Mohd Rashid
The uncertainties and the risky nature of technological startup ventures makes them susceptible to higher failure rate than traditional small businesses. Extended research has been conducted to identify Critical Success Factors (CSFs) and their relevant influences on startup success outcomes. However, no study has examined the direct impact of CSFs on specific startup life cycle stages to determine how stage transition occur and drive a startup toward success. This quantitative study examined how specific subset of relevant CSFs impact specific startup life cycle stages to predict stage transition. To conduct the study, an instrument was constructed using 35 CSFs and six startup life cycle stages in accordance with Love (2016) Startup Evolution J-Curve model. The study subjects were technology startup founders based in the U.S and were surveyed online. The study utilized three research methodologies. It applied descriptive statistics to determine the impact of specific CSFs to specific startup stages. It applied multiple linear regression and constructed frequency distribution tables to determine the relationship between CSFs and stages key performance indicators. It used standardized regression coefficient to predict stage transition. The analysis provided evidence to support the research theory that specific subsets of CSFs drive a startup to achieve a stage key performance indicator critical for stage transition to occur. However, the study finding also show that perception, not stage transitionary processes currently drives most startup outcomes.
