Social support, computer self-efficacy, online learning engagement and satisfaction among undergraduate hospitality students

Abstract

Following the outbreak of COVID-19, the adoption of online teaching and learning in universities has witnessed phenomenal growth. Importantly, online learning engagement and satisfaction have consequences on student learning outcomes and behavioural intentions. However, a model that integrates social support, computer/internet self-efficacy and students’ behavioral and cognitive outcomes in a virtual learning context has received limited scholarly attention. Consequently, this article examined the effect of social support and computer self-efficacy beliefs on students’ online learning engagement and satisfaction. Data were solicited from a convenient sample of 260 hospitality students at a Ghanaian technical university. The research model was verified with partial least square structural equation modeling. Findings indicate that family support had a strong positive effect on students’ computer/internet self-efficacy and their online communication self-efficacy. Friend support also had a positive effect on students’ computer/internet self-efficacy but not on their online communication self-efficacy. Additionally, student-teacher interaction is positively related to students’ online learning engagement and satisfaction. This finding emphasises the importance of quality interaction between students and their teachers in the latter’s online learning engagement. The implications of these results are that management of universities needs to enhance the online interactive competencies and skills of lecturers. In addition, managers of universities need to enhance the online teaching and learning pedagogy of faculty.

Keywords:

• Social support
• computer
• self-efficacy
• online learning engagement
• satisfaction and hospitality students

Review Editor:

• Darwin Stephen, Universidad Alberto Hurtado, Chile

Subjects:

• Information & Communication Technology (ICT)
• Educational Psychology
• Higher Education
• Hospitality

Introduction

The advent and spread of the novel coronavirus (COVID-19) that resulted in the closure of schools at the peak of the epidemic in 2020 reignited academic and policy discourse on online teaching and learning worldwide. In particular, universities in Sub-Saharan Africa, including Ghana, which had hitherto relied solely on in-person mode of instruction, were challenged to move to online teaching and learning platforms. The spontaneous deployment of virtual teaching and learning amidst inadequate online teaching competencies and skills of both teachers and students (Bon, 2018; Srichanyachon, 2014), poor internet connectivity, and high cost of data (Henaku, 2020; Research ICT Africa, 2012), and limited access to Information, Communication and Technology (ICT) resources (Adarkwah, 2021) raised concerns about student online learning engagement and satisfaction with consequences on learning outcomes. Online teaching and learning have been present in higher education institutions for about two decades (Singh & Thurman, 2019). However, its adoption within the higher education ecosystem worldwide has grown exponentially after the COVID-19 pandemic, generating substantial post-COVID-19 virtual learning research interest (Abdullah & Kauser, 2023; Adedoyin & Soykan, 2023; Amin et al., 2022; Greaves, 2024; Güllü et al., 2024; Slack & Priestley, 2023; Wong, 2023).

Though in-person instruction has returned to almost pre-pandemic levels, it does appear that online teaching and learning remains appealing to both students and education providers, given the opportunities it provides to both universities and students. In the post-COVID-19 Ghanaian higher education sector, a number of universities are still delivering instruction and assessment through online learning and teaching platforms. The interest in online learning is demonstrated by continuous research on the post-pandemic online learning perspectives of students (Alarabiat et al., 2023; Farrukh et al., 2023; Marandu et al., 2023). Nevertheless, poorly executed online learning programmes are likely to negatively affect overall student learning achievements and experiences. It is, therefore, imperative to continue to examine the behavioural and cognitive attitudes of students towards online learning and teaching.

Online learning engagement is the extent to which students enthusiastically take part in learning activities in an online environment (Heflin et al., 2017). Following the perspectives of Abbas (2017), online learning engagement in this study is defined as students’ active participation in online learning activities facilitated by an e-learning platform. In a virtual environment, students’ engagement remains critical given the physical and psychological distance between students and teachers. According to Tao et al. (2022), engagement influences student academic achievement and retention (Banna et al., 2015; Spitzig & Renner, 2022; Tight, 2020). Indeed, another important factor in the online learning environment with consequences for student learning outcomes is satisfaction, which is described as the cognitive attitude and emotional feeling that results from students’ overall evaluation of their experiences at an educational institution (Elliot & Healy, 2001). Satisfaction affects students’ behavioral intentions, including positive words of advertisement (Kazungu & Kubenea, 2023), intention to re-enrol (Rehman et al., 2022), loyalty (Chandra et al., 2018; Kankhuni et al., 2023) and their willingness to recommend the university (Gabbianelli & Pencarelli, 2023; Padlee & Reimers, 2015). Importantly, online learning satisfaction influences learning motivation, academic success, and commitment to a learning programme (Kuo et al., 2013).

Several factors influence student online learning engagement and satisfaction. Drawing from social constructivism theory, student-teacher interaction (STI) is reported to determine student online learning engagement (Nguyen et al., 2018) and satisfaction (Baber, 2022; Croxton, 2014). Again, based on the self-efficacy theory (Bandura, 1997), self-beliefs in ICT that include computers, internet, and online communication (Habib et al., 2020) are likely to affect students’ online learning engagement. Computer/internet self-efficacy (CIS) and online communication self-efficacy (OCS) highlight beliefs in one’s competencies to use computers, surf the internet, and communicate with others using messaging applications (Punjani & Mahadevan, 2022). Meanwhile, grounded in the self-efficacy theory, social support is an important resource that enhances students’ self-beliefs in computer/internet and online communication (Bandura, 1997). Social support is the process of social exchange that enhances individuals’ behavioural patterns, social cognitions, and values (Farmer & Farmer, 1996) obtained from numerous sources, including family members and friends. Family support manifests in the sharing of knowledge on internet usage as well as the sharing of ICT gadgets, while support from friends influences proficiency strategies and inspires each other to reduce anxiety and uncertainties (Hsiao et al., 2012).

Following the sudden and immediate transition of hospitality and tourism students to virtual teaching and learning platforms, which possibly posed varied forms of mental health concerns (Chiu, 2022), the critical question of how social support and computer/internet self-efficacy significantly enhance student online learning engagement and satisfaction remains relatively unexplored. Recent online teaching and learning experiences have generated research interest in specific areas, including instructional approaches for online teaching (Bao, 2020), educational resources, practices, and policies of online teaching services (Zhang et al., 2020), and the influence of COVID-19 restrictions on students’ learning experiences (Sánchez Amate et al., 2021), among others. Despite these research efforts, current empirical data suggests that little attention has been paid to the factors that influence student engagement and satisfaction with online learning (Agyeiwaah et al., 2022). Importantly, for institutions to be efficacious in promoting educational processes, it is imperative to measure the online learning satisfaction and engagement levels of students (Cheon et al., 2020; Latip Newaz & Ramasamy, 2020). Only a few studies have examined students’ online learning engagement and satisfaction (Cakir, 2014; Chiu, 2022; Yousaf et al., 2023) and more so among hospitality samples (Agyeiwaah et al., 2022; Shyju et al., 2021) in which the emphasis on theory and practice continues to be a crucial part of students’ learning engagement and satisfaction (Wang et al., 2015). In addition, the first-time adoption of virtual teaching and learning in response to COVID-19 critically raises the need for research from both the perspectives of theory and practice.

As a result, this article integrates social support and ICT self-efficacy imperatives to explain students’ online learning engagement and satisfaction within the context of hospitality and tourism education and, more importantly, from a developing country’s perspective, with the self-efficacy theory serving as the overarching theoretical framework for the study. This article, therefore, aims to examine the influence of social support and self-efficacy beliefs on online learning engagement and satisfaction of hospitality students (Figure 1). Specifically, this article sought to answer the following research questions: (1) what is the effect of social support on CIS and OCS?; (2) what is the influence of CIS and OCS on students’ online learning engagement?; (3) what is the association between student-teacher interaction and online learning engagement and satisfaction? and (4) what is the link between online learning engagement and student satisfaction?

Figure 1. Conceptual framework.

It is worth noting that the transition to online teaching and learning among universities all over the world has necessitated the need to clarify the mechanisms by which social resources support ICT self-efficacy perception of hospitality students in a developing country, with ripple effects on their online learning engagement. In an online learning environment, teacher–student interaction is critical because of the increasing adoption of online teaching and learning and its significance in higher education delivery in the post-COVID-19 era. It is, therefore, vital to investigate how social support will affect the digital/ICT-related self-efficacy of students in a developing country. For students who are used to the face-to-face classroom mode of instruction, social support from family and friends becomes an extremely useful resource to help build ICT self-efficacy while remotely located at home and away from their teachers. It is, therefore, important to enhance comprehension of the contribution of social support in the framework of virtual learning during and after the COVID-19 era. This study, therefore, has both practical and theoretical implications for managers of higher education institutions across the world. With a deeper insight into the contribution of lecturer–student interaction to enhancing student learning engagement and satisfaction in a virtual learning context, managers of universities are in a better position to build the interactive and social competencies of lecturers in a virtual context. Understanding the state and the determinants of student online learning engagement is critical to ensuring a successful implementation of online instruction during and after the COVID-19 era. Following the introduction, the theoretical foundation of the relationships examined in the study is presented, followed by an empirical review and the methodology of the study. The article then presents the results and a discussion of same as well as the theoretical and practical implications of the results. Finally, the article ends with a discussion of limitations, directions for future research, and a conclusion.

Theoretical framework

This study was underlined by self-efficacy theory (Bandura, 1997) and social support theory (Berkman et al., 2000). The self-efficacy theory contends that a person’s belief in his/her capabilities is a function of the collective interplay of four factors. These include mastery experience, vicarious experience, physiological and affective state, and social persuasion. Mastery experience is confidence derived from prior success in participating in an activity. Consequently, students who have accomplished tasks using the computer and the internet are more likely to indicate positive computer/internet and online communication self-efficacy and will confidently participate in online learning.

Second, vicarious experience is the efficacy acquired from the success or failure of others who have engaged in similar endeavours. Hence, the failure or otherwise of friends and family members to use computers and internet applications has an implied effect on assumed self-efficacy. Third, the physiological and affective state focuses on people’s interpretations of their emotions, which is symbolic of either success or failure. Central to this discourse is social persuasion, which emphasizes inspirations gained from members of society to acquire skills and inject efforts needed to withstand challenges and pursue intended assignments (Bandura, 1997).

The social constructionism theory perceives learning as a social phenomenon jointly developed with and guided by members of society (Guan & So, 2016; Purzer, 2011). Additionally, social cognitive theory considers cognitive development as a social process promoted by support in the social environment (Bandura, 2001). According to Woo and Reeves (2007), diverse and targeted interactions in the social environment support learning. However, in the absence of face-to-face interactions between teachers and learners, communication with other people in the social environment aids the learning process. Learning challenges are overcome through the help and care from adults and peers in the social environment (Stage et al., 1998). Hence, as put forward by the social network theory, the collaboration between an individual and others, primarily family and friends, affects goal achievement (Jin, 2001). A positive and adequate social persuasion transforms negative mastery experiences, and vicarious, physiological, and affective states by injecting energy and providing assistance to succeed. Social impacts improve self-efficacy tendencies which enhance engagement and performance (Alinejad-Naeini et al., 2021; Bandura, 1995; Wang et al., 2015).

Notwithstanding, the impact, extent, content, and type, social support are reinforced by existing patterns and interactions relative to each society. The interconnection and collaboration between individuals and others in society are construed by existing practices of the local community, especially the family (Woo & Reeves, 2007). Hence, support from family and friends predicts computer/internet/and online communication self-efficacy and the capability to participate and succeed in online learning, in the physical absence of a teacher.

Literature review and hypothesis development

Social support and ICT self-efficacy beliefs

The effect of family social support on computer/internet self-efficacy has been empirically examined in several studies (Gao et al., 2021; Hsiao et al., 2012; Wahyu Saefudin et al., 2021) with findings suggesting family support as the strongest predictor of computer/internet self-efficacy. Social support provided by family members (partners and parents) and others in an educational setting (teachers and peers) determines the success of students enrolled in online learning programs (Taplin & Jegede, 2001). Similarly, it is expected that support provided by family members would enhance students’ perception of online communication competencies. Continuous interaction between students and family members contributes significantly to students’ online learning potential (Mudrák et al., 2020).

The contribution of friends/peers to both formal and informal learning processes is supported in the literature (Brouwer et al., 2022; Deechuay et al., 2016; Singh & Mishra, 2022). Within social interaction theory, learner–learner interaction affects computer and academic self-efficacy (Lim et al., 2016). In a study involving high school students, perceived peer support had a significant and positive correlation with general computer self-efficacy and advanced computer self-efficacy (Hsiao et al., 2012). Among vocational high school students, peer support had a greater influence on students’ internet self-efficacy (Kung et al., 2012). Following the same argument and empirical evidence, peer support is expected to contribute to the online communication self-efficacy perceptions of students. Based on the aforementioned empirical evidence and theoretical perspectives, the following hypotheses were stated:

H₁:

Family support will relate positively to computer/internet self-efficacy

H₂:

Family support will relate positively to online communication self-efficacy

H₃:

Friend support will relate positively to computer/internet self-efficacy

H₄:

Friend support will relate positively to online communication self-efficacy

Computer/internet self-efficacy and online communication self-efficacy

According to Trang and Thang (2023), online communication involves exchanging information and ideas through electronic communication technologies, such as the internet, social media platforms, and messaging applications. Online communication self-efficacy measures one’s confidence to engage with others through electronic communication (Yasin, Ong, & Ab Aziz, 2020). Specific empirical support for the relationship between computer/internet self-efficacy and online communication self-efficacy is scarce. Consequently, related literature is relied on to develop the hypothesis. In a study of undergraduate students selected from universities in Southwestern China, internet self-efficacy predicted social media networking use (Wang et al., 2015). Given that online communication to a very large extent depends on one’s proficiency in the use of ICT gadgets, it is expected that self-beliefs in the use of computers and the internet would influence a person’s confidence to engage with others through electronic communication. Following the above discussion, it is hypothesized that:

H₅:

There is a significant positive relationship between internet/computer self-efficacy and online communication self-efficacy.

ICT self-efficacy beliefs and online learning engagement

Based on social cognitive theory (Bandura, 2001), self-efficacy in a variety of situations is related to students’ learning engagement (Chen, 2017; Jung & Lee, 2018; Kuo et al., 2021; Sun & Rueda, 2012). In a study by Jung and Lee (2018), academic self-efficacy positively affected learning engagement in the massive open online courses (MOOC) context. Among students aged 55 years and older, computer self-efficacy was positively related to learning engagement (Chen, 2017). Using respondents selected from MOOC platforms in Taiwan, learners’ web-based learning efficacy had a positive direct effect on online learning engagement in MOOC settings (Kuo et al., 2021). Stan et al. (2022) indicated that engagement in online learning increases with an increasing value of online self-efficacy perceptions among university students in Romania. In a related study, online communication self-efficacy positively influences students’ readiness to engage in blended learning (Yasin, Ong, & Utmspace, 2020). Contrary to the afore-discussed works, in the study of Sun and Rueda (2012), computer self-efficacy was not a significant predictor of behavioural engagement, emotional engagement and cognitive engagement. Due to the above empirical review, it was hypothesized that:

H₆:

Internet/computer self-efficacy will relate positively to online learning engagement

H₇:

Online communication self-efficacy will relate positively to online learning engagement of students

Student–teacher interaction and online learning engagement

The effectiveness of student–teacher interactions affect the learning outcomes of students. The academic and social development of students are related to student and faculty interactions (Roberts, 2014), learning achievement (Sun et al., 2022), and student learning motivation (Liu & Chiang, 2019). There is adequate empirical evidence supporting the influence of student–teacher interactions on students’ learning engagement (Chhetri & Baniya, 2022; Miao et al., 2022; Xie et al., 2023). For example, in a study aimed at promoting learning engagement in an online environment among undergraduate students in Chinese public universities, teacher–student interaction directly affected student online learning engagement (Miao et al., 2022). Among a Latino youth population, various dimensions of student–teacher interactions were positively associated with student learning engagement (Li, 2018). Based on the foregoing, it is therefore, hypothesized that:

H₈:

Student–teacher interaction will positively relate to student-online learning engagement

Student–teacher interaction and online learning satisfaction

The success of online learning and teaching programmes depends on student online learning satisfaction. Engagement and academic performance are associated with student online learning satisfaction (Croxton, 2014; Meyer, 2014). The determinants of online learning satisfaction have been empirically examined in many studies. Among the several determinants, student-teacher interaction has received empirical validation as a significant factor in online learning satisfaction (Alqurashi, 2019; Baber, 2020; Kuo et al., 2013; She et al., 2021; Xu et al., 2017). Based on online survey data collected from graduate and undergraduate students in a college of education, Kuo et al. (2013) found learner–instructor interaction as a significant predictor of online learning satisfaction. Based on the foregoing, it was hypothesized that:

H₉:

Student–teacher interaction will have a positive effect on online learning satisfaction

Online learning engagement and online learning satisfaction

Another important factor that affects online learning satisfaction is student engagement (Baloran & Hernan, 2021; Cheng & Chau, 2016; El-Sayad et al., 2021; Gray & DiLoreto, 2016; Kim & Kim, 2021; Zhang, 2022). Using a partial least squares structural equation model to analyze survey responses from 270 tourism management students from two universities in China, emotional engagement affected course satisfaction in an online course (Zhang, 2022). In an African study, results from online survey data solicited from online undergraduate students who experienced full online teaching and learning during the COVID-19 pandemic in Egypt, student satisfaction was significantly and directly influenced by behavioural engagement and emotional engagement (El-Sayad et al., 2021). Following the foregoing empirical evidence, it was hypothesized that:

H₁₀:

Online learning engagement will positively relate to online learning satisfaction.

Methodology

Study setting

The data for the study were collected at a technical university in Ho in the Volta region of Ghana with a student population of 4900 as of March 2020. The University launched an online learning management system (LMS) to facilitate teaching and learning following the closure of schools in the country after cases of coronavirus were recorded in March 2020. Before the closure, teaching and learning were carried out solely through an in-person classroom mode of delivery without the infrastructure for online teaching and learning. The programmes at the university include Engineering, Business and Management, Agriculture, Mathematics and Computer Science, and Hospitality and Tourism Management, with a population of about 350 students.

Sampling

Out of a population of 350 hospitality students, a convenient sample of 260 students responded to the surveys. On the likelihood of meeting a substantial number of the targeted hospitality students in the classrooms, convenience sampling was considered suitable. Using Krejcie and Morgan (1970), with a population of 350, a sample size of 260 was determined to be beyond the recommended sample size of 188. Hospitality students were selected for the current study because previous online teaching and learning research has paid less attention to this population. Meanwhile, given that the hospitality programme at the university from which the respondents were selected is vocational in orientation with intense practical courses, investigating how the students were engaged and satisfied with their online learning experience will add different perspectives to the student online teaching and learning literature. Data for the study were collected in August 2020, when students returned to campus to complete the suspended academic calendar after restrictions were lifted for students to return to the university to complete and write end-of-semester examinations. In view of the strict adherence to the COVID-19 measures at the university, it was considered safe to use paper-and-pencil questionnaires for the data collection. The authors of the study contributed to the data collection exercise that took place in classrooms. Students were assured of their voluntary participation, confidentiality, and anonymity. Students were asked to drop completed questionnaires in a box placed at the classroom entrance. This approach was adopted to ensure that students did not feel compelled to participate in the surveys because their lecturers facilitated the data collection. As of the time of data collection, only sophomores and third-years participated in the study.

Research instrument

Information on the measured constructs and their respective sources is provided in Table 1. All the items of the investigated constructs were measured on a five-point Likert scale anchored on strongly agree = 5 to strongly disagree =1.

Table 1. Items drawn from literature and modified for this study.

Construct	Source
Social support
Family social support Friend social support	Zimet et al. (1988)
Internet/computer self-efficacy
Online communication self-efficacy	Hung et al. (2010)
Student–teacher interaction	Mbwesa (2014)
Online learning engagement	Diemer et al. (2012)
Online learning satisfaction	Artino (2007)

Data analysis

The analysis of the study’s data took place in two primary steps. The first step involved entering and processing data using the Statistical Package for Social Sciences (SPSS). Descriptive statistics were used to assess the respondents’ demographic profile by means of SPSS version 21.0. The study model was examined in the second stage using Smart Partial Least Squares Structural Equation Modelling (PLS-SEM) version 3.0. In the second stage, there were two steps in the data analysis. The first phase involved evaluating the measurement model to determine its superiority in measuring various indices using estimates from the PLS-SEM algorithm. The structural model was evaluated in the second stage using bootstrapping with 5000 resamples.

Results

Profile of respondents

In terms the respondents’ gender, a greater proportion of the study participants were female (90.6%) and largely aged between 20 and 25 years old. A majority of the respondents were unmarried (96.2%). About 64.4% of the students were in their second year, while third/final year students constituted 35.6% of the sample.

Measurement model

In accordance with Hair et al.'s (2014) recommendations, the measurement model was initially evaluated to ascertain its validity and reliability. Factor loadings, Cronbach’s alpha, and composite reliability indices were estimated to evaluate the measurement model’s internal consistency dependability. All of the measuring items for the constructs evaluated in the study met the minimal threshold value of 0.70, as shown in Table 2. demonstrating that each construct explains more than 50% of the variance in the indicator, resulting in adequate item dependability (Hair et al., 2019). As shown in Table 2, the composite reliability indices for all the constructs are over 0.70, indicating the internal consistency and reliability of the measurement model. Next, the composite reliability scores of the constructs were checked.

Table 2. Factor loading and reliability scores of constructs.

Constructs and items	Factor loadings	Composite reliability	Cronbach’s alpha	Average variance explained
Family support (FaS)		0.909	0.867	0.714
FaS1	0.844
FaS2	0.908
FaS3	0.774
FaS4	0.848
Friend support (FrS)		0.871	0.805	0.628
FrS1	0.818
FrS2	0.737
FrS3	0.775
FrS4	0.835
Computer/internet self-efficacy (CIS)		0.806	0.701	0.581
CIS1	0.783
CIS2	0.727
CIS3	0.774
Online communication self-efficacy (OCS)		0835	0.703	0.628
OCS1	0.802
OCS2	0.834
OCS3	0.739
Student–teacher interaction (STI)		0.858	0.783	0.603
STI1	0.773
STI2	0.853
STI3	0.830
Online learning engagement (OLE)		0.844	0.754	0.577
OLE1	0.814
OLE2	0.806
OLE3	0.854
Online learning satisfaction (OLS)		0.858	0.794	0.830
OLS1	0.906
OLS2	0.916

Another internal consistency reliability statistic that was assessed was Cronbach’s Alpha. Similar to factor loadings and composite reliability indices, a Cronbach’s Alpha of 0.70 is regarded as a sufficient indicator of the internal consistency dependability of the measuring items. The internal consistency reliability of the measuring model was demonstrated by the fact that all of the Cronbach’s Alpha indices for the six constructs assessed in the study were greater than the threshold value of 0.70.

Once the measuring model’s internal consistency reliability was established, the convergent validity of each of the seven constructs was assessed. Convergent validity, according to Hair et al. (2019), refers to how well a construct converges to explain the variance of its measurement items. The Average Variance Extracted (AVE) method was used to evaluate this. All seven constructs met the AVE cut-off of 0.50, indicating that they each accounted for at least 50% of the variance in the measurement items. (Table 2).

Each construct’s empirical distinction from other constructs in the structural model is determined by the discriminant validity stage of the measurement model evaluation (Hair et al., 2019). Using the Fornell-Larcker criterion and the heterotrait–monotrait ratio, the distinctiveness of each of the seven constructs tested in the study was assessed. Typically, an AVE's square root for a construct should be higher than the correlation between constructs’ squared values. (Table 3). The greatest values in any column or row in Table 3's diagonal figures, which represent the square root of the construct’s average variance extracted (AVE), show the measurement model’s discriminant validity. For instance, the correlation coefficients between FaS and the other constructs are lower than the AVE of FaS (0.845), which is greater. (Table 3).

Table 3. Discriminant validity (Fornell–Larcker and Heterotrait–Monotrait ratio criteria).

	Fornell–larcker criterion							Heterotrait–monotrait ratio criterion
Construct	FaS	FrS	CIS	OCS	STI	OLE	OLS	FaS	FrS	CIS	OCS	STI	OLE	OLS
FaS	0.845
FrS	0.417	0.792						0.490
CIS	0.340	0.317	0.762					0.437	0.436
OCS	0.437	0.327	0.562	0.792				0.555	0.420	0.795
STI	0.147	0.184	0.268	0.308	0.777			0.197	0.249	0.375	0.427
OLE	0.118	0.158	0.243	0.356	0.474	0.760		0.146	0.211	0.346	0.478	0.587
OLS	0.154	0.143	0.190	0.195	0.452	0.557	0.911	0.190	0.187	0.273	0.261	0.556	0.706

The diagonal figures are the square root of the AVE of the constructs and indicate the highest in any column or row. HTMT was employed in addition to the Fornell-Larcker criterion to evaluate the measure model’s discriminant validity (Table 3). According to Henseler et al. (2015), this metric’s acceptable cut-off value is 0.85 or less. Based on the HTMT values listed in Table 4, which are much below the threshold value of 0.85, there was no issue with discriminant validity (Henseler et al., 2015). This demonstrates that the seven constructs examined in the article were empirically distinct.

Table 4. Hypothesis assessment and effect sizes.

Hypothesized path	Path co-efficient	T-statistics	P-values	F2 (effect size)	(Q2	R2	VIF values
FaS->CIS	0.252	3.753	.000	0.062			1.210
FaS->OCS	0.250	4.442	.000	0.080			1.285
FrS->CIS	0.211	3.039	.002	0.044			1.210
FrS->OCS	0.079	1.477	.140	0.008			1.263
CIS->OCS	0.453	8.630	.000	0.282			1.180
CIS->OLE	0.029	0.424	.672	0.000			1.482
OCS->OLE	0.225	3.239	.001	0.047			1.520
STI->OLE	0.376	5.984	.000	0.198			1.121
STI->OLS	0.274	4.324	.000	0.071			1.289
OLE->OLS	0.420	7.260	.000	0.236			1.289
CIS					0.080	0.146
OCS					0.225	0.382
OLS					0.272	0.265
OLE					0.136	0.351

Estimated model fit indices: SRMR = 0.080; d_ULS = 1.939; d_G = 0.519; X²=783.719; NFI = 0.700.

Assessment of the structural model

After confirming the appropriateness of the measurement model, the structural model was assessed. The coefficient of determination (R²), the blindfolding-based cross-validated redundancy measure (Q2), and the overall supremacy of the structural model were examined, together with the statistical significance and relevance of the path coefficients (Hair et al., 2019). Collinearity was assessed using the Variance Inflation Factor (VIF) prior to the structural model to ensure that the regression results were not biased. The ideal VIF values ought to be close to 3 or lower (Hair et al., 2019). Table 4's display of the constructs’ VIF values reveals that collinearity was not a problem because they are all much less than 3. The construct’s R², which measures the model’s explanatory capacity, was evaluated given that collinearity was not an issue (Shmueli & Koppius, 2011). The R² has a range of 0 to 1, and higher R² values indicate a model with greater explanatory power (Hair et al., 2019). As indicated in Table 4, the R² for online learning engagement was 0.351 meaning 35% variance in online learning engagement is explained by the variables in the model, while 26% variance in online learning satisfaction was explained by the constructs in the model.

Evaluation of structural model

Findings of the path relationships examined in the study are shown in Table 4. The results confirmed hypothesis 1 (β = 0.252; t = 3.753; p = .000). Demonstrating the effect of family support on the confidence of students in the use of computers and the internet. Students’ self-efficacy in online communication was also positively influenced by family support (β = 0.250; t = 4.42; p = .000), supporting hypothesis 2. The results provide support for a positive link between the support received from friends and its impact on computer/internet self-efficacy (β = 0.211; t = 3.039; p = .002), just as it was previously documented in the case of family support and computer/internet self-efficacy, in support of hypothesis 3. Contrary to expectations, the study’s findings did not support the association between friend support and hospitality students’ online communication self-efficacy (β = 0.079; t = 1.477; p = .140), thereby rejecting hypothesis 4.

Online communication self-efficacy and computer/internet self-efficacy had a positive and statistically significant association, supporting hypothesis 5. The study’s findings imply that as students’ self-belief in computer/internet increases, so does their online communication self-efficacy. Contrary to expectations, the study’s findings did not support the anticipated association between computer/internet self-efficacy and online learning engagement, thereby rejecting hypothesis 6 (β = 0.029; t = 0.424; p = .672). Hypothesis 7 is supported by the statistically significant path relationship between online communication self-efficacy and online learning engagement (β = 0.225; t = 3.239; p = .001). In support of hypotheses 8 and 9, student–teacher interaction positively correlated with online learning engagement and satisfaction among hospitality students (β = 0.376; t = 5.984; p = .000). In confirmation of hypothesis 10, there was a positive and statistically significant relationship between online learning engagement and satisfaction (β = 0.420; t = 7.260; p = .000).

Discussion

This study investigated the effect of social support on the ICT self-beliefs of hospitality students. The effect of students’ self-perceptions of computers, the internet, and online communication on their engagement in online learning was also assessed. The study explored the association between online learning satisfaction and hospitality students’ engagement in online learning context. The study’s findings support the conclusions of prior studies (Gao et al., 2021; Wahyu Saefudin et al., 2021), which found that friends and family had an impact on students’ computer/internet self-efficacy. The observed effects of support provided by family members and friends on the computer, internet, and online communication self-beliefs of students in the present study could be explained by the self-efficacy theory, where social support is an important resource that enhances students’ self-beliefs in any learning situation (Bandura, 1997). Social support in the form of tangible resources such as time, money, constructive feedback, a feeling of trust, advice, and suggestions emanating from family members and friends enhances the ICT self-efficacy beliefs of hospitality students.

The results of the study did not support the anticipated impact of students’ self-beliefs in computers and the internet on their online learning engagement, as stated in the work of Sun and Rueda (2012). This suggests that students’ belief in their ability to utilise computers and the internet did not have an impact on their decisions to actively engage in online learning management systems. However, the perception of self-efficacy in online communication was positively related to the online learning engagement of students, as reported in earlier works (Yasin, Ong, & Utmspace, 2020). Participation in any online learning management system requires competencies in online communication as students need to download, participate in quizzes, and submit assignments. It is, therefore, not surprising that online communication self-efficacy was considered a significant predictor of online learning engagement in the study.

Consistent with the results of earlier studies (Chhetri & Baniya, 2022; Miao et al., 2022; Xie et al., 2023), student–teacher interaction predicted the extent to which students would engage in online learning. This finding emphasises the importance of quality interaction between students and their teachers in the latter’s online learning engagement. This result implies that enhanced interaction between students and teachers encourages online learning engagement among students. Similarly, student-teacher interaction also enhances online learning satisfaction among students. Lastly, consistent with earlier studies (Baloran & Hernan, 2021; El-Sayad et al., 2021; Kim & Kim, 2021; Zhang, 2022) the results of the study confirmed a positive influence of learning engagement on online learning satisfaction. This means that students’ online learning satisfaction increases with online learning engagement among hospitality students.

Theoretically, this study tested a model integrating social support systems, self-efficacy perceptions, and cognitive perspectives of hospitality students in an online learning context. In so doing, the study clarifies the significance of social systems in the technology/ICT self-beliefs of students and its effects on virtual learning engagement and satisfaction. Self-efficacy perceptions in computer/internet and online communication incentivize students to engage in virtual learning platforms. Practically, the findings of the study mean that efforts should be directed at building and maintaining social networks among students, both at family and peer levels, to build ICT self-beliefs among students. In higher education institutions, events that enhance social interaction among students should be organized to help students forge beneficial friendships that would support their online learning engagement and satisfaction of students.

Limitations and future research

This article provides a new perspective on how social support, computer self-efficacy, and students’ engagement and satisfaction with online learning are related. Nevertheless, similar to every other study, this one does have certain limitations. To begin with, the study used standardized tools from previous research to measure the constructs. The study recognizes that measuring these constructs quantitatively has its limitations and advises that qualitative aspects be included in future research to represent the notions completely. The second shortcoming of the study relates to how broadly applicable the findings are given that respondents were selected from only one technical university in Ghana. Future studies should gather information from hospitality students at various educational institutions to guarantee validity from several angles and to further comprehend the construct. Third, it is advised that the random sampling method be used to replicate studies in other contexts, even though researchers in the management discipline frequently recommend the use of convenience sampling due to the quick access to data (Frankfort-Nacmias & Nachmias, 2008). Fourth, while social support was considered in this study as an independent variable standing alone, future research can increase the explanatory power of the proposed model by including additional components that could fully account for how computer self-efficacy influences students’ satisfaction with their online learning experiences. Finally, this study was restricted to Ghanaian hospitality students in a single university. Future research should examine hospitality students from various backgrounds and identities to gather comprehensive data that will help with comparisons.

Conclusion

The adoption of online teaching and learning is on the ascendency in universities all over the world. The rapid growth of online teaching and learning has been accentuated following the emergence of covid-19 pandemic. However, poorly executed online teaching and learning programmes would negatively affect student learning outcomes. It is therefore imperative to continuously assess the factors that influence students’ online learning engagement and satisfaction.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Christopher Mensah

Christopher Mensah is an Associate Professor in Hospitality and Tourism at the Department of Hospitality and Tourism Management, Ho Technical University. His research interests are in the fields of: sexual harassment in hospitality and tourism/customer satisfaction; hospitality human resource management; academic dishonesty; festivals and trade fairs; and hospitality education.

Mawufemor A. Kugbonu

Mawufemor Abla Kugbonu holds a PhD in Tourism Management and she’s currently a lecturer at the Department of Hospitality and Tourism Management, Ho Technical University. Her research interests are in the field of tourism souvenirs, event and leisure management.

Melody E. Appietu

Melody Enyonam Appietu is a lecturer in the Department of Hospitality and Tourism Management at Ho Technical University. She holds a Master of Philosophy in Hospitality Management. Her research interests comprise food safety, human resource management and its related issues, mistreatment of students in higher education institutions, and workplace spirituality in hospitality and tourism organizations.

Gifty A. Nti

Gifty Akpene Nti is a lecturer in Hospitality and Tourism at the Department of Hospitality and Tourism Management, Ho Technical University. Her research interests are in the fields of food preferences of customers; health and safety practices of housekeeping staff; risk perception of housekeeping staff of hotels; and hospitality and tourism education.

Mavis Adjoa Forson

Mavis Adjoa Forson works as a hospitality and tourism Lecturer at the Department of Hospitality and Tourism Management, Ho Technical University, Ghana, and is also a PhD Candidate in the School of Hotel and Tourism Management at the Hong Kong Polytechnic University. She holds a Master of Science degree in Leisure Tourism and Environment from Wageningen University, the Netherlands, and a Master of Arts degree in Hospitality Management from the University of Cape Coast, Ghana. Her research interests focus on Human Resource Management and Leadership; Fair Work; Gender and Workplace equality; Career Management; and Sustainable Tourism Development and Livelihoods.