Questionnaire-survey

Questionnaires have been distributed by post mail to all SMEs program’s participants, two weeks after the mailing, a second phone call was made for companies that had not replied. The whole procedure take around 1 month and finally yielded a response rate of 55.78% from 65 questionnaires sent out. From the return mails, 6 of them are certify that they did not participate in the programs. Another 7 of SMEs respondents also deny ever participated in the programs when making second phone call. Therefore, from 65 questionnaire sent out, we got 29 SMEs respondents who are willing to give feedback about their involvement in accelerator programs.

Those respondents were consists of 8 Cloud Computing firms, 5 Biotechnology firms, 5 ICT firms, 4 Green Energy firms, 3 Machinery firms, 2 Logistic firms, and 2 Culture &

Creativity firms. Moreover, 19 of them are SMEs which is choose as pioneer companies in the end of the program. Those companies consists of 4 Cloud Computing firms, 3 Biotechnology firms, 4 ICT firms, 4 Green Energy firms, 2 Machinery firms, 1 Logistic firms, and 1 Culture & Creativity firms.

From the questionnaire respondents, 59% of them have size of employee around 11-30;

21% less than 10 people; 10% between 31-50 and 10% more than 71 people. The average amount of respondents capital; 31% between NT$510K-NT$1,000K; 17%

between NT$1,100K-NT$5,000K; 14% more than NT$50,000K; 10% less than NT$500K; 4% between NT$11,000K-NT$50,000K, and another 4% between NT$5,100K-NT$10,000K. From those collected data, we can see that most of the accelerator program’s participants are come from new venture companies with currents employee among 11-30 people within capital amount between NT$510K-NT$1,000K.

From 29 questionnaire respondents, 43% of them wrote agree and 8% wrote strongly agree. The result show that half of respondents are agree with the statement accelerator programs has an influences for SMEs firm resource. And for further research, all return questionnaire data will be analyzed using IBM SPSS Statistics 20 linear regression. The purpose of this analyses is to investigate the relationships between each SMEs firm performance dimension with accelerators services items.

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Reliability and Validity

To begin the research study, first we are try to tests questionnaire reliability and validity problem. Cronbach’s coefficient were used to determine the stability of financial dimension, management dimension, and satisfaction. Another spearman correlation coefficients also were used to assess the strength of accurately concept of the questionnaire items. All tests were two-sided and with assumption a 5% significance level. Result of the test is shown in the table 10.

Variables Cronbach’s Alpha 0.05 (2-tailed)

Financial 0.977 Sig

Management 0.979 Sig

Satisfaction 0.963 Sig

Table 10 Analysis of Variance for reliability and validity

From result of table 10 show that the reliability and validity testing of all items dimensions are significant at alpha 0.05 level. According Lee Cronbachto theory “if a coefficient testing is above Cronbach’s alpha 0.7, thereby its lending support to indicate suitability of the items in each dimension”.

Regression Model

We begin regression analysis with creating a regression model of average each item in financial dimension with funding, mentoring, and networking accelerator’s services.

= 0 + 1funding + 2mentoring + 3networking

Dependent variable ( ) is an average of each item in financial dimension of all survey samples; independent variable is an average of funding ( 1) , mentoring ( 2), and networking ( 3) in each item question of financial dimension. Next, all data were testing with coefficient’s alpha 0.05 level, result from regression linear testing revealed that funding was significant with a p-value = 0.002, networking significant with p-value

= 0.001, only mentoring is not significant with p>0.05. Overall results from testing showed that performance of financial dimension SMEs has a close relationship with funding and networking accelerator programs.

Another regression analysis with regression model for average items of management

FN = 10 , MN = 11, SN = 5

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dimension also has been tested with coefficient’s alpha 0.05 level, dependent variable ( ) are average each item of management dimension and independent variable is funding ( 1), mentoring ( 2) and networking ( 3) in each item question of management dimension. The result of testing was only mentoring variance is significant with p-value

< 0.001.

= 0 + 1funding + 2mentoring + 3networking

Finally, regression model for average items of satisfaction dimension has been tested with coefficient’s alpha 0.05 level. Dependent variable ( ) is an average of each item in satisfaction dimension and independent variable are funding ( 1), mentoring ( 2) and networking ( 3) in each item question of satisfaction dimension.

= 0 + 1funding + 2mentoring + 3networking The result of all testing are shown in the table 11 below:

Table 11 Result of testing overall average financial, management and satisfaction In the next research, we tried to group alike category together with giving an initial name for each group’s. In the financial dimension; revenue, sales, and net profit are grouped in firm income ( . Turnover rate capital ( is standing alone;

marketing channel, market share and International expansion are grouped in market expansion ; funding resources and foreign investment are grouped in additional funding . In management dimension; long-term strategy, short-term strategy, and business model are grouped in firm objective ; human resources, firm regulation, IP management, R&D, and quality control are grouped in firm internal assist ; sales advice, overseas assist, and products exports assist are grouped in firm external assist . Furthermore, more detail of regression model for all group’s category are shown in the table 12.

Dependent Variable Independent Variables

Beta

Coefficients

P – Value (sig)

Avg Financial Funding 0.406 0.006

Networking 0.499 0.001 Avg Management Mentoring 0.751 < 0.001 Avg Satisfaction Funding 0.388 0.013

Networking 0.384 0.020

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Table 12 Regression Model for All Group Category Result of testing with group categories are shown in the table 13 below:

Dimension

Networking 0.441 0.012 Networking Networking

Market

0.029 Networking Funding Foreign

Mentoring 0.751 <0.001 Mentoring Mentoring Firm

regulation

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Mentoring 0.729 <0.001 Mentoring Mentoring Overseas assist

Product export

Table 13 Result of Group Category Testing

As we can see in table, result of testing shows that firm income, capital turnover rate, and firm expansion performance have a strong correlation with accelerator networking programs, and other financial programs (funding resources and foreigner investment) has influences for firm to obtain an additional funding; besides, accelerator mentoring program has a solid correlation with firm management performance.

Other testing with same regression model has been tested with 24 pioneer companies, who have received a fully accelerate from Start-up Taiwan Accelerator and other participants, who has not been selected as pioneer companies. The aim of this testing is tried to examine and investigate questionnaire respondents from different side of angles.

The ways we examine are still the same, first, we are testing their overall average for financial, management and satisfaction dimension. Table 14 was showed the result of the testing.

Dimension Dependent Variables Beta Coefficients P – Value (sig) 24 Pioneer SMEs

(N = 19)

Financial 0.463 0.042 Funding

Management 0.728 < 0.001 Mentoring

Satisfaction 0.438 0.051 Funding

Other SMEs (N = 10)

Financial 0.700 0.008 Networking

Management Mentoring 0.848 Networking 0.432

0.003 Mentoring 0.052 Networking

Satisfaction - -

Table 14 Result of Testing Average Financial, Management and Satisfaction between 24 pioneer SMEs and other SMEs

Results showed that according to 24 pioneer companies, accelerator programs which have an influences in their firm’s financial performance the most is accelerator funding

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program. Furthermore, accelerator mentoring program has been helping them in increasing management performances.

Overall, most of them are still satisfied with accelerator funding programs. Another results from other SMEs participants, who is not being selected as pioneer companies, they feel that accelerator networking program has helped their firm financial performance increase. They also claimed that accelerator mentoring and networking programs had influences for their firm management performance. However, when we are testing satisfaction dimension, the result is they are unsatisfied with accelerator programs.

Second, the same examination regression model with grouping similar category items together for financial and management dimension has been tested with 24 pioneer companies and other SMEs participants. Results of testing were showed in the table 15 below:

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Table 15 Result of Testing Average Financial, Management and Satisfaction of 24 Pioneer Companies

When we are doing this research testing we found a differences result between we testing the entire questionnaire respondents with separate them to 24 pioneer companies and other SMEs participants. Why it is happened? We speculate the main cause of this is because if we were doing grouped tested with just 24 pioneer SMEs it may cause insignificant samples in statistics analysis tool. However, in the insignificant condition, results of table 5.5 are still showed an additional funding is significant at p-value 0.008.

It is mean that from the result of grouping category test for 24 pioneer companies still showed an agreement with the statement “Start-up Taiwan Accelerator funding program do helping SMEs in improving their financial performance”.

5.2 Descriptive Data

In the descriptive data that we have received from Start-up Taiwan Accelerator, for this period cycle of the programs, 28 SMEs participants have achieved additional investments and offer orders from big companies and VCs. All investments that Start-up Taiwan Accelerator has got in total was accounting for 20.2 million NT dollar and offer order in total accounting for 7.7 million NT dollar.

From total investments and offer orders that SMEs has achieved, 19 of them are list as pioneer’s SMEs. Different with questionnaire-survey analysis, in descriptive analysis, we will just focus on those 19 pioneer SMEs to do further analysis and investigation due to they has passed 3 phrase selection process set by Start-up Accelerator Company with excellent performance; also, they are priority participants, who has fully received accelerator programs assistance, therefore it is very sufficient to identify their

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characteristic and performance in the aims to understanding the effect of accelerator program on Taiwan SMEs.

Firstly, we divided 19 SMEs into more specific criteria, which is SMEs with net income (2012) ≦ NT$ 1 million, SMEs with net income > NT $ 1 million to NT$ 10 million , SMEs with net income > NT$ 10 million to NT$ 50 million, and SMEs with net income

> NT$ 50 million; Next, we also divided 19 SMEs with capital (2012) ≦ NT$ 10 million, SMEs with capital > NT$ 10 million to NT$ 50 million, and SMEs with capital

> NT$ 50 million. This classification is aims to see what influences of accelerator program between small net income and capital scale companies with big net income and capital scale companies.

Net Income > 1,000,000 ~ 10,000,000

1 NT$ 4,710,000 NT$15,000,000 NT$ 27,000,000 3.18471338 5.7324841 1 NT$ 5,920,000 NT$ - NT$ 120,000,000 0 20.27027 2 NT$ 4,600,000 NT$ 2,000,000 NT$ - 0.43478261 0

3 NT$ 1,884,771 NT$ 220,000 NT$ 45,000,000 0.11672506 23.87558 Net income > 10,000,000 ~ 50,000,000

1 NT$ 15,650,652 NT$ 1,500,000 NT$ - 0.09584265 0

38 industries; 3 is ICT industries; 4 is green energy industries; 5 is machinery industries; 6 is logistic industries and 7 is culture & creativity industries.

The result of this classification testing showed that companies which have small-scale net income with less than NT$1 million, impact of accelerator programs are more perceive then SMEs with net income more than NT$1 million; besides, investment which they get from accelerator programs has helped them increasing their net income up to 20 times, and even some of them has increased 100 times more than their previous net income. In additional, Offer order which they got from accelerator programs also helped them in increasing net income 3 to 4 times from previous one.

Another detail information of capital classification and result of the studies are show in the table 17;

Capital > 10,000,000 ~ 50,000,000 (NT$)

1 NT$ 20,000,000 NT$ 1,500,000 NT$ - 0.075 0 1 NT$ 25,000,000 NT$ - NT$ 120,000,000 0 4.8 3 NT$ 50,000,000 NT$ 21,720,000 NT$ - 0.4344 0

3 NT$ 42,000,000 NT$100,000,000 NT$ 50,000,000 2.38095238 1.1904762 Capital > 50,000,000 (NT$)

2 NT$ 185,050,000 NT$ 2,000,000 NT$ 55,890,000 0.01080789 0.302026 2 NT$ 89,130,000 NT$ 20,000,000 NT$ 16,000,000 0.22439134 0.179513

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2 NT$ 120,000,000 NT$ - NT$ 125,000,000 0 1.041667 3 NT$ 120,000,000 NT$ 20,000,000 NT$ - 0.16666667 0

3 NT$ 200,000,000 NT$ 32,200,000 NT$ - 0.161 0 3 NT$ 80,000,000 NT$ 220,000 NT$ 45,000,000 0.00275 0.5625 4 NT$ 500,000,000 NT$ 28,000 NT$ 105,000,000 0.000056 0.21 4 NT$ 250,000,000 NT$ 1,488,000 NT$ - 0.005952 0 5 NT$ 480,000,000 NT$ 28,000,000 NT$ 1,200,000 0.05833333 0.0025 6 NT$ 60,000,000 NT$ 16,720,000 NT$ 29,000,000 0.27866667 0.483333

Table 17 Capital Classification

Table’s 17 showed that companies who has amount of capital below NT$10 million, offer order in accelerator programs has effect in increasing some of the SMEs capital amount 1 to 4 times from previous and investment from the accelerator programs has helped increasing their capital 1 to 8 times. However, for those SMEs who has capital more than NT$50 million, accelerator programs just have a minor effect on their financial performances.

Therefore, in conclusion, we can say that accelerator programs do help SMEs in improving their performance, especially for those SMEs with small scale of net income and capital, and has minor effect for SMEs with big scale of net income and capital.

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