Innovation capability

In today's competitive world, competition from the level of companies is drawn to the competition between their supply chain. The purpose of this study was to design and validate a suitable model for identifying and ranking effective factors and key indicators related to the performance of the organization in the supply chain of Saipa Company . The research method is objective and applied in terms of method, descriptive and correlation. The research tool was in the qualitative section, interview and in the quantitative section, a questionnaire. Data analysis in the qualitative section was used to identify the dimensions of the variables with the help of the industry's elite and through the analysis of the fuzzy Delphi, which ultimately resulted in 179 components in four factors: " T echnology C apability", " I nnovation C apability", " C ompetitive A dvantage" and " Organizational P erformance" is identified. Fuzzy hierarchy analysis was used to rank the identified factors . In the quantitative section, structural equations were used to fit the model. In this regard, after reviewing theoretical literature and drawing up the original model, the research questionnaire was distributed along with the dimensions of 350 experts and managers of the Saipa group and supply chain. The reliability of the questionnaire was confirmed by factor load method, Cronbach's A lpha and composite reliability coefficient (CR) and its validity was confirmed by convergent validity (AVE) and divergent validity. To evaluate the structural model, three coefficients of determination (R 2 ), index (Q 2 ) and statistics (GOF) have been used . The dimensions of each of the variables and the prioritization of the dimensions of the variables were determined. The results were analyzed using Structural Equation Modeling (SEM) and Software (SPLS) and the relationship between the factors was obtained and the proposed model was confirmed using factor analysis and structural equation modeling techniques . According to the results of the verifiable analysis of this research model, it was found that the performance of the organization depends on the dimensions of " I nnovation C apability", " T echnology C apability" and " C ompetitive A dvantage" in the supply chain, and has a positive and significant effect .

Innovation capability refers to a complete set of characteristics of the organization that facilitates innovation strategies, and the food industry plays a pivotal role in the processing of agricultural raw materials and food supply. The aim of the present study was to Identify and Analyze of the dimensions of innovation capability, clustering and ranking indexes in Urmia food industries. The sample size of the statistical population encompassing 221 companies was equal to 143 according to Cochran's formula. Obtaining required information was based on field study method. In the applied part of the study, exploratory factor analysis, clustering with K-MEANS algorithm and cluster ranking based on AHP technique were used. The results of the exploratory factor analysis showed that the relationship of each item with the factors classified into three categories was properly expressed. Companies with similar features but different from other clusters were put into 8 clusters. Finally, according to the weights of study variables, "human", "operability" and "structural” factors ranked first, second and last respectively.

The objective of this study was to examine the relationships between entrepreneurial self-efficacy, technological capability, and innovation capability among startup leaders. Specifically, the study aimed to determine the extent to which technological and innovation capabilities predict entrepreneurial self-efficacy, providing insights into the factors that drive entrepreneurial success in startup environments. A cross-sectional research design was employed, involving 290 startup leaders selected based on the sample size recommendations from the Morgan and Krejcie table. Participants completed a structured questionnaire assessing entrepreneurial self-efficacy, technological capability, and innovation capability. Data analysis included descriptive statistics, Pearson correlation, and multiple linear regression using SPSS version 27. Assumptions of linearity, normality, and homoscedasticity were checked and confirmed to ensure the validity of the regression model. Pearson correlation analysis showed significant positive relationships between entrepreneurial self-efficacy and both technological capability (r = 0.52, p < 0.001) and innovation capability (r = 0.56, p < 0.001). Multiple linear regression analysis revealed that technological capability (B = 0.37, SE = 0.08, β = 0.30, t = 4.63, p < 0.001) and innovation capability (B = 0.45, SE = 0.09, β = 0.35, t = 5.14, p < 0.001) were significant predictors of entrepreneurial self-efficacy, explaining 40% of its variance (R^2 = 0.40, F(2, 287) = 46.83, p < 0.001). The study concludes that both technological capability and innovation capability significantly enhance entrepreneurial self-efficacy among startup leaders. These findings underscore the importance of fostering these capabilities to drive entrepreneurial success. The study contributes to the literature by highlighting the critical role of self-efficacy and providing practical implications for entrepreneurs, policymakers, and educators aiming to support entrepreneurial development.