Identification of Determinants Entrepreneurial Intentions during the Covid Pandemic on Open Unemployment in Tasikmalaya

The pandemic had weakened the economic sector, increasing the number of unemployed. On the contrary, dozens of new entreprises had been arisen during the pandemic, indicating the increase of individuals who opt to become entrepreneurs. The aim of this study is to investigate the formation of entreprerenurial intention and its contributing factors. In this study, a modified version of the Theory of Planned Behavior is utilized, adding resilience as additional relevant variables to predict the behavioral intention during crisis. A total of 247 responses from unemployed in Tasikmalaya City and Tasikmalaya Regency was gathered and processed using multiple linear regression. The analysis revealed the positive influence of atttitude, subjective norms, perceived behavioral control, and resilience to unemployed entrepreneurial intention. This research highlighted the importance of resilience to reinforce the individual decision to become an entrepreneur during the adverse situation.


Introduction
Since its emergence at the end of 2019, the Covid-19 pandemic has become an event that severely hampers various aspescts of society, including the health system and the global economy (Hernández-Sánchez et al., 2020).Consecutively, the economic downturn increased the number of unemployed.In Indonesia, 15.6% of workers lost their jobs at the start of 2020.Most workers affected by layoffs are between 15-24 years (Ngadi & Purba, 2020).Tasikmalaya City is no exception; in 2021, 26,236 employees has to be laid off.This number has decreased by 4% compared to 2020 (Open Data Kota Tasikmalaya, 2022a).On the other side, the available vacancies in 2021 were only 1020 (Open Data Kota Tasikmalaya, 2022b).
Economic pressures that arise from the loss of jobs and income (Blustein et al., 2019) will encourage individuals to look for other alternatives to income generation like starting a business (Parker, 2018;Ramadani, 2013).The Covid-19 pandemic also created several business opportunities like delivery and health services (Galindo-Martín et al., 2021).As an illustration, the number of MSMEs in Tasikmalaya City in 2021 will increase by 6% compared to 2020 (Open Data Jabar, 2022).
Previous researches concerning the formation of entrepreneurial intention highlight the importance of internal factors, namely personal attitude (Del-Aguila-Arcentales et al., 2022), self-efficacy, and risk tolerance (Welsh et al., 2021;Cater et al., 2021).External factors like family support (Ragazou et al., 2022) and societal perceptions (Lopes et al., 2021) are also crucial in developing entrepreneurial intention.In this research, the Theory of Planned Behavior (TPB) (Ajzen, 1991) is selected as a theoretical model to predict the formation of entrepreneurial intention.This model is quite reliable and has been commonly utilized in various research fields, including entrepreneurship (Krueger et al., 2000;Lortie & Castogiovanni, 2015).Based on TPB, the intention to perform a certain behavior is triggered by personal attitudes, subjective norms, and perceptions of behavioral control (Ajzen, 1991).
The Covid-19 pandemic presented an unfavorable economic situation for entrepreneurs (Cepel et al., 2020;Diandra & Rahmatullah, 2020).Facing the adverse nature of the pandemic, most people would not opt to start a business (Bullough et al., 2014;Ruiz-Rosa et al., 2020;Hernández-Sánchez et al., 2020).In contrast, Botezat et al. (2022) and Lopes et al. (2021) present the increasing trends of people choosing entrepreneurship as a career choice during the pandemic.These conflicting conclusions from various research highlight the importance of individual entrepreneurial intention during the pandemic, particularly the relevant factors for individuals to start a business (Liñán & Jaén, 2020).
When facing adverse conditions, resilience is crucial to maintain a meaningful life (Tedeschi & Calhoun, 2004).Resilience is required for entrepreneurs to help adapt so that they can look to the future when facing unstable market conditions and unexpected situations (Ayala & Manzano, 2014) and provide the strength to manage crises so that they can stay firmly on their feet and adjust their business to the incoming risks (Sharma et al., 2021).Therefore, our research aims to investigate the relations between attitude, subjective norms, perceived behavioral control, and resilience with entrepreneurial intention.

Methodology
In this study, four hypotheses were proposed, which are arranged in a research model presented in Figure 1.

Figure 1. Research model
The proposed research hypotheses is as follows: • H1: attitudes positively influences entrepreneurial intentions.
• H3: perceived behavioral control positively influences entrepreneurial intentions.• H4: resilience positively influences entrepreneurial intentions This research employed the quantitative approach, which processed numerical data using statistical methods (Saunders et al., 2007).The research data was obtained from the respondents' responses as outlined in the research questionnaire.The participant responses were measured using a Likert scale, with a scale of 1 stating "strongly disagree" to a scale of 7 which means "strongly agree".To ensure the questionnaire's validity and reliability, a total of 30 participants were involved in an initial survey.The next phase of the study was distributing the questionnaire to the individuals who met certain criterion.As such, the purposive sampling was the most appropriate method for gathering the suitable respondents (Sekaran & Bougie, 2016).The multiple linear regression was selected to process the gathered data.
The population in this research was unemployed in Tasikmalaya City and Tasikmalaya Regency.Unemployment was restricted for those above 15 years old who are not in employment but still searching for job vacancy, preparing a new business, or not actively searching for job vacancies.Those who already got a job offer but have not yet started working were also involved.Individuals who are in education, taking care of the household, or doing non-personal related activities were omitted in this study (Badan Pusat Statistik, 2022).
Since multiple linear regression analysis was used for data processing, the required samples must be at least ten times of variables in the research model (Sekaran & Bougie, 2016), so that the minimum number of samples required is 50.For more satisfactory results, it is recommended to use of 100 observations (Hair et , 2010).In this study, we distribute 350 questionnaires to selected participants in the Tasikmalaya region.After further selection for valid responses, 247 questionnaires were used for further analysis.

Result and Discussion
Validity and reliability test Validity test with 30 initial respondents is needed to ensure that all questions in the questionnaire used provide valid responses.Determining the validity was achieved by bivariate correlation; each of the questions with the r-count value higher than the r-table were selected as valid questions (Ghozali, 2018).The results of validity test of each of the questions were presented in Table 1.Source: primary data processing Table 1 shows that the r calculated value of all questions exceeds the r table value, so that the validity of the questions listed in the questionnaire is proven and the questionnaire is feasible to use for research.
The purpose of reliability test is to determine the level of consistency of the answers from respondents recorded by the questionnaire.A questionnaire is said to with the Cronbach Alpha value exceeding 0.70 is regarded as reliable (Ghozali, 2018).The results of the reliability test are presented in Table 2. Source: primary data processing Table 2 shows the Cronbach Alpha value for each variable exceeding 0.70.Thus, the questionnaire has consistent results that are reliable (reliable).

Test for classical assumption
To ensure that the regression model meets the BLUE requirements, the collected data must undergo the test for classical assumption, which consists of normality test, multicollinearity test, heteroscedasticity test, and autocorrelation test.
The normality test was carried out to ensure the gathered data has a normal distribution (Ghozali, 2018).Based on the criteria of the Kolmogorov-Smirnov test, the data has a normal distributed if the significance value exceeds 0.05.Table 3 reveals the significance value of 0.200, exceeding the required value of 0.05.In conclusion, the data set in this research were normally distributed.Source: primary data processing .The heteroscedasticity test is conducted to check the uniformity of the variance of all observations.An observation is said to have heteroscedasticity if there is a difference between the variance of one observation and the variance of other observations.Meanwhile, if the variance in all observations is uniform, then the observation has symptoms of homoscedasticity (Ghozali, 2018).The method used is the Gjelser test, with the provision that heteroscedasticity symptoms will appear if the significance value of the regression to the residual is below 0.05.Results of Table 4. revealed all of the significance level of the variables were higher than 0.05.Therefore, no heteroscedasticity was found in all of the observation.Source: primary data processing The multicollinearity test is important for examining any existence of a correlationnal relationship between all of the independent variables in the research model.A good regression model should not correlate with its independent variables (Ghozali, 2018).The VIF value is used to check the multicollinearity; no multicollinearity symptoms are found if the VIF is less than 10.Based on the VIF testing results in Table 5., there was no multicollinearity found in the independent variables of the research model.Source: primary data processing The autocorrelation test aims to determine the existence of a correlation between residual errors in an observation and errors in previous observations.The method used in the autocorrelation test is the Durbin-Watson test.Source: primary data processing

Goodness of fit test (F-test) and hypotheses testing (t-test)
The goodness of fit test (F-test) is required to ensure the usability of the regression model to influence the dependent variable (Ghozali, 2018).The method used in the F-test is to calculate the F value and then compare it with the F-table value.Source: primary data processing Referring to Table 8, the calculated t value for all proposed hypotheses exceeds the t table value (1.971), so that all proposed hypotheses can be accepted.Table 9 shows the details of the calculated regression model, the regression model used is Y = 0.27 + 0.454X1 + 0.212X2 + 0.393X3 + 0.300X4+e Source: primary data processing

Correlation and determination test
The test for correlation (R) and determination (R 2 ) is necessary to measure the level of influence of all of the independent variables on the selected dependent variables.The method deployed in correlation and determination tests was the Pearson Product Moment method, with the results shown in Table 10.Source: primary data processing The results of data analysis confirmed all of the independent variables (attitudes, subjective norms, perceived behavioral control, resilience) have a positive relationship on entrepreneurial intention, with a correlation coefficient value of 0.877.The coefficient of determination of 0.766 indicates the effect of all independent variables of 76.6%, while the other 33.4% is influenced by other variables not involved in the regression model.

Discussion
Results of the hypotheses testing confirmed the proposed hypotheses testing; attitudes, subjective norms, perceptions of behavioral control, and resilience were crucial for entrepreneurial intention development.
Specifically, the personal attitude had a positive influence on entrepreneurial intentions.In accordance with the findings of Al-Jubari (2019) and Ozaralli & Rivenburgh (2016), personal attitudes also become a key factor on entrepreneurial decisions.Someone who has a strong interest in entrepreneurship as a career choice will exert higher entrepreneurial intention (Al-Mamary et al., 2020).
Subjective norms, emerging from external parties who an individual regarded as important (Ajzen, 1991), was positively influenced the entrepreneurial intention, consistent with findings from Ridha et al. (2017) and Al-Jubari (2019).Family members, friends, and relatives can provide material and mental supports that are necessary for individual to become entrepreneur (Al-Mamary & Alraja, 2022).Favorable societal or national support (Lopes et al., 2021;Del-Aguila-Arcentales et al., 2022) also boost the intention to become entrepreneur.Without external support, people tends to be reluctant to start a business (Al-Mamary et al., 2020).
Perceived behavioral control is also a contributor to entrepreneurial intentions with a significant and positive effect, as also found in Misoska et al. (2016) and Kristiansen & Indarti (2004).Individuals who have the confidence to control the business processes and overcome the risks will prefer to become selfemployed rather than work with other people (Al-Mamary & Alraja, 2022).
We also investigated the influence of resilience on entrepreneurial intention during the Covid-19 pandemic.The results of hypotheses testing confirmed the statement.Pérez-López et al. (2016) study also revealed the direct and positive influence of resilience for entrepreneurial decision in difficult times.The effect of resilience on entrepreneurial intentions tends to be stronger during crisis compared to peaceful times (Bullough & Renko, 2013;Renko et al., 2021).

Conclusion
The findings of this study uncovers the positive influence of attitude, subjective norms, perceived behavioral control, and resilience in the formation of the unemployed entrepreneurial intention during crisis situations, specifically the Covid-19 pandemic.In regard with resilience, the results reveals the importance of resilience as a crucial factor for individual to start a new enterprise during adverse situation.This results of this study contribute for the entrepreneurial research, particulary the formation of entrepreneurial intention in the time of crisis.
By using unemployed as the societal segment of the study, this study brings different perspectives on the entrepreneurial intention in specific demographics.The results can be compared to other study of entrepreneurial intention in various segments.like refugees (Almohammad et al., 2021) or migrants (Del-Aguila-Arcentales et al., 2022).
Several limitations are present in this study.The participants involved in this study are restricted to the unemployed in the Tasikmalaya region.Therefore, future research on various population segments in geographical regions can be conducted to enhance the generalization of the research findings.This study is also only observing the intention to start a new business; future studies may explore the relationship between entrepreneurial intention and actual business establishment.The model used in this study only investigates the direct effects of independent variables.Thus, future research models may include mediating or moderating variables.Further research models can also use additional variables such as entrepreneurship education (Misoska et al., 2016)or need satisfaction (Al-Jubari, 2019), or combined with other models such as the Entrepreneurship Orientation Model from Lumpkin & Dess (1996).

Table 6 . Autocorrelation test results
The calculated Durbin-Watson value is then compared with the table dU value and the table 4-dU value.Autocorrelation symptoms will not be found if the calculated Durbin-Watson value is between the table dU value and the table 4-dU value.Table 6.showed the calculated Durbin-Watson value (2.026) is between the table dU value and the table 4-dU value (1.8094 < 2.026 < 2.1906).In conclusion, no autocorrelation presents in the data set.

Table 7 . F-test results
The calculated F value shown in Table 7 is 183.979; this value exceeds the value of F table (2.25).Thus, the regression model obtained is able to influence the dependent variable properly.Testing the research hypotheses proposed in the research model is tested by t-test.The proposed hypotheses will be accepted if the calculated t value exceeds the t table value.