As someone from Rangsit, I want our city to develop talented people and give them opportunities to turn knowledge into work, businesses and benefits for the community. This ambition leads me to a practical question: how can we help people starting a business make better decisions before their time and money run out?
The research points towards real customers, teams with clear responsibilities, measurable experiments and sustained support. This article proposes seven evidence checkpoints for new ventures, then connects them to a Silicon Valley Rangsit approach that we can pilot and evaluate together.
01 / EVIDENCE
Define survival before asking why ventures fail
New businesses and startups overlap, but startups often seek business models that can be repeated and scaled. Some are still looking for a market; others need years to develop their technology. Evaluation should therefore distinguish business survival, profit, growth and community benefit. Raising investment or winning a competition is one event along that journey. [2]
Cantamessa and colleagues examined 214 startup post-mortem reports and highlighted the lack of systematic business development. A 2025 study by Bethlendi and colleagues, covering 40 failed ICT startups in Hungary, emphasised dynamic capabilities and differences across venture stages. These datasets reveal patterns of problems; they cannot establish a failure rate for all Thai startups. [1][2]
Evidence on success brings people and decisions into focus
A 2024 literature review emphasised decisions about the core team, product definition, market selection and partnerships. A meta-analysis of 31 studies by Song and colleagues found relationships between venture performance and several factors, including marketing and industry experience, financial resources and founding teams. Readiness should be developed across these areas; simply adding people or money does not guarantee success. [3][5]
Camuffo and colleagues conducted four randomised trials involving 759 firms. Training entrepreneurs to formulate and test hypotheses affected idea termination and patterns of strategic change. My practical takeaway is to build evidence-based decision skills and be willing to end experiments that do not justify further investment. The study does not guarantee that every venture will survive. [4]
Support must continue after the event
An evaluation of Start-Up Chile found benefits when basic funding and coworking services were combined with entrepreneurial learning. Qin’s 2025 study also found that accelerator benefits depended on the institutional context of the ecosystem. For Rangsit, the lesson is to connect mentors, pilot customers, resources and follow-up rather than expect the same activity or service to work equally well everywhere. [7][8]
02 / SEVEN CHECKPOINTS
Seven missteps and the evidence needed before moving on
These seven checkpoints are a practical synthesis of the research. No study establishes this exact number of steps as sufficient for success. Teams can revisit earlier checkpoints and change the sequence to suit their venture.
The misstep
Guessing the customer’s problem
The next step: Listen to customers and validate the problem
A team may begin with what it wants to build, substituting the opinions of friends for market evidence. Talk to people facing the problem, observe their current solutions, and distinguish users, payers and decision-makers. Before moving on, record the problem, its frequency and consequences, and why a customer might switch to a new solution. [1][3]
Evidence before moving onRecord problems, existing solutions and payers
The misstep
Having a product without a clear route to market
The next step: Choose a market and revenue model
A working product still needs an accessible market. Choose an initial customer segment, explain the value offered, specify pricing and sales channels, then test real behaviour. Depending on the venture, relevant evidence may include a trial, a meaningful commitment of time or actual payment, rather than praise and likes. [1][3]
Evidence before moving onSpecify customers, value, pricing and channels
The misstep
A capable team without clear responsibilities
The next step: Align team roles and mentoring
Skills need owners who are accountable for outcomes. Agree who handles customers, product development and financial information, and who decides what. Find mentors who fill specific gaps. Evidence includes agreed responsibilities, delivered work and a shared follow-up process. [3][5]
Evidence before moving onAgree owners, responsibilities and follow-up
The misstep
Investing heavily before testing
The next step: Test a prototype and measure results
A prototype should answer important questions with proportionate resources: can customers use it, which work does it reduce, and why would they return? Write down the hypothesis, test method, measures and criteria for continuing, changing or stopping before seeing the results. This helps limit selective interpretation of encouraging data. [4]
Evidence before moving onSet criteria to continue, change or stop
The misstep
Sales without sufficient cash
The next step: Understand costs and plan cash
Sales, profit and cash receipts occur at different times. Understand unit costs, payment obligations, collection periods and how long cash can last under explicit assumptions. McKenzie and Woodruff linked better marketing, inventory, record-keeping and financial-planning practices to survival and faster sales growth in small firms. These are associations and need to be adapted to each type of startup. [6]
Evidence before moving onTrack cash received, obligations and cash remaining
The misstep
Selling once without being able to repeat delivery
The next step: Demonstrate repeat use and delivery
After the first customer, examine why customers return, whether delivery can maintain its quality, and how costs change as customer numbers increase. Track repeat use, complaints, delivery time and cost per cycle. One order is not sufficient evidence for full-scale expansion. This checkpoint proposes testing market demand and operating practices together. [1][3][6]
Evidence before moving onTrack repeat use, quality and costs
The misstep
Expanding before reviewing readiness
The next step: Scale with evidence and follow-up
Before hiring, adding premises or raising capital to expand, check whether customer evidence, delivery systems, cash and team capabilities can support the extra work. Match resources to the venture’s stage. Reviews should allow decisions to continue, narrow the scope or end part of the work, with reasons and subsequent results recorded. [2][4][8]
Evidence before moving onCheck readiness and review over 30–90 days
AI can help organise interview notes, draft prototypes or compare options. Customer evidence must still come from real data collection, with human review before decisions. This is a proposed way of working, not a research finding that using AI ensures survival.
03 / LEARNING IN PRACTICE
A hypothetical example from a Rangsit business problem
Suppose SMT students want to build an order-tracking tool for local retailers. They first listen to real shops to discover where orders are lost, then test a small prototype. They measure working time, order errors and repeat use, and ask which benefits the shop would pay for.
If shops do not keep using it, the team revisits the problem and approach. If they continue but support costs are high, the team improves delivery before expanding. This example illustrates a decision process and the data to collect; it is not a report of an already successful project.
04 / SILICON VALLEY RANGSIT
Towards a Silicon Valley Rangsit Model
As someone from Rangsit, I want new ventures to have a place to practise and people who help ask necessary questions. I therefore propose a Silicon Valley Rangsit Model as a pilot approach connecting people, problems and resources. It still needs real implementation data and evaluation; it is not an empirically validated causal model.
Universities and students could contribute knowledge and prototypes; entrepreneurs could provide real problems and test sites; communities and customers could provide feedback; mentors and networks could connect experience; and sponsors could supply resources appropriate to the work’s readiness. These proposed roles invite collaboration and do not confirm that any organisation has agreed to participate.
Silicon Valley Rangsit Model
Connect people • Run experiments • Follow up
- Universities & students
- Entrepreneurs & test sites
- Communities & customers
- Mentors & networks
- Sponsors & resources
Connect
Connect people and problems · checkpoints 1–3
Learn
Experiment and understand costs · checkpoints 4–5
Grow
Repeat and scale with evidence · checkpoints 6–7
Sustain
Review over 30–90 days · follow up at 3/6/12 months
↺ Feed results back into the problem, team and support decisions
This diagram shows proposed roles and workflow. Its arrows are not statistically confirmed causal relationships.
FranchiseX Community can organise the work around Connect • Learn • Grow • Sustain: connect people with real problems, learn through experiments, develop repeatability, then follow up and share knowledge. This is consistent with the OECD’s emphasis on university–industry connections, infrastructure and evaluation of startup support in Thailand. [9]
Funding for learning activities and community work should be distinguished from capital invested in ventures. Define each purpose, accountable person and reporting requirement so that sponsors can see which work their resources support.
05 / PILOT & FOLLOW-UP
A proposed 90-day pilot, followed by longer-term evaluation
The following is a proposed learning pilot, not a research-backed deadline by which every venture must succeed. Teams developing long-cycle technologies should use appropriate progress evidence rather than be required to earn revenue within 90 days.
| Period | Work together | Evidence for review |
|---|---|---|
| Days 1–30 | Choose a problem, listen to customers, organise the team and mentors, and define the key questions. | Problem records, users and payers, test scope, roles and available budget. |
| Days 31–60 | Test a prototype with users, collect results and examine delivery costs. | Usage behaviour, before/after observations, feedback, costs and limitations. |
| Days 61–90 | Review what to continue, change or stop and prepare resources for the next cycle. | Decision rationale, repeat use or suitable progress evidence, cash plan and accountable owners. |
Measure what happens after the activity
Follow every team in the same cohort at 3, 6 and 12 months. Separate learning outcomes, such as evidence quality and decisions, from business outcomes, such as repeat use, cash and operating status. Also track community benefit and who continues the work. Ending an unsuitable idea can be valuable learning, but it must not be counted as a surviving business.
When reporting a survival rate, define survival, the starting date and the original cohort denominator. Retain the status of closed, paused and uncontactable teams. Post-training data alone cannot establish that the programme caused success. Where feasible, compare results with similar eligible teams that have not received the support.
A new future starts with work that someone carries forward
I want Silicon Valley Rangsit to begin with teams willing to listen to customers, experiment and reconsider their work, supported by a network that helps carry it forward. The ambition to develop talented people and new ventures becomes more credible when we can explain what was learned, how decisions changed and who actually benefited.
The more we give, the more we gain • The more we do, the more we enjoy itConnect • Learn • Grow • Sustain
06 / RESEARCH LIBRARY
Research and references
Eight research studies and one OECD report, with findings and evidence limitations.
Cantamessa, M., Gatteschi, V., Perboli, G., & Rosano, M. (2018). Startups’ Roads to Failure. Sustainability, 10(7), 2346.
Evidence: Analysis of 214 startup post-mortem reports using the SHELL framework highlights systematic business-development problems.
Limits: Retrospective reports from selected failed ventures cannot estimate a failure rate for all startups or for Thailand.
Open original source ↗ 10.3390/su10072346Bethlendi, A., Hegedűs, S., & Szőcs, Á. (2025). What could we learn from startup failures? Journal of Innovation and Entrepreneurship, 14, 35.
Evidence: Questionnaire, component and cluster analyses of 40 Hungarian ICT startups in voluntary liquidation highlight dynamic capabilities and venture-stage differences.
Limits: This is a small, failed-only observational sample, not a causal experiment or direct evidence for all Thai ventures.
Open original source ↗ 10.1186/s13731-025-00493-wGonzález M, M. A., Terzidis, O., Lütz, P., & Heblich, B. (2024). Critical decisions at the early stage of start-ups: a systematic literature review. Journal of Innovation and Entrepreneurship, 13, 83.
Evidence: A systematic review identifies nine decision domains, including the core team, product definition, market selection and partnerships. Its abstract reports 56 studies.
Limits: Frequency rankings are not causal effect sizes. One passage in the paper reports 57 studies, differing from its abstract.
Open original source ↗ 10.1186/s13731-024-00438-9Camuffo, A., Gambardella, A., Messinese, D., Novelli, E., Paolucci, E., & Spina, C. (2024). A scientific approach to entrepreneurial decision-making: Large-scale replication and extension. Strategic Management Journal, 45(6), 1209–1237.
Evidence: Four randomised trials involving 759 firms find effects on idea termination and patterns of strategic change, supporting systematic hypothesis formation and testing.
Limits: The outcomes concern entrepreneurial decisions and idea search, not guaranteed survival; ending every idea is not success.
Open original source ↗ 10.1002/smj.3580Song, M., Podoynitsyna, K., van der Bij, H., & Halman, J. I. M. (2008). Success Factors in New Ventures: A Meta-analysis. Journal of Product Innovation Management, 25(1), 7–27.
Evidence: A meta-analysis of 31 studies of new technology ventures finds associations with marketing and industry experience, financial resources and founding teams, among other factors.
Limits: These are associations across older studies and varied settings; increasing team size or funding alone does not necessarily improve results.
Open original source ↗ 10.1111/j.1540-5885.2007.00280.xMcKenzie, D., & Woodruff, C. (2017). Business Practices in Small Firms in Developing Countries. Management Science, 63(9), 2967–2981.
Evidence: Studies small firms across countries, covering marketing, buying and inventory, records and financial planning. Panel data from three countries link better practices to survival and faster sales growth.
Limits: The sample is small businesses rather than exclusively high-growth startups. These associations do not prove that a particular training programme ensures survival.
Open original source ↗ 10.1287/mnsc.2016.2492Gonzalez-Uribe, J., & Leatherbee, M. (2018). The Effects of Business Accelerators on Venture Performance: Evidence from Start-Up Chile. The Review of Financial Studies, 31(4), 1566–1603.
Evidence: A regression-discontinuity study of Start-Up Chile finds benefits when basic cash and coworking services are combined with entrepreneurial schooling, but no detected effect for basic services alone in this setting.
Limits: The estimates concern a specific programme and selection threshold; they do not establish that capital or workspace is universally ineffective.
Open original source ↗ 10.1093/rfs/hhx103Qin, F. (2025). Oasis in the desert or icing on the cake? The impact of entrepreneurship accelerators across ecosystems. Journal of International Business Studies, 56, 659–676.
Evidence: The initial dataset covers 7,056 ventures, 289 programmes and 132 economies; the matched sample has 3,468 ventures. Matching and multilevel analysis link accelerator growth benefits to institutional ecosystem quality.
Limits: This is observational evidence despite matching, not a randomised trial. Growth benefits are not direct measures of long-term survival.
Open original source ↗ 10.1057/s41267-025-00780-4OECD. (2025). Start-up Asia: Chasing the Innovation Frontier. Chapter: Promoting start-ups in Thailand. Development Centre Studies, OECD Publishing.
Evidence: The Thailand policy chapter discusses university–industry links, research, infrastructure and resources, alongside coordination and evaluation of startup support.
Limits: This policy report uses data from different periods. It neither tests the proposed Silicon Valley Rangsit Model nor confirms participating organisations.
Open original source ↗ 10.1787/a9b71040-en
07 / TAKE IT FURTHER
Download and keep learning
The original Thai article: nine A4 pages with research references and illustrations.
The complete English version is available on this webpage.
