Modeling Iranian innovation network in nanotech for policy: applying an adopted version of SKIN model

Abstract

Purpose Iran Nanotechnology Initiative Council could significantly increase the production of scientific articles in the field by imposing ten-year incentive policies so that Iran ranked 7 in this area in the year 2015. But this progress was insufficient to speed up the production and commercialization of nanotechnology products and Iran ranked 44 with a share of 0.03 per cent of nanotechnology production in the world. Therefore, Iran Nanotechnology Initiative Council as a governmental policymaker institution in this area has sought for the policy threefold increase of funding to speed up the production and commercialization of products in this field. But given that the result was not so clear, this research was formed in the form of modeling Iran Nanotechnology Innovation Network and testing various scenarios to increase its efficiency. Design/methodology/approach This paper uses simulation framework of innovation networks (SKIN) in an attempt to model the production innovation network in Iran in the field of nanotechnology that can measure the effect of incentive policies in changing the network structure and, consequently, increasing the level and pace of innovation in it. Given that the volume of articles produced in Iranian universities in the field of Nanotechnology had a high speed and volume in comparison with the volume of technical knowledge produced by companies, and because the SKIN framework did not consider the distinction between the two, in the first step, the framework is developed using the model of absorptive capacity of knowledge provided by Cohen and Levinthal (1990) and then the developed model was used to model the Innovation Network. Findings Finally, two policies of threefold increase of budget (Scenario 1) and increasing the support for joint projects (with maintaining the current budget level) (Scenario 2) were tested in this model. The social network analysis method was used to analyze the results of the two scenarios, where innovation network topology was compared (as an index to measure the network efficiency) in three states of current status of the network (the baseline scenario), implementing the first and second scenarios of extraction and with each other. Originality/value This paper models Iranian Nanotechnology Innovation Network for studying the evolution of the network as a result of executing different supportive scenarios.

Collaborative network of SMEs innovation projects: influence of scientific and technological institutions

“…This perspective makes it possible to obtain insights regarding the dynamics of the network's evolution. With each project as a reference, undirected and unweighted connections were assumed, such as Mahmoudzadeh and Alborzi (2017). These choices are due to the premise that knowledge is not directed in only one direction and the difficulty of giving weight to the participation of members of the project teams.…”

“…It is also widely accepted that the network structure and its properties may be able to influence the performance of the network (Angelini et al, 2017;Barabási, 2009;Burt, 1992;Mahmoudzadeh & Alborzi, 2017;Watts & Strogatz, 1988), it is possible to observe the actors of the networks through configuration mechanisms such as centrality (Freeman, 1979;Wasserman & Faust, 1994).…”

“…In this perspective, many studies are concerned with interactions between actors in innovation networks formed spontaneously or with networks as an instrument of sectoral policies (Franco, Câmara & Parente, 2017;Hernández & González, 2017;Mahmoudzadeh & Alborzi, 2017;Schütz et al, 2018). Furthermore, as pointed out by Brem and Radziwon (2017), those networks used to support actors' performance in the development of national and regional innovation, opening space for studies with scope in local projects with the participation of SMEs.…”

Designing a multi-division model of national innovation capability promotion based on social network analysis

“…Other applications of social network analysis can be used to construct the actors of innovation capability such as universities, government and knowledge-based firms as the agents, and the interaction between these actors can be examined. In agent-based modeling instead of modeling a set of variables by performing causal relationships, the interaction between the agents is examined (Mahmoudzadeh and Alborzi, 2017). Also, agent-based modeling can be used to test political factors identified in this research by simulation of their effects. Designing innovation capability model at micro-level and comparing the performance of knowledge-based firms in a competitive environment by using multi-period and multi-division DEA. Investigation of important variables such as regulations and political factors to improve national innovation capability.…”

“…Other applications of social network analysis can be used to construct the actors of innovation capability such as universities, government and knowledge-based firms as the agents, and the National innovation capability promotion interaction between these actors can be examined. In agent-based modeling instead of modeling a set of variables by performing causal relationships, the interaction between the agents is examined (Mahmoudzadeh and Alborzi, 2017). Also, agentbased modeling can be used to test political factors identified in this research by simulation of their effects.…”