{'fraction_non_alphanumeric': 0.03486238532110092, 'fraction_numerical': 0.0199962553828871, 'mean_word_length': 5.054409430968034, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The role of perceived social support in the theory of planned behavior in predicting entrepreneurial intention: Evidence from a Nigerian university

Despite the usefulness of the theory of planned behavior in predicting intention and behavior in different domains, the sufficiency of its use in predicting and determining intention has been debated by many scholars. This paper extended the theory of planned behavior by including social support as a possible determinant of intention in the entrepreneurship domain while looking at one of the largest universities in Nigeria. Data were collected from 432 final year students of Ahmadu Bello University in Zaria using a simple random sampling technique. Structural equation modeling was adopted using partial least square technique for data analysis. Perceived social support, attitude towards entrepreneurship, and perceived behavioral control all were found to have a significant effect on entrepreneurial intention, while subjective norms had an insignificant effect. The study found perceived social support to be an important social influence factor in the theory of planned behavior because of its influence on entrepreneurial intention. Hence, perceived social support is recommended to be included as a major construct in the theory of planned behavior.


Introduction
The theory of planned behavior (TPB) is a model considered useful and influential by many authors in predicting social behavior (Ajzen, 2020). It is one of the best supportive social psychology theories applied to various behaviors in understanding individual behavior (La Barbera & Ajzen, 2020). According to TPB, the closest determinant of behavior is the intention to participate in the behavior and is determined by three structures: attitude, subjective norms (SN), and perceived behavioral control (PBC). Despite the usefulness of TPB in predicting behavior in different domains, its sufficiency in predicting and determining intention has been debated by many scholars (La Barbera & Ajzen, 2020;Yzer, 2012). Botetzagias et al. (2015) proposed the addition of other constructs that may increase the predictive power of the model.
Overall, TPB has strong support for predicting intention and behavior; however, many of the differences are still unexplained. Therefore, researchers have recommended adding additional variables to improve TPB's predictive power (Yzer, 2012). Ajzen (2020) supported this by arguing that, in principle, the theory can include other predictable variables as long as there are strong theoretical reasons to join them and that these reasons capture most of the unique differences in intention or behavior.
Social support has been one of the social influence constructs tested and has been found to be able to predict intention within TPB in the exercise domain (Courneya et al., 2000). The rationale for incorporating perceived social support (PSS) in the theory considers the difficulty encountered in executing some behaviors; hence, assistance from others might be helpful beyond perception of attitude, behavioral control, and approval from significant others. This is supported by Grube et al. (1986) who argued that SN were not found to sufficiently capture social influence, especially on behaviors that do not have complete volitional control. Similarly, La Barbera and Ajzen (2020) believed that good attitudes and SN can form good intentions only when people believe they are capable of such behavior. Tornikoski and Maalaoui (2019) added that although TPB is the preferred model applied to entrepreneurship research, people rarely perceive they have perfect behavioral control. This implies that, although others approval of one's behavior may be important, it may not sufficiently capture social influence, especially in the entrepreneurship domain. Entrepreneurship is not an activity seen as completely under the control of potential entrepreneurs; hence, there is a need for assistance from members of the society. Therefore, this paper extended the TPB by including PSS as a possible determining factor leading to intention in the entrepreneurship domain.

The Theory of Planned Behavior (TPB) and Its Application in Previous Studies
According to TPB (proposed by Icek Ajzen in 1991) human behavior follows three beliefs: (a) attitude towards entrepreneurship (ATE): this implies beliefs about possible outcomes or consequences of behavior, and assessment of those outcomes (behavior beliefs) that produce favorable or negative attitudes; (b) SN: normative expectations and motivations that meet the expectations of others (normative beliefs), and (c) PBC: beliefs about factors that may promote or hinder behavioral performance (Ajzen & Fishbein, 1973). The combination of these three constructs (ATE, SN, and PBC) leads to the formation of behavioral intention. As a good predictor of planned behavior, intention itself shows the extent to which people try to perform a given behavior like entrepreneurship (Ajzen, 2020). Empirically, various studies (La Barbera & Ajzen, 2020;Utami, 2017) have found TPB useful in explaining students' entrepreneurial intention (EI). Hence, the application or adoption of TPB to study students' EI had been considered viable and has been applied in different studies. However, studies found that the three components of TPB seem to play different roles in the formation of entrepreneurial intention, and that the role played by SN appears insignificant in the formation of intention (Bulfone et al., 2020;Tornikoski & Maalaoui, 2019). Specifically, assessing nursing students in Italy, Bulfone et al. (2020) found an insignificant relationship between SN and EI to be self-employed. Similarly, in a critical examination of TPB in the entrepreneurship domain, Tornikoski and Maalaoui (2019) concluded that SN was a weak predictor of EI. Kolvereid (1996) was one of the first to apply TPB in his study predicting individual intention to go into entrepreneurship. He found that PBC, SN, and attitude all significantly affected intention. Similarly, Steinmetz et al. (2016) found support for the three main forerunners of EI on ATE, SN, and PBC. In addition, Utami (2017) found a positive relationship between attitude, PBC, and SN on EI. Yang (2013) found that although perceived behavior control significantly affected entrepreneurial willingness of Chinese undergraduate students, its impact was much less than that of entrepreneurial attitudes and SN. Similarly, Potishuk and Kratzer (2017) found that ATE, PBC, and SN all have high explanatory power and are relevant in increasing EI.
On the contrary, Ruiz-Rosa et al. (2020) found support for attitude and PBC, while SN were negligible. Similarly, other studies (Dinc & Budic, 2016;La Barbera & Ajzen, 2020) found a positive and significant relationship between attitude and PBC, while SN did not have significant effect. Other studies (Bulfone et al., 2020;Tornikoski & Maalaoui, 2019) also found partial support for TPB as ATE and PBC were significant while SN was insignificant. Yurtkoru et al. (2014) used the revised TPB to explain the intention to start a business, eliminating SN. They found that personal attitude and PBC predicted the EI.
In summary, although TPB puts forward three constructs (ATE, PBC, and SN) as predictors of intention, previous empirical studies on the application of TPB have found that attitude, SN, and perceived behavior control have had a significant influence on EI. However, other studies (Solesvic et al., 2014;Zhang et al., 2014) have partially supported TPB. Although, ATE has been consistent in terms of its prediction of EI, SN and PBC have been inconsistent (La Barbera & Ajzen, 2020;Muhammad et al., 2015;Zapkau et al., 2015). Hence, to add to the existing literature, the following hypotheses where formulated.
• H1: ATE is significantly related to EI • H2: PBC is significantly related to EI • H3: SN are significantly related to EI

Social Influence (Perceived Social Support) in TPB
Social support is one of the most appropriate and frequently used social influence constructs and helps in understanding and predicting individual behavior (Courneya et al., 2000;Kacperczyk, 2013). This implies that social support is an integral part of the social influence that predicts individual behavioral intention. Another construct under the domain of social influence is normative social influence, also referred to as SN (Chung, 2019). Regarding the persistently weak role of SN when tested in TPB, Grube et al. (1986) argued that SN inadequately represented social influence. This result was similar to Ajzen's (2020) argument that SN as a construct in TPB is weakly related to intention. A limitation attached to the construct is its inability to differentiate between group influences (like peer groups) and influences from significant figures (like parents) because these influencers have different ways of presenting their influences (La Barbera & Ajzen, 2020). Although knowing that people approve our behavior may be important, it may not be an adequate manifestation of the same behavior, especially in the entrepreneurship domain. Social support has been found to be a better social influence factor in exercise intention (Courneya et al., 2000). It is also vital in developing EI (Sahban et al., 2016). The theoretical argument for the inclusion of social support in TPB is in line with the argument given by Ajzen (2020) for extending the theory of reasoned action (TRA). The extension came as results of Ajzen's belief that TRA is applied toward behaviors that are voluntarily controlled behaviors that can be done at will and with fewer constraints (Tornikoski & Maalaoui, 2019).
Because some behaviors may not be under complete volitional control, Ajzen (2020) extended the TRA by introducing PBC to cover for such behaviors. However, no consideration was given to the consistently weak role of SN in TRA when developing TPB, which is the only social influencing factor in the theory. Hence, for behaviors that are not completely volitional, assistance from others (social support) would be more important than mere approval from them (subjective norms). Therefore, this study argues theoretically that the less volitional a behavior, the more important and relevant social support is to the behavior. Tornikoski and Maalaoui (2019) argued that entrepreneurship is one of those behaviors that is not under complete volitional control. This is supported by Aldrich and Zimmer (1984), who posited that entrepreneurship is facilitated or constrained by linkages between aspiring entrepreneurs, resources, and opportunities. This implies that entrepreneurs must establish connections to resources and niches in an opportunity structure; hence, the support for the inclusion of social support as one of the determinants of intention to venture into entrepreneurship.
While the benefits of social support to individuals have long been appreciated, PSS is perceived as a better precursor to psychological activity (Johnson-Siegel, 2017;Sahban et al., 2016). Thus, based on this argument and the recommendations of Eagle et al. (2019), social support may become a core construct in TPB. This study looked at how social support can be a possible determinant of EI (see Figure 1). No study has yet tested TPB while also including PSS in the same model. This study tested the extended TBP by including PSS in the model to predict the willingness of individuals to venture into entrepreneurship. Based on this argument, social support is assumed to improve the prediction of EI. Hence, the following hypothesis is also presented.
H4: PSS is significantly related to EI

Methods
This study adopted a cross-sectional research design.

Data Collection
This study used a self-administered questionnaire to collect data from 9,836 final year students of Ahmadu Bello University, Zaria during the 2018/2019 session. The university was chosen because it is one of the first to introduce entrepreneurship education into its curriculum, and students were chosen because they were found to be the ideal respondents for futuristic entrepreneurship behavior (Krueger et al., 2000).

Sample
The sample size of the study was determined to be 370 students using Dillman et al.'s (2014) sample size formula. However, including Israel's (2013) suggestion, 30% of the minimum sample (111 students) was added to the computed sample size for a total of 481. Of those, 432 were correctly completed and returned. Based on the rule of thumb on minimum sample size determination, the model with four independent variables would need at least 40 observations to achieve statistical power of 80% with a probability of 5% (Hair et al., 2017). Hence, the sample size of the study was adequate to achieve high statistical power. Simple random was employed where each element was given equal chance to be selected in the sample. The sample frame (students lists) was imputed into the excel sheet where random numbers were generated, and list ranked in ascending order.

Measurement of Constructs
The questionnaire was adopted from Liñán and Chen (2009) to measure ATE, SN, PBC, and EI.
All questions were in close ended form and responses were on a 5-point Likert scale (strongly agree, agree, not sure, disagree, and strongly disagree). ATE consisted of 5 items, for example, Entrepreneurship as a career is attractive to me and; Being an entrepreneur implies more advantage than disadvantage. SN consisted of 3 items, for example: My parents are positive and accept my future career as an entrepreneur and My friends see entrepreneurship as a logical choice for me. PBC consisted of 6 items, including Starting a firm and keeping it working would be easy for me and I'm prepared to start a viable firm. EI was measured by 6 items, including I will do anything to be an entrepreneur and I am determined to start a business in the desired future. Finally, PSS was measured with 6 items adapted from Sarason et al. (1987), including I have someone I can talk to when in need of advice and I have someone who can go out of their way to help me when faced with business problems. After adapting the questionnaire from Sarason et al. (1987), a confirmatory factor analysis was run using Jamovi v1.1 statistical software and all the fit indices were satisfactory (RMSEA = .94; CI Upper = .125 -Lower = .067; CFI = .97; DF = 9; X 2 = 40.9; X 2 /DF = 4.54; p < .001). To address issues of common method bias, a procedural approach was adopted during the design and administration of the questionnaire to prevent the damaging effect of erroneous responses (Podsakoff et al., 2012).

Assessment of PLS Path Model
Prior to the main analysis, normality and multicollinearity assumptions were met (Hair et al., 2017). After successfully satisfying all assumptions, the data collected were further analyzed using SmartPLS software for partial least squares path modeling is a statistical process designed to estimate the causal network between two or more constructs and is defined in terms of a theoretical framework (Vinzi et al., 2010). To validate and evaluate the model adopted for this study, Hair et al. (2017) recommended a two-stage assessment: measurement models (external models) and structural models (internal models).

Measurement Model
To evaluate the measurement model of this study, the researchers evaluated the reliability of the individual items of each potential construct, the reliability of internal consistency (i.e., composite reliability and Cronbach's alpha), discriminant validity, and convergence validity of each structure (Henseler et al., 2009). Although Hair et al. (2017) proposed an indicator for the scale of development, an outer loading of .70, AVE of .50, composite reliability/Cronbach's alpha of .70 is reliable and acceptable. The validity and reliability results are presented in Table 1. All constructs in Table 1 were reliable because their respective composite reliability and Cronbach's alpha values were above the threshold of .70. Again, all constructs had indicator reliability and convergence validity because the AVE level of each structure was higher than .50. Furthermore, to ascertain the discriminant validity, Duarte and Amaro (2018) proposed the use of heterotrait-multimethod (HTMT) matrix as an alternative approach. Ab Hamid et al. (2017) added that the traditional Fornell-Larcker criterion and cross-loading are insufficient and insensitive in detecting the effectiveness of the identification compared to the HTMT criterion. Therefore, the discriminant validity was evaluated using the HTMT matrix (see Table 2). The HTMT statistics presented in Table 2 were based on the correlation between all reflective constructs. Since the HTMT value is lower than the 0.85 threshold proposed by Hair et al. (2017), the reflective latent structure of this study has discriminant validity.

Structural Model
After all the measurement model conditions were met, the structural model was evaluated. The first part of the structural model evaluation involved the examination of theoretical relationships. Specifically, standard bootstrap was adopted on a sampled case of 432 using 5,000 bootstrap samples to assess the importance of path coefficients for the relationships (Hair et al., 2017).

Figure 2. Structural Model
The bootstrapping result in Table 3 shows that all the relationships were significant except SN. Specifically, ATE and EI was significantly related (t = 8.10, p < .05), PBC was also significantly related with EI (t = 7.91, p < .05). Results also showed a significant relationship between PSS and EI (t = 8.30, p < .05). However, an insignificant relationship was found between SN and EI (t = 1.41, p < .05) at 5% level of significance. Table 3 results are represented graphically in Figure  2, where the T-values are expressed on the lines between the independent variables and dependent variable.

Coefficient of Determination (R2), Effect Size (ƒ2) and Predictive Relevance (Q2)
The coefficient of determination or assessment of the R 2 level was assessed to evaluate the amount of variance explained by the exogenous latent variables on the endogenous latent variables. According to Chin (2010), R 2 values are .67, .33, and .19 respectively, and are considered substantive, moderate, and weak respectively. The ƒ 2 values provided an overview of the potential effect or impact of a particular exogenous variable on the endogenous variable. The general criterion for evaluating ƒ 2 values of either small, medium, or large. ƒ 2 value was measured by .02, .15, and .35 respectively (Cohen, 1988). However, in this study, the predictive correlation (Q 2 ) of external latent variables was examined using cross-validated redundancy criteria, reflecting endogenous latent variables (see Table 4). The findings for ATE, SN, PBC and PSS explained 61% (.61) variance in EI. The R 2 value explained by these latent variables on the target endogenous latent variable was moderate. On the effect size, ATE, PBC, and PSS had a medium effect size, while SN had no effect on EI. Therefore, since Q 2 is greater than zero, a predictive correlation is assumed because the higher the Q 2 , the higher the predicted correlation (Duarte & Amaro, 2018).

Importance-Performance Map Analysis (IPMA)
The importance performance diagram (Figure 3) shows the x-axis, which represents the overall (non-standardized) impact of ATE, PBC, PSS, and SN on the target construct EI (that is, its importance). The y-axis represents the average rescaled (and unstandardized) latent variable scores (i.e., their performance) of ATE, PBC, PSS, and SN on the target construct EI.
Both Figure 2 and Table 5 show that a one unit increase in ATE led to 53% change in EI. In addition, one unit increase in ATE, PBC, PSS, and SN led to 60%, 49%, and 49% leads to a corresponding increase in EI, respectively. Hence, PSS emerges as the most important variable among the four with .36, followed by ATE (.32), PBC (.31), and finally, SN with the least important score of .05. This implies that it would be logical to invest on the performance improvement of PSS, ATE, and PBC because of their high impact on the target construct (EI).

Conclusions
The findings of this study conclude that ATE, PBC, and PSS are good predictors of EI given their significant contributions. SN, on the other hand, was found to have an insignificant effect on EI. This implies a logical focus on developing ATE, PBC, and PSS to enhance student EI. This conclusion is in line with the findings of previous studies that established a positive and significant relationship between ATE and PBC on EI (Dinc & Budic, 2016;La Barbera & Ajzen, 2020). The findings also concur with other studies (Muhammad et al., 2015;Zapkau et al., 2015) which found that SN is a weak predictor of EI. Therefore, the findings suggest a need for another social influence factor (i.e., PSS) in TPB to improve its application, especially in entrepreneurship studies.

Theoretical Implications
The study expands the use of the TPB framework to provide useful information on PSS and entrepreneurship intentions. Although, Ajzen (2020) considered TPB one of the most influential models in predicting social behavior, Courneya et al. (2000) argued that without social support, the model is incomplete, especially on behaviors like entrepreneurship that is incompletely volitional. The study was the first to include PSS as a predictor of EIs in the TPB model. Therefore, this research is helpful to the development of the theory. A similar study conducted in Indonesia also found a positive relationship between social support and students' inclination towards entrepreneurship (Sahban et al., 2016). Ip et al. (2017) also found that PSS was the most prominent antecedent of social EI. In the health sector, social support was also found to influence health promoting behavior like physical activity (Johnson-Siegel, 2017).
The findings showed that PSS is of prominent importance for the development of intention to go into entrepreneurship. These findings lend support to other studies (Courneya et al., 2000;Jairam & Kahl Jr., 2017) which highlighted the buffering strength of PSS to enhancing individual intention. In this study, the R 2 of 0.61 was found to be comparatively higher than most studies on TPB that excluded PSS in the model (Botetzagias et al., 2015;Primandaru, 2017), demonstrating the relevance of the construct in the model. PSS was found to be the most important construct among the four predictors of EI with the highest in terms of importance, while SN were the least in terms of performance and importance. Among the social influence factors, PSS was relevant to the TPB when predicting EI. This is in line with the suggestions of many studies positing those entrepreneurs would find supportive behavior more important than normative behavior (Asimakopoulos et al., 2019;Eagle et al., 2019). Thus, given the involuntary nature of entrepreneurship behavior, PSS can be an equally important factor and theoretically relevant social influence construct in the TPB.

Practical Implications
From a practical perspective, TPB-based interventions will need to facilitate strategies that generate supportive behaviors and foster positive social norms (Asimakopoulos et al., 2019;Sahban et al., 2016). Although the literature review revealed SN and social support as two social influence factors, this study found that social support is greater than SN at predicting intention to perform a behavior. Hence, practitioners should focus more on social support than SN as it markedly improves EI. Practitioners need to teach supportive behavior generally to those in a position to facilitate and assist entrepreneurship behavioral change beyond soliciting the approval of entrepreneurship from family and friends. This support will enable governments and other agencies involved in policy making to channel their resources and ideas towards continuously approving, motivating, and supporting young people, who then may develop the intention to go into entrepreneurship.
Compared to other sub-Saharan African countries, Nigeria's global ranking of EI is consistently low (Acs et al., 2018;Singer et al., 2014). This study also unveiled another variable for practitioners to consider as they attempt to boost EI in Nigeria. Although, the Nigerian government has introduced entrepreneurship education to boost business start-ups, the study's findings suggest the need for the government to go even further by creating a supportive society that promotes and encourages entrepreneurship across the country.

Limitations and Suggestions for Further Studies
Such a society of encouragement will broaden the understanding of the social support construct in terms of its relationship with belief, attitude, intention, and behavior. For example, within different social supports identified in the literature, researchers can explore ways of generating specific social supports that are more relevant to entrepreneurship behavior. Again, among the sources of normative behaviors (e.g., family, friends, teachers etc.), researchers can attempt to identify the most important sources of normative behavior. In addition, future research can also study behaviors that are not volitional to build consistency in demonstrating the importance of PSS in predicting behavioral intention. With the consistently weak role of SN in relation to EI, future studies should also consider adopting an indirect approach to the relationship. This may explain why the direct relationship has been consistently weak.
Despite the contributions of this study, several limitations were highlighted. This research suggested the need for further studies, taking the following into consideration. First, this study adopted a cross sectional research design; for example, data was collected from students in Nigeria at a specific point in time. Cross sectional design does not address issues of causality; hence, one should exercise caution when generalizing. Therefore, future studies can address this limitation through longitudinal studies, which would collect data at two or more points in time to compare to results of this study; and could appropriately produce a causal effect. Another limitation was the choice of only students as respondents of the study; other studies can consider respondents who have not been exposed to entrepreneurship training (non-students). Again, the study adopted path analysis, which only checks for predictive relevance between independent and dependent variables. Future studies can look at multi-group analysis to compare different population groups.