Research on the Sustainable Development Strategy of Online Learning: A Case Study of YouTube Users

Abstract

Amid the COVID-19 pandemic and the widespread adoption of mobile devices, video-based online learning has emerged as a critical mode of education. However, empirical research on the determinants of online learning behavior and intention among video users remains scarce. To explore the factors influencing the continuous intention of users to engage in YouTube video-based online learning, the present study drew on the perceived value theory and the ECM perspective to construct a model. This study is a quantitative study in which 669 valid data were collected from online users of online learning and communication communities through online questionnaires distributed by non-probability sampling, and the constructed model was tested using SPSS 27.0 and AMOS 27.0. The results revealed that perceived value had a positive direct effect on continuous intention and an indirect effect through satisfaction on continuous intention. Therefore, to effectively and sustainably promote video-based online learning, measures should be taken to enhance users’ continuous intention and retention. Thereafter, suggestions for further research were proposed.

1. Introduction

Prior to the outbreak of the COVID-19 pandemic in 2020, the use of educational technology had already grown rapidly and became widespread. EdTech investments in 2019 reached a staggering $18.66 billion [1], and online education is expected to reach $250 billion by 2025. Since the outbreak of COVID-19, the use of language apps, virtual tutoring, video conferencing tools, or online learning software [2] has increased significantly. While the infection rates of COVID-19 vary among countries, the education system has been completely disrupted by the pandemic, and many assert that education is no longer keeping up with the times. Will the shift to online learning lead to a more effective method of teaching? Many individuals are concerned that the rapid transition to online learning may hinder this goal, while others plan to embrace the benefits of online learning and make it a part of their “new normal” of learning.

Online learning has broken through the limitations of time and space, making learning not only limited to the classroom but also breaking through the limitations of age and identity, making lifelong learning possible. Through the COVID-19 pandemic, people have realized the importance of cross-sector, cross-company, and knowledge dissemination in various fields of society. Early research on “online learning” focused mainly on platform forms, with the change in mindset being the shift from offline physical teaching to the Internet. Research on online learning still follows the traditional physical teaching model, and there are still characteristics, such as the difference in teacher–student identity and generalized teaching (Roberts, 2005) [3]. However, this research perspective and path are not sufficient to explain the changes and trends in online learning today. With the rise of social media and mobile technology, mobile learning, such as live streaming platforms, social Q&A communities, and mobile digital reading, has become a new form of ‘online learning’ (Maziriri et al., 2020) [4]. Currently, most of the relevant theories used in empirical research on online learning focus on information system acceptance theory, including the technology acceptance model (Davis, 1989) [5], the theory of planned behavior (Ajzen, 1991) [6], and the integrated technology acceptance and use theory model (Venkatesh, 2003) [7]. Although the initial adoption of information systems by users is crucial, the success of information systems ultimately depends on the level of user activity. Therefore, compared with initial adoption behavior, users’ willingness to continue using the platform and their behavior are crucial to the successful operation of the platform information system (Bhattacherjee, 2001) [8].

With the advent of social media, people are increasingly using videos for online learning. The use of video platforms provides the public with an array of learning content ranging from astronomy and geography to casual conversations. Therefore, online learning through videos offers a new approach to the public. Additionally, while information technology is rapidly developing, in the context of fragmented information, information overload, and uneven distribution of teaching resources, users are gradually changing how they acquire information and knowledge. Users are more easily attracted to social learning resources, which makes many video platforms stand out in education (Alkhudaydi, 2018; Camm et al., 2018) [9,10]. A video-sharing platform such as YouTube, the world’s largest and most popular, allows users to watch and upload videos for free. YouTube is estimated to receive billions of views per day and to host over 6 billion hours of video per month, making it the second-largest search engine in the world after Google (Statista, 2021) [11]. People around the world use it for a variety of purposes, ranging from entertainment to marketing to social interaction (Moghavvemi et al., 2018) [12].

In view of the fact that users are the ultimate beneficiaries and the primary driving force behind online education and video platforms (Tarantino et al., 2013; Ray et al., 2021; Wang et al., 2021) [13,14,15]. In addition to being able to access higher-quality educational and learning resources through these platforms, they are also able to learn and communicate at any time and from any location. In order to better meet the needs of users and improve user satisfaction and loyalty, online education and video platforms are constantly being improved and optimized based on user feedback. At the same time, for users of video-based learning, understanding these factors can also help them better select and utilize video platforms and educational resources, enhancing learning effectiveness and motivation. Therefore, this study aims to explore the key factors influencing users’ continuous interest in engaging in video-based online learning in order to promote the sustainable development and advancement of online learning models.

Based on existing research, there is currently no universally accepted definition of “online learning.” Additionally, research on video online learning behavior from a user perspective is relatively scarce. Furthermore, it is important to study the differences and effects of online video learning compared to other forms of learning from a new perspective. To address these two issues, this article utilizes the Perceived Value Theory and takes an Expectation Confirmation Model (ECM) perspective, combined with the characteristics of YouTube video online learning, to explore the factors that influence users’ sustained intention to learn on YouTube. The findings can provide valuable insights for the sustainable development of YouTube and other social media online learning forms, as well as marketing strategies for the future online education market.

2. Literature Review

2.1. Perceived Value Theory

Various definitions of perceived value have been provided by different scholars due to the dynamic and subjective nature of this concept. In particular, Zeithaml (1988) proposed the concept of perceived value, which has since gained widespread acceptance and has been developed into a theory [16]. Perceived value is defined from the perspective of consumer psychology as the overall evaluation of a product or service’s utility that users perceive after balancing the benefits and costs they receive during the acquisition process. In addition, Zeithaml (1988) asserts that perceived value impacts usage intentions and behaviors.

The dimensions of perceived value have been examined through two-dimensional theory, dichotomy, three-dimensional theory, and multidimensional theory. In recent years, researchers have also started to examine the dimensions of perceived value in the context of the Internet and consumer behavior. Sweeney (2001) developed the PERVAL scale to measure user-perceived value, which divides perceived value into four dimensions: emotional value, social value, price value, and quality value [17]. Social value is a novel concept that refers to a product’s ability to enhance a consumer’s self-concept within the social category. Sheth et al. (1991) proposed five aspects of perceived value: social value, emotional value, functional value, cognitive value, and situational value [18]. Lai and Chih (2011) further expanded this theory by dividing perceived value into five dimensions: social value, price value, content value, interaction value, and interface design value, based on the study of factors influencing the usage intention of an e-book reading client [19].

Building on existing research and the characteristics of online learning through YouTube videos, this study will use five dimensions of perceived value—functional value, content value, social value, interaction value, and emotional value—to analyze factors that influence users’ willingness to learn through YouTube videos.

• Functional value refers to the primary purpose of using YouTube videos to acquire knowledge and skills and users’ overall perception of the benefits they receive from completing online learning through YouTube, such as increasing efficiency, solving practical problems, and acquiring new knowledge and skills more quickly (Meng and Li., 2023; Kahan et al., 2017) [20,21];
• Content value refers to the richness and novelty of information and knowledge provided in YouTube videos, as well as the clarity of organization, the reasonableness of chapter organization, and the level of interest, professionalism, and inspiration conveyed in the content (Wu, 2010; Poch and Brett, 2015; Poturak and Softic, 2019) [22,23,24]. Users tend to prefer videos that are useful, practical, interesting, and in-depth, as these videos can satisfy their need for knowledge and help them solve problems;
• Social value refers to the help YouTube videos provide for users’ social and career development (Davis, 1989; Khalifa, 1997; Gan and Wang, 2017) [25,26,27]. For example, users can expand their interpersonal networks and obtain the latest industry trends and information through video watching, which can improve their professional skills and enhance their self-image;
• Interaction value refers to the benefits users receive from online communication and group interaction during YouTube video learning, including making friends, enhancing influence and attraction among others, and engaging in activities such as commenting, liking, sharing, and discussing. Users prefer interactive videos because they can interact with other users and share their opinions and views (Anderson et al., 1994; Reichheld and Thomas, 1996) [28,29];
• Emotional value refers to the emotional utility that users derive from learning through YouTube videos, such as enjoyment and pleasure (Wang et al., 2004) [30]. For example, videos that can make users feel excited, thrilled, happy, satisfied, and so on can positively influence their willingness to learn (Disney, 1999; Bell, David, et al., 2002) [31,32].

2.2. Expectation Confirmation Model

The Expectation Confirmation Theory (ECT) was first proposed by Oliver (1980) to explain the relationship between consumer satisfaction and continued purchasing behavior before and after consumption [33]. Bhattacherjee (2001) built on this theory and proposed the Expectation Confirmation Model (ECM), which suggests that users of information technology, similar to consumers in the marketing field, base their decisions to continue or discontinue usage on the difference between their expectations and actual experiences.

The ECM has been applied by researchers to investigate issues related to users’ intention to continue using digital textbooks, online education platforms, and learning spaces. Therefore, based on the ECM theory, this study will explore the factors that influence users’ intention to continue using YouTube videos for online learning.

3. Hypotheses and Research Model

3.1. Perceived Value and Satisfaction

Fornell (1992) found through research that user expectations, perceived quality, and perceived value positively and significantly affect customer satisfaction in advertising development [34]. Building upon this, Haemoon (1999) found through research that perceived value not only affects customer satisfaction but also indirectly affects their behavior through customer satisfaction [35]. Daneji et al. (2019) studied the impact of perceived value on student satisfaction and willingness to continue using massive open online courses (MOOCs). The results showed that perceived value is an important factor affecting student satisfaction and willingness to continue using MOOCs [36]. Mohd et al. (2020) studied the impact of perceived value and satisfaction on students’ willingness to continue using e-learning platforms during the COVID-19 pandemic [37]. The results showed that perceived value and satisfaction are important factors affecting students’ willingness to continue using e-learning platforms, especially in response to the demand for online learning during the pandemic. Wu et al. (2011) studied the impact of perceived value on learner satisfaction and willingness to continue using online learning communities in China. The results showed that perceived value is an important factor affecting learner satisfaction and willingness to continue using online learning communities, especially in terms of community interaction and learning resources [38].

In addition, according to public evaluations of YouTube, we can see that ‘YouTube users are accustomed to shorter video content, and it is difficult for users to finish longer videos if the content does not convey value’, ‘Creating lectures, demonstrations, etc. does not require a significant budget and is cost-effective’, ‘Diversity of content’, ‘At the level of relationship stickiness, YouTubers and users have a strong and close social relationship, which can highly consolidate user stickiness’, ‘Unique and creative videos made by people like themselves can be found’. Thus, this study proposes the following research hypotheses:

H1: 

Functional value has a significant positive (+) effect on Satisfaction.

H2: 

Content value has a significant positive (+) effect on Satisfaction.

H3: 

Social value has a significant positive (+) effect on Satisfaction.

H4: 

Interaction value has a significant positive (+) effect on Satisfaction.

H5: 

Emotional value has a significant positive (+) effect on Satisfaction.

3.2. Perceived Value and Continuous Intention

Kahan et al. (2017) investigated the relationship between perceived value, satisfaction, and continuous intention to use in online learning communities. The results showed that perceived value and satisfaction had a significant positive effect on continuous intention to use. They argued that perceived value is an important factor in enhancing user loyalty and continuous intention to use online learning communities [21]. Poch et al. (2015) studied the relationship between user-generated content creation intention, extrinsic motivation, and intrinsic motivation, with perceived value as an intrinsic motivator. The results showed that perceived value had a positive impact on user-generated content creation intention, indicating that perceived value can motivate users to engage in creative behavior [23]. Wu et al. (2010) investigated satisfaction and continuous intention to use in a blended e-learning environment and found that perceived value and satisfaction were key factors influencing students’ continuous intention to use. The study also found that perceived learning effectiveness had a more significant impact on continuous intention to use [22].

In summary, perceived value plays an important role in the continuous intention to use online learning. By providing valuable content and good services, user satisfaction and continuous intention to use can be improved, promoting the development and growth of online learning. Based on this, this study proposes the following hypothesis:

H6: 

Functional value has a significant positive (+) effect on Continuous Intention.

H7: 

Content value has a significant positive (+) effect on Continuous Intention.

H8: 

Social value has a significant positive (+) effect on Continuous Intention.

H9: 

Interaction value has a significant positive (+) effect on Continuous Intention.

H10: 

Emotional value has a significant positive (+) effect on Continuous Intention.

3.3. Satisfaction and Continuous Intention

Continuous intention to learn online through YouTube videos refers to users’ willingness and intent to continue using YouTube videos for online learning, including completing a course and participating in other video-based learning. Satisfaction, based on the expectation confirmation theory, refers to the degree to which customers feel that their explicit, typically implicit, or mandatory needs or expectations have been met. Oliver (1980) believes that satisfaction is a feeling of pleasure or disappointment formed by comparing expectations before purchase with perceived effects after purchase. Khalifa (1997) introduced satisfaction from consumer behavior into the field of information systems and believes that satisfaction is the level of pleasure felt by information system users towards system attributes and service quality. Therefore, satisfaction not only has a significant positive impact in the field of consumer behavior but also in predicting customers’ continued use of information systems. Based on this, this study proposes the following hypothesis:

H11. 

Satisfaction has a significant positive (+) impact on Continuous Intention.

3.4. Research Model

Based on the above hypotheses, the conceptual model of this study is proposed, as shown in Figure 1.

4. Methods

4.1. Scale Design and Data Collection

The use of survey methodology is a common approach for understanding the subjective opinions and experiences of research participants. When investigating the factors influencing YouTube users’ sustained intention to learn through online videos, survey data can provide a vast and systematic collection of data, which can be analyzed and synthesized statistically within a short time frame. Therefore, this study collected data through a survey in order to achieve a comprehensive understanding of the research topic. The respondents to this questionnaire were online users of online learning communication communities (community users have more representative attitudes and behaviors towards online learning, and because they have certain common interests and backgrounds, they can better control the survey variables and improve the reliability and validity of the research results). The online survey was conducted using “Google Workspace” from 5 July 2022 to 5 October 2022 for a period of 90 days. During the specific survey process, the study paid attention to controlling the personal circumstances of the survey participants to a certain extent. A total of 700 survey questionnaires were distributed, and 700 questionnaires were collected. After performing a consistency test (KAPPA test) on the 700 survey questionnaires, 31 invalid questionnaires were eliminated, and a total of 669 valid questionnaires were obtained, with a valid questionnaire response rate of 95.5%.

To ensure the reliability and validity of the questionnaire, mature scales that have already been developed were used to measure the variables involved in this study. In order to ensure that the respondents could accurately understand the measurement items and to optimize the wording of the measurement items based on the specific context of users’ continuous willingness to use YouTube videos for online learning, minor modifications were made to the wording of the measurement items. The questionnaire consisted of two parts: the first part included basic demographic information, and the second part included measurement items for the core variables. The core variables included functional value (FV), content value (CV), social value (SV), interaction value (IV), emotional value (EV), satisfaction (SA), and continuous intention to use online learning (CI). After designing the questionnaire, 35 students who frequently used YouTube videos for online learning were invited to fill out the questionnaire, and some of the items were revised based on their feedback. Finally, a small-scale pretest of the questionnaire was conducted, and 110 questionnaires were collected. Through exploratory factor analysis, a final set of 27 variable items were determined. All measurement items were tested using a Likert 5-point scale (“1” indicating “strongly disagree”, “3” indicating “neutral”, and “5” indicating “strongly agree”).

4.2. Sample Description

The basic profile of the respondents, obtained after statistical analysis of the valid questionnaires, is shown in

Table 1. Descriptive statistics of respondents.

Statistical Variables	Statistical Variables	Number of Samples	Percentage %
Gender	Male	305	45.6
	Female	364	54.4
Age	10’s	263	39.3
	20’s	184	27.5
	30’s	97	14.5
	40’s	90	13.5
	Over 50	35	5.2
Education	Below Secondary School	139	20.1
	Secondary School	151	22.6
	University	274	41.0
	Graduate School	109	16.3

. The present study indicates a greater proportion of female respondents than males, which is inconsistent with some previous research in the field (Liaw et al., 2011; Chang et al., 2014) [39,40], which has suggested that male students have more positive e-learning attitudes than female students. However, this finding is consistent with research indicating that women tend to participate more in discussion and communication in online communities and are more willing to express their opinions and views than men, while men may be more inclined to search for and read the information in online communities, rather than actively participate in discussions (Hampton et al., 2011; Tsai et al., 2015) [41,42]. Moreover, the higher proportion of respondents under 30 years of age compared to those over 30 years of age may be due to the younger age group being more receptive to emerging technologies and new learning methods (Ke, 2013) [43]. The educational qualifications distribution of the respondents in the present study is also in line with the age distribution of the overall sample.

4.3. Reliability and Validity and Correlation Analysis

Reliability refers to the consistency, stability, and accuracy of test results. In this study, we used SPSS 27.0 to examine the internal reliability of the scale using Cronbach’s Alpha and Composite Reliability (CR). The overall reliability was 0.888, and the Cronbach’s Alpha coefficients of each latent variable, as shown in

Table 2. Reliability and validity analysis results.

Variable	Cronbach’s α	AVE	CR	N of Items	VIF
FV	0.857	0.6059	0.8599	4	1.244
CV	0.885	0.6647	0.8875	4	1.19
SV	0.911	0.7228	0.9123	4	1.167
IV	0.87	0.6301	0.8716	4	1.232
EV	0.821	0.5401	0.8238	4	1.109
SA	0.905	0.7082	0.9065	4	1.683
CI	0.851	0.6581	0.8522	3	/

, exceeded 0.7, indicating good reliability of the sample survey and high internal consistency of the scale and questionnaire used, meeting the requirements of the study.

Validity refers to the extent to which the measured results reflect the content being investigated. In this study, we used Amos 27.0 and examined the convergence validity of the model using factor loading, CR, and Average Variance Extracted (AVE). As shown in Table 2, the CR values of each latent variable were above 0.6, meeting the standard and indicating good convergence validity of the model. From

Table 3. KMO and Bartlett’s test results.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy.	0.884
Bartlett’s Test of Sphericity	Approx. Chi-Square Sig.	10,356.464
	df	351
	Sig.	0.000

, the overall data’s KMO value was 0.884, Bartlett’s test of sphericity’s approximate chi-square was 10,356.464 with 351 degrees of freedom and a significance probability p = 0.000 < 0.01, indicating good structural validity of the questionnaire and enabling factor analysis. In summary, the validity of each latent variable was good, and the scale overall had high quality.

To test the discriminant validity of each latent variable, we used Pearson correlation analysis, as shown in

Table 4. Correlations.

	FV	CV	SV	IV	EV	SA	CI
FV	0.904	0.151 **	0.150 **	0.164 **	0.193 **	0.437 **	0.164 **
CV	0.151 **	0.919	0.145 **	0.190 **	0.125 **	0.395 **	0.202 **
SV	0.150 **	0.145 **	0.929	0.329 **	0.096 *	0.290 **	0.372 **
IV	0.164 **	0.190 **	0.329 **	0.904	0.112 **	0.363 **	0.381 **
EV	0.193 **	0.125 **	0.096 *	0.112 **	0.892	0.307 **	0.124 **
SA	0.437 **	0.395 **	0.290 **	0.363 **	0.307 **	0.885	0.477 **
CI	0.164 **	0.202 **	0.372 **	0.381 **	0.124 **	0.477 **	0.873

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

. The correlation coefficients of each variable with other variables were all smaller than the square root of AVE, indicating good discriminant validity of the questionnaire. At the same time, each variable’s Variance Inflation Factor (VIF) was much smaller than 10, indicating no multicollinearity problems among variables.

4.4. Model Fitness Test

According to the results of reliability and validity tests, this study is suitable for analysis using structural equation modeling. After analyzing the 669 collected questionnaires with Amos 27.0, the model fit indices are shown in

Table 5. Structural equation model fitting results.

Items	CMIN/DF	NFI	TLI	CFI	RMSEA	GFI	AGFI
Ideal value	>1, <3	>0.9	>0.9	>0.9	<0.05	>0.9	>0.9
Acceptable value	>1, <3	>0.8	>0.8	>0.8	<0.08	>0.8	>0.8
Results	1.400	0.960	0.986	0.988	0.024	0.988	0.945

, which indicates that the model fit indices are within the acceptable range, suggesting that the model has a good fit and can be accepted.

4.5. Model Parameter Estimation

This study employed structural equation modeling analysis using Amos 27.0 to examine the direct causal effects and path coefficients between variables in order to investigate the influence of YouTube video user satisfaction on their continued willingness to participate in online learning. The specific analysis results, including the model’s parameter estimation, are shown in

Table 6. Structural equation model parameter estimation results.

Hypotheses	Estimate	SE.	CR.	P	Outcome
H1	0.330	0.037	8.863	***	Supported
H2	0.262	0.033	7.947	***	Supported
H3	0.104	0.033	3.124	0.002	Supported
H4	0.210	0.037	5.711	***	Supported
H5	0.210	0.042	5.034	***	Supported
H6	−0.081	0.040	−2.009	0.045	NS.
H7	−0.029	0.036	−0.809	0.419	NS.
H8	0.194	0.035	5.497	***	Supported
H9	0.176	0.039	4.468	***	Supported
H10	−0.044	0.044	−1.016	0.31	NS.
H11	0.427	0.054	7.961	***	Supported

*** indicates P < 0.01.

, and the significant relationships between variables are depicted in Figure 2. Structural Equation Model Diagram. There can be inferred that most of the hypotheses in this study model are supported (p = 0.000 or p < 0.05).

In support of the original hypothesis, the path coefficients for Functional Value, Content Value, Social Value, Interaction Value, and Emotional Value are 0.330, 0.262, 0.104, 0.210, and 0.210, respectively. According to the degree of influence of these five factors on Satisfaction, Functional Value > Content Value > Interaction Value > Emotional Value > Social Value, and Satisfaction play a significant role in Continuance Intention. The results also support the original hypothesis, and the path coefficient is greater than 0.3, which indicates the importance of satisfaction in YouTube users’ willingness to continue learning online.

4.6. Mediation Analysis

Using structural equation modeling analysis, the mediating role of satisfaction was further examined in the model, and the results are presented in

Table 7. Bootstrap intermediation test results.

Path					Effect Value	Boot S.E.	Bias-Corrected 95% CI
B							Lower	Upper
Functional Value	--->	Satisfaction	--->	Continuous Intention	0.156	0.025	0.117	0.214
Content Value	--->	Satisfaction	--->	Continuous Intention	0.131	0.023	0.090	0.181
Social Value	--->	Satisfaction	--->	Continuous Intention	0.52	0.016	0.022	0.087
Interaction Value	--->	Satisfaction	--->	Continuous Intention	0.101	0.018	0.068	0.143
Emotional Value	--->	Satisfaction	--->	Continuous Intention	0.085	0.022	0.049	0.132

. Bootstrap Intermediation Test Results. Based on the results obtained, satisfaction was found to be a partial mediator of the effect of the five perceptual variables on the willingness to continue online learning.

5. Results and Discussion

This study found that perceived value (functional value, content value, social value, interaction value, and emotional value) was the antecedent variable affecting users’ satisfaction with YouTube video online learning and had an indirect effect on their continuous intention. Perceived value (social value, interaction value, and emotional value) and satisfaction had a direct and significant effect on users’ continuous intention to use YouTube video online learning.

• Perceived Value and Satisfaction

The test results of the structural equation model in this study showed that perceived value positively and significantly influenced satisfaction: functional value (β_H1 = 0.33, p < 0.001), content value (β_H2 = 0.262, p < 0.001), and social value (β_H3 = 0.104, p < 0.001). Interaction value (β_H4 = 0.21, p < 0.001) and emotional value (β_H5 = 0.21, p < 0.001) positively affect satisfaction. That is, hypotheses H1, H2, H3, H4, and H5 are valid. Among them, the functional value has the greatest effect on satisfaction and is significant at the 0.001 probability level.

This is consistent with previous research findings (Peechapol et al., 2018; Kundu, 2020) [44,45] that the greatest impact of functional value on satisfaction when YouTube users are online learning through video may be due to the importance users place on the usefulness, reliability, and effectiveness of the video content. Functional value provides the features and tools that users need to complete a specific task or achieve a goal, such as the clarity, accuracy, and information content of the video, which can directly affect user satisfaction. In addition, users compare and match the features and tools in the video with their learning goals, and they may have higher satisfaction and continuous intention using the video content if the video provides enough features and tools to meet their learning needs. Therefore, providing video content and features that are useful, reliable, and effective can increase user satisfaction and loyalty to video online learning.

• Perceived Value and Continuous Intention

The results of the structural equation model test in this research showed that Social Value (β_H8 = 0.194, p < 0.01) and Interaction Value (β_H9 = 0.176, p < 0.001) positively influenced users’ YouTube videos online learning continuous intention. In addition, functional value (β_H6 = −0.081, p = 0.045), content value (β_H7 = −0.029, p = 0.419), and emotional value (β_H10 = −0.044, p = 0.31) positively influenced users’ online learning continuous intention. The correlation does not hold. That is, it is assumed that H8 and H9 are valid, and it is assumed that H6, H7, and H10 are not valid.

Although the content value and sentiment value are critical to user experience and satisfaction in online learning environments with YouTube videos, the reason for their insignificant impact on continuous intention to use online learning may be due to several factors: First, utility and usefulness are more important to users in online learning environments. Compared to other types of online content consumption, such as entertainment and social media, users may be more focused on the functionality and usefulness of videos, i.e., whether they provide the knowledge and skills they need to better perform specific learning tasks or achieve their goals. Thus, users may focus more on the utility of the video content and relatively ignore the emotional and content value of the video. Second, there may be individual differences in the degree to which users need emotional and content value, i.e., different users may value video content and emotional value differently, and thus the impact of these factors on continuous intention to learn online may vary for different users. Finally, there may be sample bias or research methodological limitations in the current study, which may also lead to the finding of insignificant effects when analyzing the effects of affective and content value on the online learning continuous intention.

• Satisfaction and Continuous Intention

The results of testing the structural equation model in this research showed that satisfaction (β_H11 = 0.427, p < 0.001) has a significant positive effect on users’ online learning continuous intention, i.e., hypothesis H11 is valid.

There is a general consensus in the academic community that ‘satisfaction drives loyalty’ [28,29,31,32]. This study analyzes the relationship between YouTube video user satisfaction and the “continuity” of video online learning intention in the post-epidemic online education context, and the role of satisfaction in predicting behavior has been tested again in practice. This is consistent with the results of most existing studies and further confirms the strong explanatory power of satisfaction on users’ continuous intention. Users’ continuous intention to use YouTube videos is stronger when they have positive satisfaction experiences with the various knowledge and skills they learn through YouTube videos.

• Intermediation Effect of Satisfaction

When YouTube users learn online through videos, the bootstrap intermediation test shows significant results for all product terms with a 95% interval that does not include the number 0. This indicates that the satisfaction sub-oh for the mediating variable in the relationship between Functional Value, Content Value, Social Value, Social Value, Interaction Value, and Emotional Value has an intermediation effect in the relationship between Functional Value, Content Value, Social Value, Interaction Value, and Emotional Value in influencing the online learning continuous intention. That is, Functional Value, Content Value, Social Value, Interaction Value, and Emotional Value can all indirectly and significantly and positively influence online learning continuous intention through satisfaction.

These value factors can increase user satisfaction with online learning. For example, functional value can increase the usefulness and utility of videos to help users better accomplish their learning tasks; content value can provide useful knowledge and skills to meet users’ learning needs; social value can promote interaction and communication between users and other learners to enhance the sense of participation and belonging in learning; interactive value can increase interaction between users and video content to enhance users’ emotional value can increase users’ motivation and emotional engagement by providing enjoyable learning experiences and emotional stimulation. Second, these value factors can enhance users’ learning experience and engagement, thereby indirectly increasing their online learning’s continuous intention. For example, a video online learning environment that provides rich functionality and usefulness, valuable content and interactive social experiences, and emotional stimulation will attract and retain more users, increase their engagement and involvement in online video learning, and increase their online learning’s continuous intention. Ultimately, these value drivers can inspire user loyalty and word-of-mouth (WOM) referrals, which in turn drive online learning’s continuous intention. Satisfied users are often more likely to recommend the video online learning environment to their friends and colleagues, which helps to expand the user base and increase brand awareness, thereby increasing the likelihood of continuous online learning intention.

6. Implications

The results also suggest that YouTube video online learning has become an important form of online learning in the post-epidemic era, and its importance is in increasing user satisfaction and continuous online learning intention.

• Theoretical Value

The theoretical value of this study lies in the in-depth investigation of the influence mechanisms of functional value, content value, social value, interaction value, and emotional value on user satisfaction and conline learning continuous intention, which is important for enriching and improving the theoretical framework of online learning satisfaction and continuous intention. It is important to enrich and improve the theoretical framework of online learning satisfaction and conline learning continuous intention. In this study, the analysis of YouTube videos shifts from video content research to video user research, from cultural research perspective to an empirical research perspective, and combines online education practice, consumer behavior theory, and social psychology to explore the analysis. From the original theory of ‘consumer value’, we deeply explore the mechanism behind users’ YouTube videos online learning continuous intention; we also cite “social value” and “social benefit” in relationship marketing theory as variables to make users use YouTube videos for online learning. The study also uses factors such as “social value” and “social value” from the relationship marketing theory as variables, which makes the study more relevant to the social characteristics of the YouTube platform and users’ psychology, and further explains the logic behind users’ online learning continuous intention, giving a fresh view from a user-centered perspective. The extent to which the five dimensions of perceived value, namely functional value, content value, social value, interactive value, and emotional value, influence user online learning satisfaction and continuous intention is examined, opening up a new direction for subsequent research on online videos on platforms such as YouTube as learning objects rather than just secondary content.

• Practical Importance

At the same time, this study has important implications for practical applications. The results of this study can also provide guidance to online educational platforms. Platforms such as YouTube should pay attention to users’ perceived value and increase users’ satisfaction in the learning process in order to increase users’ online learning’s continuous intention, thereby improving user experience and retention rates and increasing the platform’s competitiveness. Video platforms and creators can try to combine emotional value factors with video content, for example, by creating touching storylines or providing interesting learning experiences to stimulate users’ emotional resonance, thereby increasing user satisfaction and continuous online learning intention. When creating videos, creators can consider combining multiple value factors, such as combining social and functional values, to provide users with a more complete and rich learning experience, thereby increasing user satisfaction and continuous online learning intention. Video platforms and creators can collect and analyze user feedback to understand users’ needs and preferences for different value factors in order to provide users with a more personalized learning experience and further increase their satisfaction and online learning’s continuous intention. Finally, video platforms and creators can seek to use advanced technological tools, such as artificial intelligence and big data analytics, to better understand users’ behaviors and needs and optimize video content and platform features, thereby increasing users’ satisfaction and continuous online learning intention.

7. Conclusions

This study presents a comprehensive discussion and analysis of the factors that influence users’ satisfaction and online learning’s continuous intention with YouTube videos. Drawing on Perceived Value Theory and Expectancy Confirmation Theory from a user research perspective, we examine in-depth users’ online learning continuous intention, analyze the application of different dimensions of Perceived Value Theory, and the influence of different dimensions on users’ satisfaction and online learning continuous intention, further validating the findings of Kahan et al. (2017). It can be concluded that this paper provides a comprehensive discussion and analysis of the factors influencing users’ satisfaction and continuous online learning intention for YouTube videos. It can be concluded that perceived value directly influences users’ satisfaction (Fornell, 1992; Wang et al., 2020), social value, interaction value, and satisfaction directly influence users’ continuous intention to learn online (Oliver, 1980; Khalifa, 1997; Poch et al., 2015), especially perceived value has the strongest effect on users’ continuous intention to learn online through the mediating effect of satisfaction (Haemoon, 1999; Alzahrani et al., 2018), which may indicate that increasing user satisfaction by increasing user involvement and engagement in video learning becomes an important condition for effective and sustainable online learning with video. This may indicate that increasing user satisfaction by increasing user involvement and engagement in video learning becomes an important condition for effective and sustainable online learning videos. This study shifts the analysis of YouTube videos from video content research to video user research, from the analysis of YouTube videos in this study shifts the analysis of YouTube videos from video content research to video user research, from cultural research perspective to empirical research perspective, and combines the reality of online education, consumer behavior theory, and social psychology to explore the analysis, which has some research novelty.

However, this study has certain limitations, as it mainly focuses on the impact of revenue-oriented consumer value, while future research could examine the impact of risk-oriented consumer value. As advertising is the main source of revenue for video platforms, future research on users’ intention and behavior in choosing video platforms for online learning could consider the moderating effect of platform advertising, native advertising, and other types of advertising on users’ online learning continuous intention. Additionally, there may be sample bias or methodological limitations in the current study, and differences among different user groups should be considered. Future research could further explore the moderating effect of demographic variables on the proposed model.