An Empirical Study on the Effectiveness of E-commerce Entrepreneurial Learning: The Mediating Effect of Social Media Flow Experience

Abstract

This study investigated students’ flow states and attitudinal beliefs in a social media-mediated e-commerce entrepreneurship education (EE) context that mediates the entrepreneurial intention-behavior relationship by a structural equation model using the theory of planned behavior (TPB) and the flow theory. This study collected questionnaire data from students to examine the conceptual model. Such learning environments enhanced students’ perceived behavioral control, increased their flow experience, improved attitudes toward social media-assisted entrepreneurial learning, and facilitated the intentions, which led to learning behavior performance achievement in a digital economy. Notably, the flow and attitudes constructs mediated the linkage between students’ perceived entrepreneurial feasibility and the intention-behavior relationship in the social media-assisting EE context. This study contributed to theory by emphasizing the importance of digital entrepreneurial learning in facilitating prospective entrepreneurs’ TPB cognitions, by identifying the mediation effects of learners’ flow and attitudes on the intention-behavior relationship, and by adding the flow concept into the TPB to understand the mechanism through which EE influences students’ exploratory behavior. The findings highlighted EE practices in computer-mediated environments that facilitate students’ active and meaningful learning for flow experience and attitude enhancement, which likely increases their entrepreneurial intentions and behavior in a digital business environment.

Plain language summary

This study aimed to investigate students’ flow experience and attitudes as mediators in influencing students’ entrepreneurial intention-behavior in a social media-mediated e-commerce entrepreneurship education (EE) context by survey data and a research model using the theory of planned behavior (TPB) and the flow theory. Such learning environments enhanced students’ perceived behavioral control, increased their flow experience, improved attitudes toward social media-assisted entrepreneurial learning, and facilitated the intentions, which led to learning behavior performance achievement in a digital economy. Notably, the flow and attitudes constructs mediated the linkage between students’ perceived entrepreneurial feasibility and the intention-behavior relationship in the social media-assisting EE context. This study contributed to theory by emphasizing the importance of digital entrepreneurial learning in facilitating prospective entrepreneurs’ TPB cognitions, by identifying the mediation effects of learners’ flow and attitudes on the intention-behavior relationship, and by adding the flow concept into the TPB to understand the mechanism through which EE influences students’ exploratory behavior. Future research suggestions include a more significant sample number to examine entrepreneurship education and its impact on an intention-behavior link, using the PLS-SEM technique to assess indirect effects of flow and attitudes, and considering more learners’ psychological dimensions as additional constructs. Doing so advances the insights of EE practices in computer-mediated environments that facilitate students’ active learning and cognition, suggesting the possibility of increasing their entrepreneurial intentions and behavior in a digital business environment.

Keywords

entrepreneurship education (EE)social media theory of planned behavior (TPB)attitudes flow experience entrepreneurial learning behavior performance

Introduction

The rise of Web 2.0 technologies facilitates digital entrepreneurship and dramatically changes entrepreneurs’ characteristics, users, and marketplaces (Garcez et al., 2022; Sussan & Acs, 2017). Digital technology, such as social media networking, impacts entrepreneurship, businesses, and higher education (Bakeman & Hanson, 2012; Mao, 2014; Nakara et al., 2012; Neier & Zayer, 2015; Vandeyar, 2020). Therefore, Tseng et al. (2022) suggested that equipping students with cyber-related entrepreneurship abilities and beliefs is vital to facilitate initiating or continuing entrepreneurial behavioral intentions in a digital economy. In the entrepreneurship education (EE) domain, scholars (Cope, 2003; Maritz et al., 2015; Neck et al., 2014; Pittaway & Cope, 2007) understood entrepreneurial learning as a self-reflection, experimenting process. The mechanism through which EE influences learning outcomes thus deserves closer examination (Eesley & Lee, 2021; Martínez-Gregorio et al., 2021; Rauch & Hulsink, 2015). However, current research on digital technologies and skillsets in EE that influence students’ entrepreneurial skills, beliefs, and intentions appears scant (Bakeman & Hanson, 2012; Sussan & Acs, 2017; Tseng et al., 2022).

The majority of studies emphasize the presence and the essence of EE in improving students’ attitudes toward entrepreneurship and increasing their entrepreneurship intentions, thus developing an entrepreneurial workforce and stimulating economic growth (Fayolle et al., 2006; Kelley et al., 2015/2016; Packham et al., 2010; Rauch & Hulsink, 2015; Y.-C. J. Wu & Wu, 2017). However, the literature offers mixed, contradictory results on EE effectiveness (Bae et al., 2014; Martínez-Gregorio et al., 2021; Nabi et al., 2017). Some studies report that EE programs do not accomplish their intended impacts on students’ entrepreneurial skills or intentions (Marques et al., 2012; Oosterbeek et al., 2010), whereas others suggest that positive effects of EE exist (Anwar et al., 2020; Souitaris et al., 2007; Zhang et al., 2014). Therefore, to advance EE research, outcome measures of entrepreneurial learning should be the focus of inquiry (Honig, 2004; Martínez-Gregorio et al., 2021; Nabi et al., 2017; Pittaway & Cope, 2007).

Given the limitations outlined above, this study aimed to investigate students’ flow psychological state and attitudinal beliefs as mediators to influence students’ entrepreneurial intention-behavior in a social media-mediated e-commerce entrepreneurial learning context. Consequently, to achieve this goal and advance literature concerning the outcome measures of EE, a structural equation model using the theory of planned behavior (TPB) and the flow theory is applied. The TPB is an effective model for studying the predictors of respective entrepreneurs’ intentions (Alferaih, 2017; Iakovleva et al., 2011; Zaremohzzabieh et al., 2019) and provides validated determinants to observe learners’ attitudes, perceptions, and behavior after entrepreneurship programs (Fayolle et al., 2006; Martínez-Gregorio et al., 2021; Y.-C. J. Wu & Wu, 2017; Zhang et al., 2014). Additionally, this study integrated the flow concept into the TPB model to examine changes in students’ internal conditions affecting their performance. Flow is a heavily researched construct as predictions in complete task absorption, including human-computer interaction, computer-mediated learning environments, high-performance cognition, and many others (Abuhamdeh, 2020; Finneran & Zhang, 2005; Guo & Ro, 2008; Webster et al., 1993). The flow experience of social media-mediated e-commerce learning, specifically in this study, was postulated to mediate the relationships between students’ perceived behavioral control, intentions, and learning behavior performance.

The results contributed to EE literature and practice by highlighting the essence of equipping prospective entrepreneurs (i.e., students) with the feasibility of digital skills and beliefs but rarely discussed in digital entrepreneurship times (Bakeman & Hanson, 2012; Kang & Lee, 2020; Nambisan, 2017; Sussan & Acs, 2017; Tseng et al., 2022), by providing evidence to the meditation effects of learners’ flow and attitudes in a digital entrepreneurial learning context to effectively enhance learning performance (Martínez-Gregorio et al., 2021; Nabi et al., 2017; Sussan & Acs, 2017; T. Wu, 2020), and by adding the flow concept into the TPB to advance knowledge to the mechanism behind EE and its results (Eesley & Lee, 2021; Martínez-Gregorio et al., 2021; Rauch & Hulsink, 2015). Following the introduction, this study reviews the TPB and the flow theory drawn to develop research hypotheses. The survey data examine a conceptual model linking students’ perceived behavioral control, attitudes, and flow experience to entrepreneurial intentions and learning behavior performance. The paper then discusses the results and concludes by presenting the implications for research and practice.

Relevant Literature Review and Hypothesis Development

The Theory of Planned Behavior

The theory of planned behavior (TPB) proposed by Ajzen (1991) is one of the most influential and popular theoretical models for studying human action. The TPB is an extension of the theory of reasoned action (TRA), which posits that subjective norm and individuals’ attitudes affected by external factors and beliefs facilitate their intentions and subsequent behaviors (Ajzen & Fishbein, 1980). Given that people may have incomplete volitional control, the TPB explicitly incorporates perceived behavioral control as a third antecedent to behavioral intentions (Ajzen, 1991, 2002, 2020). Accordingly, the TPB postulates three independent determinants of intention: attitude toward the behavior, subjective norms, and perceived behavioral control. Intention can be used to demonstrate a self-prediction to engage in a behavior; attitudes (AT) refers to the degree to which individuals have a favorable or unfavorable appraisal of the behavior; subjective norms (SN) is the perceived social pressure to perform or not to perform the behavior; and perceived behavioral control (PBC) refers to individuals’ perceptions of their ability to perform a given behavior (Ajzen, 1991, 2002, 2020). Attitude toward the behavior, subjective norms, and perception of behavioral control lead to a behavioral intention; the intention is thus assumed to be the immediate antecedent of behavior (Ajzen, 2002).

Although the TPB is commonly viewed as one of the most widely-used theories in social psychology in general, it has become a prominent theory in the entrepreneurial intention and behavior domain (Alferaih, 2017; Iakovleva et al., 2011; Zaremohzzabieh et al., 2019). Entrepreneurial intentions (EI), defined as one’s desire to start a business, is the first essential step toward entrepreneurial behavior (Bae et al., 2014; Krueger & Carsrud, 1993). In the entrepreneurship context, EI can be used to predict the planned and goal-oriented entrepreneurial behavioral outcomes: becoming an entrepreneur, performing entrepreneurial behavior, and advancing entrepreneurial-process behaviors, thus many studies regard EI as one of the vital antecedents of actual entrepreneurial actions at the individual level (Bae et al., 2014; Krueger & Carsrud, 1993; Liñán & Fayolle, 2015; Rauch & Hulsink, 2015). According to Alferaih (2017), EI is the most frequently mentioned dependent variable in the empirical literature on entrepreneurship to understand an individual’s planned behavior, such as starting a new venture; meanwhile, the determinants of the TPB, SN, AT, and PBC, are the most three frequently used independent variables to predict EI. In social entrepreneurship, Zaremohzzabieh et al. (2019) found that the best correlate of social EI was PBC, followed by SN and AT. The constructs of the TPB significantly enable the researchers to understand and predict one’s intention by considering personal and social factors in the entrepreneurship literature (Alferaih, 2017; Iakovleva et al., 2011; Zaremohzzabieh et al., 2019).

In addition to the applicability and usefulness of the TPB in entrepreneurship, it has been increasingly validated as a theoretical model to explore one or several antecedents of student EI in the EE context and, therefore, assess student behavior outcomes and EE’s effectiveness (Fayolle et al., 2006; Martínez-Gregorio et al., 2021; Y.-C. J. Wu & Wu, 2017; Zhang et al., 2014). For example, the meta-analysis results of Martínez-Gregorio et al. (2021) indicated that the most frequently examined outcome measures in EE are EI, control, self-efficacy, and attitude toward entrepreneurship. Researchers (Amofah & Saladrigues, 2022; Marques et al., 2012) identified that AT and PBC of students positively influence intentions, suggesting favorable attitude toward EE and enhanced perceived control over entrepreneurship lead to the actual formation of EI. The previous literature revealed that EE has direct or indirect effects on the relationships between the TPB antecedents and EI (Anwar et al., 2020; Fernández-Pérez et al., 2019; Souitaris et al., 2007; Tseng et al., 2022). Therefore, the TPB determinants could shape and foster EI under EE environments. Notably, the TPB containing the independent determinants of intention, AT, SN, and PBC, observe possible changes in learners and measure their planned behavior outcomes, such as new venture initiation in the EE context (Martínez-Gregorio et al., 2021).

Despite the significant applicability of the TPB in entrepreneurship and EE, however, the elements of the TPB model might have different importance and relevance when studying student EI after EE (Fayolle et al., 2006; Marques et al., 2012). Additionally, the literature suggests more examination of the following issues: the role of psychological traits in the relationship between EE and EI (Martínez-Gregorio et al., 2021) and the antecedents of student EI in the context of cyber entrepreneurship (Tseng et al., 2022). To address these concerns, some scholars (Fayolle et al., 2006; Marques et al., 2012; Zaremohzzabieh et al., 2019) suggested considering the relevance of the TPB determinants for EE assessment, including additional constructs in the original TPB and adapting TPB constructs linked to the precise areas of study. Furthermore, to further observe changes of respective entrepreneurs (i.e., students) in beliefs and attitudes toward performing a behavior, Bae et al. (2014) recommended considering other outcome variables rather than EI, such as entrepreneurial knowledge and skills, actual behavior or performance, to evaluating students’ post-education behavior. Due to contextual differences used by previous TPB-based studies in the EE literature, much remains to be done, particularly in adding construct in the TPB model to comprehensively investigate students’ changes in perceptions and attitudes that, in turn, influence their intentions and subsequent learning behavior performance in a digital entrepreneurial learning context.

The Flow Concept

Flow is a state that posits individuals’ total absorption and concentration in an activity and is aware of nothing else (Buil et al., 2019; Csikszentmihalyi & LeFevre, 1989; Kim & Park, 2021). The primary condition for entering flow depends on one’s perceived balance between action capabilities and opportunities (Nakamura & Csikszentmihalyi, 2002). Flow experienced by learners during learning activities is characterized by concentration, control, and enjoyment (Guo & Ro, 2008). Suppose flow experience in terms of perceived enjoyment, control, satisfaction, and attention exceeds individuals’ threshold values. Flow likely makes performance feel effortless and enjoyable (Csikszentmihalyi & LeFevre, 1989). Flow has been employed to address positive user experience in various fields, including educational contexts, computer-mediated environments, recreation and leisure sciences, marketing, game design, and many others (Abuhamdeh, 2020; Finneran & Zhang, 2005; Guo & Ro, 2008).

In the contexts of information systems and computer-mediated environments, the flow construct has gained considerable interest in investigating the relation to task performance, exploring human-computer interactions, assessing individuals’ acceptance of and holistic experience with new information and communication technology (ICT), and determining user perception, intentions, and behavior in web-based environments (Chang & Zhu, 2012; Finneran & Zhang, 2005; Hong et al., 2017; Pelet et al., 2017; Valinatajbahnamiri & Siahtiri, 2021; Webster et al., 1993; Zhou & Lu, 2011). Computer-mediated environments lead to flow, which may generate the cognition-, affect-, and behavior-related outcomes that provide promising research avenues to investigate more insights into the effect of individual flow experience on perceptions and performance (Valinatajbahnamiri & Siahtiri, 2021). Similarly, the concept of flow is heavily researched in computer-mediated learning settings. Flow allows researchers to explore students’ inner states influenced by education technology in different learning contexts and their subsequent learning outcomes. The literature identifies direct or indirect effects of flow experience on increased exploratory behavior: learning results (Choi et al., 2007; Kim & Park, 2021; Palomäki et al., 2021), entrepreneurial attitudes and intentions (Fellnhofer, 2015), perceived learning and satisfaction (Buil et al., 2018, 2019), and learning intentions (Hewei & Youngsook, 2022).

Although flow theory has a significant impact on research concerning the effects of computer-mediated learning environments, it has yet to frequently be applied in prior studies associated with EE except for the research of Fellnhofer (2015). Furthermore, the literature highlights the importance of internet-based and ICT-mediated environments in facilitating student engagement, equipping digital knowledge and skills, and cultivating student EI in a digital business sector (Abrahams & Singh, 2010; Bakeman & Hanson, 2012; Gikas & Grant, 2013; Mao, 2014; Ngai, 2007; Sussan & Acs, 2017; Tseng et al., 2022). However, the relationship likelihood of experiencing flow and performance in a social media-assisting EE context still needs to be determined. This study thus integrated the flow experience construct as a mediator into the TPB to understand how changes in students’ internal conditions influenced by education technologies impact their subsequent behavioral consequences.

Hypotheses Development

To draw on the theory of planned behavior (TPB) and the flow theory, Figure 1 represents the relationships among perceived behavioral control, attitudes, and flow experience that directly and indirectly affect students’ entrepreneurial intentions and subsequent learning behavior. This study did not consider subjective norms. The reasons were the relative importance of the three predictors (i.e., attitude, subjective norms, and perceived behavioral control) of intentions is expected to vary across behaviors and situations (Ajzen, 1991); beliefs of friends and family cannot be influenced by EE (Rauch & Hulsink, 2015); and social norms might infrequently predict intentions (Zaremohzzabieh et al., 2019).

Figure 1.

Research model.

The TPB Model

Students’ perceived behavioral control (PBC) refers to the perceptions of the behavior’s feasibility in predicting their behavior under the social media-mediated e-commerce entrepreneurial learning context. EE likely provides required resources and opportunities that affect student’s learning processes and enhance their beliefs about the ability to control and master, which increases students’ perceptions of the feasibility of entrepreneurial behaviors (Fayolle et al., 2006; Rauch & Hulsink, 2015). Individuals’ confidence in their beliefs and abilities exhibits a high degree of PBC that thus significantly predicts their entrepreneurship attitudes and intentions (Fernández-Pérez et al., 2019; Liu et al., 2022; Yi, 2017). Given the difficulty and risk concerning entrepreneurship, Tantawy et al. (2021) emphasized that a strong belief in one’s perceived control over persevering and succeeding is vital to engaging in entrepreneurship and increasing entrepreneurial intentions. Moreover, personal confidence in prior entrepreneurial experience and management education helps students concentrate on entrepreneurial learning and gain more value from EE (Kirkwood et al., 2014; Pittaway & Cope, 2007; Politis, 2005). In a digital economy context, personally controllable knowledge and experience are vital to entrepreneurship. For example, Peterman and Kennedy (2003) noted that previous experience and perceptions of market need are characteristics of internet-based entrepreneurs. Abrahams and Singh (2010) indicated that project management knowledge is foundational for student engagement in e-commerce learning. An individual’s underlying beliefs and behaviors reshape the collective nature of digital entrepreneurship (Nambisan, 2017) and dramatically predict cyber entrepreneurship intentions (Tseng et al., 2022). Based on the above, under the social media-mediated e-commerce entrepreneurial learning context, students with high confidence levels in their capabilities to start a business venture are likely to concentrate on e-business learning tasks and promote attitudes toward entrepreneurial learning, which may influence their EI directly and indirectly. Therefore, this study hypothesizes:

Hypothesis 1a. Students perceived behavioral control positively impacts their flow experience in the social media-mediated e-commerce entrepreneurial learning context.

Hypothesis 1b. Students perceived behavioral control positively impacts their attitudes toward social media-mediated e-commerce entrepreneurial learning.

Hypothesis 1c. Students perceived behavioral control positively impacts their entrepreneurial intentions.

Students’ attitudes (AT) regarding the favorable or unfavorable evaluation of the social media-mediated e-commerce entrepreneurial learning context are used to predict intentions. The attitude-intention model provides opportunities to study the link between EE and entrepreneurial behavior (Bae et al., 2014; Nabi et al., 2017; T. Wu, 2020; Y.-C. J. Wu & Wu, 2017). For example, Packham et al. (2010) suggested that completing an entrepreneurship course affects learners’ attitudes toward pursuing an entrepreneurial career from a European perspective. Tseng et al. (2022) identified attitude as an effective predictor of cyber entrepreneurship intentions under cyber EE. In student EI studies, scholars suggested that changes in students’ attitudes toward entrepreneurship and entrepreneurial learning activities positively impact their intentions (Amofah & Saladrigues, 2022; Anwar et al., 2020; Fellnhofer, 2015; Fernández-Pérez et al., 2019; Iakovleva et al., 2011; Marques et al., 2012). Hence, this study hypothesizes the following:

Hypothesis 2. Students’ attitudes toward social media-mediated e-commerce entrepreneurial learning positively impact their entrepreneurial intentions.

Students’ entrepreneurial intentions (EI) indicate the willingness to apply to display entrepreneurial behaviors within a social media-mediated e-commerce EE context. Intentions in the TPB are perceived as immediate antecedents of actual behavior (Ajzen, 1991, 2002). Following the TPB, this study adopted EI to predict individuals’ subsequent behaviors. The EE literature reports that entrepreneurial behavior is difficult to observe or display in time (Marques et al., 2012), and improved entrepreneurial knowledge and skills can be used to understand students’ behavior under EE (Bae et al., 2014). Accordingly, in this study context, subsequent behavior is entrepreneurial learning behavioral performance concerning the desired outcome behavior of students in a social media-supported e-commerce EE setting. The entrepreneurial learning behavior performance construct refers to deep, self-reflective learning in knowledge acquisition and capability development (Benbunan-Fich & Arbaugh, 2006; Hytti et al., 2010), entrepreneurial mindsets and actions (Neck et al., 2014), and entrepreneurial self-efficacy that posits changes in students’ attitudes (Bae et al., 2014). These elements above will likely facilitate students’ future entrepreneurship practices and generate behavior toward venture creation (Kirkwood et al., 2014; Pittaway & Cope, 2007; Rauch & Hulsink, 2015). Based on the meta-analysis results of the EE-relevant studies, Liñán and Fayolle (2015) suggested that the potential of the concepts, such as individuals’ commitment to an entrepreneurial process and their goals, plans, knowledge, and self-efficacy, likely account for one’s actions and behavior in entrepreneurship. Accordingly, students’ increased willingness facilitates their learning engagement and achievement behavior that benefits in initiating future entrepreneurship. This study proposes the following hypothesis:

Hypothesis 3. Students’ entrepreneurial intentions positively impact their entrepreneurial learning behavior performance in the social media-mediated e-commerce entrepreneurial learning context.

The Mediation Effect of the Flow Experience

The flow experience construct relates to a pleasant, holistic experience where students are involved in social media-mediated e-commerce entrepreneurial learning. From an educational perspective, flow experience is viewed as the student perception of or learners’ control of computer-mediated education technology interactions and learning environments, suggesting its potential for empirically understanding students’ learning, affect, attitudes, and behavior (Finneran & Zhang, 2005; Webster et al., 1993). In the e-learning and online course situations, several previous studies revealed that flow experience is positively related to learners’ attitudes, satisfaction, and learning outcomes (Choi et al., 2007; Kim & Park, 2021) and has a mediating role in affecting learning intentions (Hewei & Youngsook, 2022). Concerning educational technology, Pelet et al. (2017) suggested that once students immerse themselves in social media use, flow leads to exploratory behavior in terms of time distortion and the frequency of social media use; Buil et al. (2019) noted that flow in clickers using contexts positively predicts perceived learning and satisfaction. Scholars (Buil et al., 2018; Fellnhofer, 2015; Hong et al., 2017) indicated that the flow experience of game learning environments is related to varying positive learning outcomes of students, including skill improvement, satisfaction, learning progress, attitudes, and intentions. Specifically focused on the moderating effect of task experience, Palomäki et al. (2021) suggested that flow is directly linked to one’s performance expectancy. Little is currently known about the effect of flow on the relationships between social media-mediated entrepreneurial learning and students’ cognition changes and subsequent behaviors. However, adopting Web 2.0 and media-based technologies can lead to students’ meaningful learning and digital entrepreneurship skillset development, which may support this study’s proposal (Bakeman & Hanson, 2012; Mao, 2014; Sussan & Acs, 2017). Therefore, this study postulates that:

Hypothesis 4a. Students’ flow experience positively impacts their attitudes toward social media-mediated e-commerce entrepreneurial learning.

Hypothesis 4b. Students’ flow experience positively impacts their entrepreneurial learning behavior performance in the social media-mediated e-commerce entrepreneurial learning context.

Methodology

Participants

This study recruited 110 undergraduate students who participated in the three-credit business and management classes from 2018 to 2019. As the social media-supported e-commerce learning required students’ attendance and engagement in entrepreneurial group projects with their own portable devices and social media accounts, only 103 questionnaires were collected to analyze because of incomplete data. The sample comprised 65 (63.1%) male and 38 (36.9%) female participants. The age was 18–27 (M = 21, SD = 2.445). 55.3% of participants were in the 18–20 age range, 29.1% were in the range of 21 to 23, and the rest were above 23 years old. Participants majored in various fields of business and management: 33 (32%) majored in logistics and supply chain management; 29 (28.2%) majored in information management; 22 (21.4%) majored in business administration; 19 (18.4%) majored in accounting and finance management. Rauch and Hulsink (2015) indicated that many people start their business ventures years after completing their education. EE in a business school environment is valuable, suggesting participants’ age and business major are suitable for examining students’ perceptions and behaviors in a social media-assisting EE context. Furthermore, followed by Memon et al. (2020), this study employed the G*Power 3.1.9.7 analysis tool to calculate the sample size for an SEM model. This study specified the parameter settings of the effect size at .15, α at .05, power at .80, and 7 predictors. G*Power estimated that the minimum sample size required for this study’s model is 103, demonstrating that the sample size is acceptable.

Measures

The survey collected students’ responses to test the research model (Figure 1) at the end of the social media-mediated e-commerce learning courses. The questionnaire contained survey measurement items developed based on the literature. All items used 5-point Likert scales anchored by “strongly disagree or very unlikely” and “strongly agree or very likely.” The perceived behavioral control construct (PBC) used four items originating from the studies of Ngai (2007) and Pittaway and Cope (2007). The attitude construct (AT) was measured using three items adapted from Choi et al. (2007) and Rauch and Hulsink (2015). The flow experience variable included three items borrowed from the studies of Choi et al. (2007), Zhou and Lu (2011), and Chang and Zhu (2012). The intention construct included three items adapted from Iakovleva et al. (2011). Entrepreneurial learning behavior performance was measured using four items, which draws upon the previous literature (Bae et al., 2014; Benbunan-Fich & Arbaugh, 2006; Hytti et al., 2010; Neck et al., 2014).

Data Analysis

The covariance-based structural equation modeling (CB-SEM) with the EQS software program was used to test the hypothesized relationships among perceived behavioral control, attitudes, and flow experience that directly and indirectly affect students’ entrepreneurial intentions and subsequent learning behavior. This study used CB-SEM for the following reasons. CB-SEM allows the researchers to test theory and explain the phenomena (Dash & Paul, 2021; Hair, Babin, & Krey, 2017; Hair, Matthews, et al., 2017) and enables the researchers to examine all the hypotheses in a single model (Williams et al., 2009), suggesting CB-SEM is appropriate for this study’s research objective.

A two-step SEM approach suggested by Anderson and Gerbing (1988) was conducted. First, a test was conducted to assess the validity of the measurement scales using confirmatory factor analysis (CFA), and second, a test to determine the goodness-of-fit between a theoretical hypothesized model and the sample data. The measurement model assessment and structural model assessment were conducted. Additionally, while this study hypothesized the direct links between the bivariate constructs rather than hypothesized flow and attitudes serially mediated the association between perceived behavioral control and learning behavior performance, the roles of flow and attitudes in a conceptual model demand a mediation analysis. Thus, the PROCESS macro for the SPSS tool with the bootstrapping technique was employed to illustrate the indirect path effect for flow and attitudes variables (Dash & Paul, 2021; Hayes, 2013).

Results

Measurement Model Assessment

This study used survey data from the recruited participants to examine the research model. The measurement model was assessed through confirmation factor analysis using SPSS and EQS statistics software with a maximum likelihood approach (Byrne, 2013). Table 1 displays the results of the measurement reliability. Cronbach’s alpha (α) values of most constructs are above .7; the α value of the intentions construct is above .5, higher than the acceptable value of .5 (Hair et al., 2006). All variables’ composite reliability (CR) values range from .793 to .928, close to or above the acceptable value of .8 (Hair et al., 2006). These results support the reliability of the measurement model.

Table 1.

Survey Items and Measurement Reliability.

Constructs	Items	Loading	α	CR	AVE
In the social media-mediated e-commerce context, I had adequate knowledge and skills regarding
Perceived behavior control	Business planning and processes	.767	.773	.857	.600
	Opportunity recognition	.778
	Project and human capital management	.785
	Communications and collaboration	.767
I perceived that learning entrepreneurship in the social media-mediated e-commerce context
Attitudes	Increased my interest in entrepreneurship	.931	.882	.928	.811
	Increased my intentions toward entrepreneurship	.865
	Motivated me to be an entrepreneur	.904
In the social media-mediated e-commerce context, I felt that
Flow experience	I enjoyed creating and running the e-business process.	.861	.839	.904	.758
	I focused on activities to foster knowledge, skills, and mindset.	.864
	I was involved in learning activities.	.886
In the social media-mediated e-commerce context, I felt that
Intentions	I had thought about starting a firm.	.791	.582	.793	.564
	I would likely start a firm shortly after graduation.	.624
	I would likely consider entrepreneurship as a potential career.	.823
After completing learning in this context, my…in entrepreneurship was developed to help me become a potential entrepreneur.
Entrepreneurial learning behavior performance	Knowledge	.849	.828	.887	.662
	Capability	.813
	Mindset	.755
	Self-efficacy	.835

Convergent validity was used to construct validity (Table 1). The factor loading values of all items range from .624 to .931, which most exceeds the recommended level of .7 except for one indicator. The average variance extracted (AVE) ranges from .564 to .811, surpassing the suggested .5 value (Fornell & Larcker, 1981). Hence, all three conditions for convergent validity were identified.

A discriminant validity test was performed. Table 2 presents the square root of the AVE value for each construct on the diagonal, ranging from .751 to .901. The square root of the AVE value surpasses the correlation between that construct and any other construct (Fornell & Larcker, 1981). Table 2 also shows descriptive statistics and construct correlations. The results suggest that the measurement scales are acceptable in discriminant validity.

Table 2.

Descriptive Statistics, Correlations, and Discriminant Validity.

Constructs	Mean	SD	1	2	3	4	5
1. Perceived behavior control	3.813	0.531	.775
2. Attitudes	3.825	0.781	.478**	.901
3. Flow experience	3.913	0.643	.476**	.611**	.870
4. Intentions	3.874	0.405	.631**	.664**	.564**	.751
5. Learning behavior performance	3.716	0.582	.718**	.640**	.590**	.707**	.814

Note. Correlations among constructs are on the off-diagonal. The square root of AVE is on the diagonal.

**Correlation is significant at the .01 level (two-tailed).

Structural Model Assessment

This study tested the theoretical model and hypotheses based on structural equation modeling using EQS statistical software. Five common model-fit measures were used to examine the goodness of fit of the research model: chi-square normalized by the degree of freedom (χ²/df), the normed fit index (NFI), the non-normed fit index (NNFI), the comparative fit index (CFI), and the standardized root mean square residual (SRMR). The five fit indices are as follows: χ²/df = 269.332/97 = 2.776, NFI = .852, NNFI = .875, CFI = .899, and SRMR = .090. The model-fit indices, NFI, NNFI, and CFI, are close to the .9 suggested cutoff value, and the SRMR is close to the .08 threshold value (Doll et al., 1994; Hu & Bentler, 1999; Marsh et al., 2004). Therefore, the results indicate an acceptable goodness-of-fit between the hypothesized model and the observed data.

Figure 2 and Table 3 present the standardized coefficients of the direct path and their significance in SEM analysis. Hypotheses 1a, 1b, and 1c examined the links among learners’ PBC, flow experience, attitudes, and intentions. PBC was significantly related to flow experience (β = .622, t-value = 3.677, p-value < .001). Hence, Hypothesis 1a was supported. The impact of PBC on students’ intentions was significant (β = .764, t-value = 3.693, p-value < .001); thus, Hypothesis 1c was supported. However, the relationship between learners’ PBC and their attitudes was insignificant (H1b, β = .158, t-value = 1.092). Hypothesis 2 investigated the relation between attitudes and students’ intentions. Students’ positive attitudes toward social media-supported e-commerce learning positively impacted their intentions (β = .304, t-value = 2.694, p-value < .01). Hypothesis 2 was supported. Hypothesis 3 contained an intention-behavior link (β = .956, t-value = 4.406, p-value < .001), supporting a positive effect of intentions on students’ learning behavior performance. Hypotheses 4a and 4b examined the effect of flow experience. Flow was found to have a significant influence on attitudes (β = .582, t-value = 3.834, p-value < .001) rather than on behavior performance (β = .063, t-value = 0.509). Thus, Hypothesis 4a was supported, but Hypothesis 4b was not. Moreover, variables of PBC, flow experience, and attitudes explained a significant percentage of the variance in participants’ intentions (R² = .918).

Figure 2.

Results of structural model analysis.

Table 3.

The Results of Direct Paths.

Hypothesis	Relationship	β	t-values	p-values	Supported
H1a	PBC → Flow	.622	3.677	.000***	Yes
H1b	PBC → Attitudes	.158	1.092	.277	No
H1c	PBC → Intentions	.764	3.693	.000***	Yes
H2	Attitudes → Intentions	.304	2.694	.006**	Yes
H3	Intentions → Performance	.956	4.406	.000***	Yes
H4a	Flow → Attitudes	.582	3.834	.000***	Yes
H4b	Flow → Performance	.063	0.509	.672	No

Note. Path coefficients are estimated by CB-SEM using EQS software and are significant at.*p < .05, **p < .01, and ***p < .001.

An indirect analysis was performed to see the relationships between PBC and learning behavior performance by including flow and attitudes as intervening variables. Although this study’s hypothesized model differs from model 6 of Hayes’s (2013) PROCESS macro, it used model 6 because of its appropriateness for the serial multiple mediator model. Therefore, this study presented the indirect effects concerning the hypothesized relations. The sample was bootstrapped 5,000 times at 95% bias-corrected confidence intervals. Table 4 showed that PBC did not indirectly impact learning behavior through flow experience (95% CI [−0.0230, 0.1757]), and other mediation relationships existed because the confidence intervals did not cross or include zero (Hayes, 2013). Flow and attitudes variables indirectly affected the relationship between students’ PBC and their subsequent performance. Hayes (2013) suggested that reporting a mediation analysis with clarity is necessary when multiple analytical strategies are used. Note that PROCESS macro generated the path coefficients, unlike EQS, which used a maximum likelihood approach. The values of indirect effects were thus different from the values of the product of paths estimated by the SEM approach. Table 4 results mainly identify the significant indirect effects of flow and attitudes on hypothesized relationships between students’ PBC and their learning behavior.

Table 4.

The Results of Hayes’s (2013) PROCESS Macro on the Indirect Effects.

Relationship	Effect	SE	95% confidence interval
Relationship	Effect	SE	Lower	Upper
PBC → Flow → Performance	0.0663	0.0502	−0.0230	0.1757
PBC → Intentions → Performance	0.0892	0.0365	0.0288	0.1722
PBC → Attitudes → Intentions → Performance	0.0231	0.0114	0.0045	0.0484
PBC → Flow → Attitudes → Intentions → Performance	0.0225	0.0093	0.0062	0.0424

Note. Model 6 of Hayes’s (2013) PROCESS macro estimates seven serial mediation relationships. This table only presents the results concerning four serial mediation relationships hypothesized in this study.

Discussion

This study found that the TPB determinants of PBC and attitude significantly predicted students’ EI in a social media-mediated e-commerce learning context, leading to enhanced learning behavior performance. The results also suggested that psychological traits such as flow experience and attitudes mediate the intention-behavior relationship. The discussion is illustrated below.

First, the results revealed that students’ PBC positively impacted their flow experience (β = .622) and intentions (β = .764), while not their attitudes toward entrepreneurial learning (β = .158). The positive relation between PBC and flow showed that students’ perceptions of the feasibility would promote their flow experience influenced by social media-assisting EE environments. The results supported the notion that students’ strong confidence in their abilities drives them to concentrate on tasks such as e-business projects and then experience flow in learning (Tantawy et al., 2021). The potential of individuals’ control over entrepreneurship and management knowledge likely immerses them in a self-learning, computer-mediated EE mechanism, thereby benefiting learning to become entrepreneurs (Cope, 2005; Kirkwood et al., 2014; Neck et al., 2014; Pittaway & Cope, 2007). In line with the previous studies (Alferaih, 2017; Zaremohzzabieh et al., 2019), the findings confirmed one’s PBC as a promising predictor of EI. This study’s TPB model showed that enabling students to perceive control over performance of behavior can facilitate their intentions, adding evidence to the research on a significant direct relationship between PBC and EI (Amofah & Saladrigues, 2022; Anwar et al., 2020; Fernández-Pérez et al., 2019; Marques et al., 2012; Souitaris et al., 2007; Tseng et al., 2022; Yi, 2017) and the essence of perception of behavior control in the formation of a behavioral intention (Ajzen, 1991, 2002). This study did not find a positive link between students’ PBC and their attitudes. The possible explanation could be that student participants’ positive feedback in a social media-mediated EE context increased their perceptions of feasibility, which directly leads to flow and performance enhancement (Palomäki et al., 2021), suggesting that flow may have a robust mediating effect on the relationship between PBC and attitudes.

Second, this study identified the positive effects of attitudes on intentions (β = .304). Within this study, the potential of e-commerce practice with social media use helps students develop a positive evaluation of entrepreneurial learning, increasing their intentions related to the willingness to generate behavior toward venture creation. More favorable attitudes foster more viable intentions to pursue entrepreneurship (Ajzen, 1991). The findings were consistent with meta-analysis evidence by Alferaih (2017) and Zaremohzzabieh et al. (2019) to note attitudes as frequently regarded as a solid antecedent to individuals’ intentions related to entrepreneurship. The results also aligned with prior empirical studies on the significant positive attitude-intention relationship in the EE context (Abrahams & Singh, 2010; Amofah & Saladrigues, 2022; Anwar et al., 2020; Fellnhofer, 2015; Fernández-Pérez et al., 2019; Marques et al., 2012; Packham et al., 2010; Souitaris et al., 2007; Tseng et al., 2022).

Third, the positive relationship between intentions and behavior was confirmed (β = .956). Within a digital technology-mediated EE context, learners’ enhanced entrepreneurial intentions facilitate the desired results specifying learning achievement behaviors benefiting in pursuing future entrepreneurship practices, such as knowledge construction, self-reflection with entrepreneurial mindsets, digital skill development, and entrepreneurial feasibility (Cope, 2003; Kirkwood et al., 2014; Neck et al., 2014; Tseng et al., 2022). Hence, the findings added evidence to the literature by confirming intention as a significant positive predictor of performance of the learning behavior (Ajzen, 1991; Martínez-Gregorio et al., 2021; Rauch & Hulsink, 2015). Although the behavior in this study is the desired learning outcome behavior of participants rather than their actual self-employed behavior, it is vital to strengthen students’ willingness and feasibility to perform learning behavior relevant to entrepreneurship as entrepreneurship difficulties may impede students’ EI (Martínez-Gregorio et al., 2021). Moreover, according to Ajzen (1991), if the extent to one’s PBC is realistic, together with behavioral intention, the two determinants can be used to predict the probability of behavioral achievement. Therefore, from an educational perspective, it is essential to help students have a positive experience with entrepreneurial learning, which likely improves their attitudes, facilitates entrepreneurial thinking, and enhances their competence in and mindsets toward future entrepreneurial actions (Cope, 2005; Packham et al., 2010; Politis, 2005; Rauch & Hulsink, 2015; Tantawy et al., 2021). This study’s TPB results supported the essence of EE’s practice suggestions concerning conceptualizing entrepreneurial learning programs relevant to future entrepreneurs (Rauch & Hulsink, 2015; Tantawy et al., 2021) and advancing technologies and course content in cyber EE (Tseng et al., 2022), thus providing the potential to facilitate entrepreneurship cognitions on EI that, in turn, increases the likelihood of behavioral achievement in entrepreneurship.

The last discussion addresses the mediating effects of flow and attitudes. The two paths from PBC to attitudes (H1b, β = .158, p-value > .05) and from flow experience to students’ performance (H4b, β = .063, p-value > .05) were insignificant. Table 4 also shows the significance of the indirect effects of flow experience and attitudes. The indirect impact of students’ PBC on their performance through attitudes and intentions was identified; PBC indirectly affected participants’ behavior through flow, attitudes, and intentions. In line with the studies (e.g., Choi et al., 2007; Hewei & Youngsook, 2022; Tantawy et al., 2021), the results suggested that flow and attitudes are the significant mediators of participants’ confidence in abilities and intentions and subsequent learning behavior. This study also provided evidence to the extant EI literature by examining and confirming the strong mediation effects of increased flow experience and favorable attitudes in explaining a positive entrepreneurial intention-behavior relationship (Martínez-Gregorio et al., 2021; Nabi et al., 2017). The intervening effects of flow and attitudes showed that the stronger students’ experience and engagement in e-business and social media, the more favorable the attitudes concerning entrepreneurial learning, and the higher the probability of facilitating increased intentions and effective results. Furthermore, the flow integration into the TPB provided a deep understanding of the EE mechanism through which letting learners immerse themselves in learning to a greater extent is crucial to facilitate cognitions and behavioral achievement, particularly in a digital entrepreneurship domain (Eesley & Lee, 2021; Garcez et al., 2022; Martínez-Gregorio et al., 2021; Sussan & Acs, 2017; T. Wu, 2020). Notably, in contrast to the previous research, for example, Choi et al. (2007) and Kim and Park (2021) reported that flow is directly linked to learning outcomes. The social media flow experience construct was identified as a full mediator connecting PBC to attitudes and EI within the context of this study. Increasing participants’ total absorption in social media-assisted EE environments did not directly lead to behavior outcomes but indirectly through the impact on cognitions. Therefore, the mediation effect of flow provided insights into the link between self-efficacy and flow and its effect on performance expectancy (Palomäki et al., 2021; Valinatajbahnamiri & Siahtiri, 2021).

Conclusions and Implications

Given the impactful effect of the technology revolution on entrepreneurship and EE, this study drew upon the TPB and the flow theory to examine students’ flow experience and attitudinal changes as mediators in influencing students’ entrepreneurial intention-behavior relationship in a social media-mediated e-commerce entrepreneurial learning context. Data collected from students who participated in social media-mediated entrepreneurial learning supported the conceptual model empirically. The results revealed that students’ PBC significantly impacted flow experience; PBC and participants’ attitudes significantly affected their intentions. This study also confirmed the positive link between intentions and students’ learning behavior performance. The flow and attitudes constructs mediated the link between students’ perceived entrepreneurial feasibility and the intention-behavior relationship.

Theoretical and Practical Implications

This research has five theoretical implications. First, concerning EE practices in web-based, media-mediated settings, this study provided empirical evidence for the feasibility and applicability of digital technologies that help students increase perceived control over cyber mindsets and skillsets, which is essential but rarely discussed in entrepreneurship (Bakeman & Hanson, 2012; Kang & Lee, 2020; Nambisan, 2017; Sussan & Acs, 2017; Tseng et al., 2022). Second, this research contributed to EI, the TPB, and the flow literature by identifying the mediation effects of flow and attitudes on an intention-behavior relationship, suggesting a cognitive pathway leading to EI and exploratory entrepreneurial behaviors (Martínez-Gregorio et al., 2021; Nabi et al., 2017). Third, beyond supporting the TPB as an effective model to examine outcome measures in entrepreneurship, this study contributed to the theory by integrating the flow theory into the TPB, which provides a more comprehensive framework for understanding the mechanism behind entrepreneurial learning and its results (Eesley & Lee, 2021; Martínez-Gregorio et al., 2021; Rauch & Hulsink, 2015). Fourth, flow as a mediator in an intention-based model advanced knowledge of how one’s immersive experience in a media-mediated EE context facilitates TPB cognitions and, after that, desired learning outcomes related to entrepreneurial potentials (Garcez et al., 2022; Sussan & Acs, 2017; T. Wu, 2020), and of the complex relationships between an individual’s self-efficacy, flow, and performance expectancy (Palomäki et al., 2021). Finally, this study added evidence concerning a positive intention-behavior link under the EE context that needs further research (Honig, 2004; Martínez-Gregorio et al., 2021; Nabi et al., 2017). Although the behavior outcome within the context of this study is learning behavior performance rather than actual entrepreneurial behavior, this research highlighted the importance of the TPB cognition determinants in strengthening learning behavior, which likely led to a positive appraisal of entrepreneurship and then the desire for business startups (Packham et al., 2010; Pittaway & Cope, 2007; Rauch & Hulsink, 2015).

Two practical contributions are addressed. First, the results emphasized the need for computer-mediated environments to be further linked to the TPB model in the EE context. Such entrepreneurial learning enables to facilitate students’ active involvement and self-reflection in entrepreneurial learning tasks (Maritz et al., 2015; Neck et al., 2014; Pittaway & Cope, 2007); meanwhile, digital technologies such as e-commerce and social media support context-aware learning and advance cyber-related skills (Bakeman & Hanson, 2012; Gikas & Grant, 2013; Sussan & Acs, 2017; Tseng et al., 2022), thus demonstrating the necessity of a supportive internet-based media-mediated environment for significantly facilitating students’ TPB cognition and encouraging and cultivating their future entrepreneurial potentials. Second, the findings identified the mediating impacts of social media flow experience and attitudes in explaining an intention-behavior link. Guiding learners in intense concentration on the tasks and favorable evaluation of the learning setting thus becomes the focus of EE in a digital economy. For education practice purposes, this study suggested paying attention to digital EE systems containing active involvement and time duration of teachers and students (Liu et al., 2022; Martínez-Gregorio et al., 2021; Palomäki et al., 2021; Tseng et al., 2022), students’ preparedness in digital entrepreneurial learning (Kang & Lee, 2020; Nambisan, 2017; Peterman & Kennedy, 2003), and dynamic internal and belief changes between students’ self-efficacy, flow, and behavior results (Choi et al., 2007; Palomäki et al., 2021).

Limitations and Future Research

This study has four limitations. The first limitation concerns that the sample size of this study may influence the explanatory power of the conceptual model in a social media-assisting EE context (Westland, 2010). Although this study’s measurement and structure models were acceptable, and the sample size recommended by G*Power was also acceptable (Memon et al., 2020), carefully considering the conclusion application was suggested. As the TPB being investigated is well established, the CB-SEM analysis employed in this study is applicable. However, when small sample sizes are a concern, Hair, Matthews, et al. (2017) recommended using PLS-SEM to obtain meaningful solutions while achieving explanation and prediction benefits. The second limitation concerns the default serial mediation models of PROCESS, in which the model the researchers intended to estimate might be different from the exact model recommended by PROCESS that will estimate all direct effects in a serial mediation (Hayes, 2013), like this current investigation. Third, the adverse effects of ICTs in education could be a limitation. Although digital technologies offer opportunities for increasing students’ absorption experience and attitudes that foster their entrepreneurial capabilities and mentality, this study did not consider students’ distractions and use of social networks for social somewhat educational purposes that likely affect learners’ behavior performance (Mao, 2014; Neier & Zayer, 2015). Consequently, as mentioned earlier, issues such as well-designed digital EE settings, student preparedness, and learners’ flow and cognition dynamics should be significantly considered when integrating ICTs into a learning setting. The last limitation would be the limited variables in predicting entrepreneurial potential. The proposed model on the TPB and flow advanced a theoretical understanding of students’ psychological and cognitive changes and their relations with an intention-behavior link. However, in addition to the flow concept, the recent literature suggested the potential of personal internal dimensions in better understanding of outcome measures in entrepreneurship and EE, such as the Entrepreneurial Event model (Martínez-Gregorio et al., 2021; Zaremohzzabieh et al., 2019), self-efficacy expectations (Tantawy et al., 2021), control beliefs (Tseng et al., 2022), need for achievement (Liu et al., 2022), and many other psychological features (Alferaih, 2017; Marques et al., 2012; Martínez-Gregorio et al., 2021). Combining all or some of these variables in the TPB might provide further insights to explain prospective entrepreneurs’ actions toward venture creation.

This study provided suggestions for future research. First, a research direction regarding increasing the number and scope of samples to explore an intention-behavior relationship in digital entrepreneurship is recommended. Students’ characteristics and gender issues (Kelley et al., 2015/2016; Martínez-Gregorio et al., 2021) could be considered for determining EI and outcome measures. As suggested, PLS-SEM is recommended because this method can facilitate complex SEM solutions in the structural model and constructs (Hair, Matthews, et al., 2017) and allow users to estimate a model that combines observed and latent variables and discuss mediation analysis with latent variables (Hayes, 2013). Third, preventing distraction difficulties while enhancing cyber-related skills, future research could explore the perceptions of instructors and learners in influencing the computer-mediated learning progress and students’ EI (Neier & Zayer, 2015; Tseng et al., 2022). Finally, researchers could consider learners’ psychological and affect dimensions as additional constructs in the TPB to improve the quality of entrepreneurship intention-based studies by direct or indirect, moderating or mediating, experimental or survey method, and longitudinal or each education stage viewpoints (Alferaih, 2017; Bae et al., 2014; Martínez-Gregorio et al., 2021; Zaremohzzabieh et al., 2019).

Footnotes

Acknowledgements

The authors would like to sincerely thank the National Science and Technology Council of Taiwan for the financial support (MOST 106-2511-S-344-001; MOST 110-2511-H-344-001-MY3). The authors are grateful for the valuable suggestions of the anonymous reviewers.

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: The research was funded by the National Science and Technology Council, Taiwan. MOST 106-2511-S-344-001; MOST 110-2511-H-344-001-MY3.

Ethical Approval

Ethical review and approval were waived for this study due to the following conditions issued by the Governance Framework for Human Research Ethics at National Cheng Kung University in Taiwan: (1) participants of this study are not homeless, children and adolescents, native citizens, new immigrants, pregnant women, handicappers, or psychiatric patients; (2) the likelihood of damages or discomfort derived from participating in this study is not higher than the chance of any other damages or discomfort in participants’ daily life; (3) decisions to take or not to take part in this study do no influence participants’ rights and benefits; (4) participants do not provide personal information; (5) the collection data is appropriately saved by researchers’ institutions and used only for this study, and (6) the collection data is unrelated to any specific participant, organization, or circumstance.

Consent

This study verbally informed participants about information concerning the research background, aims, methods, analysis tools, class requirements, and future publication of this study. The paper questionnaires did not contain personal identification data.

ORCID iD

Tienhua Wu

Data Availability Statement

Data are not publicly available as they were used for the current study with the institution’s permission.

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