Table 1: Summary of literature review in each factor.

In Thailand, medical or health technology is becoming increasingly significant, and the Thai government has established a 20 year industrial development strategy plan (2017-2036) that identifies new industries that will drive the countrys economy in the future (New S-Curve). The health tech business is linked to three target industries: robotics, digital, and medical hub, and the COVID-19 epidemic has heightened the importance of health tech startups in Thailand.

Currently, there are researchers and companies with potential and capability to develop medical and health innovations. Some of them have been established as start ups and reached the seed round stage. The number of startups in the medical and health industry was rated fifth in a study of startups conducted by the Office of National Higher Education, Science, Res-earch and Innovation Policy Council (NXPO) and the Thailand Tech Startup Association (TTSA) in 2019-2020 and health companies, on the other hand, are distinct from other businesses in that they have a long life cycle, particularly in the seed or development stage. It will take extra time to compare products with those of other businesses and to test products before they are released to the market. As a result, a different business model than other enterprises may be required, and success indicators for business must be identified. The findings of this study will assist various parties in developing a mechanism to support health tech businesses, as well as serve as a guideline for new health tech startups to follow in order to ensure long-term success.

Literature Review and Hypothesis

Startup key success factors

Fig. 1: Conceptual framework and variable component with hypotheses. 

The success of each startup may be valued or defined differently, such as Gelderen et al. (2005) indicated that the success was seen by market share or number of customers, profits or sales that increase or exceed the market average. Wong et al. (2005) claimed that success came from the founders achievement of targets, such as owning a business or promoting better quality of life for society. A number of studies are currently underway to look at the factors affecting the success of startups in many fields, which are quite extensive and varied. This research is based on the elements of factors contributing to the success of entrepreneurs according to the concept of Isenberg (2011), who has more than 22 years of entrepreneur development expertise and looked at the success of entrepreneurs who used innovations in conjunction with previous research. It has been discovered that the success of startups is determined by six key elements: government policy factor, startup support factor, human capital factor, market factor, finance factor and culture factor. The 6 key elements that drive startup success are the measure of success and the characteristics that drive startup success. The 6 key elements of Isen-berg (2011) was studied by many researchers around the world (Timmons & Spinelli, 2004; Wong et al., 2005; Mueller et al., 2012; Sefiani & Bown, 2013; Ng et al., 2014; Okrah et al., 2018; Prohorovs et al., 2018; Thanapongporn et al., 2021). By reviewing Isenbergs concept (2011) and the relevant literature, variables in the study could be determined. This leads to the 6 hypotheses as following:

H1 The government policy factor influences success of health tech startups in Thailand.

H2 The startup support factor influences success of health tech startups in Thailand.

H3 The human capital factor influences success of health tech start-ups in Thailand.

H4 The market factor influences success of health tech startups in Thailand.

H5 The finance factor influences success of health tech startups in Thailand.

H6 The culture factor influences success of health tech startups in Thailand.

From the literature reviews, it was discovered that the success of startups consists of 6 latent variables and 23 observed variables. The composition can be sum-marized as shown in Table 1 and Fig. 1.

The population in this research is health entre-preneurs registered as a juristic person with the goal of serving as a manufacturer and a wholesaler of pro-ducts in three groups: pharmaceuticals (drugs), med-ical devices, and cosmetics and dietary supplement products, with a period of incorporation of a juristic person not exceeding 5 years and with regards to the business still under operations (2020). From retrieval of information from the Department of Business Development, the Ministry of Commerce. It was discovered that at the end of 2020, there were 4,562 entrepreneurs who met the criteria. Structural Equation Modeling was used to calculate the minimum sample size. As a result of the huge sample size, there is a greater likelihood that the variable will be nor-malized than the smaller sample. Meanwhile the research by Hair et al. (2013) proposed that the mean sample size of Structural Equation Modeling should be 10 times the observed variables. Therefore, in this study, the number of observed variables from the relevant literature review was 23, which was then multiplied by 10 as a result; the sample size for this study is 230 people. The sample approach was based on the stratified random sampling concept, which is a type of probability sampling. In other words, the sample group was established based on the companys aims and the populations proportion. The information was gathered through an online survey from December 2021 to February 2022. For the quantitative research method, the researcher created a survey questionnaire to determine the link between relevant variables, questionnaire items and synthesis based on a review of the literature review. The researcher used the 7-point Likert Scale, and used the data from the sample as a unit for analysis. Basic statistical analysis and confirmatory factor analysis were employed to analyze the questionnaire data based on the principle of latent variables in Structural Equation Modeling (SEM) using AMOS program. Second order confirmatory factor analysis was used for hypothesis testing because the variables studied were complex theoretical variables. The researcher presented the good-ness of fit analysis results and correlation of each variable component through statistical values to deter-mine the conditions of the model fit as follows: regression weight (factor loading) representing the significant weight that each latent variable is influ-enced or extracted from the preceding variable, Chi-square/degree of freedom (CMIN/df) representing the overall goodness of fit of the correlation model which should be less than 3 (Bentler & Bonett, 1980). The goodness-of-fit index (GFI) must be greater than 0.8 (Seyal et al., 2002), the incremental fit index of improved NFI (CFI) must be greater than 0.9 (Bentler & Bonett, 1980), and the root-mean-square error of approximation (RMSEA) must be less than 0.08 (Hair et al., 2013).

The researcher was able to collect 240 completed questionnaires, which was more than the target num-ber cosmetics and dietary supplement goods accounted for 65 percent of the respondents, followed by pharmaceuticals (22.5 percent), medical devices and services (12.5 percent), and medical devices and services (12.5 percent). When considering the length of time in business, it was discovered that 24.17 per cent had been in operation for three years, followed by 4 years (22.5%), 5 years (20.83%), 2 years (16. 67%) and 1 year (15.83%) respectively as detailed in Table 2. 

Table 2: General information of sample.

As shown in Fig. 2 and Table 3, the findings of the AMOS program with second order factor analysis revealed that all six factors influence the success of health tech startups. The model in Fig. 2 presents such statistics and Indices as CMIN/df =1.917< 2.0 with df=182 and P=.00<.05; RMSEA=.076<.08; GFI=.851>.80; CFI=.91>.9, presenting the close fit of the model to the data.

Fig. 2: Result of final 2^nd order CFA model with standardized coefficient. 

In Table 3, the square root of AVEs shows higher values in comparing with the inter-construct correlation values. Therefore, the constructs discri-minant validity is proven. When P-values are less than 0.001, however, all hypotheses are supported (significant level = 1%).

The standard coefficients show that, the most influential factor was culture; this is supported by a number of studies. (Castrogiovanni, 1996; Prohorovs et al., 2018), which discovered business inspiration and the founders awareness of success stories play an important role in success. The startup support element is the next most essential aspect, which is consistent with the research by Cheah et al. (2016) and Thanapongporn et al. (2021), which discovered having a place and infrastructure enabling startups to run their business is a key factor for the success of startup entrepreneurs. This is also in accordance with the research by Teeter and Whelan-Berry (2008) it was discovered professional services will contribute to the success of startups. 

Table 3: Model discriminant validity. 

Furthermore, Radojevich-Kelley & Hoffman, (2012) discovered that an incubators or accelerators actions contribute to a start-ups success while the research by Lee, (2010) found that startup activities, such as seminars to share experiences, business matching, business collaboration and business network, etc., play a key role in a startups success. The third important factor was the human capital factor, which is consistent with the study by Lin et al. (2006) which found that entrepreneurial capability of entrepreneurs and founders have a beneficial impact on company success rates.

Table 4: Standardized regression weights. 

This is because competence, skills and knowledge are factors that help startups gain business advantage (Lee, 2010). According to research by Khongkhai and Wu, (2018), a significant component supporting the success of startups is the ability of entrepreneurs and founders to apply innovation to corporate products. Additionally, the research by Geibeland Manickam, (2016) discovered that the startup team has a vital impact in the success of a company. Many studies found that the experience of the founder team in conducting research, organizational management, and business operations is important for success (Yoo et al., 2012; Arruda et al., 2013; Hyder & Lussier, 2016; Thanapongporn et al., 2021). The market element is the fourth and most essential factor, which is consistent with the research by Prohorovs et al. (2018), which found that outstanding products or services that can solve customer pain point problems and create satisfaction play a crucial part in the success of startups. In the other words, startups with innovative products or services are more likely to succeed than those with less in-novative products. This is in consistent with the findings of the research by Geibeland Manickam (2016), the capacity to scale up manufacturing for both domestic and foreign customers while preserving quality standards correlates to startup entrepreneurs success, according to the study. Furthermore, having a network to extend ones business or a business partner is a crucial component. Lee, (2010) found that participation in the startup cluster or business grouping is positively correlated with the success of startups. The same is true for the study of Sefiani and Bown (2013), which found that the increased number of business partners enhances the level of startup success. The fifth factor was the government policy factor, which is consistent with the research by Geib-eland Manickam, (2016), which discovered that government financial support in the form of funds or gifts, especially in the early stages, had a significant impact on the startups performance. Meanwhile, the research by Okrah et al. (2018) found that incentives play a critical part in the development of startup companies. The last factor is finance, which is consistent with the research by Bocken (2015) which found that sufficient private funds of the owner, especially in the early stage, directly affect the success of startup entrepreneurs.

According to the findings of the study, it was found that health tech startups in Thailand can be successful because of the 6 elements, which are government policy, startup support, human capital, market, finance, and culture. Culture is the most influential factor, followed by startup support, human capital, market, government policy and finance. It is worth noting that tax support or access to low-interest funding sources is of the last priority. However, internal factors such as business inspiration or knowledge, experience & product prominence are the top priority.

This work was supported by the grant from the National Science & Technology Development Agency, Thailand.

The author certifies that there are no conflicts of interests in the study, data collection and analysis.