An Empirical Study of the Relationship Between Social Networking Sites and Students’ Engagement in Higher Education

Abstract

This article investigates how students’ online social networking relationships affect knowledge sharing and how the intensity of knowledge sharing enhances students’ engagement. It adopts the social capital theory as the basis for investigation, and the partial least square structural equation modeling was used to examine the hypothesized model. Responses from 586 students in higher education were analyzed. The findings provided empirical evidence which contradicts the argument that students perceive social networking sites as an effective tool for learning. Also, contrary to previous studies which posit that knowledge sharing impacts engagement, it was observed that there is no relationship between the two. However, as social networking sites differ in terms of member behavior norms, it is envisaged that if a similar study is conducted and limited to a specific academically inclined social networking site such as Academia.edu, ResearchGate, Mendeley, and so on, different findings may be observed.

Keywords

students’ engagement knowledge sharing social networking sites social capital theory higher education

Introduction

Teaching strategies continue to evolve with the dramatic improvement in technology. There is no argument about the fact that education is gradually shifting its focus from brick and mortar facilities to virtual environments. Nowadays, more attention is given to self-learning activities rather than teaching. As a result, curricula are designed to place emphasis on learning outcomes rather than course content (Tam, 2014). Some have argued that learning outcomes (e.g., critical thinking and problem-solving skills) are influenced mainly by student engagement (Junco & Clem, 2015). As teachers seek to improve student engagement, it is vital that attention is given to the seven principles suggested by Chickering and Gamson (1987). These principles include student–faculty contact, prompt feedback, active learning, diversity, high expectation, time on task, and student cooperation.

As recent technologies are effective for promoting such principles (Crews, Wilkinson, & Neill, 2015), attention is often given to learning with technology (O’Flaherty & Phillips, 2015). This is because, active learning and interaction between students require some amount of technology engagement (Blasco-Arcas, Buil, Hernández-Ortega, & Sese, 2013); and accordingly, a number of studies have investigated the relationship between technology and student engagement (Domingo & Garganté, 2016). Others have also tried to explain the effective role played by social media in facilitating student engagement (Tess, 2013). Yet, current research on how social networking sites (SNSs) influence engagement is not adequate (Eid & Al-Jabri, 2016).

To this regard, this study contributes to knowledge by examining the relationship between SNSs and student engagement. Specifically, it investigates how students’ online social networking relationships affect knowledge sharing (KS) and how the intensity of KS enhances students’ engagement. It provides empirical evidence which contradicts the argument that students perceive SNS as an effective tool for learning. In doing so, it employed the social capital theory (Chiu, Hsu, & Wang, 2006) to measure the relationships between SNS and student engagement in higher educations. It starts with a discussion on existing relevant literature on SNS and student engagement, followed by the fundamental theory adopted for the study. The research hypothesis formulation, data collection approach, analysis, and discussion were presented before conclusions were drawn.

SNS and Students’ Engagement

Student engagement is the physical and psychological effort students devote to academic experience. It involves both in-class and out-of-class activities (Coates, 2007). Thus, it includes collaborative learning and support. It is the effort students dedicate to everyday school activities. As mentioned earlier, there is enough evidence that supports the existence of a relationship between student engagement and learning outcomes (Lee, 2014; Soffer & Yaron, 2017).

Existing research that relates to engagement are mostly in the domain of student learning outcomes. This has received more prominence in recent times (Bryson, 2014). Studies have shown that the amount of interaction and level of quality directly influence student learning and development (Croxton, 2014; Shadiev et al., 2014). As an attempt to promote and improve the effectiveness of student engagement, several studies have emphasized the use of social media. This is because, existing research has demonstrated that social media and networks play an important role in student engagement (George, 2017).

SNSs are online platforms that provide affordances for users to create personal profiles, create content, and share them by connecting with others on the platform (Boyd & Ellison, 2007). Due to the pervasive and viral properties of SNS, it has been largely successful for information sharing and dissemination. It is considered as the way of life for almost all Internet users (Karpinski, Kirschner, Ozer, Mellott, & Ochwo, 2013) and has gained popularity in higher educational institutions. Although one can argue that the ubiquitous nature of SNSs at universities has impacted on how students collaborate and communicate (Dabner, 2012), its impact on student engagement and KS has not been adequately explored.

Currently, students are exposed to various technologies than ever before. Often, they use interactive and computing devices to engage in more social activities such as social networking, text messaging, blogging, online learning, content sharing, and many others (Lim & Richardson, 2016). Approximately, 49 million millennial own a smart phone and 75% of them have a social networking profile (Zickuhr, Rainie, Purcell, Madden, & Brenner, 2012). Studies have also shown that majority of students are familiar with and are also actively engaged in the use of social networking platforms (Paliktzoglou & Suhonen, 2014; Scott, Sorokti, & Merrell, 2016).

SNSs promote active learning (Seifert, 2016), effective communication (Cunha, van Kruistum, & van Oers, 2016), and information sharing (Osatuyi, 2013). It has the potential to increase student engagement (Sadaf, Newby, & Ertmer, 2012) because there is some level of relationship between SNS use and student engagement (Dyson, Vickers, Turtle, Cowan, & Tassone, 2015). It has been confirmed that majority of students who spend more time on SNS platforms believe that they are more connected to their friends and colleagues than those who do not (Burke & Kraut, 2014). Mbodila, Isong, and Muhandji (2014) explained that SNS significantly increases students’ collaboration. This is made possible by the network between students and their colleagues on these platforms: They support collaborative learning and increases their involvement in class activities (Azeta, Eweoya, & Ojumah, 2014).

Nonetheless, the use of SNS tools for promoting students’ collaboration that may facilitate engagement is faced with privacy and security concern (Waycott, Thompson, Sheard, & Clerehan, 2017). Due to the lack of face-to-face interaction and the ability to collaborate with other students who one may not have met physically, they can put students in danger as they may deal with individuals they do not know (Kwon, Park, & Kim, 2014). In addition, SNS exposes them to an enormous amount of data and information, which may be difficult to process and identify the quality levels of the information received (Bright, Kleiser, & Grau, 2015). Exposure to inappropriate online content, cyberbullying as well as online harassment also adds to the major challenges of using them (Fox & Moreland, 2015). In some cases, it has been considered to distract students (Lederer, 2012) and reduce the amount of time they commit to academic work (Alt, 2015). Some researchers have argued that faculty in higher education should be discouraged from using SNS (Malesky & Peters, 2012). This is because, although it increases collaboration and potentially promotes engagement, it discourages face-to-face interaction (Lederer, 2012) which is essential for student engagement.

Considering this, the debate about the potential of SNSs to increase student engagement appears to be overshadowed by its challenges and concerns. Amidst these concerns raised, some countries are faced with major barriers and challenges which hinder the successful implementation and adoption of technology for education. Although these countries are catching up, the digital divide is considerably wide (Pick & Sarkar, 2015). Challenges that discourage the use of Internet facilities in them penetrate all sectors including education. There have been reports that students in such areas mostly use commercial cybercafes or their mobile phones due to low bandwidth, connectivity, and poor Internet infrastructure on campuses (Mtebe & Raisamo, 2014; Odero & Mutula, 2017). Consequently, some students reluctantly use the Internet for such activities because it is expensive. Interestingly, in such countries, the Internet and social media are mainly used for entertainment (Hadebe, Owolabi, & Mlambo, 2017). The biggest challenge is whether students from such countries perceive SNS as an effective educational and research tool.

Considering all this, the use of SNSs for academic purposes is still limited and therefore calls for investigations in different educational institutions, geographical, and sociocultural settings (Rodríguez-Hoyos, Salmón, & Fernández-Díaz, 2015).

Theoretical Foundation

The student engagement theory (Astin, 1984) is one of the most used theories for discussing issues relating to improved teaching and learning in higher education (Junco, 2012). The theory proposes that an engaged student is one who devotes more effort to studying, frequently interacts with faculty and colleagues, spends more time on campus, and actively participates in student activities. This theory has been applied in areas that include the evaluation of institutional role and students’ motivation in relation to SNSs (Alhazmi & Rahman, 2013). Here, it was observed that the integration of SNSs into education is mainly an institutional responsibility. Furthermore, students are motivated to use SNSs for academic purposes if it is introduced by the institution and faculty. Although the theory provides adequate constructs for analyzing students’ engagement, it is limited in terms of measuring factors that affect students’ motivations.

Due to this limitation, some researchers prefer the application of the gratification theory proposed by Katz, Blumler, & Gurevitch (1974) for explaining issues regarding SNS use and learning outcomes (Asogwa, Ugwu, & Ugwuanyi, 2015). The gratification theory addresses the limitation of the student engagement theory and argues that SNS features serve as a motivational influence on students’ behavior. This theory postulates that people are attracted to a medium if it satisfies their needs. Thus, users can make intentional decisions about what activities to perform within a social media network. This theory has been mostly tailored to investigate students’ motivations, behaviors, and experiences on the use of SNSs for educational purposes.

The gratification theory is not ideal for this study because online social networks thrive on interactions among people with similar goals and interests. Inherent within these interactions are tangible and intangible resources. However, studies that focus on Internet use and behaviors should be different from others that seek to uncover benefits embedded within social networks.

The social capital theory suggests that benefits are derived because of associations among people (Coleman, 1988). The theory enables the acquisition of productive resources (Putnam, 1995). It is the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual. Its benefits include access to knowledge and information, employment, influence, and reputation. There is a tendency of increased KS when individuals maintain close social interactions. Therefore, social capital can be considered as a social unit that does not reside in an individual but within a network (Coleman, 1988). This study adopts the social capital theory for investigations.

The Social Capital Theory

The social capital theory has a multidimensional construct consisting of structural, relational, and cognitive dimensions.

Structural dimension denotes the pattern of connections between the members of a network. The core of this dimension is the strength of ties between members. This also reflects how often members of a network interact with each other. The intensity of interaction and thus the structural dimension of social capital is associated with KS. In this research, social interaction ties are considered as the main indicator of structural social capital.

Relational dimension is the formation of opinions and behaviors based on the relationships people maintain (Nahapiet & Ghoshal, 1998), and these relationships facilitate the formation of mutual identification and trust (Bolino, Turnley, & Bloodgood, 2002). Hence, norms such as trust and reciprocity are essential indicators of social capital. The main facets of this dimension, however, include identification, norms of reciprocity, and trust (Chow & Chan, 2008).

Cognitive dimension on the other hand is the element that enables the formation of shared interpretation and meanings within a network (Chow & Chan, 2008; Wasko & Faraj, 2005). It is characterized by common language, shared goals, and norms. In any social setting, these properties promote the development of cognitive social capital. It is facilitated by the frequent interaction among individuals who share similar vision and language (Wasko & Faraj, 2005). As mentioned earlier, knowledge does not reside in one person, thus people solicit knowledge from external sources. This makes social interaction a repository of knowledge (Wang, 2016). Consequently, KS cannot be achieved by coercion (Mu, Peng, & Love, 2008) but through social relationships made available by social capital. External sources enable individuals to attain knowledge that would naturally not be accessible to them. Although some researchers argue that social capital is less useful in scientific studies because it is too vague, Cao, Simsek, and Jansen (2015) explained that it can be made useful by treating it as a multidimensional construct. In such an instance, emphasis is placed on the relationship between individuals’ social networks and their anticipated impacts. Studies such as Scheufele and Shah (2000) and Howard and Gilbert (2008) have successfully applied this approach.

This study adopts Chiu et al. (2006) social capital model but includes SNSs and student engagement as new constructs (see Figure 1). This is because, the study seeks to explore SNS impact on KS and subsequently its effect on student engagement.

Figure 1.

Research model indicating relationship between constructs adopted from (Chiu et al., 2006).

As argued earlier, SNS supports the creation and sharing of content, which is imperative in KS. Several studies have looked at the relationships between social capital and KS; however, the one that is of major relevance to this study is Aslam, Shahzad, Syed, and Ramish (2013)’s research. The researchers examined the relationship between social capital, KS, and how KS affects student achievement. Particularly, they were interested in students’ offline social networks. They concluded that academic achievement is not a function of KS.

Hypotheses Formulation

Social interaction ties represent the connections between networked members and act as medium for information exchange. Members of a network are more inclined to share knowledge if they are familiar with each other. Thus, the intensity at which group members interact affects KS. However, this relationship is reciprocal, effective KS may strengthen ties among group members. Indeed, it is believed that social interactions and relationships could be articulated and improved through KS (Hsu & Lin, 2008). Some studies have found a significant relationship between social interaction ties and KS (Chiu et al., 2006; Mu et al., 2008; Tsai & Ghoshal, 1998). It is therefore hypothesized that:

Hypothesis 1a: There is a significant relationship between social interaction ties and KS among students in a social network.

Trust is a key in every knowledge society because it is the enabler for social exchange and KS (Ritala, Olander, Michailova, & Husted, 2015). Individuals are more willing to share what they know if they trust each other within a group. The fear of being exploited by others is eliminated when individuals trust each other. Similarly, mutual exchanges between actors enable the formation of trust. When network members are actively involved in KS activities, they are able to showcase their abilities, benevolence, and integrity which enable other members to determine their trustworthiness (Evans, Wensley, & Frissen, 2015). It is therefore argued that there is a relationship between trust and KS (Kasemsap, 2016). Similarly, it is expected that within a social network of students, this relationship exists. Hence, it is hypothesized that:

Hypothesis 1b: There is a significant relationship between trust and KS among students in a social network.

Norm of reciprocity is the exchange of knowledge that is mutual and perceived by members to be fair (Chiu et al., 2006). In a social network, individuals expect their actions toward others to be reciprocated. This impacts KS (Kwahk & Park, 2016). Individual’s KS will induce knowledge exchange. It is expected that within a social network of students, KS and norm of reciprocity will have a significant relationship. It is therefore hypothesized that:

Hypothesis 1c: There is a significant relationship between norm of reciprocity and KS among students in a social network.

Identification embodies a member’s sense of belongingness and positive feeling toward a group. Identification serves as a resource that influences opinions of value for knowledge exchange (Omotayo & Babalola, 2016). It therefore explains a member’s readiness to stay engaged as an active group member. It is essential in facilitating KS behavior, and there is a correlation between the identification and KS (Chiu et al., 2006). Research also indicates that members of effective KS groups tend to offer affective support and encouragements to each other (Jeng, He, & Jiang, 2015). This creates a sense of belongingness. It is therefore expected that students in a social network will share their knowledge when they feel a sense of belongingness to the network. It is hypothesized that:

Hypothesis 1d: There is a significant relationship between identification and KS among students in a social network.

Language is an essential enabler of KS (Chiu et al., 2006; Omotayo & Babalola, 2016). To facilitate effective KS, there must be a shared vocabulary. A common vocabulary enables members to understand each other. It supports communication efficiency which motivates members to participate in discussions within the network (Aslam et al., 2013). Hence, students within a social network are expected to share knowledge once they share a common language. Thus, it is hypothesized that:

Hypothesis 1e: There is a significant relationship between the use of a common language and KS among students in a social network.

Shared vision entails common goals and objectives of members within a social network. The existence of a mutual procedure that guides interaction eliminates misunderstanding and provides better opportunities for resource sharing. Omotayo and Babalola (2016) and Tsai and Ghoshal (1998) have all explained that the presence of a shared goal among members of a social network leads to sharing of resources. Similarly, as people continue to share resources, they are likely to form common beliefs and objectives. As such, it is expected that the existence of a collective goal would impact how knowledge is shared within students’ social network. It is therefore hypothesized that:

Hypothesis 1f: There is a significant relationship between shared vision and KS among students in a social network.

One major method of establishing access to knowledge is using electronic communication networks (Abdillah, 2017). SNS makes it possible to distribute content quickly within larger groups of individuals irrespective of their geographical location. Particularly, social media is an enabler for KS (Hamari, Sjöklint, & Ukkonen, 2016; Lim & Richardson, 2016), as it facilitates distribution and comprehension. Hence, the presence of a social network (in this case, an electronic online social network) among students will impact KS. Hence, it is hypothesized that:

Hypothesis 1g: There is a significant relationship between SNS use and KS among students in a social network.

Finally, it has been proven that the ability to effectively create and share knowledge across the hierarchies of an organizational structure provides competitive advantage for the organization. However, the key challenge is the processes that allow the collection of information and how to make it accessible to others. Yet, it is expected that as long as individuals within the social network continue to share knowledge, it will encourage other members of the group to be engaged in the activities of the group (Nahapiet & Ghoshal, 1998; Sigala, 2007). Similarly, committed members of a group will actively participate in KS activities (Martinez, 2015). It is therefore expected that this property of organizations will be demonstrated within student networks. Thus, it is hypothesized that:

Hypothesis 2: There is a significant relationship between KS and engagement among students in a social network.

Research Method

Data Collections

As the study sought to measure the behavior of students using social networks, it was designed to ensure that all participants or respondents are computer literates and also actively use computers. As such Google forms was used for designing and administering the questionnaire. The distribution was done using Facebook and WhatsApp. The decision to use social networks for collecting data was strategically planned to ensure the viral distribution of questionnaire among the target population. It also ensured that participants are somehow connected in a social network group. Respondents were requested to forward the questionnaire to members of their networks. Responses were limited to students of higher educational institutions, particularly those in colleges, polytechnics, and universities.

Self-reporting approach was used for data collection, and participation was purely voluntary. Respondents were asked to indicate their positions on a 5-point Likert-type scale on their agreement or disagreement on the various relevant issues regarding social networks use and its potential to keep them engaged in learning activities (see Appendix A). The measurement for the items was adopted from prior research and modified to suite the research context. The construct definitions and their sources are shown in Table 1, whereas the question items and their sources are shown in Appendix B.

Table 1.

Construct Definitions and Sources.

Construct	Definition	Reference
Social interaction ties	It represents strength of the relationships, the frequency, and intensity of interactions among members of a network	(Chiu et al., 2006; Tsai & Ghoshal, 1998)
Trust	It is the belief that group members will adhere to acceptable norms and not knowingly cause harm to other members	(Nahapiet & Ghoshal, 1998; Wasko & Faraj, 2005)
Reciprocity	It implies that people are compelled to return reactions when they believe that others would return the same favors to them	(Aslam et al., 2013; Chiu et al., 2006)
Identification	Identification refers to an individual’s perception of self-inclusiveness in a community	(Bagozzi & Dholakia, 2002; Chiu et al., 2006)
Shared language	It is a common vocabulary that enables actors to communicate with common understanding	(Evans et al., 2015; Nahapiet & Ghoshal, 1998; Tsai & Ghoshal, 1998)
Shared vision	It entails common goals and objectives of members within a social network	(Aslam et al., 2013; Chiu et al., 2006)
Knowledge sharing	The willingness of individuals in a network to share with others the knowledge they have acquired	(Bock et al., 2005; Chiu et al., 2006)
Engagement	The amount of physical and psychological energy that the student devotes to the academic work	(Astin, 1984; Zhao & Kuh, 2004)s

To improve the quality of the questionnaire, the questions were pretested with 10 students to check for clarity of language used and its appropriateness. The comments, suggestions, and observations gathered in the pretesting process were considered, and the questionnaire was amended appropriately. Again, during the pretesting, an initial reliability analysis was performed using Cronbach’s α. All questions were identified to be reliable. The final version of the questionnaire is presented in Appendix A. The questionnaire was sent to 50 students through WhatsApp. Also, a link to it was posted at each researchers’ Facebook page.

Respondents Demographics

After a month of administering the questionnaire, a total of 586 responses were received. However, 26 of the responses were incomplete, and thus they were discarded. Out of the remaining 560 responses used for the analysis, males were 52% and females 48%. In terms of age distribution, 156 of the respondents were under the age of 25 years but not less than 16 years. This age-group typically represents students who are not considered to be matured. Mature students (i.e., those above 25 years) were grouped into two, those below 40 years and those above 40 years. Respondents who were between 25 and 40 years formed 57% which is more than half of the sample, whereas those above 40 years were 15%.

College students (students from professional training institutes, e.g., teaching college, nursing college) formed 3%, undergraduates were 14%, postgraduates including those doing diplomas were 25%, whereas graduate students (masters and Doctor of Philosophy) formed 58% of the total responses. Table 2 is a detailed presentation of the demographics of respondents.

Table 2.

Demographics of Respondents.

Demographics	Value	Frequency	Percentage
Sex
	Male	292	52
	Female	268	48
Age
	16–24	156	28
	25–40	320	57
	Above 40	84	15
Education
	College	16	3
	Undergraduate	80	14
	Postgraduate	140	25
	Graduate	324	58

Data Analysis and Hypothesis Testing

Measurements

Structural equation modeling was adopted for the analysis of the data collected. This is because, it is effective for observing relationships among variables, as it provides a comprehensive approach to testing validity of hypotheses (Hoyle, 1995). Partial least square structural equation modeling (PLS-SEM) was used instead of Covariance-based structural equation modeling because although the two approaches share a common aim for testing relationship between constructs and indicators, PLS-SEM is more appropriate for situations where the research goal is to extend an existing structural theory (Hair, Ringle, & Sarstedt, 2011). Again, PLS-SEM has been proven to be effective in predicting the effects of target constructs (Hair, Hult, Ringle, & Sarstedt, 2016).

It was also observed that the variables were highly reflective. Indicators of reflective variables are highly correlated and interchangeable. For example, the variable identification in the data set comprises the following indicators: belongingness, togetherness, positive feeling, and proudness. All these are highly correlated and can be used interchangeably to measure identification.

Reliability and Validity

Although some researchers prefer to test internal consistency reliability using only Cronbach’s α, additional checks such as composite reliability and average variance extracted (AVE) in PLS-SEM is more appropriate because it provides a confirmation of reliability from various perspectives. Table 3 is a summary of the various reliability test performed on the variables. It was observed that all the values were greater than 0.7, and hence, they were considered to be reliable. The convergent validity was measured using AVE as suggested by Wixom and Watson (2001). AVE values are expected to be equal to or greater than 0.5 to be valid. From Table 3, all AVE values were greater than 0.5, and thus they were considered to be valid.

Table 3.

Coefficient of Latent Variables.

	Composite reliability	Cronbach’s α	AVE	(AVE)^1/2
Social interaction ties	0.925	0.963	0.754	0.868
Trust	0.935	0.969	0.744	0.863
Norm of reciprocity	0.879	0.941	0.707	0.841
Identification	0.909	0.956	0.715	0.846
Shared language	0.892	0.947	0.734	0.857
Shared vision	0.863	0.933	0.677	0.823
Social networking sites	0.935	0.969	0.706	0.840
Knowledge sharing	0.918	0.961	0.738	0.859
Student engagement	0.840	0.919	0.724	0.851

Note. AVE = average variance extracted.

To measure discriminant validity, the square roots of the latent variables were matched against the correlation values. From Tables 3 and 4, it can be seen that the relationship holds for all the observed variables.

Table 4.

Discriminant Validity Analysis.

Correlations among latent variables
	SIT	TR	NR	ID	SL	SV	SNS	KS	SE
SIT	0.868
TR	0.086	0.863
NR	−0.005	−0.094	0.841
ID	−0.111	−0.045	0.078	0.846
SL	−0.005	−0.093	−0.104	0.071	0.857
SV	0.256	0.228	−0.020	0.003	0.071	0.823
SNS	0.056	0.154	0.143	0.051	0.003	−0.006	0.840
KS	0.237	0.390	−0.091	−0.133	0.051	0.099	0.082	0.859
SE	−0.007	−0.171	0.101	−0.080	−0.133	−0.007	−0.142	0.035	0.851

Note. SIT = social interaction ties; TR = trust; NR = norm of reciprocity; ID = identification; SL = shared language; SV = shared vision; SNS = social networking sites; KS = knowledge sharing; SE = student engagement.

For example, the AVE for KS is 0.738 (see Table 3) and the corresponding square root for 0.738 is 0.859 as indicated in Table 4 (the diagonal entries of Table 4 is the square root of the AVE for each latent variable). From Table 4, it can be observed that 0.859 is greater than all the values on the row and column for KS. These values are 0.237, 0.390, −0.091, −0.133, 0.099, 0.082 for those on the row, and 0.035 for the column. Performing similar comparison for all AVE values in Table 4 indicates that the latent variables are valid.

The Structural Model

Table 5 and Figures 2 and 3 provide values for evaluating the performance of the hypotheses proposed in this study. Specifically, it describes the relationship among the latent variables that make up the model. With the assistance of bootstrapping process, the following observations in Table 5 and Figures 2 and 3 were made. In Table 5, the values in the T statistics column define the significance of the path coefficient. Using 5% significance level, path coefficients were considered to be significant if the T statistics value is greater than 1.96. Thus, it can be observed that only a few of the hypotheses were supported (Relationships that are significant are indicated in bold). Shared language and shared vision had a significant effect on KS with values 4.815 and 2.399, respectively. Although the relationship between trust and KS was significant with a T statistic value of 2.039, trust negatively impacted KS. The effects of the other independent constructs on KS irrespective of their directions were not significant. Table 5 provides values of T statistics for the relationships between the various constructs.

Table 5.

Statistical Path Coefficients of Inner Model.

	Original sample	Sample mean	Standard deviation	T statistics	p
ID -> KS	−0.064	−0.055	0.086	0.753	.452
KS -> SE	−0.045	−0.036	0.085	0.530	.596
NR -> KS	−0.063	−0.056	0.086	0.732	.465
SIT -> KS	−0.098	−0.097	0.082	1.188	.236
SL -> KS	0.207	0.199	0.086	2.399	.017
SNS -> KS	0.097	0.104	0.096	1.010	.313
SV -> KS	0.381	0.376	0.079	4.815	.000
TR -> KS	−0.158	−0.167	0.078	2.039	.042

Figure 2.

The structural model. SIT = social interaction ties; TR = trust; NR = norm of reciprocity; ID = identification; SL = shared language; SV = shared vision; SNS = social networking sites; KS = knowledge sharing; SE = student engagement. ***p < .001. **p < .05.

Figure 3.

Importance performance map analysis. SIT = social interaction ties; TR = trust; NR = norm of reciprocity; ID = identification; SL = shared language; SV = shared vision; SNS = social networking sites; KS = knowledge sharing; SE = student engagement.

The different values in Figures 2 and 3 help explain the strength of the model or relationships that were tested in the study. The values indicated on the arrows in the model explain the strength for the significance of the path. Thus, higher values are preferred to lower ones.

In Figure 2, the R² values are coefficient of determination, and they represent the percentage of variance. Hence, social interaction ties, trust, norm of reciprocity, identification, shared language, shared vision, and SNS explain 24.7% of the KS. KS explains 0.2% of student engagement.

Discussion

The use of SNSs has aided interconnectivity of students. They are embodied with tools such as forums, wikis, and collaborative platforms that enable students to interact with each other. This study adopted constructs from the social capital theory to scrutinize the impact of SNS use on student engagement. The theoretical model was examined using a PLS-SEM, and the results indicate that relationships between some of the proposed hypothesis are not significant. For instance, the findings indicated that social interaction ties do not support KS. This agrees with findings from Aslam et al. (2013) but disagrees with Tsai and Ghoshal (1998). Aslam et al. (2013) conducted their research within an academic environment while Tsai and Ghoshal (1998) performed their study in an organizational setting. Consequently, it can be argued that within academic circles and particularly concerning students’ engagement, social interaction ties do not play a significant role on how knowledge is shared, although the opposite may hold in other organizations.

Among the other relational dimension indicators (identification and norm of reciprocity), trust was the only indicator that significantly affected KS negatively. This observation deviates from Tsai and Ghoshal (1998)’s assertion that there is a significant positive relationship between trust and KS. As mentioned earlier, their study was conducted within an industry setting, hence the nature of setting or domain influences the relationship between trust and KS. On online social networks, communication is a precursor of trust. The formation of trust depends on repeated exchanges and an individual’s assessment of the other party’s past behavior (Doney & Cannon, 1997). Hence, satisfaction with KS with other network members is an antecedent of online trust (Grabner-Kräuter & Bitter, 2015). The cognitive dimension is recorded to have the highest influences on KS. Shared vision is found to be the most significant indicator of KS followed by shared language. This finding confirms Chiu et al. (2006) claims.

The result also indicated that SNS does not significantly impact KS within the academic context. This finding is particularly interesting considering the fact that a number of studies have identified a positive correlation between the two constructs. Faraj, Jarvenpaa, and Majchrzak (2011) and Majchrzak, Faraj, Kane, and Azad (2013) have all confirmed that SNS is an instrument for facilitating KS. Although their assertion may be true, the challenge is whether the students use SNS to share knowledge that is related to their academic work. Hence as observed from this study, there is no evidence that students use SNS to share academic-related knowledge.

Contrary to previous studies that posit that KS impacts engagement (Sigala, 2007), it was observed that there is no relationship between the two constructs, as we drew our sample from students. However, Sigala (2007) assertion was not supported by empirical research. Thus, this finding calls for further investigations on the relationship between the two constructs.

Conclusion

The main aim of this study was to investigate the impact of SNS on student engagement. SNS use was found not to be a function of KS and therefore had no significant impact on student engagement. The findings can be attributed to the nature of the SNS used for the study. This is because, although online social networks have similar functionalities, they exhibit different social norms and organization. As some social networks are designed to fit within a particular market niche, their features may be different for particular users. The SNS platforms used in this study were generic and not specifically designed for academic work. It is, however, envisaged that if a similar study is conducted using academically inclined SNS such as Academia.edu, ResearchGate, Mendeley, and so on, different findings may be observed. As argued by Papacharissi (2009), SNSs such as Facebook, LinkedIn, SmallWorld, and so on differ in terms of members’ behavioral norms. Facebook is more publicly open with weaker behavioral norms as compared to LinkedIn that is strict. This is because, Facebook is designed mainly to support and maintain friendly interactions, whereas LinkedIn is primarily used for professional networking. It is therefore suggested that future research should investigate specific SNS that are designed for academics or students to measure the relationship between SNS use and their engagement.

Footnotes

Appendix A: Research Questionnaire

Appendix B: Individual Item Measurements and Sources

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Felix N. Koranteng is a private researcher with interest in human behavior and technology with particular emphasis on collaborative learning technologies, human–computer interaction, social media and sentiment analysis, and information and knowledge management. Currently, he holds a master of science in Management Information Systems from the Ghana Institute of Management and Public Administration. He obtained a bachelor of science degree in Computer Science from the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

Isaac Wiafe holds a PhD in Informatics and an MSc in Applied Informatics from the University of Reading, UK. He also holds a bachelor’s degree in Mathematics from the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. His research is focused on human–computer behavior change. He is the key inventor of the 3-Dimensional Relationship between Attitude and Behavior Model. Currently, Wiafe is the head of the Intelligence Spaces and Machine Learning lab at the Department of Computer Science, University of Ghana, where he is leading research on how technology can be used to change human behavior intentionally. Specifically, he is interested in how artificial intelligence and sensor technology can be deployed in spaces to sense occupants’ mood and behavior to compose persuasive messages that will optimize persuasion experience. This study is under the theme Persuasive Spaces.

Eric Kuada is a senior member of the Institute of Electrical and Electronic Engineers (IEEE), and the current chairman of the IEEE Ghana Section. He is the current Head of the Department of Computer Science at the Ghana Institute of Management and Public Administration. He obtained a PhD degree in Information Technology in 2014 from Aalborg University, Denmark. Prior to that, he obtained an MSc degree in Network Services and Systems from the Royal Institute of Technology—KTH, Sweden, and a BSc in Electrical and Electronic Engineering from the Kwame Nkrumah University of Science and Technology in 2009 and 2003, respectively. He also worked as a lecturer at the Department of Electronic Systems, Aalborg University.

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