The Effects of Blended Learning Environment on College Students’ Learning Effectiveness: Exploring the Role of Behavioral Intentions and Self-Efficacy Through the UTAUT2 Model

Abstract

With the rise of “Internet + Education,” blended learning has become a new trend in higher education. This study aims to explore how behavioral willingness and self-efficacy affect the learning effect of college students in this new learning mode. The Unified Theory of Acceptance and Use of Technology (UTAUT2) was used to construct a theoretical model, and data on the blended learning effectiveness of 167 college students from a university in Southwest China were collected through questionnaires, and the theoretical assumptions and model were verified using Structural Equation Modeling (SEM) in Smart PLS software. It was found that in blended learning, performance expectation, effort expectation, hedonic motivation and facilitation all significantly and positively affect college students’ behavioral intention of blended learning, which in turn positively affects the effect of blended learning, among which hedonic motivation is a more important influencing factor in the behavioral intention of blended learning. Self-efficacy also has a significant positive effect on blended learning effects. Finally, the study proposes countermeasures for teaching improvement from three aspects: teachers’ teaching design, students’ motivation and behavior, and optimization of e-learning platform content and function.

Keywords

blended learning learning effect behavioral willingness self-efficacy UTAUT2 model

Introduction

With the rapid development of Internet technology and the wide-scale application of 5G technology, the acquisition, and application of knowledge is changing at an unprecedented rate, and these changes have also profoundly altered the way of human learning. Many new ways of learning have also emerged, promoting the continuous improvement of learning concepts and learning methods, and blended learning is one of them. Blended learning, as a product of this change, combines traditional classroom teaching with modern technology, which not only poses new challenges to educational concepts and methods, but also offers unprecedented opportunities. 2023 EDUCAUSE Horizon Report: Teaching and Learning Edition released by the American Association for Higher Education Informatization (EDUCAUSE) emphasizes the social trend of blended and online learning, promotes the mainstreaming of the blended learning model, and Achieve innovative and integrated development, emphasizing the need for higher education institutions to create integrated blended learning spaces, build a teaching and learning ecosystem that integrates online and offline, and provide continuous input and support for blended learning to improve learning outcomes (Zhong et al., 2023). China’s active implementation of the “Double Ten Thousand Plan” has realized 10,000 national and provincial first-class undergraduate courses in just 3 years, including about 4,000 national online first-class courses and 6,000 national online-offline blended first-class courses, which has made online-offline blended learning become one of the research hotspots of current curriculum construction and teaching practice one of them (Gaihua, 2020). Coupled with the catalyst of the new crown epidemic that has lasted for 3 years, online learning has received a high degree of attention, which to a certain extent promotes the new integration of technology and teaching education and creates conditions for the implementation of the blended learning model.

This study not only responds to the current trend of change in the field of education, but also lies in its importance to the education of adolescents, especially its far-reaching impact on the learning outcomes of college students. Adolescence is a critical stage in the development of cognitive, emotional, and social skills of an individual, and education during this period has a profound impact not only on influencing his or her personal future, but also on the long-term prosperity and progress of society (Simion, 2023). College students in adolescence undergo rapid psychological and physiological changes, and these changes present special challenges and opportunities for the development of educational practices and strategies (Hamdan, 2023). Blended learning, as a flexible, interactive and student-centered learning method, plays an indispensable role in meeting the unique needs of college students and promoting their overall development (Li et al., 2022). By combining traditional offline classroom learning with online e-learning, it fully meets the needs of university students for autonomy and interactivity in learning, provides them with a more personalized and flexible way of learning, and helps to cultivate their critical thinking and creative problem-solving abilities (Islam et al., 2021).

Blended learning is based on mobile communication devices to create an online learning environment and classroom interaction combined with the learning scene, through the Internet and other mobile technologies, and face-to-face teaching combined to create a highly participatory and personalized learning experience for students (Goodyear & Dudley, 2015). In the field of higher education, the application of blended learning promotes the development of independent learning, personalized learning, cooperative learning, and the sharing of learning resources, through which learning motivation is stimulated and learning effects are improved (Liang et al., 2020). American scholars Garrison and Vaughan’s (2013) survey of six colleges and universities in Florida and tens of thousands of college students also showed that students’ performance in blended courses was significantly higher than that of pure face-to-face courses and purely online courses. Compared with single online teaching, college students have higher recognition of the quality of blended learning and believe that the learning effect of blended learning is not lower than that of traditional face-to-face learning in the classroom.

In terms of the current status of blended learning research, despite the fact that blended learning-related research is relatively abundant, the research on the effects of blended learning still appears to be insufficient and limited, with most of the research focusing on exploring the design and application of blended teaching modes based on the teaching cases of courses (Fengqing et al., 2018), as well as the implementation methods of blended teaching, such as flipped classroom, Mucous Class, and collaborative learning which can be categorized as the implementation of blended learning methods. Empirical studies that evaluate the factors influencing the learning effects of blended learning from the students’ perspective are rare and lack a sound theoretical model.

From the viewpoint of blended learning research hotspot, scholars mostly carry out blended learning research from the implementation method, purpose, object and essence, etc. There is not much difference in the implementation of the object of the research, are mainly concerned about the students of science and technology colleges and universities, but there is a difference in the implementation of the method, purpose and essence (Mohamed Zabri et al., 2023; Zhu et al., 2024). However, most of the current research is mostly based on teachers as an entry point to explore blended learning in mobile technology platforms and other teaching aids brought about by the reform of teaching and learning and the improvement of student performance, ignoring the role of education and teaching turned to the implementation of the essence of the role of education and teaching should be more concerned about the motivation of the students’ learning, and to promote the enhancement of the overall quality of students. This study has explored those factors in blended learning that help enhance the willingness of college students to continue learning from the perspective of students and help them improve the effect of blended learning.

In summary, the current research on blended learning is still about the practical research of various blended learning modes or teaching modes, and there are fewer analyses of the influencing factors of blended learning and their relationships. Therefore, an in-depth study of the factors affecting the effect of blended learning carried out by college students has become a proposition that needs to be solved urgently and is worthy of in-depth exploration in the current research on blended learning. On the basis of literature combing and questionnaire survey, this study determines the variables of influencing factors of students’ learning effects in blended learning environment, and constructs a relationship model of influencing factors of learning effects, with a view to helping students change their blended learning behaviors according to their own needs, improving their learning effects, and providing certain theoretical basis and practical guidance for the teaching design and implementation methods of blended learning.

Literature Review and Research Modeling Assumptions

Blended Learning

Blended learning first originated from foreign enterprise training, since the late 1990s to the present, blended learning at home and abroad has experienced three stages from the technology application version 1.0 to the current “Internet +” version 3.0 that focuses on students’ learning experience (Xiaoying et al., 2018). With the development of the information age, which is mainly characterized by digitalization, networking, and intelligence, the blended learning model, which combines online network learning (rain classroom, live recording, and broadcasting) and offline classroom learning, has been widely used in major universities in China. The sharing of learning resources and the teaching mode of flipped classrooms in blended learning breaks the time and space limitations of learning and enables students to have a better learning experience. However, although blended learning is a hot topic, its single definition has not been established (Hrastinski, 2019). For example, Wasoh (2016) believes that blended learning is a teaching scenario based on the combination of mobile communication devices, web-based learning environments, and classroom discussions, and Goodyear and Casey (2015) defines blended learning as a mix of teaching and tutoring methods in a learning environment that is “centered on the student learning experience.” According to Genfu (2015), blended learning is a “student-centered learning approach”, and the acceptance of blended learning by college students has an important impact on the final learning effect. In this paper, we define blended learning as the use of online technology and classroom interaction to create a truly highly participatory and personalized learning experience for students in an online learning environment. Academic research on blended learning focuses more on its learning strategies and evaluation, and research on the effects of blended learning rarely uses structural equations to verify its influencing factors. Instead, this study will explore the influencing factors of blended learning effects from the perspective of students, which has unique research significance.

The UTAUT2 Model

The process of blended learning cannot be separated from information technology such as mobile communication devices, and the effective implementation of most information systems (IS) and information technology (IT) depends on user acceptance (F. D. Davis, 1989). Through continuous development in recent decades, many theoretical models have been developed to predict and explain user acceptance of IT or IS in the fields of psychology, education, information systems (IS), and social sciences. The most widely cited framework in the field of information technology (IT) is the Technology Acceptance Model (TAM) (Chauhan & Jaiswal, 2016; Sumak & Sorgo, 2016), however, the TAM has some drawbacks (Sumak et al., 2017; Tsai et al., 2017). To address the shortcomings of the TAM model, Venkatesh et al. (2003) proposed a more complete IT based on a systematic analysis and comparison of models commonly used in academia: the theory of rational behavior (TRA), the theory of technology acceptance (TAM), the theory of planned behavior (TPB), the model of motivation (MPCU), the social cognitive theory (SCT), and the theory of diffusion of innovation (IDT). Acceptance model, namely the Unified Theory of Technology Acceptance and Experimentation (UTAUT) model. The UTAUT model integrates significant features to make its unique addition (Granić, 2023). Venkatesh et al. (2012) proposed the UTAUT2 model based on the UTAUT model by incorporating the three dimensions of price-value, hedonic motivation, and habit, and the explanatory strength of this model for technology use behavior proved to be significantly higher compared to the UTAUT model.

In existing studies, scholars mostly use the UTAUT2 model to analyze mobile learning (Arain et al., 2019), online shopping (Soh et al., 2020), mobile payment (García et al., 2023), and other users’ willingness and behavior toward information technology. Few people have come to apply the extension UTAUT2 to study the learning effects of blended learning. Whereas in blended learning, the use of information technology is a core component of the entire learning process, and the blended learning effect is essentially the learning benefits acquired based on the acceptance of mobile technology. Students’ performance expectations of learning outcomes, as well as enabling conditions, process fun, and habit development all affect learning outcomes. Therefore, this paper takes the UTAUT2 model as the theoretical basis, and the use behavior in the model is replaced by the learner’s learning effect, on which the construction of the research model and the formulation of hypotheses are carried out.

Research Variables and Research Hypotheses

Many studies have combined the UTAUT2 model with other theories to fill gaps in applications, by integrating or expanding them to facilitate the analysis of technology use in diverse scenarios, and by adding external variables to enhance the explanatory efficacy of the model (Jiang, 2021).

Learning Effect

Learning Effect (LE) refers to the evaluation of the learner after a period of learning activity and the realization of the expected effect of the learning activity, that is, the learner’s change in knowledge, skills, affective attitudes, or values after learning (Baharudin & Ismail, 2014; Jettka & Stein, 2014). The blended learning effect in this study, on the other hand, is the changes in knowledge, skills, affective attitudes and values, and social communication that occur in college students after they learn relevant knowledge or complete relevant learning tasks in a blended learning way, as well as college students’ comprehensive evaluation of this way of learning. The effectiveness of blended learning is affected by a variety of factors, ranging from the reasons of learners’ factors to the reasons of learning environment factors such as course design (Barbara et al., 2013; Shameem et al., 2023), teacher factors and network design (Bernard et al., 2014). In this paper, we focus on the learner’s perspective and the environmental factors of blended learning.

Performance Expectancy

Performance Expectancy (PE) is defined by Venkatesh et al. (2012) as the degree to which an individual believes that the system contributes to improving job performance, which in this paper indicates the learning efficiency that college students can improve by means of blended learning as well as the results of the expected accomplishments and gains that can be obtained. Since the UTAUT model is a summary integration of eight classical models, many variables have similar roles, among which the variables similarly defined with performance expectation (PE) are perceived usefulness (in the TAM model), extrinsic motivation (in the MM model), job adaptation (in the MPCU model), relative advantage (in the IDT model), and outcome expectation (in the SCT model), and in this paper, the related dimensions are categorized under the same dimension. Research surveys have shown that performance expectations have a direct impact on behavioral intention to use technology (Raman et al., 2014). Performance expectations in the UTAUT model are one of the main factors that influence individuals to accept and use new technologies. In schools, it is indicated that students are more likely to accept and actively use blended learning if they perceive it to be effective in enhancing learning and teaching. Therefore, the research hypothesis:

H1: Performance Expectation (PE) has a positive effect on Behavioral Intention (BI) in blended learning

Effort Expectancy

Effort Expectancy (EE) is an important predictor of technology acceptance in the UTUTT2 model, Venkatesh et al. (2003) stated that Effort Expectancy (EE) is “the degree of ease associated with the use of a system,” and there have been existing models of perceived ease of use in the TAM model, complexity in the MPCU model, and IDT ease of use in the model are antecedents of effort expectancy (Cimperman et al., 2016). In this study EE is defined as the degree to which college students are easy to use the web-based technologies used in blended learning, that is, the lower the level of effort required for college students to learn and use the technologies in blended learning, the higher their behavioral intention toward blended learning. Therefore, the research hypothesis:

H2: Effort Expectation (EE) has a positive effect on Behavioral Intention (BI) for blended learning

Hedonic Motivation

Hedonic Motivation (HM) is the degree of fun or pleasure perceived by college students in blended learning (Cimperman et al., 2016). The rich online learning resources in blended learning and some interesting classroom interactive designs (pushing courseware for pre-study before class, pop-up classroom discussion, classroom exercise quiz/response system, etc.) make blended learning gamified, and college students will participate in blended learning more actively when they feel fun in the blended learning context. Many existing studies have also shown that hedonic motivation (HM) has a positive effect on user behavioral intention (Brown & Venkatesh, 2005; Lee, 2009; Leong et al., 2013; Siyu & Qinjian, 2020). Therefore, the research hypothesis:

H3: Hedonic Motivation (HM) has a positive effect on Blended Learning Behavioral Intentions (BI)

Facilitating Conditions

Facilitating Conditions (FC) contains three different concepts: perceived behavioral control (TPB/DTPB), facilitating conditions (MPCU), and compatibility (IDT) (Venkatesh et al., 2012), and is defined as the perception of resources and support that an individual receives to perform a behavior (Brown & Venkatesh, 2005). Considering the idiosyncratic nature of the research population, the facilitating condition (FC) in this paper can be understood as the condition of blended learning. Therefore, the research hypothesis:

H4: Facilitating conditions (FC) have a positive effect on blended learning behavioral intention (BI)

Students’ Self-Efficacy

Self-efficacy is an assessment of an individual’s own efficiency or competence in accomplishing a specific task, which is not related to personal skills, but mainly judges how those skills are used (Fengjuan, 2021). Self-efficacy can be understood as the degree of confidence in students’ level of their own competence in participating in blended learning (Wanqing & Xufeng, 2020), and improving self-efficacy and identifying factors will have an impact on students’ literacy, where college students analyze the degree of complexity of a specific learning task in participating in blended learning, and judge the degree of match between their own competence and the learning task in order to assess their own competence. Related studies have shown that self-efficacy has an impact on behavioral intention (Ameen et al., 2019). Therefore, the research hypothesis:

H5: Student self-efficacy (SSE) has a positive effect on blended learning effectiveness (LE)

Behavioral Intention

Behavioral Intention can be understood as a motivational factor that influences whether an individual will perform an action in a given situation, and it is a comparative evaluation of expectations and feelings after the experience (Oliver, 1981). In this study, behavioral intention is defined as a comprehensive evaluation of college students’ adoption of learning through blended learning. Existing studies have also demonstrated that blended learning behavioral intention is an important factor influencing learning effectiveness (Chun & Zetian, 2018; Wu & Liu, 2013). Therefore, it is hypothesized:

H6: Blended Behavioral Intention (BI) has a positive effect on Blended Learning Effectiveness (LE)

The research hypothesis model is shown in Figure 1.

Figure 1.

Research model.

Research Methods

Research Procedure and Samples

The purpose of this paper is to investigate the learning effects of blended learning among college students, using a quantitative research design with students from a university in Sichuan Province, all of whom completed the blended learning course under the same instructor’s teaching in the current semester, minus other potential confounders (e.g., differences in teaching methods and educational resources).

According to Barclay et al.’s (1995) rule of ten, the minimum sample should be 10 times the largest structural path in the structural model for a given cross-section. The structural model of this study involves six constructs (i.e., five independent variables, one mediator variable, and one dependent variable) and six structural paths, and according to this tenfold rule criterion, the sample size in this study is much higher than the required number of multiples of ten, and thus the use of data in this sample is scientific. The survey was conducted from February 9, 2023 to March 21, 2023. Questionnaires were distributed to all students who participated in the blended learning course of study, totaling 180, and 180 questionnaires were received. However, during the data screening process, there were some questionnaires with abnormal or missing data. After deleting the questionnaires with less or missing information, we received 167 valid questionnaires, of which 79 male respondents (47%) and 88 female respondents (53%). There were 75 respondents in science and engineering majors, accounting for 45%, and 92 respondents in arts and management majors, accounting for 55%. The students of undergraduate programs accounted for the vast majority of students, accounting for 89%. The main population characteristics of the experimental sample are very similar to the study of Grivokostopoulou et al. (2019). In the study of this paper, all respondents were involved in studies related to blended courses for more than 6 months before the survey, and the questionnaire can reflect the feelings of the respondents more realistically. Therefore, the sample of this study is representative and fair.

This paper adopts the questionnaire method to conduct research, drawing on the existing UTAUT2 mature scale, combined with the actual production of a questionnaire on the learning effect of blended learning for college students, such as Table 1, using a 5-level Likert scale, in which “strongly disagree”“not quite agree”“Generally”“Agree”“Strongly Agree” five effects are represented by 1 to 5 points in order. The study used random sampling to survey the college students who participated in the course, a total of 180 questionnaires were collected, 167 valid questionnaires and the effective questionnaire rate is 92.8%, which meets the test requirements. Finally, SmartPLS was used for analysis.

Table 1.

Blended Learning School Effectiveness Measurement Question Items.

Constructs	No.	Items	Refs.
Performance expectancy	PE1	Blended learning is very applicable to me	V. F. D. Davis (2000)
	PE2	Blended learning can improve my learning
	PE3	Blended learning can improve my learning effectiveness
	PE4	Blended learning can allow me to learn more content
Effort expectancy	EE1	It was easy for me to adapt to blended learning	Venkatesh et al. (2003)
	EE2	Access to learning resources through blended learning is easy	Venkatesh et al. (2003)
	EE3	Interactive learning in blended learning is clear and easy to understand	Chao (2019)
Hedonic motivation	HM1	Blended learning is a good learning opportunity for me	Venkatesh et al. (2012)
	HM2	The online discussions and interactive aspects of blended learning made it more interesting.
	HM3	Blended learning gives me a sense of achievement
Students’ self-efficacy	SSE1	Blended learning helps me to accomplish my learning tasks better.	Pintrich and Groot (1990)
	SSE2	I can achieve the learning objectives of the course through blended learning.
	SSE3	I believe I can overcome the difficulties in blended learning.
Facilitating conditions	FC1	In general, the country where my university is located is supportive of mobile learning (infrastructure, policies, etc.)	Alyoussef (2021)
	FC2	In general, the university environment supports me in blended learning
	FC3	There are abundant learning resources in blended learning to meet my needs.
Behavioral intention	BI1	Blended learning is beneficial to me	Chang et al. (2010)
	BI2	I would like to see more blended learning in my future courses.	Venkatesh and Bala (2008)
	BI3	I would recommend blended learning to others.	Venkatesh and Bala (2008)
Learning effect	LE1	I can learn more through blended learning.	Mahdi (2018)
	LE2	Blended learning has improved my creativity	McGill and Klobas (2009)
	LE3	Blended learning has improved my ability to work in a team.
	LE4	Blended learning has improved my problem solving skills

Research Instrument

In this study, Structural Equation Modeling (SEM) techniques were used to test the research model. There are two different types of SEM methods: covariance-based methods and variance-based methods. In contrast to the covariance-based structural equation modeling tool (SEM), partial least squares (PLS), a variance-based method, is very suitable for small sample studies where normal distribution is not required, and with the number of valid questionnaires in this study being 167, Smart PLS is just right for analyzing small sample data. Second, in this study, the data were not normally distributed (p < .01), but based on the Kolmogorov-Smirnov test. SmartPLS is based on Partial Least Squares Path Modeling (PLS-SEM), which does not require a multivariate normal distribution of the data, and is better suited for exploratory studies and for dealing with complex models, which is essential for understanding how behavioral intention and self-efficacy affect the learning outcomes, a complex phenomenon that is critical to understanding how behavioral intentions and self-efficacy affect learning outcomes. In addition, Smart PLS, with its intuitive user interface and ease of operation, enables more focus on model construction and theoretical interpretation rather than dwelling on technical details. It also provides comprehensive model evaluation tools, such as path coefficients, R² values, effect sizes, predictive relevance, and cross-validation, which help to comprehensively assess the explanatory and predictive power of models. In terms of empirical research, Smart PLS is widely used in business and social sciences, therefore Smart PLS is appropriate as a data analysis tool for this study, which can effectively support the research objectives and improve the validity and reliability of the findings. In this study, the research model will be validated through two aspects: measurement modeling and structural modeling.

Data Analysis

Reliability and Validity Tests of the Measurement Model

In this paper, the reliability and validity of the variables were tested using a validated factor analysis (CFA). The Cronbach’s α coefficient and the CR value of the combined reliability were chosen as the indicators of the reliability of the measurement model. As shown in Table 2, the internal consistency reliability indicators Cronbach’s α value and combined reliability are distributed between .855 and .926 respectively, which are greater than the standard of .8, indicating that the reliability of each scale of the measurement model is high.

Table 2.

Standardized Item Loadings, AVE, CR, and Alpha Values.

Factor	Item	Standardized loading	AVE	CR	Cronbach’s alpha
Behavioral intention	BI1	.926	.850	.944	.911
	BI2	.924
	BI3	.916
Effort expectancy	EE1	.858	.775	.912	.855
	EE2	.886
	EE3	.897
Facilitating conditions	FC1	.935	.863	.950	.920
	FC2	.932
	FC3	.919
Hedonic motivation	HM1	.933	.819	.931	.889
	HM2	.923
	HM3	.857
Learning effect	LE1	.915	.819	.948	.926
	LE2	.888
	LE3	.906
	LE4	.911
Performance expectancy	PE1	.932	.853	.946	.914
	PE2	.923
	PE4	.915
Students’ self-efficacy	SSE1	.914	.838	.939	.903
	SSE2	.927
	SSE3	.905

In this paper, the standardized factor loading coefficients and average variance extracted values (AVE) were mainly selected as the test indicators of aggregation validity. The standardized factor loading coefficients of all the indicator items are greater than .7 (Table 2), so the reliability of the indicators is high. Validity generally includes both convergent and discriminant validity. In addition, the mean variance extracted values (AVE) of the variables are distributed between .775 and .863, which are all greater than the evaluation standard of .5, indicating that all the variables have convergent validity. In this paper, the Fornell-Larcker criterion is mainly selected as the test index of differential validity. The square root of each latent variable’s average variance extracted value (AVE) is greater than the correlation coefficient between the latent variable and other latent variables, indicating that the measurement model has a good differential validity among the latent variables (Table 3).

Table 3.

The Square Root of AVE, Factor Correlation Coefficients.

	BI	EE	FC	HM	LE	PE	SSE
BI	.922
EE	.775	.880
FC	.805	.703	.929
HM	.812	.745	.723	.905
LE	.883	.744	.772	.751	.905
PE	.826	.806	.787	.843	.832	.924
SSE	.810	.772	.796	.800	.782	.857	.915

Note. Diagonal elements are the square root of the average variance extracted from each construct; Pearson correlations are shown below the diagonal. PE = performance expectations; EE = effort expectations; HM = hedonic motivation; FC = facilitating conditioning; SSE = self-efficacy; BI = behavioral intentions; LE = learning effects.

Testing of Structural Models

Based on the comprehensive model of UTAUT2 theory and self-efficacy theory, this paper explores the factors that have influenced the blended learning effectiveness of college students from the students’ perspective. The research results show that performance expectation, effort expectation, hedonic motivation and self-efficacy have direct or indirect positive effects on the learning effect of blended learning. This study enriches the theoretical foundation of blended learning effect research, and the specific findings are as follows. The results of the structural equation modeling test are shown in Figure 2 below and illustrated in Table 4. It provides the explanatory power and path coefficients, R², and associated t-values for each path in the research model. The partial least squares method was used to test that all hypotheses are valid and the relevant interpretations of these results will be further explained later in the discussion section.

Figure 2.

Structural equation model and paths.

Table 4.

Hypothesis Test of Structural Model.

Hypotheses	Structural path	Path coefficients	t-Statistics	p-Value	Supported
H1	PE → BI	.169	1.955	.025	Yes
H2	EE → BI	.193	2.680	.004	Yes
H3	HM → BI	.290	3.099	.001	Yes
H4	FC → BI	.326	4.082	.000	Yes
H5	SSE → LE	.196	1.714	.043	Yes
H6	BI → LE	.724	7.488	.000	Yes

Note. PE = performance expectations; EE = effort expectations; HM = hedonic motivation; FC = facilitating conditioning; SSE = self-efficacy; BI = behavioral intentions; LE = learning effects.

In this study, 167 samples were repeatedly sampled through the SmartPLS software self-help method (bootstrapping) to validate the relationship between the model variables, and Figure 2 shows all the path coefficients and explains the variance of the model. The data shows that the R² value for behavioral intention is .787 and for learning effectiveness is .793, indicating that the model is very effective in explaining the relationship between students’ behavioral intention and learning effectiveness. The standardized mean square residual (SRMR) value of the model was .045 (less than .08) and the normative fit index (NFI) was .832 (greater than .8) indicating that the model was well-fitted (Wu & Liu, 2013).

Table 4 shows the results of hypothesis testing. Performance expectancy (β = .169, p < .05) and effort expectancy (β = .193, p < .05) positively influence behavioral intention, so hypotheses 1 and 2 are valid. Hedonic motivation (β = .290, p < .05) and enabling conditions (β = .326, p < .05) positively influence behavioral intention, so hypotheses 3 and 4 are valid. Self-efficacy (β = .196, p < .05) and behavioral intention (β = .724, p < .05) positively influence the learning effect, so hypotheses 5 and 6 are established.

Based on the above analysis, it was concluded that both the measurement model and the structural model were validated. Moreover, these results show that the theoretical model of this study has significant predictive relevance and explanatory power.

Conclusions

Discussion

Based on the UTAUT2 model, this paper explores the influencing factors affecting blended learning effectiveness of college students from the students’ perspective. The findings confirm that the proposed integration model and research hypotheses are empirically supported, and the study enriches and extends the research and theoretical foundation of blended learning effectiveness of college students. The specific findings and discussions are as follows:

The values of the total effect of the three influences of students’ own perceptions of performance expectations, effort expectations, and hedonic motivation on behavioral intentions are .169, .193, and .290, respectively, with the values of the total effect of hedonic motivation being significantly higher than that of the other two influences. It can be seen that hedonic motivation is a more important influence factor in blended learning behavioral intention, which is consistent with the findings of Arain et al. (2019). The design of gamified learning sessions in blended learning, fun and entertainment will motivate the behavioral willingness of college students to use blended learning, thus increasing their engagement and learning effectiveness. Blended learning not only enhances students’ hedonic motivation, but also subconsciously promotes deep understanding and long-term memory of learning content (Mitchell & Co, 2023). The results of this study show that a blended learning environment with some gamified learning sessions is a complex but productive learning session that stimulates hedonic motivation in college students while balancing performance expectations and effort expectations to ensure that learning is both fun and school effective. The design of gamified learning sessions in blended learning, fun and entertainment will motivate college students’ behavioral intention to use blended learning. The path coefficient of enabling conditions, another influencing factor of behavioral intention, is .326, which is higher than the above three influencing factors on the students’ side, which indicates that the support of the learning environment in blended learning is important and that college students’ perception of support and resources related to technology in blended learning significantly affects their behavioral intention to use blended learning (Hair et al., 2012). However, there are also related studies that are contrary to the results of this study, for example, in a survey of intention studies in Vietnam, it was found that the facilitating conditions are not statistically significant in affecting usage behavior (Pham, 2023), but this does not mean that the facilitating conditions are not useful in affecting the intention to use blended learning consistently, which may be due to the differences in cultural backgrounds and education systems, and also provides a direction for subsequent research.

The standardized path coefficient of performance expectation and blended learning behavioral intention is .169, which is in line with the positive influence of performance expectation in the UTAUT2 model proposed by Venkatesh et al. (2012), indicating that college students engage in blended learning to a large extent expect to find resources and learning effects in blended learning that meet their own learning needs, and their willingness to use them will be enhanced accordingly. Some studies have shown that college students in the learning process, technology period access to a wealth of resources, in the effective time to complete more learning tasks, improve learning efficiency, and further affect academic achievement. In blended teaching, the classroom accessing the students’ online independent learning situation and online Q&A can be very good to help teachers understand the learning needs of college students, and then better adjust the learning progress in the classroom with targeted, personalized solutions to the college students’ learning confusion. There are also online platforms for teacher-student interaction in the classroom, which can well meet the expectations of college students. At present, China’s blended learning policy, and teaching system is gradually improved, the next step, also to further meet the learning needs of college students, the design of blended learning should increase the gamification of the learning process to explore the potential needs of blended learning learners, so that college students in blended learning to achieve sustainable learning.

Performance expectancy is typically prioritized among the core elements of UTAUT2. However, the current study reveals that effort expectancy has a more substantial standardized path coefficient (.193) with blended learning behavioral intention than performance expectancy does. This divergence from previous findings (Chao, 2019) may stem from the unique characteristics of the sample population, which consists of post-millennial college students adept at using internet technology due to their upbringing during the digital age. For this cohort, networked and intelligent environments are not only a lifestyle but also a preferred mode of learning. Blended learning, with its integration of online platforms and offline courses, aligns well with their learning preferences. Additionally, the recent widespread shift to online learning during the pandemic has equipped these students with valuable experience, enabling them to navigate the challenges of a blended educational approach that combines both online and offline elements upon their return to college classrooms.

The analysis indicates that college students’ willingness to engage with blended learning technologies is affected by a myriad of factors, such as performance and effort expectations, hedonic motivation, and social support. These elements collectively influence students’ attitudes and continuance intention to utilize blended learning. The empirical data from this study suggest that willingness has a significant direct impact on the effectiveness of blended learning, denoted by an effect size of .726, thereby affirming the positive relationship between students’ willingness and learning outcomes in such environments. This willingness also extends to their level of engagement and ultimately, the success of their learning. Accordingly, it is vital to consider and refine these determinants in the design and implementation of blended learning initiatives to enhance students’ acclimation and educational achievements.

The total effect value of college students’ self-efficacy on the effectiveness of blended learning is .196, indicating that self-efficacy has a positive effect on the effectiveness of blended learning, which is consistent with the results of related studies (Abdullatif, 2022). Learning at the university level is very different from learning at the basic education level, and college students are more free in learning compared to learning at the primary and secondary school levels. Therefore, to improve college students’ learning self-efficacy enhancement first requires learners to dynamically adjust their self-perception, strengthen their psychological success beliefs, and on this basis, improve their self-control ability and optimize their learning behaviors and habits, otherwise, it is difficult to achieve the desired learning results. Educators or educational institutions can enhance college students’ self-efficacy by encouraging college students to fully recognize their own abilities and potentials, giving positive feedback and support in time, as well as fostering college students’ self-directed learning ability, which will help to improve college students’ motivation and learning engagement in blended learning environments (Ling, 2022), to achieve blended learning effectiveness.

Implications

Based on the UTAUT2 model, this study explores the factors affecting the effectiveness of blended learning from the perspective of college students and enhances the understanding of the theory and practice of blended learning. The empirical study of the integration model and research hypotheses provides a new vision for optimizing the effectiveness of blended learning to achieve educational reform and innovation.

The apparent influence of hedonic motivation on behavioral intentions highlights the need for college and university faculty to create learning experiences that are rich in learning content as well as engaging and enjoyable. The success of gamified learning courses in motivating students’ willingness to engage in blended learning suggests that the incorporation of games and entertainment elements is not frivolous, but rather a strategic enhancement of the learning process (Li & Phongsatha, 2023). This approach to learning meets the intrinsic need for joyful learning, which is a powerful driver of student engagement, making college students learn and enjoy learning, leading to deeper and more lasting learning outcomes (Ong & Young, 2023).

In addition, the study emphasized the importance of enabling conditions, revealing the key role of the learning environment in supporting students’ use of blended learning. This implies that the construction of instructional technology infrastructure and the investment of digital teaching resources in colleges and universities are conducive to the creation of a blended learning environment and the building of a platform for teachers and students to interact and collaborate without barriers, so that college students can learn in blended learning. This not only requires technical infrastructure support, but also simultaneously requires college teachers to carefully set up online and offline courses for blended courses to enliven the learning atmosphere, so that students can make full use of the blended learning platform to realize their learning potential and enhance their learning results.

Findings suggest that performance expectations and effort expectations have a contributory role in motivating students to engage in blended learning and enhancing learning outcomes. Given the rapid development of information technology and web-based technologies, especially among the millennial generation and subsequent college student cohorts, this generation’s proficiency with digital technology means they are accustomed to seamlessly integrating technology into the learning experience (Rudhumbu, 2022). The ease of use of blended learning technology can better support the blended learning of college students. The higher the level of college students’ performance expectations for learning, the easier it is for them to accept the various learning tasks designed by teachers in teaching, and the higher the level of their behavioral willingness to learn, the better their learning results will be. Blended learning, which integrates modern information technology with education, is an innovation of higher education model and learning mode, and the elasticity and ease of use of the blended learning model improves the degree of participation and flexibility of learning, so that college students can have more diversified choices and autonomy in the process of learning, which in turn realizes the optimization of the teaching effect of blended learning.

Behavioral willingness has a strong direct impact on learning outcomes, highlighting that continuous learning in blended learning contributes to deeper learning among college students, which inspires instructors to be fully aware of the importance of college students’ willingness to learn when designing blended learning activities.

The positive correlation between self-efficacy and blended learning effectiveness emphasizes the importance of promoting students’ confidence in their own abilities. The design of blended courses should focus on “student-centeredness,” and the flexible learning and personalized experience of college students in blended courses make it easier for them to gain a sense of accomplishment in learning tasks and internalize their learning knowledge easily.

In conclusion, the significance of this study lies not only in exploring the factors affecting the effectiveness of blended learning, but also in adopting a unique perspective to consider the interactions between learning enjoyment, learning environment, students’ self-efficacy and behavioral willingness and the tailor-made needs of the student population from the learner’s point of view, which will provide insights for colleges and universities to create a more efficient ecosystem of blended learning to adapt to the development trend of higher education. The development trend of higher education.

Limitations and Future Research Recommendations

Although this study can draw some insights and provide some valuable insights, there are still some limitations. First, although the sample data collected through the questionnaire method may be affected by the sample size, this study ensured the reliability and validity of the findings by using a dataset of 167 samples, which is larger than the 70 datasets required by the law of least 10 times, to obtain representative and reliable conclusions. Future research can further validate these findings by expanding the sample size and increasing the generalization ability of the study. In addition, the study of learner characteristics and influencing factors of learning effects in blended learning may be a hot research topic and research trend in the coming period of time. This study conducted a kind of new exploration of blended learning effects based on the UTAUT2 model, and future studies can consider more influencing factors of blended learning effects as a way of enriching the related research on blended learning effects. The enhancement of blended learning effect is a gradual process that requires continuous refinement and improvement.

Conclusion

Based on the UTAUT2 model and from the perspective of college students, this study explores the key factors affecting the effectiveness of blended learning, reveals the role mechanisms affecting the effectiveness of college students’ blended learning, enriches the theory of blended learning, and moreover provides a basis for how to optimize the blended learning environment and improve students’ learning effectiveness. The findings emphasize the importance of hedonic motivation, favorable conditions and self-efficacy in promoting learning effectiveness. Although the study did not consider the effects of gender and age, it is because hedonic motivation, enabling conditions, and self-efficacy remain key factors in influencing learning outcomes, even across student populations of different genders, ages, levels of experience, and levels of voluntary participation. Second, future studies will use larger samples and incorporate more moderators such as experience level and voluntary participation, thus making the model and findings more generalizable. The potential integration of UTAUT2 with other theoretical frameworks is expected to enrich the understanding of blended learning mechanisms and improve support for the development of more effective and targeted educational strategies. The findings of this study provide new perspectives for understanding the effectiveness of blended learning among college students and assist future educational practices.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Higher Education Talents Training Quality Project of Sichuan Province (JG2021-1332) and Chengdu University of Technology “Double First-Class” initiative Construction Philosophy and Social Sciences Key Construction Project (ZDJS202303).

ORCID iDs

Na Lv

Zhigang Li

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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