarding prior empirical studies, some were conducted in settings that could influence the results, as cultural and contextual factors may vary across countries, especially between developed and developing nations Notably, no known studies have examined the determinants of blended learning adoption among students in Vietnam, particularly in Ho Chi Minh City This lack of local references poses challengesforresearchanddevelopmentinthisarea.Withintheeducationalcontext, itisessentialtothoroughlyinvestigatetheuniquecharacteristicsandtargetaudiences of various programs operated within an institution to obtain accurate results Additionally, as blended learning has gained prominence, especially in the wake of the COVID-19 pandemic, understanding the factors influencing its adoption in internationalp a r t n e r s h i p p r o g r a m s w h o s e i n s t r u c t i o n a l a p p r o a c h e s h a v e b e e n

Foralltheaforementionedreasons,theauthordecidedtocarryouttheresearch with the title “Factors influencing students’ blended learning adoption: A case study of

Western Sydney international partnership program at UEH-ISB in Ho Chi Minh City” This research is expected to serve as a reference source for future related research topics Furthermore, this research will also help the school managementtopositivelytransformstudents’perceptionstowardsblendedlearning methods, enhance their capabilities and adaptability, and foster greater student collaborationwithinthisnewlearningapproach.Thisinturnwillleadtoanelevation in the student’s learning performance and satisfaction in the implementation of the blended learning method.

Researchobjectives

Generalobjective

To examineand verifyfactorsinfluencing students’ blended learningadoption at Western Sydney international partnership program.

Specificobjectives

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’ blendedl e a r n i n g a d o p t i o n a t W e s t e r n S y d n e y i n t e r n a t i o n a l p a r t n e r s h i p p r o g r a m

Researchquestions

Question1:Whichfactorscaninfluencestudents’blendedlearningadoptionat Western Sydney international partnership program?

Question 2: What are the impact levels of these factors on students’ blendedl e a r n i n g a d o p t i o n a t W e s t e r n S y d n e y i n t e r n a t i o n a l p a r t n e r s h i p p r o g r a m ?

Research space: Western Sydney international partnership program at UEH-

Research duration: The research will be conducted from November 2023 to June2024,whilethesurveytakingplacefromNovember2023toJanuary2024.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.

In this research, qualitative research methods were used in the design of the research conceptual model and measurement scale Secondary data was collected fromthepublishedresearch,articles,andbooksrelevanttotheresearchtopic.Anin- depth interview with three executives from the school management who are experienced in blended learning operations was conducted to figure out if there are any other factors that may influence the students’ blended learning adoption, and to make theadjustment to any observedvariables (measurement items) in theresearch scale that could lead to students’ misunderstanding or confusion Besides, a focus group discussion with ten undergraduate students was also set up to assess whether thedefinitionsoftheconstructsandobservedvariablesprovidedbythescholarswere consistent with the perceptions of the intended respondents.

Primary data was collected through the online survey using the Google Forms questionnairesenttostudents’emails.Afive-pointLikertscalewasappliedtogather the students’ attitudestoward factors that influencetheir blended learning adoption.

Collected data is analyzed by SPSS version 26.0, using the listed methods: descriptive statistics, Cronbach’s Alpha reliability test, exploratory factor analysis, Pearson’scorrelationcoefficient,multiplelinearregression,andOne-wayANOVA.

Firstly, the research systematizes the theories revolving around the research topic in order to identify the potential factors that could influence the level of students’ blended learning adoption at Western Sydney international partnershipprogram.

Secondly,theresearchaimstore-testtheconceptualmodelassuggestedbythe previous studies and the author’s recommendation As there is no known study that has been conducted to investigate the factors influencing the students’ blended learningadoptioninthecontextofinternationalpartnershipprogramsinVietnamup to this point, the research is expected to be a useful reference source for the related research topics carried out in the near future.

Thirdly, the research builds a scale for the research topic based on previous studiesandaddsnewscalestothescalesystemofresearchworksrelatedtostudents’ blended learning adoption.

Institutionsoperatinginhighlycompetitivemarketsneedtofindeffectiveways ofdeliveringhigh-qualityeducation.Theresultsofthisstudywillprovideeducators, school administrators, instructors, and other concerned parties with useful information regarding the students’ blended learning adoption The findings of this study will assist the school management in developing strategies that extend the quality assurance framework to support the blended learning approach It is crucial to guarantee the quality of blended education experiences for students Satisfied studentsaremoremotivatedandcommittedtotheir study and,ultimately,arebetter learners than their dissatisfied counterparts. necessity of conducting the research), research objectives and questions, research subject and scope, research methodology, research contribution, and researchstructure.

In Chapter 2, the definitions of terms used in the research are presented along with the theoretical framework associatedwith the research topic Subsequently, an overviewofpreviousempiricalstudiesintherelevantfieldsisprovided,withtheaim of addressing the gaps in previous studies to be addressed in this research.

Next the conceptual model of this research is presented, accompanied by the hypotheses used to assess the influence of selected variables on students’ blended learning adoption at 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 usedforanalyzingthecollecteddataincludedescriptivestatistics,Cronbach’sAlpha reliabilitytest,exploratoryfactoranalysis,Pearson’scorrelationcoefficient,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.

In Chapter 1, the research provides a comprehensive overview of the research problem This includes discussing the rationale for selecting the research topic, as well as clearly defining the objectives and research questions The chapter also outlines the specific subject and scope of the research.

Furthermore, the chapter presents the statistical methodologies that will be utilizedforsamplinganddataanalysispurposes.Thisservestooutlinetheanalytical approach that will be adopted 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.

The blended environment includes both the in-person component, where studentsneed to engageinphysicalactivitieswiththeirteacher andotherpeers,and the online component, which involves activities that students must complete independently through the use of support technology and systems, with the help of the Internet (Hung and Chou, 2015; Padilla Meléndez et al., 2013) Torrisi-Steele (2011)definedblendedlearningas“enriched,student-centeredlearningexperiences made possible by the harmonious integration of various strategies, achieved by combining face-to-face interaction with information and communications technology” Blended learning is described by Thorne (2003) as “a way of meeting the challenges of tailoring learning and development to the needs of individuals by integratingtheinnovativeandtechnologicaladvancesofferedbyonlinelearningwith the interaction and participation offered in the best of traditional learning.”

Lawless (2019) argues that “blended learning is an approach to education that combines online educational materials and opportunities for interaction with traditional place-based teaching methods” The concept of blended learning was identified by Heinze et al (2004) as a form of learning that involves a combination of different methods of delivery, teaching models, and learning styles, and is based on open communication between all participants in a course.

Driscoll (2002) defines blended learning as a multifaceted approach that combines various instructional methods and technologies to achieve optimal learning outcomes It encompasses the integration of web-based technologies, pedagogical approaches, instructor-led training, and workplace tasks By blending different modes of delivery and instruction, blended learning provides learners with a flexible and personalized learning experience that caters to diverse learning styles and needs.

No.12/2016/TT-BGDDT: “Blended learning is the combination of e-Learning (electroniclearning)andtraditionalteaching-learningmethods(wheretheinstructor andlearnersarepresenttogether)toenhancetheeffectivenessoftrainingandquality of education”.

To sum up, blended learning is a form of learning that combines traditional direct learning with online learning, allowing for a more flexible approach to program design This type of learning is characterized by a dynamic, changing and activelearningprocess,withpartofthelearningtakingplaceonline,whiletheother part takes place in theclassroom The onlineand offline components ofthe blended learning process are complementary, creating an integrated learning experience.

Source:TayebinikandPuteh(2013)Stu dents in blended learning are not limited to relying solely on the materialp r o v i d e d b y t h e i r t e a c h e r , b u t c a n a l s o s e a r c h f o r t h e m a t e r i a l i n a v a r i e t y o f w a y s , i n c l u d i n g l i b r a r y s e a r c h e s , o n l i n e c o n v e r s a t i o n s w i t h f r i e n d s o r c l a s s m a t e s , w e b s i t e s e a r c h e s , sea rch e ng in e se arch es, portal searches,blog se a rch e s , andot hermedia suchaslearningortutorialsoftware(TayebinikandPuteh,2013).

Researchsubjectandscope

Researchscope

Research space: Western Sydney international partnership program at UEH-

Research duration: The research will be conducted from November 2023 to June2024,whilethesurveytakingplacefromNovember2023toJanuary2024.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.

Researchmethodology

Qualitativeresearchmethod

In this research, qualitative research methods were used in the design of the research conceptual model and measurement scale Secondary data was collected fromthepublishedresearch,articles,andbooksrelevanttotheresearchtopic.Anin- depth interview with three executives from the school management who are experienced in blended learning operations was conducted to figure out if there are any other factors that may influence the students’ blended learning adoption, and to make theadjustment to any observedvariables (measurement items) in theresearch scale that could lead to students’ misunderstanding or confusion Besides, a focus group discussion with ten undergraduate students was also set up to assess whether thedefinitionsoftheconstructsandobservedvariablesprovidedbythescholarswere consistent with the perceptions of the intended respondents.

Quantitativeresearchmethod

Primary data was collected through the online survey using the Google Forms questionnairesenttostudents’emails.Afive-pointLikertscalewasappliedtogather the students’ attitudestoward factors that influencetheir blended learning adoption.

Collected data is analyzed by SPSS version 26.0, using the listed methods:descriptive statistics, Cronbach’s Alpha reliability test, exploratory factor analysis,Pearson’scorrelationcoefficient,multiplelinearregression,andOne-wayANOVA.

Researchcontribution

The research systematically reviews existing theories to ascertain the potential factors that may influence the adoption of blended learning among students enrolled in Western Sydney's international partnership program.

Secondly,theresearchaimstore-testtheconceptualmodelassuggestedbythe previous studies and the author’s recommendation As there is no known study that has been conducted to investigate the factors influencing the students’ blended learningadoptioninthecontextofinternationalpartnershipprogramsinVietnamup to this point, the research is expected to be a useful reference source for the related research topics carried out in the near future.

Thirdly, the research builds a scale for the research topic based on previous studiesandaddsnewscalestothescalesystemofresearchworksrelatedtostudents’ blended learning adoption.

Institutionsoperatinginhighlycompetitivemarketsneedtofindeffectiveways ofdeliveringhigh-qualityeducation.Theresultsofthisstudywillprovideeducators, school administrators, instructors, and other concerned parties with useful information regarding the students’ blended learning adoption The findings of this study will assist the school management in developing strategies that extend the quality assurance framework to support the blended learning approach It is crucial to guarantee the quality of blended education experiences for students Satisfied studentsaremoremotivatedandcommittedtotheir study and,ultimately,arebetter learners than their dissatisfied counterparts. necessity of conducting the research), research objectives and questions, research subject and scope, research methodology, research contribution, and researchstructure.

Chapter 2 provides critical context for this research by defining key terms and establishing a theoretical foundation It also reviews previous empirical studies in related fields, identifying gaps in the existing body of knowledge These gaps serve as the impetus for the current investigation, ensuring its novelty and relevance.

Next the conceptual model of this research is presented, accompanied by the hypotheses used to assess the influence of selected variables on students’ blended learning adoption at 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 usedforanalyzingthecollecteddataincludedescriptivestatistics,Cronbach’sAlpha reliabilitytest,exploratoryfactoranalysis,Pearson’scorrelationcoefficient,multiple linear regression, and One-way ANOVA.

Chapter 5 synthesizes research findings, offering insights to improve blended learning implementation for students It highlights potential limitations and suggests avenues for further exploration.

In Chapter 1, the research provides a comprehensive overview of the research problem This includes discussing the rationale for selecting the research topic, as well as clearly defining the objectives and research questions The chapter also outlines the specific subject and scope of the research.

Furthermore, the chapter presents the statistical methodologies that will be utilizedforsamplinganddataanalysispurposes.Thisservestooutlinetheanalytical approach that will be adopted 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.

The blended environment includes both the in-person component, where studentsneed to engageinphysicalactivitieswiththeirteacher andotherpeers,and the online component, which involves activities that students must complete independently through the use of support technology and systems, with the help of the Internet (Hung and Chou, 2015; Padilla Meléndez et al., 2013) Torrisi-Steele (2011)definedblendedlearningas“enriched,student-centeredlearningexperiences made possible by the harmonious integration of various strategies, achieved by combining face-to-face interaction with information and communications technology” Blended learning is described by Thorne (2003) as “a way of meeting the challenges of tailoring learning and development to the needs of individuals by integratingtheinnovativeandtechnologicaladvancesofferedbyonlinelearningwith the interaction and participation offered in the best of traditional learning.”

Lawless (2019) argues that “blended learning is an approach to education that combines online educational materials and opportunities for interaction with traditional place-based teaching methods” The concept of blended learning was identified by Heinze et al (2004) as a form of learning that involves a combination of different methods of delivery, teaching models, and learning styles, and is based on open communication between all participants in a course.

Blended learning encompasses a range of approaches that integrate web-based technology, pedagogical methods, and instructor-led training to optimize learning outcomes Driscoll (2002) categorizes blended learning into four key facets: (i) combining web technologies to achieve educational goals; (ii) blending pedagogical approaches to enhance learning; (iii) integrating technology and face-to-face instruction; and (iv) creating a mix of training elements and technology to facilitate task-based learning.

No.12/2016/TT-BGDDT: “Blended learning is the combination of e-Learning (electroniclearning)andtraditionalteaching-learningmethods(wheretheinstructor andlearnersarepresenttogether)toenhancetheeffectivenessoftrainingandquality of education”.

To sum up, blended learning is a form of learning that combines traditional direct learning with online learning, allowing for a more flexible approach to program design This type of learning is characterized by a dynamic, changing and activelearningprocess,withpartofthelearningtakingplaceonline,whiletheother part takes place in theclassroom The onlineand offline components ofthe blended learning process are complementary, creating an integrated learning experience.

Source:TayebinikandPuteh(2013)Stu dents in blended learning are not limited to relying solely on the materialp r o v i d e d b y t h e i r t e a c h e r , b u t c a n a l s o s e a r c h f o r t h e m a t e r i a l i n a v a r i e t y o f w a y s , i n c l u d i n g l i b r a r y s e a r c h e s , o n l i n e c o n v e r s a t i o n s w i t h f r i e n d s o r c l a s s m a t e s , w e b s i t e s e a r c h e s , sea rch e ng in e se arch es, portal searches,blog se a rch e s , andot hermedia suchaslearningortutorialsoftware(TayebinikandPuteh,2013).

Concluded from the table, a lesson can be said to be blended or hybrid when the portion ranges from 30 to 79% for the online-delivered content The blended learning encourages educators to shift from teacher-centred learning to student- centred learning.

Definition

Adoption

“Adoption is the decision (acceptance or rejection) and the subsequent implementation, discontinuance, and/or modification by an individual or an organization” (Kee, 2017) As a result, adoption is a process that occurs on an individual or organizational level and then diffuses throughout the system.

Adoption refers to the acceptance and continuous use of a particular product, service, or concept Before making a purchase decision, customers typically progress through a series of stages, including knowledge acquisition, persuasion, decision-making, and confirmation, as outlined by Rogers and Shoemaker (1971).

The decision to adopt and the act of implementing that decision are not necessarilyconcurrent.Thesetwoaspectsoftheadoptionprocesscanbedistinctand occur at different points in time (Reed et al., 1996).

Toconclude,adoptioncanbeunderstoodastheprocessbywhichanindividual or organization embraces and incorporates a new concept, method, idea, service, good, or product into their practices and routines This adoption process typically involves a temporal sequence of steps, beginning with an initial awareness or knowledge of the innovation, followed by a decision to either adopt or reject it, and then the actual implementation or use of the adopted innovation.

The process of adopting an innovation is governed by five fundamentalprinciples:

- Thelikelihoodofaninnovationbeingadoptedbyanindividualorgroup 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 individualor a group, as well as the level so if they perceive it as something they can experiment with before committing to adoption, if it is in line with their individual and professional objectives, if it is not overlycomplicated,ifitismoreeffectivethanotherinnovations(orthecurrentstate of affairs), and if it has some observable advantages (Surry et al, 2005).

Adoption may appear to be a a simple concept at first glance The concept of adoptionis,infact,complexandnotclearastoitsmeaning.Theadoptionprocessis not an event, but a complex procedure that involves multiple dimensions The key elements are as follows:

- The adoption of a technology can be either partial or incomplete, meaning that a technology can be adopted in a variety of ways;

- The process of adoption of a new technology may take place incrementally, with the use of the technology increasing in scale orintensity;

- Thenatureoftechnologyisnotimmutable,itisconstantlychangingand adapting to the needs of users;

The process of technology adoption encompasses two key dimensions: the initial discovery and decision to adopt, followed by the actual adoption or rejection of the technology The degree to which the new technology is utilized after adoption further defines its impact within the organization.

Multi-stagemodelsmaybeabletocapturerealitymoreaccuratelythansimpler models, however, itis stillunknown when the moment ofadoption happens This is duetothefactthattheadoptionprocessiscontinuous.Rogers(2003)acknowledged that the evidence for distinct stages was tentative Though it is widely agreed thatt h e r e arevariousstepswithintheadoptionprocess,noteverysteptakesplaceineach caseofadoptionandsometimesdifferentstepsco- occurandareinextricablylinked.Insomecases,stepsmayoccurinanorderdifferentfromthat predictedbythemodel.

It is challenging to accurately identify the point at which adoption occurs, and the majority of definitions appear to be circular However, the only agreement was to defineadoptionasthepointwhichseparatesanorganisation’slackoftechnology from its acquisition of technology There are various interpretations of when adoption occurs, such as when a mental commitment is made to the use of the technology, when trial is initiated, or when full commercial use is initiated. Furthermore,somehavechallengedthevalidityoftheterm“adoption”itself,arguing that the point of adoption is typically determined retrospectively by the power of evidence (Tornatzky et al., 1983).

Theearlybehaviouralistsbelievedthatknowledgewaslinkedtobehaviour,and this was reflected in the diffusion model for technology adoption This model proposed that, once a person has been informed of a new technology and has been convinced of its benefits, they will follow the rational course of action, which is to adopt it (Hooks et al., 1983) Any variation in the adoption rate among individuals wasattributedtovariationsinindividual“innovativeness”(Gartrell,1979).Withthe publicationofPresser’spaper(1969),itwasrecognisedthatinnovationiscontingent, meaning that a person may adopt one technology prematurely and another technology later, as the conditions in which they may use both technologies aredifferent.

Gradual adoption describes the progressive increase in the application of a technology over time It allows for partial adoption, where individuals utilize the innovation to varying degrees, rather than complete adoption The rate of adoption is influenced by perceived trialability, as individuals are more likely to adopt technologies they can readily experiment with This gradual adoption process allows for adaptation and refinement of the technology based on user feedback, leading to wider acceptance and integration into the social system.

The difference between trial and gradual adoption is complicated by the decreasedneedforextensivetrializationasanewtechnologybecomesadoptedbya growingnumberofindividuals(Barrand Cary,2000).Accordingto Presser(1969), once an idea has been well tested and demonstrated to be effective in a particular field,andindividualshaveadopteditwithoutextensivetrialanderror,“itisapractice rather than an innovation”.

Internationalpartnershipprogram

Decree No.86/2018/ND-CP defines partnership programs as “cooperation between Vietnamese higher education institutions and foreign higher education institutionstoimplementtrainingprogramsandawarddegreesorcertificateswithout establishing a legal entity”.

TheamendmentsinDecreeNo.24/2022/ND-CPfurtherclarifythatpartnership programs with foreign countries can take the following forms:

- Programs jointly developed by the parties, awarding foreign or both foreign and Vietnamese degrees/certificates;

- Transferred foreign programs, awarding foreign or both foreign and Vietnamese degrees/certificates.

Partnership programs may be established entirely within Vietnam or partly within Vietnam and partly abroad, as determined by the involved parties To be considered an international partnership program, it must adhere to these core requirements:

- The program is recognized and widely applied at universities globally, and student learning outcomes (degrees) are accepted at foreignuniversities;

- The program is accredited and accepted by international universities or international accreditation organizations;

- Theprogramis taught in a foreign language, mainly English.

- Educationfranchiseprogramswhereforeignuniversitiesauthorizelocal universities to deliver some courses and award their degrees;

- Pathway/articulation programs where students study 2-3 years domestically before transferring abroad to complete a foreign university’s degree.

To sum up, the international partnership program is a concept of educational programs that are not limited by national borders (cross-border education), which allows students to accumulate international knowledge and qualifications without needing to leavetheirhomecountry.Theseacademicprogramscomein avarietyof formats, allowing students to choose an option that best suits their needs Some examples of the various formats of international partnership programs include earning a foreign degree at a branch campus of an international university located within the student’s home country, earning a dual degree from two universities in two different countries, earning a foreign degree through online distance learning programs without needing to physically study abroad, or pathway programs that allowforatransitiontostudyingabroad.Theinternationalpartnershipprogramsare all taught in English, even if the degree-granting university is located in a country whereEnglish is not the official language Therefore, proficiency in English is a mandatory requirement for students participating in these international partnershipprograms. adoption of blended learning This framework consists of three key components: strategy, structure, and support.

Strategy: This component involves addressing issues related to the overall approach to blended learning It encompasses the development ofclear institutional guidelines, the establishment of advisory groups, the formulation of a coherent strategy, and the availability of necessary resources and time By focusing on strategy, institutions can determine whether to adopt blended learning and how to implement it effectively for specific purposes.

The structure of a blended learning environment involves organizational aspects such as governance structures and technology-pedagogy integration models It also encompasses scheduling of blended activities and evaluation methods for assessing the effectiveness of blended learning initiatives.

Support: The support component addresses how institutions promote faculty performance and sustain instructional design in the blended learning context It encompassesbothtechnicalandpedagogicalsupportmechanisms,suchasproviding training and resources to enhance faculty members’ technological skills and knowledge Additionally, support may involve incentives to encourage faculty engagement and commitment to blended learning practices.

Byconsideringtheelementsofstrategy,structure,andsupport,institutionscan establish a solid foundation for the adoption and successful implementation of blended learning approaches These frameworks provide guidance for institutions seeking to integrate blended learning into their educational practices.

Organizations can transition towards mature institutionalization of blended learning through a phased approach suggested by Porter et al (2016) This approach involves progressive stages of development, implementation, and integration of blended learning practices within the institution The framework proposed by Graham et al (2013) can serve as a complementary reference for understanding this transition.

Awareness/exploration: In this stage, institutions have not yet developed standardizedstrategiesforblendedlearning.However,thereisagrowingawareness and understanding of blended learning approaches among faculty members Individual faculty members may be encouraged to explore and experiment with blended learning strategies in their classrooms At this stage, there may not be an official institutional policy regarding blended learning, but there is a recognition of its potential and minimal support for faculty members to investigate itsimplementation. Adoption/early implementation: In this stage, institutions have accepted blended learning as a policy and have started implementing interventions and initiativesrelatedtoblendedlearning.Thisstageischaracterizedbytheintroduction of new programs and approaches that incorporate blended learning into the institutionalcurriculum.Thefocusisoninitialimplementationandexperimentation with blended learning practices.

In the mature implementation/growth stage, blended learning becomes deeply embedded within an institution's educational framework A robust institutional structure and support system are established, providing clear guidelines for governance, pedagogical models, scheduling, and evaluation This structured approach fosters the ongoing growth and development of blended learning initiatives As a result, blended learning practices become seamlessly integrated into the institution's ecosystem, creating a solid foundation for sustained implementation and expansion.

Social Cognitive Theory (SCT), proposed by Albert Bandura in 1986, draws upon principles from social psychology to understand human behavior within the contextofthreemainfactors:behavior,personalfactors,andtheenvironment.These factorsinteractbi-directionally,influencingbothindividualandgroupbehavior,and can be used to predict and modify behavior.

InSCT,thebehaviorfactorfocusesonissuesrelatedtousage,performance, factors that are external to the individual SCT recognizes that these three factorsareinterconnectedandconstantlyinfluenceeachother.Thistriadicstructure forms the foundation of SCT.

Whenappliedtotheevaluationofinformationtechnologyusage,SCTemploys severalconstructs.Theseincludeself-efficacy,whichreferstoanindividual’sbelief in their ability to perform a specific behavior successfully Outcome expectations relate to the anticipated outcomes or consequences of performing a behavior Performance, anxiety, affect, and personal outcome expectations are additional constructs used within the SCT model to assess 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) is an extension of The Theory of ReasonedAction(TRA),whichwasdevelopedinresponsetocriticismregardingthe explanation of behaviors for which a person does not have full volitional control.ThisextensionofTRAincludesoneadditionalfactor,PerceivedBehavioralControl

People's behavior is strongly influenced by their self-assurance in their ability to carry out the behavior.*** **Perceived behavioral control (PBC) is a determinant in both the intention to use and the actual usage behavior.****Coherent Paragraph:**Perceived behavioral control (PBC) is a crucial factor that influences both the intention to use and the actual usage behavior According to the Theory of Planned Behavior (TPB), individuals' behavior is heavily impacted by their confidence in their ability to perform the behavior PBC is defined as the individual's perception of their personal capability to successfully execute the desired action.

“people’sperceptionoftheeaseordifficultyofperformingthebehaviorofinterest” (Ajzen, 1991) TPB states that the more one perceives behavioral control, the more likelytheyaretousethebehavior;andthehighertheintentiontouse,thehigherthe likelihoodofusagebehavior(Ajzen,1991).TheTPBtheorysuggeststhatintentions create the actual behavior, whereas attitudes, subjective norms, and PCB influence these intentions.

It is essential from an information systems perspective to identify the specific causal factors that can be manipulated to enhance acceptance of a system by specifying the antecedents of the attitude towards using the system In contrast to TRA, where attitudes are formed by a sum of belief-evaluation terms, TAM, which was developed by Davis et al (1989), is composed of two beliefs, namely the perceivedusefulnessandtheperceivedeaseofuse.Theprimarytenetoftheoriginal

TAMisthatusers’acceptanceoftechnologyisdeterminedbytheirperceptionofits usefulness and ease of use Davis (1989) defines these constructs as follows:

Theoreticalframework

SocialCognitiveTheory

Social Cognitive Theory (SCT) emphasizes the reciprocal interactions between behavior, personal factors, and the environment in shaping human behavior These three factors influence individual and group behaviors and work together bidirectionally SCT's predictive and modification potential makes it valuable for understanding and changing behavior.

InSCT,thebehaviorfactorfocusesonissuesrelatedtousage,performance, factors that are external to the individual SCT recognizes that these three factorsareinterconnectedandconstantlyinfluenceeachother.Thistriadicstructure forms the foundation of SCT.

Whenappliedtotheevaluationofinformationtechnologyusage,SCTemploys severalconstructs.Theseincludeself-efficacy,whichreferstoanindividual’sbelief in their ability to perform a specific behavior successfully Outcome expectations relate to the anticipated outcomes or consequences of performing a behavior Performance, anxiety, affect, and personal outcome expectations are additional constructs used within the SCT model to assess 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.

TheoryofPlannedBehavior

The Theory of Planned Behavior (TPB) is an extension of The Theory of ReasonedAction(TRA),whichwasdevelopedinresponsetocriticismregardingthe explanation of behaviors for which a person does not have full volitional control.ThisextensionofTRAincludesoneadditionalfactor,PerceivedBehavioralControl

(PBC), which is a determinant in both the intention to use and the actual usage behavior According to TPB, people’s behavior is strongly influenced by their self- assurance in their ability to carry out the behavior, with PBC being defined as

“people’sperceptionoftheeaseordifficultyofperformingthebehaviorofinterest” (Ajzen, 1991) TPB states that the more one perceives behavioral control, the more likelytheyaretousethebehavior;andthehighertheintentiontouse,thehigherthe likelihoodofusagebehavior(Ajzen,1991).TheTPBtheorysuggeststhatintentions create the actual behavior, whereas attitudes, subjective norms, and PCB influence these intentions.

TechnologyAcceptanceModel

It is essential from an information systems perspective to identify the specific causal factors that can be manipulated to enhance acceptance of a system by specifying the antecedents of the attitude towards using the system In contrast to TRA, where attitudes are formed by a sum of belief-evaluation terms, TAM, which was developed by Davis et al (1989), is composed of two beliefs, namely the perceivedusefulnessandtheperceivedeaseofuse.Theprimarytenetoftheoriginal

TAMisthatusers’acceptanceoftechnologyisdeterminedbytheirperceptionofits usefulness and ease of use Davis (1989) defines these constructs as follows:

Perceived ease of use, while not directly influenced by perceived usefulness, impacts it Both perceived usefulness and ease of use contribute to behavioral intention to use, which reflects an individual's conscious plans regarding future usage This suggests that users are more likely to adopt technology perceived as beneficial and easy to use.

Unifiedtheoryofacceptanceanduseoftechnology2

TheUTAUTiscomposedoffourprimaryelementsthatinfluencethebehavior of an individual in terms of intention and use: performance expectancy (PE), effort expectancy(EE),socialinfluence(SI),andfacilitatingconditions(FC).Gender,age, experienceandvoluntarinessofusealsoplayasignificantroleintheacceptanceand useoftechnology,accordingtothemodel.TheUTAUTfacilitatestheassessmentof the efficacy of new technologies in the educational field, and assists researchers in comprehendingthemotivationsbehindtheadoptionofthesetechnologiesinorderto create effective interventions for thosewho are less likely to adoptthem Numerous studieshavebeen conductedtoexploretherelationshipbetween theUTAUTmodel and the acceptance of new technologies (Dečman, 2015; Yang et al., 2019) The UTAUT model focuses on the factors that influence behavior intention and use behavior,p r i m a r i l y t h r o u g h t h e l e n s o f t h e u s e r ’ s p e r c e p t i o n o f t h e i m p a c t o n themselves.

UTAUT2 is the extension theory of UTAUT by Venkatesh et al (2012). UTAUT2 provide a predictive model for the use and acceptance of technology or a system In UTAUT2 model, there are three new additional constructs compared to UTAUT,which are:hedonicmotivation,pricevalue,habit.Furthermore,individual differences in age, gender and experience moderate the impacts of these items on behavioral intention and technology use behavior The UTAUT2 has a greater capacity to explain thebehavioralintention touse technologydueto itsinclusionof the majority of external factors that directly influence the behavioral intention to adopt a technology.

Understandinghowanewtechnologyisusedandadoptedhasbecomeamajor topicofstudyintheliteratureofinformationsystems.Thishasledtotheemergence ofmanymodelsbesidesTRAandTAM,whichresultedintheproliferationofadhoc models,themixingofconceptsfromdifferenttheories,ortheuseofmodelsthatare to develop a unified understanding of user acceptance Eight prominent technology acceptancemodelswereexamined,including:TRA,TPB,TAM,combinedmodelof TAM and TPB, the motivational model, the model of PC utilization, the social cognitive theory and the innovation diffusion theory On the basis of these models, they developed a unified model that incorporates elements across all eight models - UTAUT By combining the exploratory capabilities of the individual models with key moderating effects, UTAUT is advancing cumulative theory while maintaining a disciplined structure The various studies suggest that

UTAUT2 is a highly effective model for predicting consumer technology usage, as it combines the constructs from UTAUT with three additional determinants: hedonic motivation, price value, and habit These extensions significantly enhance the explanatory power of the model, increasing the explained variance in behavioral intention from 56% to 74% and technology usage from 40% to 52% compared to previous acceptance models Consequently, UTAUT2 is the most suitable choice for research on student adoption of blended learning.

Overviewofempiricalstudies

Al-Busaidia's (2013) research sought to explore the connections between learners' embrace of a Learning Management System (LMS) in blended learning and their personal attributes like self-efficacy, technology expertise, and personal innovation, as these factors relate to their intention to fully use e-learning The findings revealed that learners' intent to use full e-learning was significantly influenced by their personal innovativeness, perceived usefulness (PU), and satisfaction with the LMS in blended learning.

Therefore, when learners adopt the LMS in blended learning, it positively impacts their intention to engage in full e-learning These results provide valuable insights for practitioners and researchers in terms of planning and strategizing for full e- learning implementation.

In the study of Nguyen Van Than (2014) on the acceptance of over-the-top technologyservice,thelinear regressionanalysiswasappliedtotestthehypotheses. Thefindingsimpliedthatfacilitatingconditionswasthegreatestinfluentialfactoron the technology acceptance; followed by effort expectancy, performance expectancy and social influence.

CaoHaoThietal.(2014)conductedastudyontheacceptanceanduseofvirtual training on cloud computing The data was collected from 320 participants through Google docs using 5-likert scale. The findings pointed out that the price value had no influence on the acceptance and use of virtual training In contrast, performance expectancy, effort expectancy, social influence, facilitating conditions, hemonic motivation significantly influenced the acceptance and use of virtual training. Blended learning has gained popularity in higher education, and the study of Yan Dang et al (2016) focused on investigating factors that can influence student learning in this relatively new and evolving environment Yan Dang et al (2016) examined factors from three perspectives: students themselves, instructors, and institutional support Specifically, the factors considered were students’ computer self-efficacy,instructorcharacteristics,andfacilitatingconditions.Aresearchmodel was developed to systematically assess the impact of these factors on students’ perceived accomplishment, perceived enjoyment, and satisfaction with the blended class. Additionally, the study explored gender differences by testing the research model separately for male and female students Interestingly, the findings indicated that for female students, all three factors (computer self-efficacy, instructor characteristics, and facilitating conditions) significantly influenced their perceived accomplishmentandperceivedenjoyment,whichinturnhad asignificantimpacton their learning satisfaction However, for male students, no significant impact was found from computer self efficacy to either perceived accomplishment or perceived blended learning in medical education through the implementation of the UTAUT2 model.Thisstudyaimedtoidentifyanddeterminethevariablesthatcouldinfluence a student’s intention to use blended learning It was conducted in a cross-sectional andcorrelationalmanner,with225Iranianmedicalstudentsasthesample.Datawas examinedusingSPSS-18andAMOS-23software,andstructuralequationmodeling was employed to test the hypothesis The model constructs were found to be acceptable in terms of their validity and reliability Performance expectance (PE), effort expectance (EE), social influence (SI), facilitating conditions (FC), hedonic motivation(HM),pricevalue(PV)andhabit(HT)allhadasignificantimpactonthe student’s blended learning adoption In addition, the researchers found that the students’behavioralintentiontoengageinblendedlearninghadasubstantialimpact on the students’ actual implementation of blended learning Furthermore, the proposed framework, which was based on UTAUT2, had a high potential to detect the underlying elements that influence the students’ behavioral intention to use blended learning.

The study of Zhaoli Zhang et al (2020) focused on identifying the key factors that influence college students’ adoption of the e-learning system in mandatory blended learning environments The successful application of the e-learning system is crucial for effective implementation, management, and continuous improvement of blended learning in higher education The researchers propose the UnifiedTechnology Acceptance and System Success (UTASS) model to investigate these factors The model integrates self-reported data from questionnaires and system log datatocapturestudents’actualonlinebehavior.Additionally,thestudyconsidersthe moderator variables of gender and major The self-reported questionnaires were distributed through the e- learning system-starC, and a total of 287 valid responses were collected System log data was also collected to record students’ actual online behavior The collected data was analyzed using structural equation modeling The findings reveal that system quality (SQ), social influence (SI), and facilitating conditions(FC)significantlyandpositivelyinfluencestudents’behavioralintention (BI) to use the e-learning system However, information quality (IQ) does not have a significant positive effect on BI Furthermore, there is no significant positive relationshipfoundbetweenFC,BI,andusebehavior(UB).Thestudyalsoidentifies a moderator effect of gender, indicating that male college students are more susceptible to the impact of system quality and social influence.

Mahboubeh Taghizadeh and Fatemeh Hajhosseini's study (2020) aimed to investigate graduate students' attitudes, interaction patterns, and satisfaction with blended learning and determine the impact of attitude, interaction, and teaching quality on student satisfaction The study, conducted with 140 TEFL graduate students at Iran University of Science and Technology, utilized questionnaires to assess learner satisfaction, attitudes, interaction types, and teaching quality Quantitative and qualitative analyses revealed positive student attitudes towards blended learning technology.

The instructors were successful in teachingboththeoreticalandpracticalconceptsofTEFL,guidingonlinediscussions byprovidingconstructivefeedback,andmotivatinglearnerstoengageinmoreonline learning activities. The most frequent type of interaction observed was learner- instructor interaction Furthermore, the results of multiple regression analysis demonstrated that the quality of teaching had a higher contribution to students’ satisfaction compared to interaction and attitude This suggests the importance of training online teachers to enhance their knowledge, skills, and strategies necessary for effective online teaching.

The purpose of the study conducted by Kurniawan et al (2021) is to identify the factors that influence learners’ adoption of blended learning in non-formal educationandtoexaminetherelationshipsbetweenthesefactorswithinatheoretical model The motivation for conducting this study arises from the lack of research on physical space in educational institutions To collect data, a questionnaire based on Google Forms was distributed to 566 users of Blended Learning in non-formal educationinstitutionsinIndonesia.Existingscaleswereusedtomeasureallvariables in the theoretical model Structural Equation Model (SEM) analysis was conducted using SPSS and Amos software This research contributes to the theoretical and practical understanding of Blended Learning adoption and provides guidance for successfulimplementationofBlendedLearninginnon- formaleducationinstitutions Out of the thirteen initial hypotheses, nine were found to be significant The three hypotheses with the greatest impact were Social Influence(SI) leading to Perceived Usefulness (PU), Compatibility with Existing Environment (CE) leading to PerceivedEaseofUse(PEU),andPerceivedUsefulness(PU)leadingtoBehavioral Intention (BI) Social Influence was identified as the most influential factor in the adoption of Blended Learning in non-formal education institutions.

A study by Raman and Thannimalai (2021) investigated factors influencing the behavioral intention to use e-learning in higher education during the COVID-19 pandemic Employing the UTAUT2 model, the researchers assessed students' behavioral intention using snowball sampling and a UTAUT2-adapted questionnaire The analysis revealed that social influence and habit significantly impacted behavioral intention, while performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, and price value had no influence Habit emerged as the most significant predictor of behavioral intention This study guides higher education institutions in considering elements crucial for successful e-learning implementation and provides a basis for further research.

A research conductedby Norman Rudhumbu (2022) focused on the predictive power of UTAUT2 model towards the acceptance of blended learning among universitystudents.Thestudyemployedaquantitativeapproach,whichincludedthe useofastructuredquestionnairetocollectdatafrom432post-graduatestudents.The collected data was validated using Confirmatory Factor Analysis (CFA) and analyzed using Structural Equation Modeling (SEM) The results of the study revealed that performance expectancy, effort expectancy, social influences, facilitating conditions and hedonic motivation had a positive effect on the student’s behavioral intentions to use blended learning methods in universities Conversely, habit and price value did not have a significant impact on university students’ behavioral intentions to adopt the blended learning mode Furthermore, the study revealed that student’s behavioral intentions had significantly influenced the student’s acceptance of blended learning To sum up, the UTAUT2 application was suitableforthemeasurementofthestudent’sbehavioralintentionsto adoptblended learning in universities.

A research carried out by Jueliang Huang, Thanawan Phongsatha (2022) focused on the factors that influence the blended learning acceptance of early childhood undergraduate students The mixed-method research approach was applied to explore the acceptance and attitudes of early childhood major students towards blended learning in China based on the Unified Theory of Acceptance andUse of Technology 2 (UTAUT 2) and College and University ClassroomEnvironment Inventory (CUCEI) The study surveyed 363 early childhood undergraduate students in China, using the SEM analysis to identify which items inUTAUT2 and CUCEI had a significant impact on blended learning The interview datasupportedthefindingsofthequantitativedata.Itwasfoundthatsocialinfluence and classroom environment had a significant impact on the acceptance of blended learning Furthermore, the ease of use and convenience of blended learning as well

BL acceptance rate and hedonic motivation, facilitating condition, and price value.

Georgios Zacharis, Kleopatra Nikolopoulou (2022) conducted a study on the factors that influence the behavioral intention of University students to utilize eLearningplatformsduringthepost-pandemicperiod,usingtheUTAUT2approach with new item Learning Value This research sought to identify the elements that influence the behavioral intention of university students to engage in eLearning in the post-pandemic era An online questionnaire was administered to 314 students from various universities in Greece The results of the study revealed that the followingelementshadamajorinfluenceonstudents’intentiontoutilizeeLearning platforms: performance expectancy, social influence, hedonic motivation, learning value and habit Additionally, facilitating conditions and learning value had a direct effect on the actual use of eLearning platforms These findings further refine the analysis of the post-Covid-19 eLearning model, combining the Learning Value, in order to investigate the intention of students to access eLearning platforms.

Jashwini Narayan and Samantha Naidu’s study (2023) focused on the contextualandcomprehensiveapplicationofamodifiedUTAUT2model(withnine variables and a moderator) in higher education during the post-COVID-19 era to examine behavioral intention of tertiary students from developing country The impactofthestudent’sbehavioralintentionontheirusebehaviorwasalsoevaluated Data were collected from 419 students enrolled in a regional university through a conveniencesamplingtechnique,andthedatawereanalysedtoconfirmtheproposed modelthroughtheuseofcovariance-basedstructuralequationmodelling.Theresults of the study suggest that there is a strong correlation between social influence, hedonic motivation, facilitating conditions, commitment, behavioral intention and use behavior However, no significant positive impact on behavioral intentions has beenf o u n d i n r e g a r d t o t h e f o l l o w i n g f a c t o r s : p e r f o r m a n c e e x p e c t a n c y , e f f o r t expectancy, price value, trust, and comfortability.T h i s s t u d y p r o v i d e s a n o v e l a p p r o a c h t o t h e c o n t e x t u a l a p p l i c a t i o n o f p o s t - C O V I D

U T A U T 2 m o d e l s T h e n e w c o n s t r u c t t h a t h a s b e e n i d e n t i f i e d i n r e c e n t l i t e r a t u r e , “ C O V I D - 1 9 f e a r ” , h a s b e e n e v a l u a t e d forthefirsttimeinthecontextofUTAUT2forthepurposeofmoderating the association between behavioral intentions and use behavior However, the moderating analysis revealed that COVID-19 fear did not show a significant interactioneffect,inotherwords,COVID-19feardidnotmoderateorstrengthenthe associationbetweenbehavioralintentionsandusebehavior.Besides,theadditionof three new independent variables (trust, commitment, and comfortability) further enhanced the predictive efficacy of the model.

A study was conducted by Tran Trong Duc et al (2022) to investigate the factorsaffectingstudents’intentiontouseIoTservicesatretailstoresinHaNoiCity The survey received a total of 355 responses, the data after collection was analyzed usingtheCFAandSEMmodelforhypothesestesting.Performanceexpectancyand social influence turned out to have the most influence on students’ intention. Hemonicmotivationandfacilitatingconditionsalsopositivelyaffectedtheintention of students to use the IoT service.

Conceptualmodelandhypotheses

Conceptualmodel

Basedonpreviousresearch,thefollowing tablesummarisesthevariablesused in the above studies as well as their influence level on adoption:

Source:SummarizedbyauthorBased on the summary table, it is evident that there are various independentv a r i a b l e s c h o s e n b y d i f f e r e n t a u t h o r s t o e x a m i n e t h e i r i m p a c t a n d i n f l u e n c e o n a d o p t i o n T h e l e v e l o f i m p a c t o f t h e s e v a r i a b l e s v a r i e s a c r o s s s t u d i e s , a n d s o m e v a r i a b l e s h a v e b e e n c o n s i s t e n t l y p r i o r i t i z e d a n d s h o w n s i g n i f i c a n t i n f l u e n c e o n a d o p t i o n A f t e r c a r e f u l c o n s i d e r a t i o n , t a k i n g i n t o a c c o u n t p r e v i o u s r e s e a r c h a n d t h e r e l e v a n c e t o t h e r e s e a r c h c o n t e x t , f i v e f a c t o r s i n h e r i t e d f r o m t h e U T A U T 2 their impact on the research context of Western Sydney international partnership program in Vietnam These factors include Facilitating Conditions (FC), Social Influence(SI),PerformanceExpectancy(PE),HedonicMotivation(HM),andEffort

Expectancy(EE).Thesefactorshavealldemonstratednoteworthyinfluenceinprior adoption studies.

Factors influencing the adoption of blended learning in a program were examined, including Self Efficacy (SE) and Instructor Characteristics (IC) SE, which measures learners' confidence in their abilities, and IC, which relates to the teaching style and qualities of instructors, were found to significantly impact students' engagement with blended learning approaches.

IC have been less frequently selected in previous studies and have not demonstrated a prominent level of influence, the author have still chosen to include thesefactorsinthemodel.Thisdecisionenablestheauthortore-evaluatetheimpact of these factors and generate new insights It is important to note that the lack of strong influence in previous studies does not exclude the possibility of positive findingsforthesefactorsinthespecificresearchcontext.ByincludingSEandICin the model, the author aim to thoroughly examine their level of impact and assess their significance, potentially uncovering valuable contributions to the study.

Furthermore, in order to enhance the explanatory power of the conceptual model on blended learning adoption, a new variable called Course Flexibility (CF) has been introduced This variable recognizes that blended learning involves a flexible transition between online and offline modes, distinguishing it from other learningmethodsthataresolelyonlineoroffline.However,thisvariablehasnotbeen included in any previous studies related to blended learning It only appeared in a study conducted by Mehmet Kokoỗ(2019) on the e-learning flexibility Therefore, the author expects that the inclusion of this new variable will yield valuable new insights in the investigation of students’ blended learning adoption.

Performance expectancy, a crucial aspect of blended learning, refers to an individual's belief that using the system will enhance their job performance (Venkatesh et al., 2003) In the context of blended learning, it measures the likelihood of students achieving their desired outcomes by engaging in blended learning This concept is similar to the perceived usefulness dimension of the Technology Acceptance Model (TAM) By assessing performance expectancy, educators can gauge students' perceptions of the potential benefits of blended learning and its impact on their academic performance.

Williams, Rana, and Dwived (2014) reported that the relationship between performance expectancy and behavioral adoption was studied in 116 of the 174 studies Out of these, 93 studies showed that performance expectancy significantly predicted behavioral adoption, with performance expectancy being the most prominent predictor.

Research conducted in the field of higher education has demonstrated that performanceexpectancyhasapositiveandsubstantialimpactonbehavioraladoption ofe-learninginformationservices(Hsu,2012;OhandYoon,2014;RamanandDon, 2013), web- based learning systems (Jong and Wang, 2009; Lwoga and Komba, 2014; Masadeh, Tarhini,

Mohammed, and Maqableh, 2016), Moodle (Decman,

2015;Olatubosun,Olusoga,andSamuel,2015),andsocialmedia(KasajandXhindi,2016).

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

Effort expectancy is the “degree of simplicity and ease of use of a system” (Venkatesh et al., 2003) This indicates the degree to which users anticipate that a system will be effortless to utilize in the execution of their responsibilities (Huang and Kao, 2015) In the blended learning context, effort expectancy is the degree to which students believe blended learning is easy to apply in their studies University students who anticipate that blended learning is easy will be more likely to incorporate it into their studies According to Asare et al (2016), Pardamean and Susanto (2012), and Venkatesh et al (2003), this construct serves as a reference for the following three constructions proposed in other theories: (a) perceived ease of use, (b) complexity, and (c) ease of use.

Intwostudiesabouttheimplementationofblendedlearning(Azizietal.,2020) andlearningmanagementsystem(Widjajaetal.,2020),effortexpectancywasfound to influence behavioral adoption Although the predictive efficacy of effort expectancy may be lower than that of the other components in the model (Morosan and Defranco, 2016), multiple authors have indicated that effort expectancy has a positive and substantial impact on the adoption of various services, including the digital library (Nov and Ye, 2009), e-learning, and online gaming services (Oh and Yoon, 2014) Chan et al (2015) found that effort expectancy was positively and significantlyassociatedwiththebehavioraladoptiontoutilizethestudents’response system with mobile devices.

H2:Effortexpectancyhasapositiveinfluenceonstudents’blendedlearningadoptio n system”(Venkateshetal.,2003).Socialinfluenceinthecontextof blendedlearning refers to the extent to which an individual perceives belief from social group to use blended learning. According to Asare et al (2016), Pardamean and Susanto (2012), andVenkateshetal. (2003),otherconstructtheoriesrefertosocialinfluenceinterms ofbehavioralmodification,including(a)subjectivenormsin theTheoryofPlanned BehaviourandTheoryofReasonedAction(Ajzen,1991;AjzenandFishbein,1980; Fishbein and Ajzen, 1975), (b) social factors in Social Learning Theory, and (c) external variables in TAM. Undergraduate students will form behavioral intention to utilize blended learning if they perceive that their peers, lecturers, or parents believe that blended learningshouldbeemployedintheirstudies.Inseveralonlinecontextsthatinclude: acceptance of blended learning (Azizi et al., 2020); adoption of e-learning (Tarhini et al., 2017); use of a learning management system (Ain et al.,2016; Widjaja et al., 2020); adoption of MOOCs (Tseng et al., 2019); adoption of emerging information technology in higher education classrooms (Lewis et al., 2013); and adoption of online teaching by school teachers

(Tandon, 2020), social influence was found to influencethebehavioraladoption.Venkateshetal.(2012)arguethatsocialinfluence isdirectlylinkedtobehavioraladoption.FidaniandIdrizi(2012)alsofoundastrong correlation between social influence and LMS adoption.

H3: Social influence has a positive influence on students’ blended learningadoption

Venkatesh et al (2003) defined facilitating conditions as an “individual’s opinion as to whether the organization provides technology facilities to augment e- learning”.I n t h e b l e n d e d l e a r n i n g c o n t e x t , f a c i l i t a t i n g c o n d i t i o n s r e f e r t o t h e l e v e l o f expectationastudenthasfororganizationalinfrastructureandtechnicalassistance during the implementation of blended learning Examples of facilitating conditions include internet connection, data, Internet connecting devices, online pedagogy and instructional strategies employed in lectures, also including the development of modulesthatenablecontenttobeaccessibletoallstudents.Thisconstructreproduces the idea of perceived behavioral control and compatibility from earlier paradigms (Asare et al., 2016; Lakhal et al., 2013; Venkatesh et al., 2003; Venkatesh et al., 2016) Students who are confident that their institution has the necessary technological and organizational resources to facilitate their learning through blended learning mode will form behavioral intention to incorporate this learning mode into their academic studies.

Oh and Yoon (2014) predicting the use of online information services in e- learning based on a modified UTAUT model with the university student in South Koreaobservedthatfacilitatingconditionssignificantlypredicte-learningandonline gaming adoption. Also, Asare et al (2016) and Masadeh et al (2016) revealed that the facilitating conditions factor has a significant positive effect on student’s e- learning adoption Consistent with the finding above, it was established that facilitating conditions significantly predicted the adoption of: (a) English language e-learning websites (Tran, 2013), (b) web-based learning systems (Jong and Wang, 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

According to Amparo (2021), hedonic motivation refers to the notion that utilizingasystemisapleasurableexperience Hedonicmotivationisdefinedas“the fun or pleasure derived from using a technology” (Venkatesh et al., 2012) In the blendedlearningcontext,hedonicmotivationisthedegreetowhichstudentsbelieve using blended learning is a fun and enjoyable experience Previous research has demonstrated that this is a significant factor in the adoption of technology (Brown and Venkatesh, 2005) In addition, if a technology provides enjoyment and amusement while being used, users are likely to experience pleasure, which influences their intention to use the technology in the future (Lee,2009). student’s intention to use the e-learning systems Furthermore, various researchstudieshavedemonstratedastrongcorrelationbetweenhedonicmotivations andbehavioraladoption,withhedonicmotivationbeingidentifiedasoneofthemost reliable predictors forthe model The findingsof studiesofVenkatesh etal (2012), Nikolopouloetal.(2020),Gharrahetal.(2021),Ho(2014)andAlalwanetal.(2017) indicated that hedonic motivation had a positive and substantial impact on the behavioral intention of participants to adopt a system, including blended learning.

However,astudyconductedbyAbu-GarrahandAljaafreh(2021)revealedthat HMdid nothave a positive and significantimpacton participants’blended learning systemadoption.DuringtheperiodoflockdownduetoCOVID-19,studentsreported a variety of stress- related issues, including isolation, loneliness, poor concentration andlowmotivation(Curelaruetal.,2022;Popa-Veleaetal.,2021),whichmayhave contributed to a decrease in hedonic motivation during that period.

H5: Hedonic motivation has a positive influence on students’ blended learning adoption

Self-efficacy, as defined by Bandura (1986), refers to an individual’s belief in their own capabilities to perform a particular behavior Students with high self efficacy possess a strong sense of self-confidence in their abilities to successfully carry out tasks using blended learning (Balakrishnan and Gan, 2016) On the other hand, students with low self efficacy require a higher level of support from instructors to compensate for their perceived deficiencies (Sawang, Newton, and Jamieson,2013).Thesestudentsmaylackconfidenceintheir ownabilitiesandmay require additional guidance and assistance to navigate and effectively engage with the blended learning systems.

By having a strong belief in their own capabilities, individuals aremore likely to approach challenges with a positive mindset and actively seek out opportunities for growth and achievement This confidence in their capabilities can significantly impacttheirbehaviorsandactions,leadingtoincreasedeffortsandultimatelyhigher chances of success It is important to note that self efficacy is context-specific and can vary across different situations or domains (Bandura, 1977) There is a limited number of studies examining the relationship between students’ self efficacy and their engagement in blended learning Understanding the relationship between self efficacy and blended learning adoption can provide valuable insights into how students’ beliefs about their academic abilities may impact their motivation, participation, and overall success in a blended learning environment.

H6: Self efficacy has a positive influence on students’ blended learningadoption

Thetitleofinstructorisassigned to an individualwhohastheresponsibility of teaching students within a particular subject field, while instructor characteristics include

“personality traits, knowledge, abilities, experience, values, and beliefs” (Janet Clinton et al., 2023).

Researchprocess

Assessingthereliabilityof the measurement scales, item- total correlations to identify and remove unsuitable observedvariables

Assessing Total Variance Explained (≥50%), KMO Value(0≤KMO≤1),and Eigenvalue (≥ 1)

Proposing research model & initial measurementscales

Conducting online survey of 400undergraduatest udentsviaGoogleFor

Assessing the model fit, analyzing the regression coefficients,andtestingthe research hypotheses

Conductingin-depth interview & focus group discussion to refine measurementscales

Source:ProposedbyauthorThe research process consisted of three distinct stages In the first stage, thep r i m a r y f o c u s w a s o n i n t r o d u c i n g t h e r e s e a r c h a n d e s t a b l i s h i n g i t s t h e o r e t i c a l f o u n d a t i o n Thisinvolvedconductingacomprehensiveliteraturerevi ew,examining previous studies, and incorporating the author’s observations The literature review and conceptual model formed the basis for this stage Moving to the next stage, the researchs c a l e w a s d e v e l o p e d T o e n s u r e t h e q u a l i t y a n d s u i t a b i l i t y o f t h e q u e s t i o n n a i r e fortheofficialsurveyandreliabilitytest, anin-depthinterviewwas the concepts and observed variables proposed by the author aligned with the perceptions of the intended respondents.

Once the data collection phase was complete, the collected data was inputted andencodedforanalysis.Statisticalmethodswereemployedtoanalyzethedataand draw meaningful insights By applying appropriate statistical techniques, the researchfindingswereexploredandinterpreted.Inthefinalstage,theresearchdrew conclusions based on the analyzed data It provided administrative implications, highlightingthepracticalimplicationsoftheresults.Theresearchalsoacknowledged its limitations, delineating the boundaries of the study, and suggested potential directions for future research endeavors.

Overall, the research process encompassed the establishment of a theoretical background, questionnaire refinement, data collection and analysis using statistical methods, drawing conclusions, offering administrative implications, and outlining limitations and future research directions.

Scaledevelopment

Scaledevelopmentprocess

Developing valid and reliable scales for measurement requires extensive researchandasignificantinvestmentoftime.However,well-constructedscalesoffer the advantage of being more accurate and dependable in evaluating the underlying construct they are designed to measure (McIver & Carmines, 1981) Gehlbach and Brinkworth (2011) developed a comprehensive and rigorous methodology for designing reliable survey scales by integrating several well-established survey development techniques This process aims to reduce measurement effort and enhance the validity of new survey scales It involves collaboration with both potential survey respondentsand subject matter experts.The procedure isdescribed in five steps as follows:

Step1:Athorough re vi e w oftherel evant literatureshould beconducted to preciselydefinetheconstructinrelationtotheexistingresearchtopic.Thisstepalso 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 builtaccordingly.

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 providefeedbackontheitems,includingsuggestionsforrevisionsordeletions.This step, in addition to ensuring the items align with the conceptualization of the construct,mayyieldfurtherinputonpotentialmissingindicators.Whenreachingout toexpertsintherelevantconstruct,besuretoprovidethemwiththedefinitionofthe 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 whetherthedefinitionoftheconstructsandmeasurementitemsprovidedbyscholars 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 academicandpublicunderstandingoftherelevantconstruct.Inthisstep,whenthere isconceptualalignmentbetweenscholarsandrespondentsbutdivergentdescriptions oftheindicatorsorsub-themes,thelanguageusedbytherespondentscanbeadopted.

Thiswillfacilitatethecompilationofalistofindicatorsorsub-themesthatcanform the foundation for item development.

Step 5: Generate official measure items The purpose of this step is to finalize theitemsusedintheofficialscalethatembodythethemesorindicatorsthatsurfaced from integrating the interview and focus group data with the literature.

Variousa s s e s s m e n t s c a n b e m a d e t o d e t e r m i n e h o w e f f e c t i v e l y t h e items conceptualization of the construct (Gehlbach & Brinkworth, 2011).

Researchscales

Qualitative research was conducted through in-depth interviews with three experts in school management to explore their perceptions of students' blended learning adoption The research aimed to refine and supplement an initial measurement scale based on insights gained from these interviews The finalized scale will be used for data collection.

Priortomeetingtheexperts,theauthordevelopedaninitialmeasurementscale grounded in the theories and related studies presented in Chapter 2 This involved listing and conceptualizing the relevant terms and constructs pertaining to the researchtopic.Theinitialscalewasthensharedwiththeexpertsinadvance.During thediscussion,thecontentsthatreceivedapprovalfromatleast2outof3participants were retained or incorporated into adjustments if required.

The initial measurement scale presented in Table 3.1 comprises a total of 46 observed variables, which was built upon the scales used by previous researchers including: 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), Sury Ravindran et al (2016), Sattari et al. (2017),Lawless(2019),Chao(2019),White(2019),Moorthyetal.(2019),Mehmet Kokoc (2019), Chen et al (2020), Georgakopoulos et al (2020), Lu et al (2020), LeeandLee(2020),Azizietal.(2020),Abu-GharrahandAljaafreh(2021),Amparo (2021), Bordoloi et al (2021).

(2003), Venkateshetal. (2012), Huang andKao(2015), Lawless(2019), Chao(2019), White(2019), Chen et al.(2020)

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 Theintegrationofblendedlearningcomponentsallows me to enhance my learning productivity.

PE5 Usingblended learning improves my understanding of the course materials.

Implementing the blended learning system is not a challenging task for me within the classroomenvironment.

Venkateshetal. (2003), Venkateshetal. (2012), Huang andKao(2015),G eorgakopouloset al (2020),Abu- Gharrahand Aljaafreh (2021), Amparo

The individuals who have a significant impact on my behaviorthinkIshouldutilizeblendedlearningformystudie s.

SI4 Ibelievethatotherclassmatesalsouseblendedlearning to support their studies. b l e n d e d l e a r n i n g

Wu and Liu (2013),Sattari et al (2017), Moorthyetal. (2019), Georgakopoulos etal (2020), Lu et al. (2020),Abu- Gharrahand Aljaafreh(2021)

FC3 Blended learning aligns well with the information and communication technology tools I use in my studies.

FC4 Blendedlearningisinharmonywithotherpedagogical methods my instructors employ in class.

FC5 I can get assistance from others if I have trouble with blended learning techniques.

HM2 I experience a sense ofenjoyment when usingblended 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 learningstyle.

IC1 Iampleasedwithhow readilyaccessibleandavailable the instructors are to assist me.

Al-Busaidiand Kamla Ali (2012), Sury Ravindranetal. (2016)

IC2 Theinstructorsprovidetimelyresponsestomyqueries and actively participate in discussions.

IC5 The instructors effectively foster interaction andengagement among students.

IC6 The instructors use blended learning technology appropriately.

CF1 Blendedlearningallowsflexibleaccesstolecturesand learning activities from anywhere and anytime.

CF2 With the help of blended learning, I can easily access the learning materials.

Blended learning courses enable me to concentrate on learning activities that align with my individual needs and preferences.

CF6 Throughblendedlearning,Iamabletogivepriorityto specific topics during my learning process.

BLA1 I would opt for blended learning whenever I intend tostudy.

Venkateshetal. (2012), Brusso (2015),Huang andKao(2015), Lee and Lee (2020), Azizi et al.(2020), AbuGharraha ndAljaafreh(2 021)

BLA3 Inmydailylife,Iwillconsistentlyprioritizetheuseof 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.

Theauthor conducted an in-depthinterviewwith threeexpertsfromtheschool management and a focus group discussion with ten students to determine if they understood the content and appropriateness of the survey questionnaire The qualitative research results indicate that the number of independent variables in the research model on factors influencing students’ blended learning adoption at the WSU international partnership program remains unchanged, consisting of: performanceexpectancy,effortexpectancy,socialinfluence,facilitatingconditions, hedonic motivation, self efficacy, instructor characteristics, course flexibility. However,thecontentand numberofobservedvariables(measurementitems) in the scale have been adjusted as follows:

Change the content of PE4 from “The integration of blended learning componentsa l l o w s m e t o e n h a n c e m y l e a r n i n g p r o d u c t i v i t y ” t o “

Add a new measured item PE6 with the content as “Blended learning significantly fulfills my learning and research needs”.

ChangethecontentofEE4from“Implementingtheblendedlearningsystemis not a challenging task for me within the classroom environment” to “Applying the blended learning system in class is not a challenge for me”.

Change the content of FC1 from “I have been provided with access to the necessary resources for my implementation of blended learning” to “My institution possesses the required resources to support my implementation of blended learning in my studies”.

Change the content of FC5 from “I can get assistance from others if I have trouble with blended learning techniques” to “The help desk is ready to assist with any issues that may arise during the blended learning implementation”.

ChangethecontentofHM3from“Ihaveafavourableattitudetowardusingthe blended learning approach” to “I hold a positive view on utilizing the blended learning approach”. AddanewmeasureditemHM6withthecontentas“Iderivethepleasurefrom engaging in both e-learning and traditional classes”.

I possess the capability to fulfill the required tasks by effectively leveraging the tools offered by blended learning Through this approach, I am able to seamlessly complete assignment submissions, ensuring timely and efficient execution of academic obligations.

AddanewmeasureditemSE5withthecontentas“Iamabletousetheblended learning system by relying solely on the system manuals as a reference guide”.

Change the content of IC6 from “The instructors use blended learning technologyappropriately”to“Theinstructorsdemonstrateahighlevelofproficiency plantocontinuetouseblendedlearningonaregularbasis”to“Ihavemadeplansto regularly incorporate blended learning into my learning activities”.

On that basis, the official measurement scale consisting of 49 observed variables is summarized in Table 3.2.

(2003), Venkateshetal. (2012), Huang andKao(2015), Lawless(2019), Chao(2019), White(2019), Chen et al. (2020)

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.

PE5 Usingblended learning improves my understanding of the course materials.

PE6 Blended learning significantly fulfills my learning and research needs.

EE4 Applying the blended learning system in class is not a challenge for me.

Venkateshetal. (2003), Venkateshetal. (2012), Huang andKao(2015),G eorgakopouloset al (2020),Abu- Gharrahand Aljaafreh (2021), Amparo (2021), Bordoloietal. (2021)

The individuals who have a significant impact on my behaviorthinkIshouldutilizeblendedlearningformystudie s.

SI4 Ibelievethatotherclassmatesalsouseblendedlearning to support their studies.

My institution possesses the required resources to support my implementation of blended learning in mystudies.

Wu and Liu (2013),Sattari et al (2017), Moorthyetal. (2019), Georgakopoulos etal (2020), Lu et al. (2020),Abu- Gharrahand Aljaafreh(2021)

FC3 Blended learning aligns well with the information and communication technology tools I use in my studies.

FC4 Blendedlearningisinharmonywithotherpedagogical methods my instructors employ in class.

FC5 Thehelpdeskisreadytoassistwithanyissuesthatmay arise during the blended learning implementation.

HM2 I experience a sense ofenjoyment when usingblended learning for my studies.

HM6 I derive the pleasure from engaging in both e-learning and traditional classes.

SE5 Iamabletousetheblendedlearningsystembyrelying solely on the system manuals as a reference guide.

IC1 Iampleasedwithhowreadilyaccessibleandavailable the instructors are to assist me.

Al-Busaidiand Kamla Ali (2012), Sury Ravindranetal. (2016)

IC2 Theinstructorsprovidetimelyresponsestomyqueries and actively participate in discussions.

IC5 The instructors effectively foster interaction andengagement among students.

IC6 Theinstructorsdemonstrateahighlevelofproficiency in using blended learning technologies.

CF1 Blendedlearningallowsflexibleaccesstolecturesand MehmetKokoc the learning materials.

Blended learning courses enable me to concentrate on learning activities that align with my individual needs and preferences.

CF6 Throughblendedlearning,Iamabletogivepriorityto specific topics during my learning process.

BLA1 I would opt for blended learning whenever I intend tostudy.

Venkateshetal. (2012), Brusso (2015),Huang andKao(2015), Lee and Lee (2020), Azizi et al.(2020), AbuGharraha ndAljaafreh(2 021)

BLA3 Inmydailylife,Iwillconsistentlyprioritizetheuseof blended learning as an effective learning method.

BLA4 I amdetermined to utilize the blended learning system continuously and extensively.

BLA5 I would advise others to consider enrolling in blended learning courses.

Questionnairedesign

Data collection for this study utilized questionnaire surveys, commonly employed to gather factual information in educational research Primary data were collected through an online Google Forms survey distributed via students' emails To enhance clarity and ensure accurate question interpretation, in-depth interviews were conducted with three school management executives.

Theirfeedbackwassoughtregardingtheunderstandabilityoftheproposedquestions and the potential for misinterpretation.

The questionnaire consisted of two parts: a demographic profile section with fourquestions,andasectionaddressingthefactorsinfluencingstudents’adoptionof blended learning at the Western Sydney international partnership program, comprising 49 questions in total The demographic profile section used a nominal scale,whichassignsnumbersaslabelstocategorizerespondentsintospecificgroups based on gender, age group, academic major, and family income level.This scale is useful for classifying subjects with shared characteristics, as recommended by Sekaran and Bougie (2019). The Likert Scale was employed to measure the factors influencing students’adoption of blended learning in the program The LikertScale is designed to assess the respondents’ level of agreement or satisfaction Each questionwasscaledusinga5- pointLikertScale,rangingfrom:1(StronglyDisagree)

- 2 (Disagree) - 3 (Neutral) - 4 (Agree) - 5 (Strongly Agree) Participants were requiredtoselectthescalepointthatbestrepresentedtheiropinions,assuggestedby Sekaran andBougie (2019).

Samplesize

The sampling method the author selected is convenience sampling, which is a non-probability sampling approach Non-probability sampling involves choosing samples based on the overall characteristics and investigative needs According to Hair et al (1998), the recommended sample size for exploratory factor analysis (EFA) is five times larger than the number of observations in the scale, with a 5:1 ratio- fiveobservationsforeachvariable.Forthisstudy,whichhas49observations, the required sample size would be 5 * 49 = 245 In the case of regression analysis, requirements for both EFA and regression analysis, a population of 400 undergraduate students from the Western Sydney international partnership program at UEH-ISB in HoChi Minh City will be selected.

Dataanalysismethods

Descriptivestatistics

Descriptive statistics offer concise numerical summaries of data sets, aiding in the understanding and description of data characteristics These statistics provide high-level overviews, representing either samples or the entire population Through brief summaries of data measures and sample characteristics, descriptive statistics simplify the interpretation and analysis of complex data sets.

Descriptive statistics encompass three key aspects: variability, central tendency, and frequency distribution Variability measures data dispersion through metrics like standard deviation, variance, and minimum/maximum values, as well as kurtosis and skewness Central tendency represents the data's central point, calculated via mean, median, and mode Frequency distribution indicates the occurrence frequency of data.

Measures of central tendency are a well-known category of descriptive statistics.

A data set can be defined and characterized using metrics like the mean, median,and mode - termsused extensively in mathematicsand statistics.Themean is calculated by summing all the values in the data set and dividing by the total numberofvalues.Descriptivestatisticsareusedtotakecomplexquantitativeinsights from large data sets and distill them into manageable summary measures.

Cronbach’sAlphareliabilitytest

Cronbach’s Alpha (α), developed by Lee Cronbach in 1951, is a measure of), developed by Lee Cronbach in 1951, is a measure of reliability or internal consistency It assesses the extent to which a set of items in a questionnaire are closely related as a group This measure is commonly used when researchers employ a Likert scale and wish to determine the reliability of the scale. The formula for Cronbach’s Alpha is as follows:

Increasing the number of items in the scale will result in an increase in Cronbach’sAlpha(α), developed by Lee Cronbach in 1951, is a measure of),asindicatedbytheformula.Additionally,iftheaverageinter- item correlation is low, Cronbach’s Alpha will also be low Conversely, as the average inter-item correlation increases (assuming the number of items remains constant),Cronbach’sAlphawillincreaseaswell.TherangeofCronbach’sAlphais from 0 to 1, providing an overall assessment of the measure’s reliability A Cronbach’s Alpha of 0 suggests that all scale items are independent of each other, while a value approaching 1 indicates high covariance among the items, indicating that they measure the same concept. Therefore, a higher Cronbach’s Alpha coefficient signifies greater covariation among the items, allowing them to be measured under a single concept.

The minimum recommended value for Cronbach's alpha varies depending on the questionnaire scale Many researchers suggest a minimum value between 0.65 and 0.80, while Nunnally and Bernstein (1994) suggest a minimum corrected item-total correlation of 0.30 Cronbach's alpha values of 0.60 or higher are generally considered acceptable, and DeVellis (2003) proposes a minimum value of 0.70, although values as low as 0.63 may still be considered usable.

Exploratory Factor Analysis (EFA) is “a multivariate statistical method designed to facilitate the postulation of latent variables that are thought to underlie and give rise to the patterns of correlations in new domains of manifest variables” (Haig, 2005). Factor analysis is a commonly used method in psychology and education to interpret self-reporting questionnaires It serves multiple purposes in descriptive statistics. Firstly, it reduces a large number of measured variables into a smaller set of variables. Secondly, it identifies underlying dimensions that connect observed variables with latent constructs, helping to develop and refine theories Lastly, it provides evidence of validity for participants’ self-reports.

TheKaiser-Meyer-OlkinTest(KMO)isusedtoassesstheextenttowhicheach variableinasetisindependentoferrorscausedbyothervariables.AccordingtoHair et al (2006), a KMO value of 0.5 or higher (0.5 ≤ KMO ≤ 1) indicates that factor analysisisappropriateforthedata.IftheKMOvalueisbelow0.5,factoranalysisis considered unsuitable for the dataset KMO values between 0.5 and 0.7 are considered mediocre, while values between 0.7 and 0.8 are considered good.

Bartlett’s Test of Sphericity is used to assess the correlation among observed variablesin afactor.Inorder for factoranalysisto beapplicable,itisnecessary that the observed variables, which represent different aspects of the same factor, are correlated with each other If the significance level of Bartlett’s Test, denoted by Œsig.’, is less than 0.05, it indicates that the observed variables are correlated within the same factor group.

1orhigherareconsideredforinclusionintheanalyticalmodel.Anothercriterionto assess the appropriateness of the EFA model is Total Variance Explained, which should be at least50% This indicates that the model can account for a substantial proportion of the observed variance In terms of variance, if 100% is considered to represent the total variance, the obtained value indicates the extent to which the extracted factors can summarize the data and the percentage of observed variables that will not be captured by the factor analysis.

Factor Loading measures the correlation between observed variables and factors in a factor analysis A higher factor loading indicates a stronger correlation between the observed variables and the factors According to Hair (2009), there are guidelines for interpreting factor loadings:

- FactorL o a d i n g a t ± 0 3 : Th i s i s t h e m i n i m u m t h re s h o l d fo r o b s e rv e d v a ri a b l e s t o b e re t a i n e d i n t h e a n a l y s i s

- FactorL o a d i n g a t ± 0 7 : I n d i c a t i n g t h a t t h e o b s e r v e d v a r i a b l e s a r e s t a t i s t i c a l l y e x c e l l e n t i n r e l a t i o n t o t h e f a c t o r However, it is important to note that the specific standard value for factor loading depends on the sample size Different sample sizes may require different weighting factors to determine the significance of factor loading.

Pearson’scorrelationcoefficientisastatisticaltoolusedtoassessthedirection and strength of the relationship between two continuous variables Its reliance on covariance makes it the most effective approach for evaluating associations Pearson’s correlation coefficient is represented by r and is constrained within a specific range by design:

Pearson’s correlation coefficient (r) and the associated p-value are computed together.T h e p - v a l u e c a n b e i n t e r p r e t e d a s f o l l o w s t o d e t e r m i n e t h e statistical

Linear regression is a statistical technique used to predict the value of an outcomevariable(Y)basedononeormorepredictorvariables(X).Theobjectiveis to establish a linear relationship between the predictor variables and the response variable,enablingestimationoftheresponse(Y)whenonlythepredictorvalues(Xs) are known.

The dependentvariable is the factor that the equation solves for, as its value is determinedbythepredictorvariables.Theindependentvariablesarethefactorsused to predictthevalueofthedependentvariable.Inlinearregression,eachobservation includes two values: one for the dependent variable and one or more for the independent variables. The relationship between the dependent variable and the independent variables is approximated by a straight line The equation can be generalized as follows:

In the equation, Y represents the predicted value of the dependent variable β0 represents the y-intercept, which is the value of Y when all other parameters are set to

0 X1 through Xn are distinct independent variables β1 through βn are the estimated regression coefficients β1X1 denotes the regression coefficient β1 associatedw i t h t h e f i r s t i n d e p e n d e n t v a r i a b l e X 1 E a c h r e g r e s s i o n c o e f f i c i e n t signifies the impact on the dependent variable Y when there is a one unit change in the corresponding independent variable The same interpretation applies to β2X2 throughβnXn.Additionally,εrepresentsthemodelerrororresidual,whichindicates the amount of variation in the estimate of Y that is not accounted for by the independent variables It represents the discrepancy between the predicted value of Y and the actual observed value.

ANOVA (Analysis of Variance) is a statistical test applied to determine significant differences between the means of multiple groups or samples It evaluates whether the variations among group means are statistically significant ANOVA is particularly effective in analyzing group comparisons and assessing the impact of various independent variables on the dependent variable.

Prior to assessing mean differences, the Levene's test determines if the variance between categorical value groups is homogeneous (equal) The null hypothesis (H0) assumes no variance difference among the groups The Levene's test statistic from SPSS's Test of Homogeneity of Variances table (Based on Mean row) is used for this analysis.

Pearson’scorrelationcoefficient

Pearson’scorrelationcoefficientisastatisticaltoolusedtoassessthedirection and strength of the relationship between two continuous variables Its reliance on covariance makes it the most effective approach for evaluating associations Pearson’s correlation coefficient is represented by r and is constrained within a specific range by design:

Pearson’s correlation coefficient (r) and the associated p-value are computed together.T h e p - v a l u e c a n b e i n t e r p r e t e d a s f o l l o w s t o d e t e r m i n e t h e statistical

Multiplelinearregression

Linear regression is a statistical technique used to predict the value of an outcomevariable(Y)basedononeormorepredictorvariables(X).Theobjectiveis to establish a linear relationship between the predictor variables and the response variable,enablingestimationoftheresponse(Y)whenonlythepredictorvalues(Xs) are known.

A regression equation is characterized by the dependent variable and the independent variables The dependent variable is the factor that is being predicted, while the independent variables are the factors used to predict the dependent variable's value In linear regression, each observation includes two values: one for the dependent variable and one or more for the independent variables The relationship between these variables is approximated by a straight line, which can be generalized as follows: Dependent variable = Constant + (Coefficient * Independent variable).

In the equation, Y represents the predicted value of the dependent variable β0 represents the y-intercept, which is the value of Y when all other parameters are set to

0 X1 through Xn are distinct independent variables β1 through βn are the estimated regression coefficients β1X1 denotes the regression coefficient β1 associatedw i t h t h e f i r s t i n d e p e n d e n t v a r i a b l e X 1 E a c h r e g r e s s i o n c o e f f i c i e n t signifies the impact on the dependent variable Y when there is a one unit change in the corresponding independent variable The same interpretation applies to β2X2 throughβnXn.Additionally,εrepresentsthemodelerrororresidual,whichindicates the amount of variation in the estimate of Y that is not accounted for by the independent variables It represents the discrepancy between the predicted value of Y and the actual observed value.

One-wayANOVA

ANOVA (Analysis of Variance) is employed to statistically analyze differences among means of multiple groups or samples It assesses whether these group means vary significantly, making it suitable for evaluating group comparisons and the impact of multiple independent variables.

Before evaluating the difference in means, the homogeneity of variance (no differenceinvariance)ofthecategoricalvaluegroupsneedstobetested.Todothis, thenullhypothesisHL-0isset:Thereisnodifferenceinvariancebetweenthevalue groups The Levene’s test is used to test this hypothesis In SPSS, the Levene’s test statistics are taken from the Based on Mean row of the Test of Homogeneity of Variances table:

"If Sig < 0.05, the null hypothesis HL-0 is rejected, indicating a statistically significant difference in variance between the value groups In such instances, the Welch test result, found in the Robust Tests of Equality of Means table, should be considered."

- IfSig>0.05:ThenullhypothesisHL-0isaccepted,meaningthereisno statistically significant difference in variancebetween the value groups. The F test result in the ANOVA table is used.

After the assessment of the difference in variance, the evaluation of the difference in means is performed The null hypothesis H0 is set: There is no differenceinmeansbetweenthevaluegroups.TheFtestorWelchtestisusedtotest thish y p o t h e s i s d e p e n d i n g o n w h e t h e r t h e v a r i a n c e b e t w e e n t h e v a l u e g r o u p s i s n i n g t h e r e i s a s t a t i s t i c a l l y s i g n i f i c a n t d i f f e r e n c e i n m e a n s b e t w e e n t h e t w o v a l u e g r o u p s

Chapter 3 outlines the research process and methodology employed in this study. The process of scale development is described which involved in-depth interview and focus group discussion to refine the questionnaire The research employed a scale consisting of 49 items, representing eight independent variables andonedependentvariable.Thisscalewasdevelopedbasedonareviewofprevious research on blended learning adoption and was refined to align with the specific requirements of the current investigation.

Data collection was conducted via an online survey using Google Forms, with 400 undergraduate students from the WSU international partnership program at UEH-ISB in Ho Chi Minh City participating Data analysis will be performed using SPSS26.0 software, employing methods such as descriptive statistics, Cronbach's Alpha reliability test, exploratory factor analysis, Pearson's correlation coefficient, multiple linear regression, and One-way ANOVA.

EconomicsHoChiMinhCity(UEH)hasconsistentlydevelopedinnovativetraining programs to improve the quality of teaching and learning, promote international integration,andcultivateVietnam’sfuturetalent.Toprovidestudentswithaccessto an internationally-standard learning environment within Vietnam, UEH has collaborated extensively with leading universities around theworld This includesa long history of training and research partnerships with prestigious Australian institutions, including Western Sydney University (WSU) as one of its strategic partnersinAustralia.TheseglobalcollaborationsarepartofUEH’sbroaderstrategy 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 12th 2021, UEH - International School of Business (UEH-ISB) andWSUAustraliaformallysignedacooperationagreement.Theagreementcovers a diverse range of collaborative areas with the shared goal of strengthening internationally-standard training programs and diversifying curriculum offerings at UEH-ISB.UEH-ISBand WSU continue to develop their collaborative relationship, working together to implement one proposed doctoral program, six new undergraduateprograms,andvariouspostgraduateofferings.Thissigningceremony is expected to elevate the quality of training at UEH-ISB and provide Vietnamese students greater opportunities to study and engage with advanced academic values within an international standard learning environment As a result of this enhanced partnership, expertise across multiple fields is being advanced and positioned for application in the global labor market.

UEH-ISB and WSU have joined forces as leading providers of higher education, aiming to be a catalyst for regional growth WSU's presence in Vietnam enhances its reputation as a prominent player in ASEAN, providing a diversified educational portfolio This strategic partnership empowers Vietnamese students, fostering their potential to drive regional progress on the global scene.

WSU Australia is a globally renowned and prestigious institution in the world of higher education The university is known for its academic excellence and focus on applied research In recent years, WSU has made remarkable progress, climbing from58thplacein2019to33thplaceonthe2023RankingoftheWorld’sTopYoung Universities - a jump of more than 25 places Furthermore, WSU is ranked within the top 1% of all universities globally, placing it among the world’s Top 250 institutions according to the prestigious Times Higher Education Rankings This strong academic foundation and global reputation are integral to the prestige and quality of the international partnership program between UEH-ISB and WSU.

WSU is at the forefront of knowledge, delivering a modern and innovative educationalexperiencethatempowersstudentstobuildsuccessfullivesandcareers The university’s research agenda is centered on areas of national significance, tacklingcriticalsocietalchallengessuchasclimatechange,publichealth,cultureand society, infrastructure engineering, and communication sciences WSU is dedicated to a modern academic approach that is interdisciplinary, creative, innovative, and global in nature This approach is also based on values that support a society committed to social, cultural, economic, and political fairness. Recognizing that students are looking for more flexibility in their learning, supportedbycurrentandnewtechnologies,WSUisstrategicallyandsystematically incorporating blended learning models into all areas of its teaching This allows the university to integrate the best aspects of in-person and online interactions for each subject area This holistic and contemporary approach to teaching and learning positionsWSUasaninstitutionthatiscommittedtoempoweringstudents,fostering innovation,and driving positive change in line with its core values.

(Advertising);Bachelorof Applied Data Science (a4-year double degreeprogram). Inaddition,studentsalsohavetheopportunitytoparticipateintheGlobalPathways transfer program Western Sydney international partnership program takes pride in the high quality of its student intake, with 60% of students having an IELTS score above 7.0 and coming from top high schools and gifted classes across the country withhighGPAs.Studentscanexperienceaninternationallearningenvironmentright inVietnam,astheyaretrainedaccordingtotheAustralianBachelor’sdegreemodel and awarded a degree directly from Western Sydney University, which meets the higheststandardsoftheAustralianeducationsystem.Theprogramcanbecompleted injust2yearsand7months,allowingstudentstostudy100%inVietnamortransfer abroad to continue their studies With 100% of the faculty holding AACSB accreditation, students will receive knowledge and skills from leading experts The program also offers up to 30 billion VND in scholarship funds and a network of alumni working at over 100 multinational companies, helping to reduce financial pressure and create many career opportunities for students.

The study collected a total of 400 responses from 400 undergraduate students participating in WSU international partnership program with data gathered through an online survey using Google Forms After data cleaning and validation, 384/400 questionnaireswereconsideredvalid,accountingfor96%ofthetotalresponses.The analysis of the collected data was performed using SPSS version 26.0 Table 4.1 presents an overview of the characteristics of the respondents.

384studentsincludedinthesample,amajority(54.9%)areabove 20 yearsofage,whiletheremainingstudents (45.1%)fallwithinthe18to20yearsage range.Th i s d i s t ri b u t i o n o fag e s u g g e s t s t h a t s tu de nt s m a y h a v e v a ry in g l e v e l s o f t e c h n o l o g i c a l proficiency,l e a rn i n g p r e f e r e n c e s , a n d r e s p o n s i b i l i t i e s , wh i c h could impacttheirreadinesstoadoptblendedlearning.

With a nearly equal gender distribution (52.1% female, 47.9% male), it is crucial to consider a gender-inclusive perspective when examining blended learning adoption Exploring potential gender-based variations in engagement and perceptions within the blended learning environment would provide valuable insights.

ThemajorityofstudentsinthestudyarefromtheMarketingmajor,comprising 31.8% of the sample The second largest group consists of students from the InternationalBusinessmajor,accountingfor27.1%oftheparticipants.22.9%ofthe studentsrepresenttheAdvertisingmajor,withatotalof88participants.Thesmallest proportionofstudents,at18.2%,belongsto theApplied Financemajor.Eachmajor due to the digital nature of their disciplines On the other hand, finance students might prioritize data security and the reliability of the platforms used for blended learning.

Thedataonfamilyincome,whichisdividedintothreegroups(10to20million VND/month, above 20 to 40 million VND/month, and above 40 million VND/month), provides valuable insights A noteworthy finding is that a substantial portion of the sample (59.1%) falls into the middle income category This suggests that the student demographic is generally from middle to upper-middle-class backgrounds.Thissocioeconomicfactoriscrucialasitindicatesthatthemajorityof studentslikelyhavethefinancialmeanstoaccessthenecessarytechnologyrequired for blended learning Consequently, financial constraints may be less of a barrier compared to contexts with lower-income student populations.

Descriptivestatistics

The study collected a total of 400 responses from 400 undergraduate students participating in WSU international partnership program with data gathered through an online survey using Google Forms After data cleaning and validation, 384/400 questionnaireswereconsideredvalid,accountingfor96%ofthetotalresponses.The analysis of the collected data was performed using SPSS version 26.0 Table 4.1 presents an overview of the characteristics of the respondents.

384studentsincludedinthesample,amajority(54.9%)areabove 20 yearsofage,whiletheremainingstudents (45.1%)fallwithinthe18to20yearsage range.Th i s d i s t ri b u t i o n o fag e s u g g e s t s t h a t s tu de nt s m a y h a v e v a ry in g l e v e l s o f t e c h n o l o g i c a l proficiency,l e a rn i n g p r e f e r e n c e s , a n d r e s p o n s i b i l i t i e s , wh i c h could impacttheirreadinesstoadoptblendedlearning.

Blended learning adoption necessitates a gender-balanced perspective due to the near-equal gender distribution (52.1% female, 47.9% male) Investigating potential gender-based disparities in engagement and perception within the blended learning environment is crucial to fully understand its impact.

ThemajorityofstudentsinthestudyarefromtheMarketingmajor,comprising 31.8% of the sample The second largest group consists of students from the InternationalBusinessmajor,accountingfor27.1%oftheparticipants.22.9%ofthe studentsrepresenttheAdvertisingmajor,withatotalof88participants.Thesmallest proportionofstudents,at18.2%,belongsto theApplied Financemajor.Eachmajor due to the digital nature of their disciplines On the other hand, finance students might prioritize data security and the reliability of the platforms used for blended learning.

Thedataonfamilyincome,whichisdividedintothreegroups(10to20million VND/month, above 20 to 40 million VND/month, and above 40 million VND/month), provides valuable insights A noteworthy finding is that a substantial portion of the sample (59.1%) falls into the middle income category This suggests that the student demographic is generally from middle to upper-middle-class backgrounds.Thissocioeconomicfactoriscrucialasitindicatesthatthemajorityof studentslikelyhavethefinancialmeanstoaccessthenecessarytechnologyrequired for blended learning Consequently, financial constraints may be less of a barrier compared to contexts with lower-income student populations.

Cronbach’sAlphareliabilitytest

ThePerformanceExpectancy(PE)factorinthisstudydemonstratesahighlevel ofreliability,withacoefficientof0.832(seeAppendix4.2),surpassingtheminimum threshold of 0.6. Furthermore, the Corrected Item-Total Correlation for all six observedvariablesareabove0.3,indicatingastrongrelationshipbetweeneachitem inthefactor.Additionally,therearenoobservedvariableswithCronbach’sAlphaIf Item Deleted higher than the overall Cronbach’s Alpha coefficient As a result, this scale proves to be suitable and appropriate for this study.

The components within the Effort Expectancy (EE) factor demonstrate a high and equal Cronbach’s Alpha coefficient Specifically, the Cronbach’s Alpha reliability coefficient for the EE factor is 0.734 (see Appendix 4.2), exceeding the threshold of0.6 TheCorrectedItem-TotalCorrelation coefficients for theobserved variablesrangefrom0.484to0.545,allsurpassingthethresholdof0.3.Itisobserved that removing any variable within this factor would result in a decrease in the Cronbach’s Alpha coefficient Additionally, the Cronbach’s Alpha If Item Deleted valuesforallvariablesarenothigherthantheoverallCronbach’sAlphacoefficient.

Consequently, all four observed variables will be retained for the exploratory factor analysis (EFA) analysis.

TheSocialInfluence(SI)factordemonstratesgoodreliability.TheCronbach’s Alpha coefficient for this scale is 0.797 (see Appendix 4.2), which exceeds the minimum threshold of 0.6 Additionally, all Corrected Item-Total Correlations are greater than 0.3.None of the observed variables show a Cronbach’s Alpha If Item Deleted value higher than the overall Cronbach’s Alpha coefficient In conclusion,

Correlations for all observed variables are above 0.3 (from 0.497 to 0.544), indicating a strong relationship with the overall factor Moreover, noneoftheobservedvariableshaveaCronbach’sAlphaIfItemDeletedvaluehigher thantheoverallCronbach’sAlphacoefficient.Thisindicatesthatthescaleisreliable and suitable for use in the subsequent steps of the analysis.

The Hedonic Motivation (HM) factor demonstrates good reliability with a coefficient of 0.751 (see Appendix 4.2), exceeding the threshold of 0.6 However, the variable HM5 has a Corrected Item-Total Correlation of -0.013, which is below the desired threshold of 0.3 Therefore, the variable HM5 will be removed from the scale before EFA analysis, and a second calculation of Cronbach’s Alpha will be conducted.Inthesecondround,theCronbach’sAlphacoefficientincreasesto0.841, further confirming its reliability All Corrected Item-Total Correlations are greater than 0.3, and there are no observed variables with a Cronbach’s Alpha If Item Deleted value higher than the overall Cronbach’s Alpha coefficient Consequently, this factor is suitable for further analysis.

TheSelfEfficacy(SE)factordemonstratesahighandequalCronbach’sAlpha coefficient,withavalueof0.809(seeAppendix4.2),surpassingthethresholdof0.6 The Corrected Item-Total Correlation coefficients for the observed variables range from0.563 to 0.628, allexceeding the threshold of0.3.Itis observed thatremoving any variable within this factor would result in a decrease in the Cronbach’s Alpha coefficient Additionally, the Cronbach’s Alpha If Item Deleted values for all variables do not exceed the overall Cronbach’s Alpha coefficient Therefore, all variables will be retained as they contribute to the reliability of the scale.

TheInstructorCharacteristics(IC)factorhasaCronbach’sAlphacoefficientof 0.823,surpassing the threshold of 0.6 (see Appendix 4.2) Furthermore, all six observed variables have Corrected Item-Total Correlation coefficients greater than0.3,rangingfrom0.550to0.622.NoneoftheobservedvariableshaveaCronbach’s

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 exhibits good reliability with a Cronbach’s Alpha coefficient of 0.784, surpassing the threshold of 0.6 (see Appendix 4.2). However, the variable CF6 has a Corrected Item-Total Correlation of 0.042, which falls below the desired threshold of 0.3 Consequently, CF6 will be excluded from the scale prior to the exploratory factor analysis (EFA) procedure, and a second calculation of Cronbach’s Alpha will be performed In the second round, the Cronbach’s Alpha coefficient increases to 0.865, further confirming the scale’s reliability All Corrected Item-Total Correlations are above 0.3, and none of the observed variables have a Cronbach’s Alpha If Item Deleted value higher than the overall Cronbach’s Alpha coefficient Hence, this factor is appropriate for subsequent analysis.

0.6.Additionally, theCorrected Item-TotalCorrelation coefficients for all variables within this factor range from 0.621 to 0.699, surpassing the threshold of 0.3 The Cronbach’s Alpha If Item Deleted values for all variables do not exceed the overall Cronbach’s Alpha coefficient Consequently, all variables are retained as they contribute to the reliability of the scale.

Following the Cronbach’s Alpha test, all items, except for HM5 and CF6, are retainedforEFAanalysis.Theresultsofthescale’sreliabilitytestusingCronbach’s Alpha coefficient are summarized in Table 4.3:

PerformanceExpectancy(PE) PE1,PE2,PE3,PE4,PE5,PE6 0.832

EffortExpectancy(EE) EE1,EE2,EE3,EE4 0.734

SocialInfluence(SI) SI1,SI2,SI3,SI4,SI5 0.797

FacilitatingConditions(FC) FC1,FC2,FC3,FC4,FC5 0.754

HedonicMotivation(HM) HM1,HM2,HM3,HM4,HM6 0.841

SelfEfficacy(SE) SE1,SE2,SE3,SE4,SE5 0.809

InstructorCharacteristics(IC) IC1,IC2,IC3,IC4,IC5,IC6 0.823

CourseFlexibility(CF) CF1,CF2,CF3,CF4,CF5 0.865

BlendedLearningAdoption(BLA) BLA1,BLA2,BLA3,BLA4, BLA5,

Exploratoryfactoranalysis

41 observed variables representing independent factors These variables were categorized into eight factor groups The KMO coefficient is 0.852, which exceeds the threshold of 0.5, indicating a strong correlation among the 41 observations and their suitability for factor analysis The Barlett’s test of sphericity yielded a chi- square value of 6089.185 with a significance level (Sig.) of 0.000, further affirming the correlation and consistency of these 41 observations within the factor analysis.

The total variance extracted is 58.124%, surpassing the 50% mark This indicates that the eight factors account for 58.124% of the variation in the dataset Moreover, the Eigenvalue is 1.150, which exceeds the minimum threshold of 1, making it eligible for factor analysis (see Appendix 4.3).

The chosen method for rotation is the Varimax procedure, an orthogonal rotationtechniquethataimstominimizethenumberofvariableswithhigh loadings on each factor After the rotation, any observations with a factor loading below 0.5 will be removed from the model Only measurements with factor loadings greater than 0.5 will be retained to explain the factors, indicating that these observed variables are statistically meaningful The EFA analysis will retain observed variableswithfactorloadingsgreaterthan0.5andorganizethemintomajorgroups Table 4.4 displays that all factor loadings surpass 0.5 and are acceptable for convergence in this study.

The results indicate that the Blended Learning Adoption factor comprises six observations (BLA1 to BLA6) and is supported by a KMO coefficient of 0.879 (higher than 0.5) with an Eigenvalue of 3.584 (greater than 1) These demonstrate thesuitabilityofthedataforexploratoryfactoranalysis.Furthermore,Bartlett’sTest yields a statistically significant result (Sig < 0.05), indicating a correlation among the observed variables as a whole.

Additionally, the total variance extracted is 59.736%, which exceeds the threshold of 50% This provides evidence that these six factors explain 59.736% of thevariationpresentinthedata.Thecomponentmatrixhasundergonerotationusing the Varimax method The outcomes reveal that all Factor Loadings exceed 0.5, signifying their significance and suitability for integration (see Appendix 4.3) Therefore, the scale remains appropriate for subsequent analysis.

PE EE SI FC HM SE IC CF BLA

PE EE SI FC HM SE IC CF BLA

Pearson’scorrelationcoefficient

Before proceeding with multiple linear regression analysis, it is necessary to examine the linear correlations between the variables Pearson’s correlation coefficient is used to measure the strength and direction of the association between two quantitative variables.

Thecorrelationmatrix(Table4.5)displaysthecorrelationcoefficientsbetween thedependentvariableBLAandtheindependentvariablesPE,EE,SI,FC,HM,SE, IC, and CF. The correlation coefficients are as follows: 0.565, 0.459, 0.533, 0.306,

0.599, 0.541, 0.208, and0.293 Thesecoefficients are statistically significantwitha significance level (Sig.) of less than 0.05, which indicate that there is a correlation

- BLA has a moderate correlation with the variables EE and FC, as the correlationcoefficientsforthesetwovariables(0.459and0.306)areless than 0.5.

- BLA has a strong correlation with the variables PE, SI, HM, and SE, as thecorrelationcoefficientsforthesefourvariables(0.565,0.533,0.599, 0.541) are greater than 0.5.

Regarding the relationships between the independent variables, the pairs of independent variables with a significance value (Sig.) greater than 0.05 do nothave acorrelationalrelationship,andthereisnopossibilityofmulticollinearityoccurring between the two variables Additionally, for the pairsof independent variableswith a significance value (Sig.) less than 0.05 and an absolute value of the correlation coefficient less than0.4, it means that these pairs of variables have a weak to moderate correlational relationship, and there is no likelihood of multicollinearity occurring between them.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.

BLA PE EE SI FC HM SE IC CF

Entermethod.Theselectioncriterionforvariableinclusionisbasedonasignificance level < 0.05 The subsequent regression analysis results are presented below:

Std.Error of theEstimate Durbin-

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.DependentVariable:BLA

TheDurbin-Watsonstatistic(d)iscalculatedtobe2.037(1