Social Media Usage of Students,Role of Tie Strength,and Perceived Task Performance

Abstract

The extensive use of social media has encouraged university teachers to consider employing social media as new e-learning platforms. This study explored the relationship between social media usage and constructive learning performance (perceived group task performance). Specifically, this work focused on the mediating role of communication in group and the moderating role of tie strength in online social networking. Survey data were collected from students in a senior-level business course. Students were asked to express their opinions on Facebook usage, communication in group, tie strength, and group performance. SmartPLS was used as the major data analysis tool. Results revealed that frequent social media usage of students led to increased communication among group members and that communication in group enhanced the perceived group task performance of students. Finally, tie strength negatively moderated the relationship between social media usage and communication in group.

Keywords

social media social constructivism theory communication via social media tie strength group task performance

Introduction

Social media are defined as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of user-generated content” (Kaplan & Haenlein, 2010, p. 61). The emergence of social media technologies has affected teaching and learning styles in higher education. From the perspective of students, many university students are “digital natives”¹ who were born into the digital world and grew up in an environment of technological advancement (Prensky, 2001). Digital natives not only possess sophisticated skills in using digital technologies but have also developed new cognitive capacities and learning styles. Learning via social media is a new learning style that enables digital natives to personalize content, share and participate online, and interact with others on a collaborative basis (Bannon, 2012; Yaros, 2009). Social media help to create an informal and relaxing atmosphere and make learning effective (Dalton, 2009). From the perspective of educators, social media contribute to a disruptive² change in pedagogy, which is known as Pedagogy 2.0 (McLoughlin & Lee, 2011). Pedagogy 2.0 is a framework to achieve learning outcomes by exploiting the potential of Web 2.0 technologies. It emphasizes collaboration, personalization, and user-generated content. Owing to the emergence of Pedagogy 2.0, classroom instructions are shifting from the traditional “teacher-centered” approach to the “student-centered” approach,³ where students take the initiative and responsibility to learn (Farkas, 2012). There is also a paradigm shift of the knowledge-creation process in education, where knowledge is possibly generated by the users instead of being static and immutable. Students in the current context are becoming “prosumers”⁴ of knowledge who may produce and simultaneously consume knowledge (Mcloughlin & Lee, 2008). Exploring the potential benefits of social media in pedagogy is essential due to the increasing importance of the pedagogic shift derived from the massive adoption of social media.

One of the notable features of social media is its capability to assist communication through a real-time exchange of information, which includes text, graphics, audio, and videos. Social media allow people to engage in direct communication anytime and anywhere without the need to meet physically. Social media also outperform a large number of its counterparts in terms of cost efficiency (Ellison, 2008). The increase in communication and interaction in the educational context is due to the use of web technology (Anderson, 2004), and social media comprise computer-mediated communication platforms to promote online connections, maintain relationships, and boost communication among students. Moreover, social networking sites, such as Facebook, respond well to the particularities and requirements of the student-centered approach, where students take the initiative to create and develop their own learning style (Ioana, 2013). Given the chance, digital natives tend to use social media to promote peer communication, collaboration, and active learning.

Social media as a pedagogic tool can not only facilitate communication but also influence the task performance of students (particularly in a group) or constructive learning outcomes. Vanwynsberghe and Verdegem (2013) argued that social media help students train themselves to perform well in a group because students wish to gain positive evaluations or reactions from others. Social media help in relationship building, which engenders a supportive social environment to facilitate learning outcomes (Klem & Connell, 2004; Reyes, Brackett, Rivers, White, & Salovey, 2012; Rimm-Kaufman & Chiu, 2007; Sturgeon & Walker, 2009).

Tie strength in social networks influences the quality and level of communication (Gilbert & Karahalios, 2009). Therefore, the impact of social ties is important and was considered in the present research. The concept of tie strength was introduced by Granovetter (1973) during his examination of the strength of interpersonal ties in a social network. He defined the strength of a tie as “a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services that characterize the tie” (Granovetter, 1973, p. 1361). Acquaintances and friends with a loose relationship have weak ties, whereas trusted friends and family have strong ties. Studies have shown that weak-tie social networks are more effective than strong-tie ones in sharing information and enhancing social activities (Granovetter, 1973; Levin, Cross, & Abrams, 2004). The primary reason is that people with strong ties possess too many overlaps due to their homogeneity, and these overlaps reduce the need to communicate. Compared with people with strong ties, those with weak ties may have a better chance to acquire and synthesize diverse pieces of information through interactions (Granovetter, 1973). Research has shown that weak ties can help friends generate creative ideas via communication (Burt, 2004). Research on weak ties is particularly meaningful in the social media context because computer-mediated networks are useful platforms in connecting weak ties (Walther & Boyd, 2002). In view of these facts, we examined the role of tie strength in the educational context when social media are used for communication.

With regard to the research context, we studied relationships among key concepts under a project-based learning⁵ environment in which university students form small teams and discuss their group project (mostly) via social media platforms (Facebook, in this case). The project-based learning context is suitable for our research objectives because it is one of the student-centered pedagogies (Markham, 2011) that social media as a computer-supported collaborative learning tool could support (Hazari & Thompson, 2015). Many social media platforms, such as Facebook groups and Google Hangouts, provide embedded tools to create virtual communities that allow team members to seamlessly shift from face-to-face discussions to virtual communication and collaboration (Everson, Gundlach, & Miller, 2013). In the present research, students in the same (physical) group were asked to join Facebook groups at the beginning of the semester. The lecturer was also added to the groups to monitor the online behavior and performance of students. Online discussions, coordination, and collaboration continued until the groups finished the projects assigned by the lecturer.

In summary, this study addressed two important research questions: (a) whether or not social media usage influences communication among group members and their perceived task performance; and (b) whether or not tie strength moderates the relationship between social media usage and communication in group. We are particularly interested in examining the mediating role of communication in group and the moderating role of tie strength in group communication. This research advances social media literature because it simultaneously investigates the effect of social media on the process of social media usage (communication) and the outcome of social media usage (task performance). This research is also among the few studies that examined the effectiveness of social media usage in a project-based learning context, where the group performance of students is the target of investigation. Moreover, introducing the concept of tie strength in a social network to the constructive learning environment enabled by social media is a pioneer work in the pedagogical field.

The rest of the article is organized as follows. First, previous studies on social constructivism theory, social media in education, and properties of strong and weak ties are explored. Second, the research hypotheses, model, and methodology are presented. Lastly, the research findings are discussed, theoretical and practical implications are explained, and conclusions are provided.

Literature Review

Social Constructivism Theory

This research is mainly supported by social constructivism theory. Social constructivism theory is rooted in the educational field. Unlike other learning theories that focus solely on how individuals construct knowledge (Piaget, 1953), social constructivism emphasizes social interaction (Vygotsky, 1978) while learning. Social constructivism theory suggests that learning is a social process and that constructing knowledge within a social context is beneficial due to the large sum of cognition in groups (Bruner, 1990; Lave & Wenger, 1991; Mishra, 2014; Slavin, 1995). Recent years have witnessed an emerging trend of social constructivism research that focuses on the role of social technologies and social media in facilitating the generation of socially constructed knowledge (e.g., Gaytan, 2013). The strengths of social media coincide with the principles espoused by social constructivists (Kelm, 2011). For example, Churcher, Downs, and Tewksbury (2014) illustrated that social media lead to the formation of online community of practice for learning. Mcloughlin and Lee (2010) mentioned that social media facilitate participation, communication, and collaboration and the construction of personal meaning that satisfies the learning condition of social constructivism. Social constructivism theory is particularly appropriate for explaining team performance because learners tend to construct knowledge as a collective activity (Löfström & Nevgi, 2006). Social media encourage people to work in groups, and group members can correct one another’s misunderstandings and improve task progress (Hilter & Paul, 2006). In other words, receiving and giving feedback to one another through social media are convenient for students in project-/team-based learning. From the perspective of teachers, using social media in a team-based pedagogy significantly enhances teaching and learning processes because it allows educators to tap into the digital learning styles of students in groups (Rasiah, 2014). In summary, social constructivism delineates the importance of social interaction in knowledge construction, and social media can help create a social environment as depicted in social constructivism theory. As the most important theoretical lens, social constructivism theory was used in this study to support the hypotheses on social media usage and its effects (communication in group and perceived task performance).

Social Media and Task Performance

Social media significantly affect task performance. In this research, task performance was roughly divided into two categories: individual academic performance or academic performance in general and perceived group task performance. Generally, researchers support a positive relationship between social media usage and academic performance. For example, Junco, Heibergert, and Loken (2011) observed that although two groups of students in their study have similar high school grade point averages (GPA), the group that uses Twitter in class demonstrates higher engagement and GPA than those that do not use Twitter in class. In their study on German students, Skiera, Hinz, and Spann (2015) discovered that students located in densely connected subnetworks obtain improved grades, especially male students. Hung and Yuen (2010) also observed that social media use can provide a sense of community, which is fundamental to successful e-learning. Means, Toyama, Murphy, Bakia, and Jones (2009) pointed out that students who receive online instructions exhibit better academic performance than those who receive face-to-face instructions. Social media allow students to learn how to use technology to communicate, gain access to unlimited information, receive support when engaged in teamwork activities, connect with experts in a particular area, and receive immediate feedback (Fosnot, 2005). All these engagements, collaborations, and interactions lead to improved task performance of students (Faizi, El Afia, & Chiheb, 2013; Hung & Yuen, 2010; Neier & Zayer, 2015; Schroeder & Greenbowe, 2009).

However, a contrasting view also exists; this view posits that social media usage is negatively related to academic performance (Kirschner & Karpinski, 2010; Paul, Baker, & Cochran, 2012). For example, Huang (2014) mentioned that social media addiction and its symptoms significantly and negatively affect the academic performance of adolescents. Kirschner and Karpinski (2010) further explained that social media use is a major distraction that impairs the academic performance of students. Using social media involves multitasking, which undermines the capacity of students to process information and engage in deep learning (Junco & Cotton, 2012; Wood et al., 2012). Walsh, Fielder, Carey, and Carey (2013) also reported that students who spend much time using social media exhibit few academic behaviors (e.g., completing homework and attending class), reduced academic confidence, and increased problems affecting school work. Meanwhile, several studies have suggested that no relationship exists between social media usage and academic performance. For example, Pasek, More, and Hargittai (2009) found no relation between Facebook use and grades. Kolek and Saunders (2008) also found no differences in the overall GPA of Facebook users and nonusers. Junco (2015) concluded that time spent on Facebook has no relation with GPA, especially for senior college students. Moreover, Lambić (2016) claimed that no correlation exists between frequency of Facebook usage for general purposes and academic performance.

These discussions indicate that research on the relation between social media usage and academic performance has yielded mixed results. Several possible reasons may explain these disparate findings. The first one is study purpose. Most prior studies did not distinguish the purpose of social media usage. For example, when social media usage involves leisure or entertainment purposes instead of being purely academic and curricular, the relationship between social media usage and academic performance tends to be negative (e.g., Junco & Cotton, 2012; Walsh et al., 2013). The second reason is degree of usage. Most studies that showed a negative relationship between the two constructs examined cases in which students were overinvolved, obsessed, and addicted to social media (e.g., Huang, 2014; Kirschner & Karpinski, 2010). The third reason is subject (student) age or year of study. Compared with adolescents (high school students), college students have better control of social media usage. Therefore, college students, especially those in senior year, do not seem to be influenced by social media, unlike their junior peers (e.g., Huang, 2014; Junco, 2015). The fourth reason is the nature, design, and measure used in different studies. Studies conducted in the early stage are usually descriptive and exploratory (e.g., Kirschner & Karpinski, 2010). Inconsistencies also exist in the measures of academic performance and social media usage. For example, Facebook use has been measured by time spent on the site (Junco & Cotton, 2012) and frequency (Lambić, 2016) or by splitting users and nonusers (Kirschner & Karpinski, 2010). Grades have also been measured either through a self-reported survey (Kolek & Saunders, 2008) or from data collected from an official channel (Junco & Cotton, 2012). To clarify the ambiguity in literature, we followed the recommendation of prior researchers to provide additional empirical support on the relationship between social media usage and academic performance of college students and proposed a positive relationship between the two constructs.

The context of the current research is a project-based learning group. Therefore, although individual performance influences group performance, task performance is more relevant to group performance when group members finish the group project mainly through a social media platform. Only a few studies on using social media for pedagogic purposes have investigated the group performance of students under the new social learning environment. Hazari and Thompson (2015) were among the few who attempted to understand group processes in the social media-enabled learning context. They identified a positive relationship between technology-based learning environment, including social media, and perceived group performance. Consequently, they called for further research on group behavior in the context of today’s technology-based learning environment. We used self-reported evaluation of project performance to measure perceived task performance due to the scarcity of similar research in existing literature. We further tested the relationship between social media usage and individual performance of students as measured by GPA to distinguish the effect of social media on groups and individuals.

Social Media and Communication

Social media facilitate communication through real-time information exchange, which includes text, graphics, audio, and videos. Pedagogic scholars have extensively studied the effect of social media on student communication and collaboration (Hung & Yuen, 2010; Yaros, 2012). Social media as a new communication tool promote online connections and relationships. A study conducted by Neier and Zayer (2015) indicated that students are willing to use social media in education due to increased interactivity, which is a primary motive of social media usage by digital natives (Kilian, Hennings, & Langner, 2012; Yaros, 2012). Hung and Yuen (2010) concluded that social media use fosters a sense of community that can enhance communication among students. Ellison (2008) defined social media as a “social lubricant” that provides a cost-efficient means of self-presentation and for broadcasting personal events to promote interaction and connection. Similarly, Vural (2015) believed that social media, with their capability to instantly connect multiple people, can effectively notify students of announcements and facilitate communication, discussion, and self-evaluation. In summary, social media are convenient and cost-efficient channel (Ellison, 2008) for students to communicate.

Communication and Task Performance

Communication is fundamental to team work and improves team performance (Loughry, Ohland, & Woehr, 2014). Many previous studies have addressed the relationship between communication in general and task performance. For example, Fransen, Kirschner, and Erkens, (2011) argued that high-quality task-specific communication is important in all stages of team work. Pöysä-Tarhonen, Elen, and Tarhonen (2016) and Vora and Markóczy (2012) demonstrated that improved communication in terms of content, frequency, quality, and responsiveness helps to enhance group learning ability and overall task performance. Similarly, Baldwin, Bedell, and Johnson (1997); Chapelain, Morineau, and Gautier (2015); and Pöysä-Tarhonen et al. (2016) proved that groups with high levels of communication are likely to achieve improved group performance. Santhiveeran (2005) discovered that integrating online communication into classes can enhance the perceived convenience of learning and boost the participation and critical thinking of students. Imlawi, Gregg, and Karimi (2015) also believed that with proper combination and use of rhetorical and relational communication processes in an online environment, students can be motivated to learn and demonstrate an increased level of satisfaction about the course. Furthermore, Baker and Woods (2004) posited that increased immediacy and cohesiveness in online communication influence online communication quality and learning. Computer-mediated communication among students can also nurture positive word-of-mouth and lead to increased instructor ratings (Edwards & Edwards, 2013), thus motivating students to learn. Although prior research has extensively examined the positive effects of face-to-face and online communication on task/academic performance, only a few have investigated the relationship between communication and task performance in the social media context. The current research intends to fill this gap.

Tie Strength

Tie strength must be considered when investigating social media’s effects because social media enable students to communicate online in social networks, and social tie is a crucial factor in these social networks. Strong and weak ties exist in social networks (Granovetter, 1973). People with strong ties are emotionally interdependent and typically provide trust and emotional support to one another. Strong ties are also believed to be highly effective in tackling complicated projects (Hansen, 1999) and forming teams for information dissemination (Shi, Adamic, & Strauss, 2007). Compared with people with strong ties, those with weak ties are less emotionally attached. Weak ties function as “bridges” that connect different social circles, support information diffusion, and provide access to diverse sources of information (Granovetter, 1973). Although strong ties provide more emotional support than weak ties do, the information overlap caused by gravitation toward homogeneity limits the inflow of different viewpoints (Botwin, Buss, & Shackelford, 1997). By contrast, weak ties can enhance creativity by providing nonredundant information and highly complex information-processing behaviors (Perry-Smith, 2014). They are likely to provide objective feedback; strong ties are less likely to do so because of the interdependent nature of the relationships involved (Wright, 2012). Weak ties can also facilitate cooperation (Melamed & Simpson, 2016) and knowledge creation (Wang, 2016) and are instrumental in new learning (Chung & Paredes, 2015).

In pedagogy, exploring ideal approaches of forming groups and achieving improved group results are necessary because group-based projects have become a common assessment mode among college students. Tie strength should be considered before assigning group work to students because the interactions and relationships among group members are important in achieving successful group results. Discussions on the effect of social ties are limited. Baldwin et al. (1997) posited that centrality in a communication network can improve the academic performance of students. Several scholars have also discovered that social ties partially contribute to academic achievement through the “peer effect” and that a positive relationship exists between average peer academic performance and individual performance (Lyle, 2007; Mayer & Puller, 2008; Poldin, Valeeva, & Yudkevich, 2013). The emotional support provided by social ties can influence the academic performance of students as well. For example, the sense of attachment derived from peer relationships can spur engagement and competency, leading to improved academic outcomes (Fass & Tubman, 2002). Social anxiety that undermines the academic achievement of students can also be alleviated by social ties (Christina & Teena, 2015).

Previous researchers have investigated the effect of social media and social ties in the educational context. However, several gaps still exist in literature. First, although many pedagogic studies have examined the effect of social media usage, most of them focused on social effects or the process of social media usage, which involves engagement, collaboration, and communication (Faizi et al., 2013; Hung & Yuen, 2010; Schroeder & Greenbowe, 2009). Minimal attention has been devoted to the study of the effect of social media on academic outcomes, such as task performance, learning outcomes, and grades (Mingle & Adams, 2015). Second, for studies that examined the effect of social media on academic performance, the exact nature of the relationship, whether it is positive, negative, correlated, or not correlated, remains ambiguous. Moreover, only a few studies have examined the effect of social media usage on group performance of students in the project-based learning context. Third, studies on the effect of tie strength in social media on students’ task completion are scarce. Fourth, a limited number of studies have examined the relationship between face-to-face or online communication and student performance in the social media context. To fill these gaps, the present research investigated the effect of social media not only on the social process (communication) but also on the direct academic performance of students (perceived group performance and individual performance). We also tested the mediating role of communication between social media usage and perceived task performance. We introduced the concept of tie strength as a moderator in the relationship between social media usage and communication in group. Specifically, we proposed that social media usage directly affects communication in group and perceived task performance of students and that tie strength moderates the relationship between social media usage and communication in group.

Hypotheses Development and Research Model

Four research hypotheses were developed based on a literature review of theories and key concepts. The research model is presented in Figure 1.

Figure 1.

Research model.

According to social constructivism theory, constructing knowledge in a social context is beneficial to learning outcomes (Bruner, 1990; Vygotsky, 1978). Knowledge is constructed via the interaction of an individual with other group members. Such an environment embraces interactivity, engagement, collaboration, and student-centered approaches (Airasian & Walsh, 1997; Paris, 2011). Löfström and Nevgi (2006) designed eight principles to describe how the use of innovative technologies applies to social constructivism. These principles coincide with the manner through which social media enhance learning (Kelm, 2011). Moreover, in project-based learning, group members usually harness the potential of social media to generate “collective intelligence” (Anderson, 2007). With these discussions, we developed H1.

H1: Social media usage is positively related to perceived task performance.

The relationship between social media usage and communication has been extensively explained by prior researchers in the educational field (e.g., Hung & Yuen, 2010; Vural, 2015; Yaros, 2012). Aside from empirical results, this relationship is also supported by social constructivism theory, which emphasizes the critical importance of culture and social context in cognitive development (Vygotsky, 1978). The theory maintains that knowledge is constructed through interaction with others, and social media provide a modern computer-mediated platform for students to interact, communicate, and learn (from their team members, in this case). On the basis of these empirical and theoretical discussions, we hypothesized that social media usage can positively influence communication in group.

H2: Social media usage is positively related to communication in group.

Communication via social media can lead to improved group task performance for the following reasons. First, when group members use social media to communicate, they tend to form social communities to collaborate with one another; the asynchronicity⁶ of social media allows students to present themselves conveniently and deliberately (Bill, 2008; Güler, 2015). Second, with sufficient communication for a project, group members can effectively state their thoughts and rationales and explain and understand the ideas of others (Friedman & Antal, 2005); they are also likely to experience additional positive effects and receive diverse sources of information for finishing a certain task (Druskat & Kayes, 2000; Losada & Heaphy, 2004). Third, several specific capabilities, such as unlimited exposure to peer progress, archiving, and backtracking, attained through social media transform the landscape of interpersonal and group communication, minimize the cost of face-to-face meetings, and thus enhance the collaborative learning and academic performance of students (Güler, 2015). Therefore, we proposed H3.

H3: Communication in group is positively related to perceived task performance.

In this research, we proposed that the effect of social media on communication intensifies when group members have weak ties, whereas the effect weakens when members have strong ties. The reason for such a proposal is that weak ties are bridges that connect different social networks and provide diverse sources of information. The nonredundant and nonoverlapping information of different group members facilitate information dissemination (Granovetter, 1973; Perry-Smith, 2014). Meanwhile, strong ties provide overlapping thoughts due to the pursuit of homogeneity (Botwin et al., 1997). This overlap may limit the diversity of transferred information and consequently influence the nature and content of communication. People with strong ties are expected to possess a similar knowledge pool and cognitive process toward a certain task. Therefore, they may not need extensive communication to work on a group project. By contrast, people with weak ties tend to create novel ideas. Sufficient communication with others possessing different viewpoints becomes necessary in this group setting. In line with these discussions, we proposed H4.

H4: Tie strength negatively moderates the effect of social media usage on communication in group.

Research Methodology

Construct Operationalization

Original survey instruments were developed based on the literature review and formal discussions with faculty members who have used social media as a teaching and learning tool in their classes. Five faculty members joined the focus group. We asked their opinions on the 22 items measuring the four constructs. Feedback and comments were obtained to further revise the items in the questionnaire, such as which types of social media activities should be included and which types of communication in group are suitable for the social media context. Problematic items were rephrased, and repetitive dimensions or items were removed. After a preliminary evaluation by professionals, the remaining items for measuring the model constructs were relevant to the concept that they intend to measure, and ambiguity in the use of the items was eliminated. Therefore, the face and content validities of the measures were guaranteed. The definition, measurement, and source of each construct are shown in Table 1. Except for the items of social media usage, all other items (i.e., those for communication in group, tie strength, and perceived task performance) were measured with a 5-point Likert scale, where 1 represents strongly disagree and 5 represents strongly agree. The items of social media usage were measured on a scale of 1 (very rare) to 5 (very frequent). No reverse questions were provided in our measures.

Table 1.

Measures.

Constructs	Measures	Source
Social media usage	Please indicate your frequency in performing the following tasks.	Hughes et al. (2012)
– The frequency at which people engage in social interaction and information exchange on social networking sites (Hughes et al., 2012)	1. I use social media to find and spread information. 2. I use social media to keep in touch with friends. 3. I use social media to keep abreast of current events. 4. I use social media to socialize with people.
Communication in group	When working on the group project through the Facebook group, from my own perspective,	Lowry et al. (2006)
– A group member’s evaluation of the level of group discussion effectiveness and development (Burgoon et al., 2002)	1. The overall quality of group communication is good. 2. The outcome of group communication is satisfactory. 3. The forms of expression in communication have high variety. 4. The detailed messages in communication are very vivid. 5. The group communication is appropriate. 6. The group communication suits the topic. 7. Communicating openly with all members of the group is easy.^a 8. When people communicate in the group, a great deal of understanding is achieved. 9. The information I receive is generally accurate. 10. The accuracy of information passed among group members does not need to be improved.^a
Tie strength	In the Facebook group,	Gilbert and Karahalios (2009)
– A (probably linear) combination of amount of time, emotional intensity, intimacy (mutual confiding), and reciprocal services that characterize the tie (Granovetter, 1973)	1. I have a strong relationship with most of my group members. 2. I feel comfortable about asking most of my group members to loan me HKD 100 or more. 3. If I were looking for a job, most of my group members would be helpful. 4. I would be upset if most of my group members unfriend me on Facebook. 5. If I left Facebook for another social networking site, I would bring most of my group members along.
Perceived task performance	Please evaluate your group’s performance.	Self-created
– Effectiveness of completing the group project as perceived by group members (Henttonen et al., 2013)	1. I think our group presentation performance is excellent. 2. I think our group presentation performance deserves an A. 3. I don’t think our group presentation needs to be improved.^a

^aDeleted items after the preliminary reliability test.

Social media usage

Social media usage in the present study is defined as the frequency at which people engage in social interaction and information exchange on social networking sites. We borrowed the definition and measure of social media usage from Hughes, Rowe, Batey, and Lee (2012). Students were expected to answer how frequently they use social media in general to finish tasks on a given list.

Communication in group

We used 10 items from the study of Lowry, Roberts, Romano, Cheney, and Hightower (2006) to measure communication in group. Lowry et al. (2006) specifically studied the in-group communication mode in the environment of computer-mediated communication. Five dimensions, namely, group discussion quality, communication appropriateness, richness, openness, and accuracy, were used to measure group communication quality in their work. For this study, we omitted duplicated items in each dimension and used 10 items to measure different aspects of communication in the social media context.

Tie strength

Tie strength is used to assess the level of closeness of the relationship among group members. According to Granovetter (1973), tie strength possesses four dimensions: amount of time, intimacy, intensity, and reciprocal services. The current research borrowed questions from the study of Gilbert and Karahalios (2009), who were among the first to map social media data with the concept of tie strength. They used five questions to assess tie strength among participants in an online social network.

Perceived task performance

Tuckman (1975) defined performance as the apparent demonstration of understanding, concepts, skills, ideas, and knowledge of a person and proposed that grades depict the performance of a student. In this research, we operationalized group task performance as the effectiveness with which a group project is undertaken (Henttonen, Janhonen, & Johanson, 2013). Specifically, it measures the effectiveness of the completion of a group project as perceived by group members. To measure the newly developed construct, we collected the group members’ self-reported perception of their group project grade and performance in general. For the individual and general academic performances of students, we used the self-reported GPA of students because GPA is the most common measure of academic performance of college students (Junco, 2015). GPA is also believed to be the “sole measure” of academic performance used in studies on Facebook (Junco & Cotton, 2012; Kirschner & Karpinski, 2010).

Data Collection

Data were collected from Global Business Strategy, a senior-level undergraduate course in one of the universities of Hong Kong. This subject was selected due to the diversified background of the students in the class. The numbers of international, mainland Chinese, and local Hong Kong students were nearly equal. We believe that adopting diversified profiles of students can help further understand tie strength in the current research. At the beginning of the semester, the lecturer asked students to form groups of five to six people. Each group was requested to create a Facebook group and invite the lecturer to join their group. Through the Facebook groups, students were required to discuss their projects, respond to comments of group members, and share or upload audios, videos, or other files related to the project. The students were expected to submit a final project report after communication and collaboration via Facebook. The final report would be assessed through a group presentation. The lecturer served as the facilitator and monitored the process by providing feedback, answering questions, and assessing established milestones to ensure that groups were on the right track throughout the semester. In the last teaching week of the semester, predesigned questionnaires were distributed to 150 students in three classes of the subject. After data cleansing, 135 completed questionnaires were used for the final data analysis. The profile information of the respondents is shown in Table 2.

Table 2.

Profile Information of the Respondents.

Category	Frequency	Total (%)
Gender
Female	82	61
Male	53	39
Age
19–20	30	22
21–22	80	59
23–24	17	13
25–26	6	4
> 26	2	2
Year of study
1	0	0
2	15	11
3	60	44
4	52	39
> = 5	8	6
Major
Accounting and Finance	8	6
Double degree	35	26
Management and Marketing	80	59
Others	12	9
Weighted grade point average
0–0.9	0	0
1.0–1.9	0	0
2.0–2.9	50	37
3.0–3.9	69	51
4.0–4.5	16	12
Work experience (years)
< 1	70	52
1–2	40	29
2–3	12	9
3–4	8	6
> 4	5	4

Data Analysis

We used SPSS V.20 for the first round of reliability assessments because several constructs in the research model are newly developed. Initial reliability assessment resulted in the elimination of three items (COMM7, COMM10, and PTP3) from the scale. After double-checking the questionnaire content, the three items were removed from the final data analysis. The Cronbach’s alpha values of the four constructs were all above .712 after the preliminary test. The data were then analyzed using SmartPLS V.3.2.4 (Ringle, Wende, & Becker, 2015). SmartPLS was used to test the measurement and structural models, the moderating effect of tie strength, and the mediating effect of communication in group.

Measurement model

Partial least squares (PLS) structural equation analysis was applied to test the measurement and structural models because of its capability to analyze a small sample size or data with nonnormal distributions (Chin, 1998). PLS is therefore suitable for the data analysis of this study. SmartPLS V.3.2.4 as a mature and widely used PLS software was employed to test the research model. The first step in PLS is to assess the internal consistency and convergent validity of constructs. Table 3 shows that all composite reliability values were greater than the accepted value, which is .70 (Fornell & Larcker, 1981), and the square roots of the average variance extracted exceeded the recommended threshold, which is .5 (Hair, Anderson, Tatham, & Black, 1998). These results demonstrated that the constructs possessed good internal consistency.

Table 3.

Composite Reliability.

Constructs	No. of items	Composite reliability	Square root of AVE
Social media usage	4	.817	.73
Communication in group	8	.927	.79
Tie strength	5	.810	.68
Perceived task performance	2	.898	.90

Note. AVE = average variance extracted.

Discriminant validity indicates the extent to which a given construct is different from other constructs. A criterion for adequate discriminant validity is that the construct should share more variance with its measures than with other constructs in the model (Barclay, Thompson, & Higgins, 1995). Table 4 illustrates that all of the square roots of average variances extracted are greater than the correlations between corresponding constructs. Another means to evaluate convergent and discriminant validity is to examine the factor loadings of indicators; indicators should load higher on their own construct than any other factor (Chin, 1998). Table 5 shows the loadings and cross-loadings of indicators, which confirmed discriminant and convergent validities of the constructs.

Table 4.

Construct Correlations and Square Roots of AVEs.

	Social media usage	Communication in group	Tie strength	Perceived task performance
Social media usage	.73
Communication in group	.43	.79
Tie strength	−.09	.24	.68
Perceived task performance	.29	.47	.27	.90

Note. Diagonal elements are square roots of AVEs. AVE = average variance extracted.

Table 5.

Loading and Cross-Loadings of Indicators.

	Social media usage	Communication in group	Tie strength	Perceived task performance
SMU_1	.666	.200	.006	.124
SMU_2	.896	.462	−.095	.332
SMU_3	.696	.242	−.068	.162
SMU_4	.631	.239	−.080	.131
COMM_1	.374	.822	.198	.389
COMM_2	.325	.875	.288	.466
COMM_3	.336	.816	.118	.422
COMM_4	.288	.828	.151	.347
COMM_5	.437	.848	.147	.357
COMM_6	.385	.744	.184	.358
COMM_8	.193	.633	.224	.325
COMM_9	.315	.682	.178	.251
TS_1	−.075	.224	.751	.155
TS_2	−.191	.021	.497	.162
TS_3	−.127	.173	.741	.196
TS_4	−.032	.136	.675	.237
TS_5	.014	.140	.711	.207
PTP_1	.333	.449	.219	.922
PTP_2	.175	.396	.270	.883

Note. SMU = social media usage; COMM = communication in group; TS = tie strength; PTP = perceived task performance.

Structural model

After confirming the validity of the measurement model, we tested the structural model. The theoretical model and hypothesized relationships were estimated by using 500 iterations of bootstrapping. Bootstrapping generated the results of path coefficients, indirect effects, total effects, loadings, R-square, interacting effects, and mediating effects. Table 6 presents the results of path coefficients in the research model. Three of the four hypotheses were significant. The path coefficients of H2 (between social media usage and communication in group, t value = 6.06) and H3 (between communication in group and perceived task performance, t value = 4.68) were significant at the .01 level. The path coefficient of H4 (between the interaction effect and communication in group, t value = 2.20) was significant at the .05 level. No significant path was found between social media usage and perceived task performance; therefore, H1 was not supported. The R-square values are shown in Figure 2. The values for the two important dependent variables (DVs) in the structural model are 0.28 and 0.23. This result means that social media usage and the interaction between social media usage and tie strength contributed 28% to the variance in communication, and all independent variables (IVs) together explained 23% of the variance in perceived task performance. Age and gender did not exert a significant effect on perceived task performance.

Table 6.

Path Coefficients.

Paths	Path coefficient	t statistics	Significance
H1: Social media usage → Perceived task performance	.11	1.30	Insignificant
H2: Social media usage → Communication in group	.47	6.06	Significant (.01 level)
H3: Communication in group → Perceived task performance	.42	4.68	Significant (.01 level)
H4: Interaction effect → Communication in group	−.12	2.20	Significant (.05 level)

Figure 2.

Results of PLS analysis. The solid lines represent significant path coefficients, and the dotted lines represent insignificant path coefficients. PLS = partial least squares. **p < .05. ***p < .01.

SmartPLS was implemented again to further test the relationship between social media usage and individual/general academic performance of students. The statistical analysis results showed no significant relationship between social media usage and weighted GPA (t value = 0.225).

Moderating and mediating effects

As shown in Table 6, the path coefficient of the interacting effect was −.12 and significant at the .05 level. This result confirmed the existence of the negative moderating effect of tie strength. The new version of SmartPLS allows direct access to the mediating effect. The data analysis results showed that the total indirect effect between social media usage (IV) and perceived task performance (DV) was significant at the .01 level (t value =3.578), whereas the direct effect was not significant (t value = 1.47). In addition, the path coefficients between social media usage (IV) and communication (mediator) and between communication (mediator) and task performance (DV) were significant at the .01 level, with t values of 6.51 and 4.91, respectively. These results proved that communication in group exerted a full mediation effect because the IV showed no effect on the DV due to the involvement of the mediator (Baron & Kenny, 1986).

Discussion

This research investigated the role of social media usage in the effectiveness of student communication in group and perceived task performance. We hypothesized that social media usage directly affects student communication in group and perceived group task performance and that the strength of ties negatively moderates the relationship between social media usage and communication in group.

The data analysis results supported most of our hypotheses. First, contrary to our hypothesis, social media do not directly influence the perceived task performance of students as a group; H1 is thus not supported. No significant relationship was observed between social media usage and perceived group task performance and between social media usage and academic performance in general. This result is consistent with that of a few previous studies (e.g., Junco, 2015; Lambić, 2016) that found no significant differences in the grades of frequent and nonfrequent social media users. However, this result is inconsistent with those of many previous studies that indicated either a positive (e.g., Faizi et al., 2013; Isidore, 2016) or negative (e.g., Huang, 2014; Michikyan, Subrahmanyam, & Dennis, 2015) relationship between social media usage and learning outcomes of students. This result is also not supported by social constructivism theory, which promotes the positive role of social media in the learning of students. A possible explanation for the nonsignificant positive relationship is that students may use social media tools for purposes other than achieving academic goals. In other words, the sole use of social media may not affect the academic performance of students, but how students use social media will. If students use social media for engaging or information-retentive purposes, then the use itself can lead to an enhanced level of perceived task performance (Wang, Woo, Quek, Yang, & Liu, 2012). Therefore, when students use social media for a serious discussion or communication within groups, then the use itself will influence the perceived task performance indirectly. This conclusion was confirmed by the full mediating effect of communication in the present research: Frequent social media usage does not necessarily lead to high perceived task performance, unless the mediating role of communication is considered.

Second, this research confirmed the full mediating effect of communication in group. Communication in group is defined as a group member’s evaluation of the level of group discussion effectiveness and development (Burgoon et al., 2002). Effective communication among group members via Facebook is an essential link in achieving group task performance. Considering that communication via Facebook is a type of computer-mediated communication, group members have to exchange high-quality, transparent, and rich information via the social media platform to facilitate group collaboration. In other words, in the absence of serious, high-quality, and effective communication and discussion, casual usage of Facebook alone would not necessarily lead to enhanced task performance and increased individual academic performance. Our research results verified the significant mediating effect of communication in group.

Third, the results indicated that social media usage is a significant predictor of communication in group (H2 is supported). This result is consistent with that of prior pedagogic research indicating that social media as a computer-mediated communication tool assist in the communication of students (Hung & Yuen, 2010), increases interactivity within groups (Yaros, 2012), fosters a sense of communication community (Vural, 2015), and enhances relational support and self-presentation (Greenhow & Robelia, 2009). In addition, this relationship is well supported by social constructivism theory, which emphasizes the critical role of social environment (via social media, in this case) in the learning process of students.

Fourth, student communication via social media positively influenced perceived group task performance (H3 is supported). This result means that increased communication among group members regarding the project leads to improved perception of the group about the outcome of their group work. This result is in line with that of literature on communication in general (e.g., Imlawi et al., 2015; Pöysä-Tarhonen et al., 2016) and communication in the social media context in particular. For example, in the social media context, scholars have proven that due to the asynchronicity of social media, students feel free to communicate efficiently and deliberately, thus enhancing collaborative learning and academic performance (Güler, 2015; Losada & Heaphy, 2004).

Lastly, our results provided evidence that tie strength among group members negatively moderates the effect of social media usage on communication in group; H4 is therefore supported. Facebook is one of the most popular online social networks used daily by university students. Students with close relationships have strong ties, whereas those with loose connections have weak ties. Our research proves that students with relatively weak ties exhibit improved performance in communication when using Facebook to discuss a group project. This result means that the effect of social media on communication in group intensifies when group members have weak ties. The effect weakens when members have strong ties. This result aligns with that of Granovetter (1973) and Perry-Smith (2014) and reconfirms the power of weak ties in group-based social networking environments, especially in the educational field.

Theoretical and Pedagogic Implications

The current research has several theoretical implications. First, it is among the few studies that simultaneously investigated the effect of social media usage on the learning process (communication in group) and learning outcomes (perceived task performance) of students. Although the majority of prior studies focused heavily on the process of using social media, which involves engagement, collaboration, and communication, this research took another step forward and verified communication as not only a direct consequence of using social media but also a significant mediator of social media usage and perceived task performance.

Second, general social media usage among students does not necessarily lead to enhanced task performance and academic performance in general. This result echoes those of Junco (2015) and Lambić (2016), who posited that no significant differences exist in the grades of frequent and nonfrequent Facebook users. The overall results indicated that the effectiveness of using social media in education depends largely on the manner and objective of such usage. Only through serious and high-quality communication among group members can the effectiveness of social media usage be maximized.

Third, prior studies comprehensively discussed the role of communication in promoting online and offline group work. However, only a few studies examined the nature of communication among group members in the social media context. This research is among the few studies that explored the role and effect of communication in online social network groups. This study confirmed that communication in group exerts a significant full mediating effect on perceived task performance and individual academic performance.

Fourth, this research introduced the concept of tie strength to the educational field. It is among the first to emphasize the role of tie strength, especially weak ties, in the communication process of students. Contrary to natural perception, the results proved that a weak tie between two group mates improves communication quality when using social media and a strong tie between two students weakens communication. In other words, this research brings a new perspective to computer-supported collaborative learning by considering the relational closeness of students.

Lastly, social constructivism theory was employed as the theoretical foundation of the current research. Social constructivism theory has a long history in the educational field, but its application in the social media context is relatively new (Gaytan, 2013). The present study confirmed the explanatory power of social constructivism theory in the effect of social media on communication in group. Nevertheless, the explanatory power of the theory on perceived group task performance was proven to be marginal.

Regarding pedagogic implications, this research extended our understanding of project-based learning in small teams that was conducted online and supported by interactions via social media. Specifically, this study contributes to grouping strategies of classes that rely heavily on online group discussions via social media. When forming project groups, aside from homogeneous or heterogeneous considerations (Lou et al., 1996), educators should also consider relationships between students. If a teacher identifies two students with a stronger relationship than others, then assigning these students to different groups would be ideal. We believe that instead of self-grouping or random grouping, this type of grouping method can increase communication effectiveness and eventually lead to improved academic outcomes. This research also proved that sole adoption of social media as a teaching and learning facilitator might not necessarily lead to improved performance of students; what matters is the manner and objective of using social media. Educators should pay special attention to academic aims when university students decide to use social media for educational purposes.

Limitations and Future Research

This study has several limitations that require further examination. First, a cross-sectional survey was conducted among senior undergraduate students in classes of one subject. The cross-sectional nature could provide only a glimpse of the research issues at a given point in time and could not depict the evolutionary process of several important constructs (e.g., tie strength and communication in group) in the research model. Therefore, we suggest employing a longitudinal research design with data collected over multiple periods to understand the social effects of using social media for educational purposes.

Second, a cross-sectional survey is prone to common method bias due to single informants of the survey. Future research should obtain multiple types or sources of data points, such as collecting data on perceived task performance from the subject lecturer, to avoid common method bias.

Third, we were unable to collect the actual marks of the group projects due to our limited access to academic results, and this inability led to a potential weakness in measuring the real group task performance of the students. We suggest the use of objective measures (actual marks) in future research to supplement the subjective measures of perceived task performance in the current research.

Fourth, because the phenomenon under investigation somehow involved concepts in group levels, such as communication in group and perceived task performance, future research must advance the research model at the group level and conduct multilevel data analysis to examine the relationships among constructs across levels.

Fifth, the measure of communication in group should be reconsidered. Ideally, to obtain a comprehensive view of communication, the construct of communication should be regarded as a high-order construct measured from five different subdimensions. We call for future research to conduct an in-depth investigation on the measure of this construct.

Sixth, although we attempted to diversify the background of the students in a class, studying tie strength in a business environment or any broader social environment is ideal. In these environments, respondents may possess real physical distance from one another and diverse personalities, characteristics, nationalities, and relationships. The current research design somehow limited the scope of studying tie strength to a small group of students who most likely have already known one another for long.

Lastly, for practical considerations, this study tested only causal relationships with one type of social media tool: Facebook. Future studies should consider testing the same model with other popular social media platforms, such as Google Plus, to reconfirm the research results.

Conclusions

Social media as an emerging computer-mediated communication and collaboration tool result in a disruptive change in pedagogy. This study sought to contribute to the growing body of research by proposing a framework for evaluating the effectiveness of social media usage among college students and the influence of tie strength on student group communication. The results corroborated the findings of past research that social media usage significantly influences communication effectiveness, and communication in group affects perceived group task performance. The confirmation of the mediating role of communication in group is one of the major contributions of this study. Another major contribution is in the moderating effect of tie strength. This study is among the first to introduce the concept of tie strength to online project collaboration of students. The results revealed that low tie strength can lead to increased communication among group members and therefore enhances perceived task performance on a project basis. However, social media usage does not directly and significantly influence perceived task performance. These results provide educators new insights into the usefulness of social media in pedagogy and how to divide students into different project groups based on the closeness of relationships in the social network.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The author received departmental teaching and learning enhancement grant (2015-2016) from the Hong Kong Polytechnic University.

Notes

Author Biography

Cong Qi is a teaching fellow at the Department of Management and Marketing, The Hong Kong Polytechnic University. She received her PhD degree in Management Information Systems from the School of Business, the University of Hong Kong. Her research areas cover enterprise social media, Bring Your Own Device (BYOD), social media and mobile computing in education, IT outsourcing, relationship management and mobile commerce, and so forth. Her studies have been published in Decision Support Systems and Information Technology and People, and her works have been presented in international conferences like PACIS, HICSS, INFORMS, and ALT annual conference.

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