Study on the Influencing Factors of Business Success Variables of Technology Startup Entrepreneurs

Abstract

Background/Objectives: The impact of demographic factors on the business performance of entrepreneurs was investigated and the results suggest the direction of government's employment policy for strengthening, revitalizing the entrepreneurial ecosystem.

Methods/Statistical analysis: We surveyed 300 young start-up entrepreneurs, 205 questionnaires were collected and analyzed using SPSS 22 and Smart PLS 3.2.9. Measurement and structural models were analyzed to evaluate path coefficients and structural suitability. Among the demographic variables, gender was introduced as a control variable, and PLS-Algorithm and Bootstrapping were performed on 8 independent paths and parameters to verify and confirm the difference in the moderating effect according to gender.

Findings: We analyzed the measurement model to analyze internal consistency reliability, focused validity and discriminant validity, and validated the structural model by evaluating the importance and suitability of the determinants (R²): effect size (f²): multicollinearity (Inner VIF): and path coefficients. As a result of estimating the path coefficients (mean, STDEV, T-value, P-value, confidence interval): EX-S→TM-A, NW-C→ TECH-P, NW-C→ TIC-A, NW-C→TM-A, TCC-C→ TC-A, TECH-C→ TC-A, TECH-C→ TECH-P, TECH-C→TIC-A, TECH-C→TM-A has been adopted. In this paper, as a result of the analysis of the regulatory effect, which is a key differentiating factor from previous studies, among the 8 demographic variables, such as gender, type of manufacturing, start-up period, etc. TECH-C-GENDER→ TECH-P, MGC-DIV→TECH-P and TECH-C-YEAR→TECH-P were found to have a significant impact on business performance. In conclusion, the results of structural modeling of the factors that affect the business success of technology startups contribute to the establishment of start-up policies for start-up agencies and governments.

Improvements/Applications: We will break down the technology sector into manufacturing, non-manufacturing, IT, AI, and big data and add data group analysis on demographic variables to conduct research on more advanced topics.