Social Networking Site Usage, Social Capital and Entrepreneurial Intention: An Empirical Study from Saudi Arabia

  • HODA, Najmul (Department of Business Administration, College of Business, Umm Al-Qura University) ;
  • FALLATAH, Mahmoud (Department of Business Administration, College of Business, Umm Al-Qura University)
  • Received : 2022.02.10
  • Accepted : 2022.05.10
  • Published : 2022.05.30


Entrepreneurship research has focused on several factors that might affect the intention of an individual to start an enterprise. Using principles from social network theory and the entrepreneurial intention model (EI), the current research intends to investigate how social capital is formed on Social Networking Sites (SNS) and how the resulting social capital influences entrepreneurial intention. Using an online survey, 151 valid responses were received from university students. Applying partial least square structural equation modeling, positive and significant relationship was found between the SNS usage and bonding and bridging social capital. Further, it was also found that online-bonding social capital does not impact any of the three antecedents of entrepreneurial intention. On the other hand, online-bridging social capital significantly influences personal attitudes and subjective norms. It was also found that both personal attitude and perceived behavioral control significantly relate to EI, while the subjective norms do not relate significantly to EI. The paper contributes to the literature on technology-based human behavior and entrepreneurship in emerging countries, opening some areas for future research, while also providing some managerial insights. It also should be beneficial to educational institutions in understanding how the use of SNS use by students may be optimized.


1. Introduction

Social Networking Sites (SNS), a form of “computer- mediated communication”, have been emerging significantly (Ellison et al., 2011; Horng & Wu, 2020; Smith et al., 2017), with the number of users amounting to 4.33 billion users at the start of 2021 (, 2020). These SNS allow individuals to perform social networking online (Boyd & Ellison, 2007; Ranney & Troop-Gordon, 2012). Social networking has been a predominant research topic in various disciplines including business and entrepreneurship (Burt, 2000; Marín et al., 2012; Nahapiet & Ghoshal, 2009). Scholarly, researchers have discussed the role of social networks in the formation of social capital, a resource embedded within the social network of individuals – both online and offline (Adler & Kwon, 2002; Huang et al., 2017; Murphy, 2011).

Entrepreneurship has been receiving a lot of attention both in research and from policymakers because it provides a holistic solution to the current socio-economic problems. One of the major problems facing the majority of the nations is unemployment which finds an effective solution through entrepreneurship (Nikolova, 2019; Thurik, 2003). The entrepreneurial action is certainly preceded by intent and is related to certain behavioral traits of an individual. Many theories, most notably the “theory of planned behavior” (TPB) (Ajzen, 1991) have explained the formation of entrepreneurial intent. Likewise, the role of social networking has also been studied as a factor influencing EI (Zimmer & Aldrich, 1987).

The current paper aims to investigate how social capital formation takes place in SNS and how the resulting social capital affects entrepreneurial intention. The evaluation of the relationship between online social capital formation and entrepreneurial intention should be a significant contribution to entrepreneurship research in general and specifically for Arab countries. The remaining sections of the paper are organized as follows. Theoretical background and research model are discussed in the second section, research methodology in the third section, the results in the fourth section, and discussion and conclusion in the last section.

2. Literature Review

2.1. Online Networking and Social Capital

Research suggests that social capital is formed by intricate relationships and interactions happening within a network (Ha et al., 2020; Smith et al., 2017). Social capital, “is the sum of the resources, actual or virtual, that accrue to an individual or a group via their relationships” (Adler & Kwon, 2002). Other researchers then further confirmed that social networks do result in the accrual of benefits in the form of resources, ideas, and information.

Social capital has been described as a multidimensional concept (Hoda et al., 2020). It has been analyzed in terms of “interaction, emotion, activity” and classified into three dimensions namely, “structural, relational, and cognitive” (Nahapiet & Ghoshal, 2009). Putnam’s classification of social capital into “bonding and bridging” has been used more often in the studies (Putnam, 2001). While bridging social capital deals with loose ties and offer specific benefits like information sharing, the bonding social capital result from strong ties and offer more tangible benefits. Past research has established the relationship between SNS usage and online social capital (Hoda et al., 2021; Mochen, 2013). Thus, consistent with previous research, we hypothesize that:

H1a: There is a positive relationship between SNS Usage Intensity and bonding social capital.

H1b: There is a positive relationship between SNS Usage Intensity and bridging social capital.

2.2. Online Social Capital and Entrepreneurial Intention

Entrepreneurial Intention (EI), defined as the intention to engage in entrepreneurial activity to create a business (Krueger et al., 2000; Lee et al., 2011; Liñán & Chen, 2009; Lu & Wang, 2018), has been much discussed in the literature and the number of studies is still growing (Fayolle & Liñán, 2014). Among the various models of EI (Kautonen et al., 2015; Krueger et al., 2000; Rauch & Hulsink, 2015), TPB is the most popular (Ajzen, 1991, 2011; Koe, 2016; Yang, 2013). The Entrepreneurial Intention Model (EIM) has proved to be a more robust model for predicting the entrepreneurial intention (Liñán et al., 2011; Liñán & Chen, 2009). Linan and Chen (2009) introduced EIM as a refined version of the theory of planned behavior, considering an indirect effect of subjective norms on EI. Research focusing on the role of social capital on EI has generally reported the positive role of social capital in EI (De Carolis et al., 2009; Jeong et al., 2021; Kwon & Adler, 2014; Liñán & Santos, 2007). One of the prominent studies examining the impact of online and offline social capital on EI found significant differences (Pérez-Macías et al., 2019).

Combining the arguments from the literature on TPB and social capital, the current paper aims to study if online social capital influences the antecedents of EI. Online bonding social capital is a result of more close and intense relationships in the SNS. It is hypothesized that:

H2a: Online-bonding social capital and personal attitude are positively related.

H2b: Online-bonding social capital and subjective norms are positively related.

H2c: Online-bonding social capital and perceived behavioral control are positively related.

Online-bridging social capital is a result of loose ties and may provide perceived benefits to budding entrepreneurs in the form of information, promotion, etc. It is hypothesized that:

H3a: Online-bridging social capital and personal attitude are positively related.

H3b: Online-bridging social capital and subjective norms are positively related.

H3c: Online-bridging social capital and perceived behavioral control are positively related.

Liñán (2005) reviewed the role of subjective norms in explaining the EI and inferred that the role of this antecedent has been inconclusive. He, therefore, postulated an indirect role of SN in EI, through PA and PBC. In line with this, we also hypothesize this relationship in the model as.

H4a: Subjective norms positively influence personal attitude.

H4b: Subjective norms positively influence perceived behavioral control.

Consistent with the TPB, the rest of EIM includes the same relationships between the three antecedents, namely, personal attitude, subjective norms, and perceived behavioral control. It is therefore hypothesized that:

H5a: Personal attitude (PA) positively influences entrepreneurial intention (EI).

H5b: Subjective norms (SN) positively influences entrepreneurial intention (EI).

H5c: Perceived behavioral control (PBC) positively influences entrepreneurial intention (EI).

The hypothesized model is presented in Figure 1 below.

Figure 1: Hypothesized Model

3. Research Methods and Materials

3.1. Data

Data collection was done via a structured questionnaire published online and shared electronically among 500 students in a university in the western region of Saudi Arabia by the snowball method. The student sample has been frequently used in entrepreneurial research as this popula-tion shows a greater propensity for entrepreneurship (Ellison et al., 2011; Liñán & Chen, 2009; Ozaralli & Rivenburgh, 2016). Moreover, SNS usage is also expected to be high among this group (Lin & Lu, 2011). A total number of 151 responses were found fit for analysis (response rate of 30%). The percentage of male respondents was 63.9, whereas the percentage of female respondents was 36.1. Among the respondents, students pursuing the Science program were the highest (33.5%), followed by Economics/Social Sciences (28.4%), Business (20%), Engineering (9.68%), and Islamic Studies (7.74%).

3.2. Measures

SNS Usage: To measure SNS usage, we adopted the SNS usage attitude scale developed by Ellison, et al. (2007). The scale asked respondents to choose the social networking site they use the most, then asked respondents to indicate their level of agreement with statements on a seven-point Likert scale.

Social Capital: We elected to use the scale developed by Williams (2006) as it has been developed specifically to measure online social capital. Three items from the online bonding social capital scale and one item from the online bridging social capital scale were deleted as factor loadings were negative.

Entrepreneurial Intention: We used The Entrepreneurship Intention Questionnaire (EIQ) to measure EI and its antecedents (Liñán & Chen, 2009). This instrument measures the perceptions of respondents on a seven-point Likert scale.

3.3. Analysis

The hypothesized model (Figure 1) was analyzed with the Partial Least Square Modelling (PLS-SEM) in the software Smart PLS 3. The PLS-SEM offers a different approach that does not impose any distributional assumptions on the data. Additionally, it is better suited than CB-SEM to handle complex models with fewer restrictions, and it is useful in dealing with data normality and the use of formatively measured latent variables (Hair et al., 2010). Research has also suggested the use of PLS-SEM for studies with small sample sizes and where the emphasis is on exploration more than confirmation (Hair et al., 2011).

The overall procedure included two stages, as suggested by Anderson and Gerbing (1988). First, the reliability and validity were checked using confirmatory factor analysis. The fitness of data in the structural model was assessed in the second stage. Subsequently, all the hypotheses were tested.

4. Results

4.1. Measurement Model

Table 1 present the results of internal consistency and convergent validity. As evident, most of the measured constructs are within the recommended range. The recommended value of Cronbach alpha is above 0.7, and the Average variance extracted (AVE) should be above 0.5.

Table 1: Internal Consistency and Convergent Validity

The results of discriminant validity are shown in Table 2. It includes the average variance extracted and the composite reliability. The numbers in italics are the squared roots of the AVE.

Table 2: Discriminant Validity

4.2. Structural Model

In the next stage, the structural model was evaluated. The analyzed model is presented in Figure 2.

Figure 2: Analyzed Model

The path coefficients and coefficient of determination are presented in Table 3. It includes the values of mean, standard deviation, T statistics, p-values, and the decision regarding the hypothesis. In the second part of the table, the values of R2 and Q2 are shown. These values show the predictive power of the model.

Analyzing the hypotheses in the proposed model, the first hypothesis (H1a) is supported, implying that SNS usage is significantly related to online-bonding social capital (path coefficient = 0.33). Likewise, the second hypothesis (H1b) regarding the role of SNS usage in online bridging social capital is also supported (path coefficient = 0.46). In a recent study (Hoda et al., 2021), similar results were reported. Other supported hypotheses include (H3a), (H3b), (H5a), and (H5c) with path coefficients values of (0.30), (0.29), (0.43) and (0.48), respectively. These results suggest that SN is not significantly related to EI directly or indirectly. The results confirm a significant relationship between PA-EI and PBC-EI. On the other hand, the rest of the hypotheses in our model were not supported. Table 3 above summarizes the results of our hypotheses.

Table 3: Structural Model Results

5. Discussion

The extant paper examined how the social capital is formed in SNS and how it affects entrepreneurial intention. Our results indicated that both online-bridging and online bonding social capital were positively affected by SNS usage. Such findings were reported in earlier studies too (Ellison et al., 2007, 2011; Hoda et al., 2021; Wellman et al., 2001). Research on social capital asserts that bridging social capital represents weak ties that could be formed through social networks to share information and provide other benefits such as job opportunities (Burt, 2000; Granovetter, 1973). On the other hand, bonding social capital represents strong ties that could be developed online and characterized by trust, which facilitates the sharing of tacit knowledge (Hansen et al., 2001).

The results related to the relationships between online social capital and the antecedents of EI revealed some interesting findings. Online-bonding social capital was not found to have a significant relationship with any of the antecedents of EI. On the other hand, the online-bridging social capital was found to be significantly related to PA and SN. These findings support the findings of earlier studies (Liu et al., 2016; Phua et al., 2017). Additionally, one could argue that even when online relationships are somehow influential, the level of such an influence is rather weak due to the lack of direct interactions that usually leads to deeper relationships.

Overall, the model analyzed in the current study suggests that both types of social capital could be formed via intense usage of social networks, but online-bonding social capital does not play a role in the EI, while online-bridging social capital does show up as a significant variable. Further, the role of SN goes in line with many earlier studies. It also fails to show any significant indirect relationship with EI (Liñán & Chen, 2009). As confirmed in numerous studies, PA and PCB were found to be significantly related to EI.

6. Conclusion and Limitations

This paper explores a new dimension in the entrepreneurship literature by studying the linkages between the usage of SNS, social capital, and entrepreneurial intention. A study dealing with the complete linkage of the three constructs was not found when we reviewed the literature. The current study does provide managerial implications as well. Results confirm a positive role of social network sites in the development of online bonding and online-bridging social capital. The paper is also one of the first studies that understand online social capital and its relationship with EI in the context of Saudi Arabia. The findings are significant for policymakers, especially those associated with technology, entrepreneurship, and commerce in general, as well as educational institutions to understand the role of SNS on entrepreneurial intention.

While our results provide solid results, given the robust process of our research, one should approach the results cautiously due to several reasons. First, our sample is relatively small, which could have affected the results. That being said, we analyzed our hypotheses using PLS-SEM, a practice suggested being used for studies with small sample sizes. Second, our sample is drawn from Saudi Arabia, a country that is not a typical developing country. It is a wealthy and influential member of the G20, which allows for more opportunities and resources than other disadvantaged developing countries.


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