The Impact of Government Stimulus Packages Towards Business Survival During Covid-19 Among Small and Medium Enterprises in
Sabah
Mat Salleh Ayub1*, Mazalan Mifli1, Azmi Majid1
1 Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
*Corresponding Author: [email protected] Accepted: 15 February 2022 | Published: 1 March 2022
DOI:https://doi.org/10.55057/ajrbm.2022.4.1.9
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Abstract: The purpose of this study is to examine the impact of government fiscal stimulus packages amounting RM100 billion for SMEs during the first wave of covid-19 to provide greater assistance to SMEs in sustaining business operations and preserving jobs, as well as to support growth in current economic conditions. This paper analyses the business survival during Covid- 19 by examining two determinants, namely government grants and financial assistance. Total number of 150 questionnaires distributed and 120 were returned. The respondents were selected from SMEs directory database and those who have been participated in SME@University program. The data were collected and analysed using Statistical Package for the Social Science (SPSS 26) and SmartPLS to analyse, and hypothesis testing. The study confirms that there are significant and positive impact between government stimulus packages towards business survival among SMEs in Sabah. The findings of this study have useful implication to help strengthen the government business survival approaches to support SMEs in Sabah during the pandemic outbreak. This paper, apart from its contribution to the entrepreneurship research, has useful implications for government in managing Covid-19 stimulus packages to SMEs in Sabah.
Keywords: Government grants, Financial assistance, Business survival, Covid-19 and Small and Medium Enterprises (SMEs)
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1. Introduction
The rapid spread of COVID-19's has wreaked havoc on people's lives, livelihoods, communities, and businesses all over the world. The outbreak of covid-19 pandemic has created new economic crisis globally which affected the business communities. Governments all over the world are enforcing lockdowns to halt the spread of the coronavirus. According to World economic forum, Covid-19 pandemic could trigger the worst economic meltdown since the 1930s Great Depression.
The Small and Medium Enterprises (SMEs) will have significant impact on this pandemic that may cause huge number of SMEs will going out of businesses.
Small and medium-sized enterprises (SMEs) are widely regarded as engines of economic growth around the world, particularly in developing countries. Unexpected events, such as financial crises, technological innovations, or market competition, frequently threaten the survival of SMEs.
According to Duchek (2017), to be successful, SMEs need a capacity of resilience that allows them to overcome such critical business-related situations. Those SMEs who believe in their ability to cope with stressful environments are more able to be resilient, and therefore, more likely to become stronger (Bullough and Renko, 2013).
Within the context of Malaysian, the SMEs have been recognized as major players to stimulate and engine of growth for Malaysian economies. Approximately 60% of SMEs had a negative outlook for the remainder of 2020. One week after the movement control order (MCO) was implemented, this figure increased to 77.7%. In relation to the economic turbulences, the government has announced fiscal stimulus packages totalling RM100 billion for SMEs in order to assist SMEs in maintaining business operations and preserving jobs, as well as to support growth in current economic conditions (SME Corp, 2021).
Malaysia's government has announced several economic stimulus packages to ensure the well- being of the people as well as the survival of businesses that have been harmed by subsequent containment measures. The packages that have been approved by the government such as the RM20 billion Economic Stimulus Package 2020 (PRE2020), the RM230 billion PRIHATIN Rakyat Economic Package (PRIHATIN), the RM10 billion Additional PRIHATIN Package for SMEs, the RM35 billion National Economic Recovery Plan (PENJANA), and the RM10 billion Kita Prihatin (SME Corp, 2021).
The impact of acute covid-19 business interruptions on SMEs is undeniable, especially given SMEs' importance in terms of economic growth, employment, innovation, and opportunities for economic local entrepreneurs (Herbane, 2015). The Covid-19 pandemic has posed greater challenges to SMEs' survival; however, many SMEs were already under pressure prior to the pandemic.
2. Literature Review
As stated by the OECD (2006), most, if not all, SMEs in developing countries face a variety of challenges, including a small market size, a labour shortage, unsuitable business premises, poor transportation and communication infrastructure, a lack of access to reliable market information, a lack of business advisory services, a lack of access to finance, and an unfavourable institutional environment.
As a result, according to Burgess (2002), governments all over the world have prioritised the success of SMEs by increasing funding for their survival. This is supported a study by Kraja et al., (2014), who agreed that governments in developing countries have focused their efforts on supporting businesses, creating incubators for such businesses, conducting market research, improving existing infrastructure, fostering private and public partnerships, developing supportive policies on financing businesses, providing access to markets locally and globally, combating corruption, and creating a favourable business climate.
Existing empirical studies, such as those conducted by Okpara (2010) and Shariff and Peou (2008), demonstrate that government support influences SMEs survival and growth. Government support for SMEs, according to Nguyen et al. (2009), in both developed and developing countries, is a
critical factor. Indeed, the nature and scope of government assistance have a direct impact on small and medium-sized business performance, survival, and growth (Borges et al., 2009).
2.1 Business Survival
Today, the whole SMEs around the globe are facing the crisis that affects the future well-being of the businesses at-large. As the death toll from the coronavirus mounts, economists warn the business world’s greatest casualty will be small and medium-sized enterprises SMEs are a major part of the industrial economies (Saleh & Ndubisi, 2006) and their survival and growth have therefore, being a prominent issue. The contributions of SMEs to employment and the countries’
gross domestic product (GDP) are highly significant. Al-Mahrouq (2010), argued that small firms are indeed the engines of global economic growth, whereas small and medium enterprises (SMEs) play an important role in promoting economic development. The business survival or failure of SMEs due to pandemic covid-19 requires a competent entrepreneurial mind-set of SMEs to analyze and understand critical success factors of the new normal markets. The market environments have change due to lockdown, social distance, lower demand and supply, supply chain disruption and global markets uncertainty.
Business survival is an extremely important part of business recovery after a natural disaster, providing jobs, good and services, and tax dollars. However, there is little research on what features are most vital to that survival at Small and medium Enterprise level (Dietch and Corey, 2011). A few studies have tried to examine factors that determine the success of recovery for individual businesses post-disaster, but results have been inconsistent regarding what matters most.
Business survival determinants are not only interesting to authorities. Commercially oriented institutions involved in new businesses, for example banks, might benefit from understanding these determinants as well when the determinants are used for the decision which starting enterprises to support with a loan. In literature, survival of a company is understood as the ability of an entity to remain on the market, that still exists during observation, which is equivalent to the lack of its liquidation and unhindered activity on the market and business environment (Kozak, 2018).
2.2 Government assistance.
Government assistance programmes are designed to facilitate and stimulate the success of SMEs' business activities (Peter et al., 2018). One of the major goals of government support programmes is to foster growth and enhance the performance of SMEs. Governments provided assistance to SMEs in order to save their businesses, boost their growth, stimulate innovation, and enhance their capabilities by improving managerial capabilities and marketing skills, ensuring they make a greater business contribution to the national economy (Mason & Brown, 2013).
According to Sana and Abbas (2005), government assistance in developing countries, particularly post-war countries, rarely promotes schemes and incentive support systems that lead to the formation, growth, and development of SMEs. Government assistance in the form of tax breaks and exemptions, fiscal fund assistance, and loan availability has a significant impact on the establishment, expansion, and performance of SMEs in developing countries (Al-Mahrouq, 2010).
SMEs form the backbone of the economy and generate substantial employment. According to the Department of Statistics, these businesses contributed 38.9% to GDP and 48.4% to employment in Malaysia in 2019. As a result, it is critical that SMEs receive assistance. But much of the government support for the sector comes from two key sources: the loan moratorium for 6 months and wage subsidies. SMEs must take advantage of government assistance aimed at creating positive externalities in the form of a conducive environment for SMEs survival (Adam & Alarifi, 2021). According to Chen (2006), providing regulatory support, such as law and order, to ensure legal ownership and a favourable business environment is advantageous to the survival and growth of SMEs, particularly in developing economies. Therefore, we formulated the following hypothesis:
H1. Government assistance significantly and positively affect SMEs survival during Covid-19 pandemic.
2.3 Financial assistance
Most small and medium-sized enterprises require financial resources to run and finance daily production. These resources are either debt expenses that are borne by interest, own investments, family investments, or equity that needs a return on profits. Small enterprises tend to have more self-financing, less liquidity, seldom stock issue, lower leverage, rely on bank financing, and use more commercial loans and owner loans. According to Beck et al., 2004, small businesses rely mainly on equity, retained profits, and bank financing. Many business surveys have identified finance has been identified as the most important factor influencing the survival and growth of SMEs in both developed and developing countries.
The findings suggest that overall SME finance assistance has a positive impact on firm performance, employment creation, and labor productivity. The government interventions in term of financial assistance such as cash hand out and grant stand out as effective in business survival and improving SMEs performance during the market turbulences. Beck and Demirguc-Kunt (2006), state that financial assistance for SMEs is critical factors to their long-term survival and growth. It enables entrepreneurs to innovate, improve efficiency, expand into new markets, and create millions of jobs.
Previous research has shown that, SMEs have been unable to withstand the effects of economic downturns (Latham, 2009). This flaw can be traced back to a lack of financial resources as well as the high cost of business capital (Domac & Ferri, 1999). To keep this vital sector from collapsing as a result of the COVID-19 crisis, many governments have provided various types of assistance to SMEs. During the COVID-19 crisis, SMEs received some financial assistance from the government and financial institutions (Song et al., 2020). As for the stimulus package, the government has launched financial assistance to all eligible SMEs such as on-off cash RM3,000.00 and micro credit scheme with a maximum loan limit of RM10,000 per company at no interest.
In theory, SMEs can benefit from government financial assistance in two ways. First, financial assistance can help a business improve its performance and thus generating more cash flows, which is a direct way of removing financial constraints. Therefore, we formulated the following hypothesis:
H1. Financial assistance significantly and positively affects SMEs survival during Covid-19 pandemic.
3. Methodology
3.1. Research Design
This study aims to examine the impact of government stimulus package; monotorium and direct cash out to SMEs towards business survival during the outbreak of pandemic Covid-19. The research variables include independent variables that are the government assistance and financial assistance and the business survival as dependent variable of this research. In order to achieve this aim, the study adopts a quantitative method via online survey using google form due to the implementation of movement control order (MCO). The questionnaire was divided into two sections: demographic information and questions about the effects of COVID 19 on SMEs. Study was successfully covered 150 participants with valuable information on the SMEs.
3.2 Sampling
Due to time and MCO constraint, the research was limited to businesses operated in area of West Cost consists of Kota Kinabalu, Penampang, Tuaran and Papar, Sabah. The study applies the purposive sampling method, where researchers can select the sample to meet specific criteria (Gergory, et. al 1995). A total of 150 respondents were identified in this study and given the questioners using the google form format. The questionnaires developed was uploaded to google form and distributed to respective respondents and were used to collect data. The respondents were selected based on the database of SMEs that have participated in the SME@University program.
Of the 150 questionnaires distributed, only 125 questionnaires were collected and there were 5 questionnaires incomplete, so the number of respondents and complete questionnaires were 120.
The data collection exercise lasted six months, from September 2020 to February 2021.
3.3 Profile of the Respondents
A wide range of demographical characteristics were examined among the 120 respondents in this sample. Demographic information that has been collected from respondents are gender, age, and education background. Whilst since this survey is focusing on SME business survival, the business information collected includes business location, business sector, business ownership status, type of business, and number of employees.
Table 1: Respondents Profile Demographic
Variables
Categories Frequency
(n=120)
Percentage
Gender Female
Male
45 75
37.5 62.5
Age 18 - 25 years
26 - 35 years 36 - 45 years 46 - 55 years
38 36 24 22
31.7 30.0 20.0 18.3 Education Certificate
Secondary Bachelor Postgraduate
24 45 37 14
20.0 37.5 30.8 11.7 Location Kota Kinabalu
Penampang Papar Tuaran
48 38 16 18
40.0 31.7 13.3 15.0
Business Type Retailing Wholesale Manufacturing Construction Services
11 1 58 4 46
9.2 .8 48.3 3.3 38.3 Ownership Sole Proprietor
Partnership Company
77 7 36
64.2 5.8 30.0 No of Employee Less than 5
6 - 10 11 -20 More than 20
39 63 12 6
32.5 52.5 10.0 5.0
The above Table 4.1 showed number of males and females involved, the researcher expects more participants but due to challenges of COVID 19 only 120 of the SMEs population was successfully contributed the finding of the study. As the above table indicates that 63% (75) of the respondents were male and 37% (45) were female. Although the representative of the study is limited according to the SMEs exits in this study, but it can be a good estimation that the male more than females in SMEs.
According to the above table, the respondents were divided into four age groups; the majority of respondents were constituted 31.7% of respondents were with the age of bracket (18 - 25 years), the second largest age group was 30% of respondents (26 – 35) and third age group was 20% of the respondents (36 – 45), and last age groups represented only 18.3% of the respondents which was (46- 55). Most of the respondents showed that they had an education at secondary level constituted 37.5% (45), the second highest education level was 30.8% (37) at bachelor level, the third highest level of education was respondents with technical certificate consists of 20% (20) and 11.7% (14) of the respondents had a postgraduate education for their academic qualification.
As for the business location, most of the respondents were located in Kota Kinabalu, capital city of Sabah that constituted 40% (48), second Penampang 31.7% (38), third Tuaran 15% (18), and Papar 13.3% (16). The strategic location of Kota Kinabalu makes it more competitive for entrepreneurs to setup their business here. Most of the respondents engaged in the manufacturing sectors especially in the food processing that contributed 48.3% (58) in this study. Second highest respondents were in the services sector constituted 38.3% (46), 9.2% (11) in the retailing sector, 3.3% (4) in the construction sector and only .8% (1) involved in the wholesale sector. The manufacturing and services sector are the main economic activities that contribute to the state gross domestic product annually.
The above table also explained the ownership of the business and most of the business ownership was sole proprietor constituted 64.2% (77), second was company comprised 30% (36), and third was partnership 5.8% (7). The choose of ownership might be due to the size of the business and sole proprietor is cheaper and easier to start-up the business. The number of employees was relatively small with the highest number of employees was 52.5% (63) with the number of employees between 6-10 persons. Number of employees less than 5 persons constituted about 32.5% (39), followed by number of employees between 11-20 consisted about 10% (12) and only
5% (6) of SMEs could afford to hire more than 20 employees. Based on this information, most of the SMEs relatively small and very vulnerable to the market turbulence.
4. Data Analysis and Results
In this section, the data of the study will be presented. Hypothesis testing is done by looking at the p-value generated by the Inner model. The first part of the analysis is the frequency tables of the respondents in Table 1and Convergent Validity in Table 4.1, Table 4.2 discussed the Discriminant validity analyses, Table 4.3 the measurement model, Table 4.4 discriminant validity and Table 4.5 discusses the result of the measurement model and the hypotheses testing.
The objective of this research is to examine the relationship between the constructs of stimulus package determinants (government assistance, and financial assistance) towards the business survival (See Figure 1). Respondents answer 11 items of the business survival determinants and also 5 items of the demographic section. The measurement of business survival determinants was adapted which included the exogenous variables such as government assistance 3 items, and financial assistance 4 items. The business survival is the endogenous variable was measured with 4 items. For the data entry, researchers are using SPSS 26.0 and statistically analysed using liner regression technique through the SmartPLS program version 3.2.8 software.
Figure 1: A model of the hypothesized relationships
*Research framework developed based on the litrutre review.
*Research framework developed based on the literature review.
Table 4.1: Convergent Validity: Loadings and cross loadings for the measurement (outer) model
Buss. Survival Fin. Assist Gov. Supp
Buss. Survival .858 .370 -.196
Buss. Survival .889 .464 -.048
Buss. Survival .924 .361 -.162
Buss. Survival .859 .347 -.205
Finan. Assist .409 .814 .080
Finan. Assist .397 .811 .038
Finan. Assist .367 .858 .080
Financial Assistance Government Support
Business survival
Finan. Assist .298 .909 .109
Gov. Supp -.053 .128 .907
Gov. Supp -.142 .092 .925
Gov. Supp -.190 .057 .815
Convergent validity was evaluated using factor loadings, and a factor loading greater than 0.50 was required (Hair, et al., 2006). The initial run of the indicator reliability on 11 latent variables which includes business survival, government assistance, and financial assistance constructs with the value ranging from -0.205 to 0.9264.
Table 4.2: Discriminant validity of constructs
No Construct Business
Survival
Business Support
Financial Assistance
1 Business Survival 0.907
2 Business Support -0.101 0.874
3 Financial Assistance 0.163 0.436 0.836
Note: Diagonals represent the average variance extracted while the other entries represent the squared correlations
By comparing the squared correlations between constructs and the variance extracted for a construct, discriminant validity can be determined. As shown in Table 4.2, each construct's squared correlations are less than the average variance extracted by the indicators measuring that construct, indicating adequate discriminant validity. Overall, the measurement model proved adequate in terms of reliability, convergent validity, and discriminant validity.
Table 4.3: Result of the measurement model Construct/laten
Variables
Measurement Items
Loadings Sdii 𝝉i 𝜶 CR AVE
Buss. Survival Buss_Surv1 .858 .043 20.007*** .905 .934 .779 Buss_Surv2 .889 .035 25.072***
Buss_Surv3 .924 .028 32.526***
Buss_Surv4 .859 .052 16.647***
Fin. Assistance Fin_Asst1 .814 .059 13.902*** ,871 .911 .720
Fin_Asst2 .811 .067 12.089***
Fin_Asst3 .858 .036 23.858***
Fin_Asst4 .909 .024 37.462***
Gov. Support Gov-Supt1 .907 .224 4.056*** .876 .914 .781
Gov-Supt2 .925 .221 4.188***
Gov-Supt4 .815 .252 3.232***
***𝜏 values were all significant at 𝜌<0.01
*Average Variance Extracted (AVE) = (summation of squared factor loadings)/(summation of squared factor loadings) (summation of error variances) *Composite reliability (CR) = (square of the summation of the factor loadings)/[(square of the summation of the factor loadings) + (square of the summation of the error variances)
Based on Table 4.3 the measurement model's results revealed that all of the estimated indices were greater than the 0.7 for Composite Reliability (CR) and 0.5 for Average Variance Extracted (Bagozzi and Yi, 1988) thresholds (AVE). More specifically, the findings show that the AVE for each of the constructs is in the ranged of 0.720 to 0.779 and the composite reliability for all of the constructs were ranged between 0.911 to 0.934. Based on Table 4.3 above, it can be seen that the
τ values were all significant at ρ<0.01, thus the results indicate that the convergent validity measurement model can be said to be valid. And it can also be seen from the Composite Reliability value above 0.6 and Cronbach's Alpha value above 0.7, thus these results indicate that each research variable meets the criteria so that it can be concluded that the overall variable is said to be reliable.
Table 4.4: Discriminant validity of constructs
No Construct Business
Survival
Business Support
Financial Assistance
1 Business Survival 0.907
2 Business Support -0.101 0.874
3 Financial Assistance 0.163 0.436 0.836
Note: Diagonals represent the square root of the AVE while the off diagonals represent the correlations
According to Table 4.4, this study's constructs all met the criterion proposed by Gefen and Straub (2005). To be more specific, all the constructs studied demonstrated acceptable discriminant validity. Table 4.4 displays the square root of AVE as well as the correlations between constructs.
The findings revealed that the square root of AVE is greater (in bold) than the correlation with other constructs. All of the items were discovered to have higher loading on their corresponding construct (bold items) than the cross loadings on the model's other constructs. According to Hair et al. (2013), the loadings should be at least 0.1 higher than the cross loadings to indicate sufficient discriminant validity. Overall, the measurement model demonstrated sufficient convergent and discriminant validity. Hence, it can conclude that discriminant validity is achieved (Chin, 2010).
Table 4.5: Measurement Model with CR, Loading and Path Coefficient Paths in Research Model Path
coefficients (𝜷)
Mean Standard Deviation
(sd)
𝝉 - statistics 𝝆 value Finn. Assist → Buss.
Survival
.457 .462 .088 5.189 0.000***
Gov. Supp → Buss.
Survival
-.211 .212 .076 2.783 0.006***
Table 4.5 shown the measurement Model with CR, Loading and Path Coefficient. A nonparametric bootstrapping (Chin, 1988) applying with 5000 replications (Hair et al. 2014) was performed to obtain the empirical 𝜏 and sd values. Table 4.5 shows with a path coefficient of B=.457, t=5.189, p-value of 0.000<0.01, for the financial assistance and a path coefficient of B=-.211, t=2.783, p- value of 0.000<0.01 for government assistance. The results shows that the significant effect of stimulus package towards business survival, meaning that the 1st hypothesis in this study is supported and second hypothesis in this study also supported.
4. Conclusion
This paper makes some valuable contributions to the study of business survival and stimulus package of Covid-19 by Malaysia government for SMEs. According to the findings of this study, all the independent variables of the stimulus package have a significant and positive relationship
with business survival, which is nearly identical to the study findings of Latham, 2009; Demirguc- Kunt, 2006; Song et al., 2020; Peter et al., 2018 and Mason & Brown, 2013. The results of structural model estimation also showed that the independent variables have a positive contribution to business survival. This analysis finds that government assistance and financial assistance play a role in enhancing business survival among SMEs in Sabah.
As a result, the findings of this study contribute to a worthy result in business survival research, and this study provides new insights in small business research concerning the widely recognised value of business survival. Overall, the adoption of a relationship between business survival and a government stimulus package for SMEs during the Covid-19 pandemic could be not only a challenge, but also an appropriate opportunity-focused response by SMEs facing fierce competition from other SMEs. Thus, as a general conclusion we can say SMEs in Sabah need immediate and adequate enhancement in government stimulus package for post pandemic business survival. The SMEs owner must ensure that the government stimulus packages are utilized in the business at the maximum level. The government and its agencies should provide the necessary assistance and consultative services to SMEs owners for them to prepare the dimensions of the government stimulus package discussed above.
The qualitative study design was ignored because the study was conducted during the MCO and data was collected at a specific point in time. Furthermore, the sample for the study was drawn from only four areas in Sabah, excluding other areas in the Covid-19 area of Sabah. Future research could concentrate on qualitative data by employing interview guides and focus-group discussions.
Acknowledgements
This research was funded by the University Malaysia Sabah Internal Grant No. SDK10826.
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