The Role of Government Financial Support Programmes, Risk-Taking Propensity, and Self-Confidence on Propensity in Business Ventures

Abstract

This study identifies the effect of government financial support services (GFSS) on the relationship between two dimensions of psychological factors in business ventures. As such, the study uses small and medium enterprises (SMEs) of various Malaysian sectors as samples, with two constructs used to represent the dimensions of psychological factors, namely, risk-taking (RT) propensity and self-confidence (SC). The study results revealed that both constructs had a significant influence on the propensity to venture into business (PVB). However, GFSS had an insignificant moderating effect on the relationship between the two dimensions and provided input for policy makers to enhance GFSS efficiency in order to positively affect the growth rate of new businesses in Malaysia. Hence, this study bridged the research gap by identifying the influence of GFSS on PVB. As GFSS are an important tool for stimulating growth of the new business venture, the findings of this study have implications for policy makers to improve the quality and effectiveness, especially the delivery of GFSS to the targeted group.

1. Introduction

Entrepreneurship is recognised as the main economic driving force, with entrepreneurs playing an essential role in entrepreneurial activities through job opportunities for community members [1]. In line with this significant economic contribution, the government consistently supports business ventures by providing financial and nonfinancial assistance [2]. Moreover, the government encourages people to actively participate in entrepreneurial activities. Therefore, several assistance programmes were developed to promote the establishment of new business entities, with more emphasis on promotional efforts, given the unfavourable current economic condition due to the COVID-19 pandemic. The world is now faced with employment crises, and millions of people around the globe are experiencing job loss. In the wake of this crisis, the government encouraged the setting up of new businesses to reduce unemployment rates, consequently positively affecting the country’s gross domestic product [3]. As such, the increase in new businesses reflects the growth of the Malaysian economy and indicates the relevance of studying and understanding the factors influencing propensity to venture into business (PVB). Additionally, policy makers may gain more input in designing more effective policies and programmes for new start-up businesses.

In Malaysia, small and medium enterprise (SMEs) represent 99.2% of the total establishments in three key economic sectors, namely manufacturing, services, and agriculture. In total, 99.2% of SMEs operate in the services sector. SMEs contributed 38.3% to overall gross domestic product, 17.3% to total exports and 66.2% to total employment in 2018. The importance of SMEs to economic stability and social well-being was manifested by the government through a large allocation to carry out SME development programs. In 2019, a total of RM 13.7 billion has been allocated for an inclusive development agenda to further strengthen the growth of SMEs. The government believes the growth of new firms will further boost entrepreneurial activities and indirectly increase SMEs’ contribution to the country’s GDP. Thus, the National Entrepreneurship Policy 2030 (NEP 2030) was launched in 2019 to provide guidelines for entrepreneurial activities and be a catalyst for the formation of entrepreneurial culture among the Malaysian society. Among the objectives is to promote entrepreneurship as a career of choice and form a Malaysian community with an entrepreneurial culture. To realise this objective, the community is encouraged to venture into entrepreneurship activities by venture into business. To draw the public’s interest, the government actively promotes entrepreneurship as a career of choice. In addition, the entrepreneurial culture is embedded in the national education curriculum. The business registration process has been improved to facilitate the registration of new businesses. Targeted assistance and intervention programs to enhance entrepreneurial capabilities and skills are also designed to stimulate new business growth.

To further stimulate the growth of new businesses, this study aimed to identify the factors that influence PVB, which include government support factors and psychological factors. Therefore, this study provides insights into the relationship between the psychological factors (risk-taking (RT) propensity and self-confidence (SC)) and PVB in relation to the provision of external support (government financial support services (GFSS)) and its role as a moderator between the psychological factors and PVB, which has never been examined before. It contributes to the greater understanding of the combination of psychological factors and external support (GFSS) as a strategy to stimulate new business venture. Furthermore, this study intends to measure the moderating effect of GFSS and their relationship with psychological factors, and it also examines how GFSS influence the psychological factors by looking at PVB. This study also measures the relationship between the psychological factors and the moderating effect of GFSS and PVB, which has also never been examined before. The result and findings of the study contribute to the resource-based-view theory and business support literature pertaining to Malaysia in particular and other developing countries in general. Empirical support for the role of GFSS is still limited in the context of government intervention in entrepreneurship development.

Little is known about the entrepreneurs’ characteristics and motives that encouraged PVB. Although previous research examined the importance of various demographic variables in new business ventures [4,5,6,7,8] the analysis of previous works found no studies on the influence of GFSS in PVB. Furthermore, studies on the moderating effect of GFSS in psychological traits and PVB remain scarce. Therefore, both the direct and indirect relationships of the variables must be analysed for a more comprehensive picture of the propensity to become involved in entrepreneurial activities. As such, this study aimed to empirically identify the influence of several factors, such as GFSS, RT propensities, and SC on PVB in Malaysia. The results of these findings are expected to help the government in formulating a more robust entrepreneurship policy as well as to formulate more effective development programs towards creating an entrepreneurial culture.

Section 2 discusses and analyses an extensive literature review leading to the development of a conceptual framework on the PVB. This is followed by Section 3, which discusses the methodological employed in this thesis with which the application of the methodology selected was justified. Section 4 reports the characteristic of the respondents and the results from the statistical analyses of the collected data using the partial least squares structural equation modelling (PLS-SEM) technique. Finally, Section 5 presents the implication of the findings for theoretical perspectives and practicality. This section includes the conclusion and the implications of the findings. It also outlines the limitations of this study and makes suggestions for future research.

2. Literature Review

2.1. Risk-Taking Propensity and PVB

Risk is defined as the probability of an occurrence and is usually associated with adverse consequences affecting a situation. Risky conditions indicate situations where the probability of negative occurrences would affect the activities involved [9]. On the other hand, RT propensity refers to tendencies in acting on risky projects to achieve a target return [10]. In the context of entrepreneurship, entrepreneurs are often interpreted as individuals with RT propensities [11].

Therefore, a risk-taker boldly engages in decision-making in situations lacking certainty and information. This risk appetite is deemed necessary, as entrepreneurs often operate in dynamic business environments that demand quick and accurate decisions. Moreover, RT acts are occasionally performed in decisions-making processes concerning investments, either for setting up a new business or introducing new products in the market [12]. From the perspective of new start-ups, the decision to venture into entrepreneurship activities is considered risky.

Studies indicated that the risk of failure for new businesses, regardless of the entrepreneurs’ assumption of taking moderate risks, is very high [13]. Thus, the tendency to take risks is largely associated with the entrepreneurial process [14]. Besides, an entrepreneurial process is incomplete without this attitude in an entrepreneur or firm. Stevenson & Gumpert [15] argued that the attributes of an entrepreneur are considered lacking without RT tendencies. In other words, ambitious individuals with goals to achieve excellence in businesses accept failure with courage, and without overly worrying about failing at every step.

Additionally, Brockhaus [16] suggested that there is no difference between entrepreneurs and non-entrepreneurs, from the perspective of RT. This opinion, however, was contradicted by many other researchers [4,8,17]. Furthermore, Zaleskiewicz et al. [18] stated that entrepreneurs and managers differ in terms of RT. Furthermore, Maslow suggested that the tendency to take risks could be seen at every stage of human needs. For example, everyone is involved in RT from meeting physiological needs, such as getting food, to the psychological needs of self-esteem and self-actualisation.

The need to achieve a goal motivates a person to participate in RT and affordability levels [3]. On the other hand, McClelland [19] suggested that performance-oriented individuals work hard and often make risky decisions, as these individuals believe that high success levels are proportionate to high-risk levels, thus shaping the approaches in risk management [20]. Novel challenges and explorations also represent risks [21]. According to Kirkwood [22], new entrepreneurs face various risk levels in ensuring the ability of new businesses to generate profit. Based on previous arguments on the necessity of entrepreneurs to have RT attributes, this study tested the influence of these attributes on an individual’s PVB. Therefore, this study tested the following hypothesis:

Hypothesis 1 (H1). 

RT propensity has a significant positive impact on PVB.

2.2. Self-Confidence and PVB

Self-confidence is one of the important psychological factors characterising entrepreneurship. Past research revealed that entrepreneurs have a higher degree of SC than non-entrepreneurs [23]. As confidence is one of the keys to success in business [24], determined individuals work hard to achieve their goals. Hence, the confidence level is important, as entrepreneurs with a higher confidence level have more opportunities in efficient business promotions.

To successfully launch a new product and innovation in the new market, new entrepreneurs require some degree of SC [25]. In other words, SC is an entrepreneurial characteristic, highly influencing entrepreneurial success. As SC is significantly responsible for business performance, this study capitalised on the roles of SC in influencing PVB. Furthermore, Driga et al. [26] argued on the possibility of SC in influencing potential entrepreneurs. This study also provided insights into studies on the role of SC in new business formations, which remain scarce. Therefore, this study tested the following hypothesis:

Hypothesis 2 (H2). 

SC has a significant positive impact on PVB.

2.3. Government Financial Support Services and PVB

Financing is one of the important issues on SMEs, specifically during the start-up period. Therefore, the inability to perpetually raise capital contributes to the failure in SMEs’ expansion [27,28,29], as the weaknesses extends far beyond financial matters. Thus, SMEs’ external participation in financial facilities is crucial [30]. The positive impact of financial assistance on SMEs is also widely discussed and supported by several studies. Grimmer et al. [31] posited that access to financial support significantly improves SMEs’ performance level. Usually, the approval of GFSS is based on the creditworthiness of new entrepreneurs. The 5 C’s Credit Model, which refers to character, capital, capacity, condition, and collateral is a popular approach used by most SMEs’ financial support providers. Character refers to the entrepreneurial credit history that focuses on repayments behaviour. On the other hand, capital refers to the amount of capital invested in a business, while capacity is the firm’s ability to generate income and ability to make repayments. The current economic situation and business environment are referred to as the condition, and, finally, the collateral refers to the amount of assets placed as security. However, in most cases, the collateral requirement is not imposed on new business ventures. In terms of an entrepreneur’s RT propensity, it is not accessible or accounted for in decisions regarding GFSS. In short, entrepreneurs’ RT propensity level is not a criterion for extending GFSS to SMEs.

Moreover, a significant effect of SMEs’ operation in financial capability was found in a study conducted by [32], which revealed that 93.6% of the SMEs confronted problems in maintaining sufficient working capital to finance day-to-day operations. As such, this situation created a negative chain effect on other business activities, such as the ability to provide sufficient training programmes and the arrangement of adequate marketing activities [33]. The result of the study conducted by [32] and [34] also highlighted the domination of working capital issues in SME-related activities.

Additionally, Bohata and Mladek [35] argued that insufficient funding to finance business expansions was one of the barriers hindering SMEs’ growth in the Czech Republic and was supported by [36] and [30], who reported that the obstacles in accessing finances and the high cost of financing became a major threat for SMEs’ growth, in addition to tax burdens. As such, the “finance gap” among SMEs, particularly with regard to bank finance, and the action taken to bridge the gap, were acknowledged by academicians and practitioners [37].

Furthermore, the government acknowledged the “gap” and difficulties faced by SMEs in raising capital to finance daily business activities. Several financial products related to working capital finances were designed and offered through various agencies and government-related institutions in assisting SMEs’ finance management. Consequently, the products (grouped according to financing purposes) available on the market, among others, financed the acquisition of land, buildings, machinery, modernisation, working capital financing, and export financing in collaboration with the SME-related support programmes provided by the government.

Moreover, GFSS strongly influence the business performance of specific start-ups. Past studies also showed that entrepreneurs, particularly new entrepreneurs, face various financial constraints that become a major obstacle for prospective entrepreneurs to pursue business ventures. Furthermore, new entrepreneurs adapting to the world of entrepreneurship are most likely unable to start a business without GFSS. Thus, this study analysed the influence of GFSS on PVB. Therefore, the following hypothesis was tested:

Hypothesis 3 (H3). 

GFSS have a significant positive effect on PVB.

2.4. The Moderating Effect of GFSS on the Relationship between RT Propensity, SC, and PVB

This study also determined the influence of GFSS on the relationship between RT and PVB, and the relationship between SC and PVB, respectively.

2.4.1. Risk-Taking and GFSS

Entrepreneurs take risks with a moderate approach and focus on positive outcomes [38]. New entrepreneurs should also practise this risk appetite. Moreover, the provision of GFSS creates more opportunities for new business formations, removes the main obstacle concerning capital constraints in start-ups, and allows new entrepreneurs to purchase equipment and provide working capital to operate. As the initial phase of the business presents a high-risk level in terms of capital and effort, the existence of GFSS enables new entrepreneurs to continue business activities and improve the level of risk tolerance [39]. In other words, the GFSS could mitigate the risk of failure when starting a business. It is suggested that the support services induce bolder risk appetites among potential entrepreneurs to PVB. Based on the argument presented, the following hypothesis was proposed:

Hypothesis 4 (H4). 

GFSS moderate the relationship between RT propensity and PVB.

2.4.2. Self-Confidence and GFSS

The financial support provided by the government to potential entrepreneurs involves grants, among other forms of aid [34,40]. The prerequisites for risk assessment are less rigid than other commercial financial institutions. Additionally, soft financing facilities would ease the burden of new entrepreneurs in search of start-up capitals, which significantly hinders most of the individuals from PVB [40]. With the availability of sufficient funding, prospective entrepreneurs can focus more on strategic business planning. Thus, with adequate funding and a comprehensive plan, the SC level of potential entrepreneurs can increase to face a dynamic and challenging business environment. Hence, the following hypothesis was suggested:

Hypothesis 5 (H5). 

GFSS moderate the relationship between SC and PVB.

3. Materials and Methods

This study adopted a cross-sectional design with the target population comprised of members of the Kelantan Chamber of Commerce, and measured the relationship between RT, SC, and PVB with the influence of GFSS as a moderating factor. To measure the impact of the constructs on PVB, the study used scales of measurement that were developed, tested, used in other studies, and deemed to be reliable. The RT measurement scale was adapted from [41], who studied the development of instruments to measure RT under individual entrepreneurial orientation, and from [42], who studied the distribution of RT across demographic factors among micro-SMEs. For SC, the measurement items were adapted from [43] and [44], who studied the character of entrepreneurs. Finally, the measurement item for PVB was adapted from [45]. Researchers commonly employed this approach in developing a survey instrument. Kitchenham and Pfleegar [46] stated that this approach had two major advantages: the trustworthiness of the instruments based on validity and reliability tests and the comparison of new results to results from previous studies.

This study used four constructs consisting of 19 items to measure the influence of independent variables (PVB) as shown in the Figure 1. The constructs consisted of five items, except for SC, which consisted of four items. The validity and reliability of the selected items were confirmed, as reported in previous studies. However, a goodness of measure test was conducted in the pilot study to further affirm the items’ reliability and validity before distributing the questionnaires to respondents, as the previous studies were conducted abroad with different cultural and social backgrounds. This study used a five-point Likert scale ranging from one, indicating “strongly disagree” to five, indicating “strongly agree” to avoid confusion and biases of fatigue in longer scales.

The study sample consisted of 106 SMEs operating in the state of Kelantan, Malaysia and utilised a random sampling method. The respondents were selected using a table of random numbers, whereas the sample size was based on numbers, as suggested by [47]. In addition, the researchers also used the Hair approach by multiplying the number of an item of the model with 5 and 10 ratios. Using this approach, the acceptability of the sample size should be fall within the range of 90 and 190. Given these figures, we confirmed that a sample size of 106 is adequate for this specific research project. This research used the partial least squares structural equation modelling (PLS-SEM) technique to analyse the relationship between RT propensity, SC, and PVB as the moderating effect of GFSS. PLS-SEM was selected because the nature of the proposed model is suitable for exploratory studies, and this study is more interested in predicting the relationships between constructs. The PLS technique is an analysis method used to assess the proposed theoretical model under SEM with simultaneous tests on the relationship between indicators, latent variables [48], and the constructs (structural model). Moreover, Henseler, Christian & Sinkovics [49] affirmed that PLS is able to handle complex models containing a high number of constructs, indicators, and relationships. Besides, PLS does not require normal data distribution and is capable of maximising the explained variance in the dependent constructs, apart from assessing data quality through the measurement model.

4. Results

4.1. Demographic Characteristic

The majority of the respondents are aged between 31 years and 50 years (80.18%). Regarding gender, 63.21% of the respondents are male and the remainder are female (36.79%). Concerning education, 30.19% of the respondents obtained first-degree qualifications, 10.38% a master’s degree, and the remainder, 55.66%, a certificate of secondary school. The majority of respondents have run their business for more than 5 years. Two-thirds (67.92%) fall under the services sector as presented in the

Table 1. Profile of the respondents.

Demographic	N	%	Demographic	N	%
Gender			Age
Male	67	63.21	Less than 30	42	39.62
Female	39	36.79	31–40	54	45.51
			Above 40	10	9.43
Education			Length of Business
Secondary School	23	21.70	Less than 1year	34	32.08
Diploma	36	33.96	1–3 year	45	42.45
Degree	32	30.19	3–5 year	14	13.21
Master	11	10.38	5–10 year	13	12.26
Gender			Sector
Male	78	73.58	Services	72	67.92
Female	28	26.41	Manufacturing	34	32.08

.

The study results are presented in two stages [50]. Additionally, ref. [14,51] suggested that the first stage focuses on reliability and validity measures (the measurement model results). In contrast, the second stage presents the structural model before the positive result in phase 1. Chin [50] also argued that structural model measurements are not required when the validity and reliability of the results are questionable.

4.2. Measurement Model Results

The first step involved evaluating the reliability and validity of the measurement items in the latent variable [51,52]. The measurements of loading and cross-loading for each item are shown in

Table 2. Loading and cross-loading.

	RT	SC	GFSS	PVB
RT1	0.812	0.232	0.294	0.591
RT2	0.898	0.311	0.271	0.631
RT3	0.892	0.320	0.364	0.546
RT4	0.728	0.273	0.294	0.327
RT5	0.805	0.447	0.265	0.583
SC1	0.193	0.686	0.399	0.297
SC2	0.457	0.888	0.422	0.500
SC3	0.207	0.842	0.357	0.313
SC4	0.340	0.900	0.416	0.421
GFSS1	0.289	0.358	0.729	0.353
GFSS2	0.310	0.370	0.879	0.447
GFSS3	0.268	0.397	0.882	0.392
GFSS4	0.333	0.377	0.755	0.339
GFSS5	0.196	0.398	0.681	0.309
PVB1	0.485	0.231	0.373	0.686
PVB2	0.562	0.349	0.366	0.850
PVB3	0.530	0.457	0.449	0.853
PVB4	0.416	0.288	0.362	0.619
PVB5	0.553	0.470	0.261	0.801

.

All the item loadings were above 0.7, with high loading on that construct and lower loading on the other construct, as shown in Table 2, thus signifying construct validity. Moreover, all the item loading values exceeded 0.6 and indicated the construct validity in this study [51]. The results also showed no commonality in constructs [49]. As such, the measurement items conceptually used were distinct [50].

4.3. Reliability Test

The composite reliability (CR) coefficient was examined to assess the consistency of the used measurement items [50,51,52,53].

Table 3. Result of the reliability test.

Latent Variable	Items	Labels	Outer Loading	CR	Cronbach Alpha	AVE
Risk-Taking
	I like to take bold action by venturing into the unknown.	RT1	0.812	0.916	0.887	0.688
	I tend to act “boldly” in situations where risk is involved.	RT2	0.898
	I am ready to take risks.	RT3	0.892
	I am willing to invest a lot of time and money in something that might yield a high return.	RT4	0.728
	I have a strong preference for high-risk projects.	RT5	0.805
Self-Confidence
	I manage all aspects of my business.	SC1	0.686	0.9	0.852	0.694
	I organise and maintain the financial records of a business.	SC2	0.888
	For me, everything is possible if I believe I can do it.	SC3	0.842
	When I take on a project, I have confidence in carrying it out successfully.	SC4	0.9
Government Financial Support Service
	I do seek financial advice from government bodies and agencies before starting a business.	FS1	0.729	0.891	0.845	0.623
	I go to government agencies to acquire information on soft financing before venturing into business.	FS2	0.879
	Before forming a business, I do seek advice on grant facilities sponsored by the government.	FS3	0.882
	I do seek assistance from government agencies to finance working capital before setting up a business.	FS4	0.755
	I do seek assistance from government agencies for seed capital.	FS5	0.681
Propensity to Form a Business
	I prefer to be an entrepreneur rather than an employee in a company.	PVB1	0.686	0.876	0.820	0.589
	I put every effort to start and run my own business.	PVB2	0.85
	I was interested in becoming an entrepreneur long before I started my business.	PVB3	0.853
	I have had thoughts of becoming an entrepreneur since completing my studies.	PVB4	0.619
	I am prepared to do anything to become an entrepreneur.	PVB5	0.801

shows that the CR coefficient value exceeded the cut-off of 0.6, thus confirming high reliability [49]. Moreover, construct validity demonstrated that the measurements fit the theory for which the test was designed. The two tests were performed to access construct validity (convergent validity and discriminant validity). As such, the convergence validity and discrimination validity tests were conducted to assess the construct validity and theory fit. A convergent validity test was used for the suitability of the construct measurement item. The test results revealed that the factor load exceeded 0.7, CR exceeded 0.6, and average variance extracted (AVE) exceeded 0.5, as suggested by [48,51]. Discrimination validity tests, on the other hand, reported the extent to which items were differentiated between constructs by examining the correlations between potentially overlapping construct measures [20,54].

Table 4. Fornell–Larcker criterion analysis for evaluating discriminant validity.

	GFSS	PVB	RT	SC
GFSS	0.789
PVB	0.471	0.768
RT	0.355	0.666	0.829
SC	0.478	0.475	0.383	0.833

shows the square correlations for each construct, thus confirming adequate discriminatory validity with no common method variant issues in this study [55]. Moreover, the value of construct correlations is lower than the recommended value of 0.85, suggesting that the model is free from the harm of a multicollinearity problem.

4.4. Structural Model Results

The second stage concerned the development of a structural model and hypothesis testing. Path coefficients were calculated to evaluate the significance of each construct and examine the t-value. Furthermore, bootstrapping procedures were performed to obtain test results [11,56].

The results in Figure 2 and

Table 5. Path coefficient and hypotheses testing.

	Standardised Estimates	t-Value	p-Value	Hypotheses Supported
RT -> PVB	0.528	6.511	0.00	Yes
SC -> PVB	0.179	2.057	0.04	Yes
GFSS -> PVB	0.198	2.306	0.021	Yes

demonstrate that all the hypotheses were supported and reveal that all the relationships were significant and positively influenced PVB. The R² value of PVB was 0.536 and showed that RT, SC, and GFSS could explain 53.6% of the PVB variance. The R² value was considered high by [50]. For the hypothesis testing, all the independent variables were found to be a significant predictor of PVB with RT (β = 0.528, t = 6.511, p < 0.01), GFSS with PVB (β = 0.198, t = 2.306, p < 0.01), and SC with PVB (β = 0.179, t = 2.057, p < 0.01). Hence, RT was found to be the most influential predictor of PVB.

4.5. Moderating Effect of GFSS

This study applied both product indicator and two stage approaches for moderation analysis to detect the moderation effect of GFSS on the relationships between RT, SC, and PVB [56]. In this stage, the researchers applied the two-stage approach as it is commonly used for the composite method as PLS-SEM. Using the recommendation of [56], the main effect of the model was executed with a PLS algorithm to generate a latent variable score of each construct available in the research model. Data from PLS output were taken and imported in the original dataset for the following analysis. At the second stage, the interaction term was built up using the product indicator approach, as shown in the Figure 3 and

Table 6. Moderating effects of GFSS.

	Standardised Estimates	t-Value	p-Value	R2	Hypotheses Supported
GFSS -> PVB	0.194	1.984	0.048 **	0.566	Yes
RT -> PVB	0.496	6.044	0.000 *		Yes
RT GFSS PVB -> PVB	−0.064	0.759	0.448		No
SC GFSS PVB -> PVB	0.085	1.089	0.276		No
SC -> PVB	0.198	2.292	0.022 **		Yes

Note: ** p < 0.05, and * p < 0.1, respectively. Source: own compilation.

, and the 5000 replication of the bootstrap application was executed to generate an estimate for all of the effects in the model. To determine the significant increase in R², the effect of size was calculated as suggested by [57].

The moderation analysis results demonstrate that GFSS did not significantly moderate the relationships between RT and PVB, and SC and PVB, respectively. In other words, the effects of the interactions were insubstantial (0.759 for RT and 1.089 for SC) and did not support the hypotheses. To confirm these results, the interaction plot analysis was performed. Based on the output given from Figure 4 and Figure 5, it shows that government financial support does not truly moderate the effect of risk-taking and self-confidence on propensity into business.

Furthermore, tests on the effect of the size indicated the presence of a moderating effect (f 2) of 0.04 for RT and 0.05 for SC, which was considered small [51]. Although the hypotheses were not supported, the moderator’s role should not be neglected despite the insubstantial interaction [51].

5. Discussion and Conclusions

This study used the psychological traits theory, which advocates that an individual’s psychological characteristics influences the tendency to engage in entrepreneurship. Past studies showed that psychology factors significantly influence the impetus to set up a business. The study results found that RT propensity strongly influences PVB as a dominant factor. Therefore, a person with a high level of RT propensity is more likely to choose entrepreneurship as a career, resembling SC and PVB. This study also found that SC significantly influences PVB and was identified as one of the entrepreneurial characteristics.

On another note, GFSS also significantly influence PVB, as the availability of financial assistance and support from the government encourages the emergence of new entrepreneurs. The study findings on the relationship between GFSS and PVB are new and of interest to policy makers in enhancing service efficiency. With a more effective programme, the presence of new Malaysian entrepreneurs could be generated and increased. As such, the offer of financial support from the government allows potential entrepreneurs to overcome the main obstacle in setting up business funds. Therefore, GFSS act as a catalyst in increasing SMEs in Malaysia. However, government intervention in the form of financial support services is not a guarantee for the viability and sustainability of a business. New entrepreneurs in particular should be aware that GFSS represent the effort of the government to help entrepreneurs to overcome financial constraints in starting a business. The viability and sustainability of a business does not just depend on financial support factors alone. Therefore, entrepreneurs need to prepare themselves by improving their knowledge and functional skills in managing a business [45].

Notwithstanding, this study also found that GFSS do not significantly moderate the relationship between RT propensity, SC, and PVB. Thus, the tendency in RT and SC was not affected by the availability of financial assistance.

Furthermore, the tendency to take risks was not affected by the availability of financial assistance. This was evident in the absence of the government’s financial assistance for many new start-up SMEs. Past studies also revealed that new entrepreneurs do not rely on the government’s financial assistance to start business ventures, as family, friends, and other informal sources become alternative financiers [33,54,58]. Therefore, a person with RT propensity and SC is more inclined to venture into business despite the absence of financial assistance from the government. Hence, this study concludes that GFSS are an inappropriate moderator variable in this study, with more suitability as an independent variable. The study findings are also in line with previous studies and found that RT propensity and SC are the most determinant factors of an individual’s willingness to become an entrepreneur [7,14,17,59]. Through the new study findings, it was found that financial support from the government is also a driving force in entrepreneurship. A systematic and focused support system has a positive impact on the role of GFSS in influencing the PVB among the Malaysian community. The strategy of delivering support services, especially financial support, to niche and dedicated markets has a significant impact on the role of GFSS. Financial support is designed according to the size of a business, namely micro, small, and medium. For example, micro and small businesses require more support in the form of seed capital compared to medium-sized businesses. Thus, the focus on business size provides the opportunity for the government to effectively formulate each type of support to meet the different needs of each business size.

GFSS are an important factor in the growth of new business, because they are more accessible to new entrepreneurs than private financial institutions, venture capitalists and business angels [60,61]. Government agencies that provide financial support services are nonprofit organisations. Instead, these agencies are more motivated by socioeconomic development. Therefore, the risk assessment for financial support decisions for new entrepreneurs is less stringent. In contrast, commercial financial institutions apply a stringent risk exposure assessment and become one of the main considerations when making a financial decision. It goes without saying that a new business venture is categorised as high risk. Thus, new businesses find it difficult to obtain financial assistance from private institutions. Therefore, it could be argued that GFSS also act as venture capitalists and business angels in the initial stage of business venture. Editor’s Furthermore, GFSS act as a catalyst for the growth of entrepreneurial activities among the community. GFSS are able to minimise risk avoidance behaviour in the decision-making process to engage in entrepreneurship. Moreover, GFSS also strengthen the level of SC of prospective entrepreneurs when adequate financial support can mitigate the failure risk of a new business venture.

This study is not without limitations despite significant contributions. The collected data were derived from new Malaysian entrepreneurs. Although the number of samples was statistically sufficient, there was inadequacy in generalising the findings. Furthermore, the insignificant impact of GFSS may reflect the low level of exposure to GFSS, thus resulting in limited knowledge on the benefits of the financial facilities provided to potential entrepreneurs. Therefore, future studies are recommended to measure the knowledge level on GFSS and investigate the cause of the nonsignificant interactional effects in this study.