Risk Analysis for the Tech Startup Projects with Fuzzy Logic

Bolat H. B., Yaşlı F., Temur G. T.

International Conference on Intelligent and Fuzzy Systems, INFUS 2021, İstanbul, Turkey, 24 - 26 August 2021, vol.308, pp.671-679 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 308
  • Doi Number: 10.1007/978-3-030-85577-2_79
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.671-679
  • Keywords: Business model canvas, Failure mode and effect analysis, Fuzzy sets, Technological startups
  • Anadolu University Affiliated: Yes


© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Nowadays; technological (Tech) startups are fast-growing businesses that target to meet the demands of the marketplace by developing innovative products, services, or platforms. Many factors have become prominent regarding the success and sustainability of the product or service offered by the startup: Investment, experience, and education of the team, leadership of the management, creativity, innovation, technological breakthroughs, surrounding community, future perspective, target marketing strategy, location and the analysis of the market, etc. Therefore, startups contend under considerable uncertainty. Considering the high failure rates of the startups, it is inevitable to examine them in terms of risk analysis. Defining the important risk factors is crucial to develop the right strategies for successful startups. In literature, there is a great effort to find out the key factors for successful and sustainable business models including intensive technology. In this study, a risk analysis for the failure of startup projects under the framework of business model canvas has been performed using Fuzzy Failure Mode and Effect Analysis (FMEA) with the field experts. While fuzzy logic provides to define the quantitative parameters of the risk analysis, the FMEA provides to present the main reasons which cause the failure of the startup projects with their priority numbers. The findings have theoretical and practical contributions to success in startup projects by showing the effects of the factors that cause startup projects to fail. The results are discussed to provide managerial strategies for mitigating the failure risks of the startup projects.