Modeling the development of knowledge absorptive capacity in startup accelerators with a interpretive structural approach

Abstract

The success of accelerators requires continuous growth and improvement of the capacity to absorb knowledge from internal and external organizational resources. In this regard, the present study is aimed to design a model for improving the capacity of knowledge absorption in startup accelerators. This study is an applied-developmental design in terms of purpose, and non-experimental (descriptive) in terms of data collection and a cross-sectional study. Also, in this study, a mixed research method (qualitative-quantitative) is used. The data collection measure in the qualitative section is semi-structured interview, and in the quantitative section, ISM questionnaire is used. The Holsti coefficient is used to validate the qualitative section, The statistical population includes the managers of startup accelerators who are selected by the purposeful sampling method and theoretical saturation was achieved with 15 interviews. Thematic analysis method and Nvivo software were used for data analysis in the qualitative. Then, using the interpretive- structural modeling method, the research model was presented. Finally, using MICMAC analysis, the type of variables has been identified based on their influence and susceptibility to other variables. The research findings showed that the human rsources emmpowerment is the most influential components and is placed at level 4. Therefore, any program or action to enhance knowledge absorption capacity in accelerators should focus on the key and fundamental role of this factor. This components have high penetration power and low dependence in enhancing knowledge absorption capacity in accelerators and is among the priorities for action in improving knowledge absorption in startup accelerators

Keywords

• knowledge absortive capacity
• startup accelerators
• exploratory mixed method
• interpretive structural modeling