How do Facilitating Conditions Influence Student-to-Student Interaction within an Online Learning Platform? A New Typology of the Serial Mediation Model

Abstract

This study investigates factors affecting university student-to-student interaction within online learning platforms. A new model was proposed based on the United Theory of Acceptance and Use of Technology (UTAUT). The single-stage cluster-sampling method was employed, and 113 university students in Hong Kong were respondents. It was found that Information Quality, Social Influence, and Facilitating Conditions affect students’ intention to interact with each other. A quasi-full mediation model was established of the mechanism from Facilitating Conditions to students’ interaction behavior. The direct effect of Facilitating Conditions on students’ interaction and the effect of System Quality on the intention of student interaction were not significant. A fast network, computing facilities, and mobile-friendly software are possible candidates of the virtual environment conditions affecting the intention of student-interaction behavior within online learning platforms.

1. Introduction

During the pandemic period, instructor–student interactions were emphasized, and student–student interactions were ignored [1]. Learning outcomes of almost all social science and business courses include student engagement and discussion of subject content. Doing group projects is one of the ways that students learn how to collaborate with each other. It makes students not only to ask the teacher when they do not understand the course materials, but also, students are encouraged to discuss among themselves and to learn from each other. It is especially important when some of the university teaching is in a large class format and when there is no compulsory attendance requirement. Students have to use different ways to communicate with each other. It is beneficial if the university can assist students’ discussions through an e-learning platform. Students have meaningful development through collaborative e-learning [2].

There are advantages in using an online discussion forum rather than a face-to-face meeting. Students do not need to be in the same location and can communicate asynchronously at a time convenient to them. University online forums usually have better security, so that third parties find it difficult to hack those systems. If there is a pandemic like COVID-19, the tutorial function, which normally requires discussion among teachers and students, could be replaced. There are some disadvantages, such as that the students cannot have physical contact, they usually cannot see each other, and they might need some technical support. Teachers might be required to work outside normal class hours to reply to some student postings [3].

However, academic discussions among students have yet to be explored for an underlying mechanism on an e-learning platform. Colleges put a lot of physical and manpower resources into information technology support. We aim for student-to-student interaction as an output measurement. Thus, it is worthwhile to examine the process in detail.

There are four research objectives in the paper, and they are listed as follows:

(a)

propose a new model of factors affecting the intention of university student-to-student interaction using e-learning platforms;

(b)

propose a new typology of the serial mediation decision tree;

(c)

investigate the underlying mechanism between facilitating conditions and the use behaviour of student-to-student interaction; and

(d)

provide recommendations to the college management for the development of using an e-learning platform

2. Literature Review

E-learning is defined as making use of hardware and software for teaching and learning through the World Wide Web [4]. Scholars provide an explanation for the term e-learning: any form of electronic education technology, including computer-based learning, computer-assisted instruction, internet-based learning, web-based learning, online education, and virtual collaboration [5]. Their definition of e-learning is used in the study. Having said that, online learning might require learners to use the internet to connect to each other or access course material. Today almost every electronic device can connect to the internet using a public or private connection. Thus, e-learning and online learning are used interchangeably.

E-learning platforms have been used in universities for decades, and their functions have been much improved [6]. From uploading materials by teachers and downloading materials by students, almost all the platforms now provide messaging, discussion forum, testing and marking functions [6].

There are several electronic learning environments that people are using. They can be classified into four categories. The first one is the university electronic learning system including Blackboard and Moodle. The second type is communication software, for example, electronic email and skype. The third type is social media websites, such as Facebook and Google+. The fourth type is application software on mobile devices, including Whatsapp, Wechat, and short message systems [7]. The last type could be conference call software like Google Meet, Microsoft Teams, and Zoom.

During the online lesson, teachers deliver the course content to students. Students can ask questions in a chat room. Sometimes, using a microphone is not a popular choice, since there are a lot of students in a virtual classroom. Previous research on online learning has focused on teacher-to-student interaction, with less attention on student-to-student interaction [1]. This study fills in the literature gap by proposing a new model on factors affecting the intention of university student-to-student interactions using online learning platforms.

2.1. UTAUT

The United Theory of Acceptance and Use of Technology consists of four independent variables (performance expectancy, effort expectancy, social influence, and facilitating conditions) and four moderating variables (gender, age, experience, and voluntariness of use) [8].

2.2. Facilitating Conditions

This refers to the education training in using a new technology, which a company offers for people when it wants them to use that technology [9]. For example, due to the pandemic situation, teachers have had to learn how to use new software, such as Zoom, Microsoft Teams, or Google Meet, to teach their students. Schools are going to offer training support to teachers. Constructive feedback given by lecturers and setting clear expectations can increase the effectiveness and student interactions in a discussion forum [10].

2.3. Social Influence

Social influence is how the opinions of friends, peers, classmates, and family members affect the perception of a person. It has been found that social influence positively associates with the intention to do something [11].

2.4. Association between Facilitating Conditions and Social Influence

We should have information technology support that could be affected by others. This is because the support alone is not enough to drive the person’s intention to do something. That is not contradictory with the original UTUAT model. In the model, several factors, including facilitating conditions and social influence, contribute to behavioral intention.

The UTAUT model and the Delone and McLean Information system success model, with ‘System Quality’ and ‘Information Quality’ constructs, were combined (Figure 1). The selection of the model was justified by an integrative approach and was to be verified by a focus group and empirical study later. The proposed framework that will be tested in this study is appended below (Figure 1).

Thus, we have hypotheses oneto four, nine and ten for serial mediation. Hypotheses five to eight are other independent variables linking to the intention of student-to-student interaction.

There are hardware and software for online learning platforms available so that students interact more with his or her fellow classmates. Social and peer pressure could also be motivating factors.

H1. 

Facilitating Conditions have a positive association with Social Influence.

H2. 

Facilitating Conditions have a positive association with the behavior of student-to-student interaction.

H3. 

Facilitating Conditions have a positive association with the intention of student-to-student interaction.

H4. 

Social Influence has a positive association with the behavior of student-to-student interaction.

Effort Expectancy comes from the scale of the “perceived ease of use” of the Technology Acceptance Model (TAM). Students that find it easier to use the platform might have more interaction with his or her classmates.

H5. 

Effort Expectancy has a positive association with the intention of student-to-student interaction.

Performance Expectancy comes from the scale of “perceived usefulness” of the Technology Acceptance Model (TAM). Students find that the platform is useful; thus, they use it for their interaction with their classmates.

H6. 

Performance Expectancy has a positive association with the intention of student-to-student interaction.

Students find more relevant information available when they have a higher intention of student-to-student interaction.

H7. 

Information Quality has a positive association with the intention of student-to-student interaction.

A more reliable system could lead to a higher intention of student-to-student interaction.

H8. 

System Quality has a positive association with intention of student-to-student interaction.

H9. 

Social Influence has a positive association with the intention of student-to-student interaction.

H10. 

The intention of student-to-student interaction has a positive association with the behavior of student-to-student interaction.

2.5. Mediation

There are two types of Multiple mediation effects: in parallel or in series (Figure 2 and Figure 3). Scholars discuss the multiple mediation model with more than one mediator [12].

In the parallel mediation model, we aim to compare the effects of more than one mediator. As a result, we know which mediator has a larger mediating effect. The overall mechanism or linkage between the antecedent and consequence are basically the same. In the serial mediation model, the focus is on the process of the mechanism. The effect of the antecedent passes through a mediator one-by-one before reaching the dependent variable.

Regarding a definition of a mediator, “it accounts for the relationship between the predicator and the criterion [13] (p. 1176).” To test the mediating effect, the three-step method was used. Three regression equations should be established:

• The mediating variable is regressed on the independent variable;
• The dependent variable is regressed on the independent variable; and
• The dependent variable is regressed on the independent variable and the mediating variable. [13] (p. 1177).

“To establish mediation, the following conditions must hold: firstly, the independent variable must affect the mediator in the first equation; secondly, the independent variable must be shown to affect the dependent variable in the second equation; and thirdly, the mediator must affect the dependent variable in the third equation. If these conditions all hold in the predicted direction, then the effect of the independent on the dependent variable must be less in the third equation than in the second equation. Perfect mediation holds if the independent variable has no effect when the mediator is controlled” [13] (p. 1177).

Mediation lets us know more about the mechanism of the relationship among variables [13]. A new mediation analytic framework was proposed [14]. Five types of mediation were presented: complementary, competitive, indirect-only, direct-only, and no-effect mediation. Complementary mediation is Baron and Kenny’s partial mediation. Indirect-only mediation is, in fact, Baron and Kenny’s full mediation type. There is no need to test the association between the dependent variable and independent variable as there could be a competitive mediation [14].

A similar situation applies to serial mediation.

3. Methodology

The research methodology consisted of two stages. The first stage consisted of a focus group, which was qualitative in nature, and the second stage was a survey. A focus group of 10 students was conducted. The recruitment method was purposive sampling. In the purposive sampling method, the researcher deliberately identifies criteria for selecting the sample. Students that had some online learning experience and were willing to join the meeting were the criteria. Data collected from a focus group can contribute to the researcher’s understanding of the research problem. A quantitative method with a cross-sectional approach was used in the second stage to examine the use of an electronic learning platform in a college so as to investigate the relationships between variables [15].

A factor analysis curtails a number of factors to a smaller number of factors for the framework design. Second, a correlation analysis was used to analyze the relationships among variables [15]. Finally, the partial least squares method was used to analyze the research framework, as one of the objectives of the thesis was testing the predictive power of the independent variables. In addition, sample size could be more flexible in the partial least squares analysis. A multiple regression analysis was also used to reconfirm the linkages among the dependent variable and some independent variables [16].

Most researches involved in e-learning platforms have used the positivist approach. The survey method is one of the most common forms of the positivist approach. It implies that a project is used to grasp information from a group of target respondents by using a survey [17]. A lot of respondents can be reached with a questionnaire. The Hawthorne effect may also occur in quantitative research using questionnaires. Although questionnaires can be anonymous, the researcher’s identity and the aim of the research usually have to be shown to respondents. Reducing the level of personal interaction between the researcher and his or her subject may reduce this problem [18].

3.1. Research Design

Figure 4, below, captures an overview of the research design.

The purpose of the focus group was to help the researcher to design the questions and choices for answering parts on the survey. The survey was undertaken in two phases: a pilot study preceding the main survey. Both the pilot test and survey questions were in English. Students were asked to complete the questionnaire in English. All students were assumed to be proficient in English, because all the courses within their studies were conducted in English. They cannot complete their studies without an understanding of general English usage. Since the questionnaires were designed to be distributed during class with the presence of the researcher, in case of any question during the process, students could ask the researcher their question. The target population in the college at that time was the student population, which was 1000.

Cluster sampling was used in the study. It is a cost-effective method, because it is easy to operate [15]. Our target students were homogeneous generally and grouped naturally in their own classes’ format. It would have been easy for us to randomly select some of the classes for our survey purpose. One minor problem was not of all the classes are the same size. A large class would have had higher chance of being in our sample because they have more students. The cluster sampling method has a similar accuracy when compared to simple random sampling [19].

There were four different programs in the four particular classes, selected randomly. The four undergraduate programms were “Bachelor of Arts in Marketing and Public Relations”, “Bachelor of Arts in Applied and Media Arts”, “Bachelor of Social Science”, and “Bachelor of Arts in Retail and Service Management”. They were all together for 13 weeks, and class meetings were conducted as a mixture of lectures, seminars, group discussions, and presentations. Students were assessed on the basis of a test, group project, class participation, and a final examination. Students had to pass both their coursework and final examination.

The focus group meeting lasted for an hour. In the first 10 min, the background of the study and focus group agenda were explained. There were 10 students that participated in the focus group. The students’ names were Paul, Peter, Eva, Mary, Irving, Tony, Susan, Ruby, Irene, and Wilson. These were not their real names. An extra 10 minutes at the end of the meeting was used to make sure everybody could speak up and that any other related issues could be covered. The focus group findings provided researchers with hints for the survey, which was the main focus of the whole study.

3.2. Scales and Measurement

All scales were modified to suit the purpose of this study. Performance Expectancy, Effort Expectancy, and Use Behavior constructs were based on scales of the Technology Acceptance Model (TAM) [20]. Social Influence and Facilitating Conditions constructs were based on the scales of the Theory of Planned Behavior (TPB).

System Quality and Information Quality constructs were based on the variables used by the Information System Success model (ISS) [21].

Behavioral intention will have a positive impact on the use of a system [8] (p. 460). The construct comes from the scale “behavioral intention to use the system” of the Unified Theory of Acceptance and Use of Technology (UTAUT).

A table of survey instruments with modifications used is shown in

Table 1. Modified survey items.

Construct Name and Abbreviation	Items
Performance expectancy (PE), [8] (p. 447)
PE1	I would find the platform useful in my information accessing and processing
PE2	Using the platform enables me to accomplish tasks of information access and processing more quickly
PE3	Using the platform increases my productivity of information access and processing
PE4	If I use the platform, I will increase my ability to get timely information
Effort expectancy (EE), [8] (p. 450)
EE1	My interaction with the platform would be clear and understandable
EE2	It would be easy for me to become skillful at using the platform
EE3	I would find the platform easy to use
EE4	Learning to operate the platform is easy to me
Social Influence (IS), [8] (p. 451)
IS1	People who influence my behavior will think that I should use the platform
IS2	People who are important to me will think that I should use the platform
IS3	The seniors in my organization have been helpful in the use of the platform
IS4	In general, my organization has supported the use of the platform
Facilitating conditions (FC), [8] (p. 453)
FC1	I have the resources necessary to use the platform
FC2	I have the knowledge necessary to use the platform
FC3	The platform is compatible with other systems I use
FC4	A specific person (group) is available for assistance with the platform’s difficulties
Behavioral intention (IB), [8] (p. 460)
IB1	I intend to use the platform in the future
IB2	I predict I would use the platform in the future
IB3	I plan to use the platform in the future
Use Behavior (UB), [20] (p. 229)
UB1	How many times do you use the platform during a week, overall?
UB2	How many minutes do you use the platform every week, overall?
UB3	You use the platform often
System Quality (SQ), [21]
SQ1	The platform provides me with a fast response time
SQ2	The platform is reliable
SQ3	The platform is easy to access
Information Quality (IQ), [21]
IQ1	The platform provides me with accurate information
IQ2	The platform provides me with relevant information
IQ3	The platform’s information is easy to understand
IQ4	The platform’s information is up to date

.

A total of 120 students were approached, and a paper-based questionnaire was used. Aa total of 113 usable completed questionnaires were received, and the response rate was 94%. Single-stage cluster-sampling was used for the main survey. There are 13 programs in the school. We randomly picked four of them and asked all students in those program. A total of 58% of respondents were female, and 42% of respondents were male. Age ranged from 18 to 21.

4. Analysis

Focus Group

During the focus group, students were asked the frequency of online learning platform, Moodle, usage in their study. They used to access Moodle once per week to get material. However, some of students relied on the efforts of their classmates to download these for them. That is to say, they were less likely visit Moodle.

“Paul used to download notes for our project groups; thus, we do not need to visit the platform so often.”, Eva said. Ruby, Irene, and Wilson agreed with Eva.

Students, including Paul, used Moodle to download their lecture notes, to submit their assignments, to ask questions, and to read course announcements. Five of them were not aware that there was a discussion forum on Moodle and never used it. They discussed their coursework through Facebook and other discussion forums, such as Skype. They could see the faces of each other, as well as sending documents at the same time. They would use the forum on Moodle only when there were marks allocated for their discussion and contribution.

“We share our ideas and answers to the exercises on Facebook, because School would not monitor our activities, unlike Moodle…except some activities count participation marks…you know…we did it on Moodle’s discussion forum.” Mary said.

Another reason for them not using the discussion forum on Moodle was that they did not want their discussion open to the teacher and other students which were not in their peer group and project group. They preferred to discuss among their peer group so that they would not lose their ‘face.’ Keeping face is a very important consideration in Chinese people’s culture.

Irving commented, “I want to keep my idea discussed within my group before submitting to the teacher. The idea could be naive and might not be correct.”

Tony said that at the beginning of the project, they would use Skype (more personalized) for brain-storming. At the middle and final stage of project, she would use Facebook, which can deal with documents and comments. Whatsapp would be used to make an invitation to have a meeting and for the clarification of questions.

I would say that the use of different software depends on the purpose of the meeting. Brain-storming or discussion needs real-time interaction. Whatsapp deals with an established task for confirmation. Another reason for them not using the discussion forum on Moodle is that it requires a username and password for the login-screen for security reasons. However, for Facebook, they are already login in.

“I know it is for the security reason, but I have to wait a minute before I access the content. In contrast, my mobile phone has already gone through Facebook. I made a response with my fingers.” Susan said.

One more reason for using Facebook was that they know the respondents are online. When they send something to their teammates, they expect the respondent will give them back a reply immediately. In contrast, email is not used normally among students as they do not check their email very often. In short, Email is asynchronous, and social networking sites are synchronous.

5. Main Study

The conceptual model in this study was examined using partial least-square structural equation modelling (PLS-SEM), with the SmartPLS 3.0 statistical software. PLS-SEM requires less stringent assumptions about normality, which is more suitable for model development in an exploratory study and for analyzing small sample sizes [12]. The minimum sample size of the study should be equal to or larger than “10-times the largest number of structural paths directed at a particular construct in the structural model” [12] (p. 20). In this study, because the largest number of structured paths used for the behavioral intention was 6, the minimum sample size should have been larger than 60. Using PLS-SEM is appropriate with a sample size of 113, as in this study.

5.1. Measurement Model

The validity and reliability of the measurement items were evaluated in the measurement model. The reliability, internal consistency, discriminant validity, and convergent validity were assessed using the following guidelines [22]:

• The indicator loadings were examined through a reflective measurement model assessment. Loadings above 0.708 are recommended, as they can explain more than 50% of the indicator’s variance and provide acceptable item reliability.
• The measurement model possessed adequate internal consistency reliability, as the composite reliability and Cronbach’s alpha of all constructs exceeded the 0.70 criteria. Composite reliability values between 0.825 and 0.970 are classified as good.
• The convergent validity of each construct was measured to explain the variance of its items. The threshold level for AVE should be 0.50 or higher to explain at least 50% of the variances of its items [22]. The average variance extracted (AVE) of each construct was higher than 0.50, ranging from 0.616 to 0.914, demonstrating convergent validity [22].
• The discriminant validity was assessed by the heterotrait–monotrait (HTMT) ratio of the correlations. The threshold value is 0.85 [23].

  Table 2. Measurement Model Assessment.

  Construct	Item	Loading	Cronbach’s Alpha	Composite Reliability	AVE
  Facilitating conditions (FC)	FC1 FC2 FC3	0.841 0.832 0.838	0.912	0.945	0.851
  Social Influence (IS)	IS1 IS2 IS3	0.938 0.929 0.720	0.835	0.901	0.754
  Behavioral Intention (IB)	IB1 IB2 IB3	0.947 0.961 0.960	0.953	0.970	0.914
  Use Behavior (UB)	UB1 UB2 UB3	0.832 0.888 0.606	0.706	0.825	0.616
  Performance Expectancy (PE)	PE1 PE2 PE3 PE4	0.844 0.891 0.790 0.782	0.848	0.897	0.685
  Effort Expectancy (EE)	EE2 EE3 EE4	0.693 0.942 0.947	0.839	0.901	0.755
  Information Quality (IQ)	Q21 Q22 Q23 Q24	0.758 0.748 0.867 0.819	0.821	0.876	0.639
  System Quality (SQ)	Q18 Q19 Q20	0.856 0.793 0.890	0.807	0.884	0.718

  shows the measurement model assessment, and

  Table 3. Assessing Discriminant Validity (HTMT).

  Construct	BI	EE	FC	IQ	IS	PE	SQ	UB
  BI
  EE	0.636
  FC	0.663	0.762
  IQ	0.616	0.526	0.465
  IS	0.652	0.628	0.542	0.459
  PE	0.596	0.773	0.580	0.648	0.626
  SQ	0.606	0.811	0.564	0.772	0.549	0.767
  UB	0.420	0.567	0.368	0.232	0.306	0.335	0.547

  shows the discriminant validity of each construct. All constructs were demonstrated to be reliable and valid. Therefore, we could proceed to assess the structural model.

The R-adjusted squares of Social Influence, Behavioral Intention, and Use Behavior were 0.231, 0.549 and 0.136, which indicates that 13.6% to 54.9% of the variances were explained. The Q-square is used to assess predictive relevance. All three Q-square values ranged from 0.071 to 0.482, indicating that the model had a small to large predictive relevance.

The path coefficients and t-statistics were evaluated by conducting a bootstrap analysis (with 5000 subsamples and 113 cases). Figure 5 shows the PLS model result. The analysis showed that all the proposed relationships were significant.

Table 4. Results of Full Mediation Hypotheses Testing.

Hypothesis	Item	Path Coefficient	t-Value	p-Value	Result
Hypotheses 1, 9 & 10	FC >> IS >> BI >> UB	0.033	1.705	0.088 +	Supported
Hypothesis 2	FC >> UB	0.154	1.190	0.234	Unsupported
Hypothesis 3	FC >> BI	0.267	2.641	0.008 **	Supported
Hypothesis 4	IS >> UB	0.042	0.417	0.677	Unsupported

(Bootstrap samples = 5000, n = 113 cases); + p < 0.1; ** p < 0.01.

shows the results of hypothesis testing.

5.2. Hypothesis Testing

Hypotheses six and eight were unsupported. Effort efficiency was not associated with behavioral intention. Hypothesis five was not supported. This might have be due to the fact that the effect from social influence was so strong. Instead, hypothesis seven was supported (

Table 5. Results of other Hypotheses Testing.

Hypothesis	Item	Path Coefficient	t-Value	p-Value	Result
Hypothesis 5	EE >> BI	0.120	0.963	0.336	Unsupported
Hypothesis 6	PE >> BI	0.035	0.342	0.732	Unsupported
Hypothesis 7	IQ >> BI	0.275	3.055	0.002 **	Supported
Hypothesis 8	SQ >> BI	0.002	0.017	0.987	Unsupported

(Bootstrap samples = 5000, n = 113 cases) ** p < 0.01.

).

6. Discussion

Facilitating Conditions, like a fast Wi-Fi speed, computing facilities, and mobile-friendly software, could lead to the intention of student-to-student interaction on an online platform. The findings of Hypothesis 3 of our study concurs with the previous result of researchers [9]. Given that the target respondents come from Hong Kong, a metropolitan city with good infrastructure, students are generally equipped with compatible hardware and software skills [24]. Thus, those “Facilitating Conditions” might not affect student-to-student interaction behavior (Hypothesis 2). The online platform system quality also cannot affect the intention of student-to-student interaction (Hypothesis 8). This is because students could use other means, like mobile phone applications and social media networking sites, to connect to each other. Instead, information quality can affect student-interaction intention (Hypothesis 7). If there is a new piece of information, like a model answer is uploaded by instructor, students are likely to share this piece of information to their classmates. Thus, quality information is a real driver of the intention of student-to-student interaction.

Our findings echo the results obtained from researchers [3]. We extend their result to the student-to-student interaction of online learning platforms. The influence from other people, like family members and peers, was found to be significant for the decision of young people. The findings of Hypothesis 1 of our study concur with the previous result of researchers [11]. Social influence is one of the mediators between the Facilitating Conditions and the student-to-student interaction behavior of online learning platforms. Social influence cannot affect student interaction behavior directly (Hypothesis 4), and a quasi-full mediation model was established.

The online platform is mature today, and students are used to making use of the platform regularly. Thus, the ease of using the platform (Hypothesis 5) and usefulness of the platform (Hypothesis 6) are not the determining factors affecting the intention of student-to-student interaction. Students’ interaction is affected by other factors, like social influence [11]. In summary, Hypotheses 1, 3, 7, 9 and 10 were supported, and Hypotheses 2, 4, 5, 6, and 8 were not supported.

For parallel multiple mediation, a decision tree for single mediation can be applied [12,14]. Regarding serial multiple mediation, one more complication has to be considered. There are partial direct effects. For two mediators run in series, there are two partial direct effects in addition to one full direct effect. In our example, the Facilitating Conditions of Behavioral Intention and Social Influence of Use Behavior were two partial direct effects. We have to test whether these two effects were significant not or not before reaching the true full mediation. Otherwise, a multiple mediation may be named as a quasi-full mediation instead (See Figure 6).

Regarding the indirect effect from the Facilitating Conditions to the Use Behavior via Social Influence and Behavioral Intention, the p-value was found to be significant.

FC >> IS >> IB >> UB, t = 1.705, p = 0.088 < 0.1

Social Influence (IS) and Behavioral Intention (IB) were two mediators in a series manner.

There are three conditions for the true full mediation that we need to check.

First, we examine the effect of the initial independent variable on the final dependent variable, i.e., from the Facilitating Condition to the Use Behavior, directly. From facilitating conditions to Use behavior, FC >> UB, the p-value was 0.234, which is not significant (Table 4).

Second, we have to check the mid-way through effects. From the facilitating conditions to the behavioral intention, FC >> BI, the p-value was 0.008, which is significant. Finally, we need to check social influence to use behavior, IS >> UB. From IS >> UB, the p-value was 0.677, which is not significant.

Here, a quasi-full mediation is proposed, as the direct effect of FC >> UB was not significant, and FC >> BI was significant. Hypotheses one, nine and ten were supported, and hypotheses two and four were not supported, but hypothesis three was supported (Table 4).

It is worth mentioning that social influence and behavioral intention are subjective perceptions, while facilitation conditions and use behavior measure objective actions. Both the starting and ending constructs are visible variables. The two mediators are invisible constructs.

Facilitating conditions do not associate with use behavior directly but via social influence and behavioral intention. The resulting effect is significant at a p-value of less than 0.1, as the effect becomes weaker as it passes through more than one mediator.

7. Theoretical and Practical Implications

This study provides a new typology for serial mediation. Previously, there has been no distinction between a full serial mediation and a quasi-full serial mediation [25]. We propose that in order to be a full serial mediation, all of the partial direct effects should be non-significant. Otherwise, it should be a quasi-full serial mediation, as illustrated in the study.

The study provides an explanation of why facilitating conditions do not associate with use behavior in the UTAUT framework. Instead, Facilitating conditions, through social influence, affect the behavioral intention of student-to-student interaction. Furthermore, the underlying mechanism between facilitating conditions and student-to-student interaction behavior was uncovered. It is through the subjective perception of social influence and the behavioral intention of student-to-student interaction.

There are two implications for the design of e-learning platforms. First, the design of this technology should support the learning processes but not interfere with it. Second, computer technologies can have an influence on how young people think, study, and learn. The effectiveness of the design of the technology can be assessed by how it promotes young people to think, study, and learn [26]. A fast network, computing facilities, and mobile-friendly software are candidates for the virtual environment for student-to-student interaction on online platforms.

8. Conclusions

A new typology for serial mediation was proposed. The underlying mechanism between facilitating conditions and student-to-student interaction was uncovered (Hypotheses 1, 9 and 10).

Online learning has been developing for decades, and there have been some successes for internal (learning and teaching) and external (pandemic) reasons. Students not only need to communicate with the teacher, but also with their fellow classmates. Student interaction behavior is not affected by Facilitating Conditions and Social Influence (Hypotheses 2 and 4). Facilitating Conditions, Social Influence, and Information Quality are important factors affecting student-to-student interaction through online learning platform (Hypotheses 3 and 7). It enables learning to happen anytime and anywhere. The intention of student interaction behavior is not affected by Effort Expectancy, Performance Expectancy, and System Quality (Hypotheses 5, 6, and 8). It was found that students’ participation rates in discussion forums varied a lot, and there were a lot of other communication tools, such as social media. Collaborative learning through student-to-student interaction and teacher-to-student interaction is also important. What college management should do is provide suitable infrastructure or an environment for students and teachers.

Researcher have time and money restrictions. Considerable information can be obtained from a population within certain time-period by the survey method [25]. The use of surveys assumes that respondents have enough recall of events and report this information accurately and completely. Other disadvantages of using the survey method are the unwillingness of respondents to provide the desired data and the ability of respondents to provide data. Furthermore, some respondents might be unwilling to tell the truth and answer personal questions [25].

The two major research designs in survey research are cross-sectional and longitudinal designs. In a cross-sectional design, a representative sample of the population is addressed at a specific moment. It is very useful for describing the status quo in a segment of the population, as in this study. However, the result of a cross-sectional survey may have a limited useful shelf-life, as variables change over time. Furthermore, it has limited application in examining the cause-and-effect relationship [26].

A longitudinal study is designed to observe and study phenomena over time. It allows researchers to track changes within particular individuals. A cause-and-effect study can be easily carried out, since it is generally agreed that cause must precede effect. However, it takes time and resources to conduct a large sample study, and lengthy studies suffer the problem of attrition. Attrition occurs when people drop out of a study before it has finished [26]. People with extreme scores have a natural tendency to become less extreme over time. A longitudinal design could be used in future studies because there is time gap between the intention of students’ interaction and students’ interaction behavior [26].