Modeling the Growth of Technology Units for Health Technology Incubators Suitable for Low-Income Areas

Extended Abstract
Background
Institutions, companies, and local governments have long recognized the benefits of establishing new businesses to commercialize research achievements in advanced and low-tech technologies, as evidenced by the rapid growth of incubator systems in the past decades. They can play an important role in the success of incubator businesses. Incubators significantly affect society's economy, as a result of which emphasis is placed on realizing better performance in incubators' regional and subsequent regional development. Therefore, it is important to understand how incubators can succeed in developed and underprivileged areas.
Methods
The current applied study used a qualitative approach. The data was collected using semi-structured interviews and the narrative method. Based on the narrative elimination rule, we extracted, coded, and categorized components and categories using community narratives and existing theoretical foundations. This study identified variables by equating and comparing existing theories with the literature on incubation. Also, using ISM interpretive structural modeling, we modeled the relationships between the variables and their effects on the development of technology units. The reason for using ISM was that this modeling method could solve complex problems by providing images and reducing complexity. The statistical population of this research included the managers of growth centers and health science and technology parks of type 2 and 3 universities in Iran, university professors who specialize in the knowledge-based economy, health entrepreneurship experts, and successful / unsuccessful technologists at growth centers in underprivileged areas. The snowball sampling method was used for sampling, and after taking a semi-structured oral interview of 31 people, the data reached the theoretical limit. The researcher recorded the narratives as audio files, lowered the volume according to the rule of eliminating additions, and selected the categories from the narratives' high points after each interview. Finally, 213 categories were extracted and coded in 2 steps, and the influencing variables were identified.
Results
The questions and answers in this research were as follows:

The first question: What factors influence the growth of technology units at health technology growth centers in less developed areas?

The influencing factors were identified in two main infrastructure and growth plan dimensions. Also, five categories and 25 sub-categories were identified. Factors affecting growth were divided infrastructure and planned growth for technology units in health technology growth centers. The innovation of this research is that two internal and external infrastructure variables were identified in the infrastructure category, and the planned growth variables, including admission, growth, and exit plans, were determined. In the following, the extra-organizational infrastructure variables are divided into six sub-indices (regional capacity; government support policies; international trade and sanctions; macroeconomic situation and growth of markets; favorable business environment, multiplicity and complexity of laws; institutional gaps in importing and smuggling goods) and the internal infrastructure variable with six sub-indicators (unavailability of resources and credits; having office equipment, workshop and laboratory facilities; physical space; organizational culture familiar with entrepreneurship; having a continuous entrepreneurship training program for students and faculty members; organizational structure suitable for the third generation university (organizations and organizational positions)). The organizational structure is divided according to the third-generation university, and by paying more attention to them, which are among the factors under the organization's control, the shortcomings of extra-organizational infrastructures in less privileged areas can be covered. Also, three sub-indexes were obtained in the exit program variable.

The second question: How are the relationships between the growth model of technology units at health technology growth centers appropriate for less privileged areas?

The ISM method was used to determine the optimal growth model of technology units at health technology growth centers in less developed areas. The external infrastructure is closely linked to both the infrastructure and the planned growth. Additionally, the acceptance, growth, and exit plans have a close relationship with the growth plan, as well as with the infrastructure at the second level, specifically the infrastructure and planned growth associated with incubation. Infrastructure and planned growth (incubation) are mediating variables and have a direct relationship with the growth of technology units which has been identified as the dependent variable in this research.
Conclusion
This research showed that the growth of technology units in less privileged areas as a dependent variable in 3 levels of planned growth variables and infrastructures is influenced by independent variables of internal and external infrastructure and acceptance, growth, and exit plans. The growth and exit plan affect the efficiency of the growth centers and the growth of technology units. The growth of the health technology centers in the areas with little benefit directly affects the two basic variables of the planned growth (the quality of the written program of the growth center) and the infrastructure. According to previous studies, identifying and introducing internal infrastructure according to the infrastructural capacities of less developed regions as an independent variable are promising hopes to compensate for the shortcomings of regional infrastructure in less privileged regions.