SMEs around the world are constantly under threats to their survival and sustainable growth. This study evaluated the variables influencing the sustainable growth of SMEs in the technological evolution of digital transformation. I propose a business model innovation strategy for the sustainable growth of SMEs. Furthermore, I evaluated the variables influencing growth by industry sector and present strategies for innovation in the business model. Data were collected from 205 CEOs in six sector industries in Korea, and I investigated the effect of variables that affected the company’s sustainability when introducing digital transformation. A partial least-squares regression was used for the statistical analysis and was performed using a data group analysis. I found that the SME performance varied by industry and that technological competency had a mediating effect. The variables that most affected the SME performance were management, technology, and technical competence in marketing and innovation. This research went beyond the limitations of studying only the effects of each of the previous literature variables through causal studies linking these variables. The variables affecting the performance of the industries were found to be different. By comparing the variables that led to a difference in performance in the industrial sector, I developed a business model innovation strategy that can affect the sustainable performance of SMEs.
Overcoming the failure of SMEs has been an important research topic. The critical research finding is that it has verified the essential elements of performance improvement. We presented a solution to the research question, "Is there a causal relationship between the effect on SMEs' success on capacity and business performance?". We analyzed whether the competence of SMEs had a mediating effect between success variables and performance. Secondary effects were empirically studied by converting independent variables to Higher-Order Component (HOC). The second-order variable of management influenced financial, non-financial, and technical performance, and the second-order variable of technology affected technical performance. As a result of introducing demographic variables as a controlling variable for performance, gender, and year of establishment showed a moderating effect on technical and non-financial performance. We expect to contribute to practical application to SME CEOs and government policymakers, support organizations, academia, and industry.
This study's research question is that there will be a crucial element in improving business performance among SMEs' success variables and competencies. Significantly, there should be different variables for performance in industry sectors. 1) The success variables of SMEs vary widely, but four characteristics of technology, management, commercialization, and exit strategies were selected. 2) The mediator is a technology innovation and technology marketing function. 3) The dependent variables are technical, financial, and nonfinancial performance. Previous literature had problems studying only the effects of each of the three variables, so we established a hypothesis and research model that focused on causality studies linking them. According to the data group analysis result for 3330 CEOs of SMEs, the six industries' performance impact factors were different. As a result of comparative analysis of changes in performance impact by industry, it was found that the largest increase in Information Technology (IT)/Software (S/W), Life/Food, and Crafts sectors. The key research finding is that it has verified the essential elements of critical performance improvement. We provided that different success variables and competencies differ in performance across industries. The results are expected to contribute to SMEs' CEO and government policymakers' practical applications, support organizations, academia, and industry. .
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.
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