فصلنامه سیاست علم و فناوری
سال شانزدهم شماره 2 (پیاپی 58، تابستان 1402)

The development of information technology underpinning the Industrial 4. revolution led to increased platform businesses, which changed the economic model, the business environment, and consumer welfare. Debates about these businesses' benefits and challenges indicate that they are still in question and how to support and regulate them as an application of new science and technology development. The issues of Iranian ride-sharing platform businesses can also develop in line with this question, which has created challenges for policymakers. Since these studies have yet to draw apparent horizons for policymakers, this article aims to explain these issues under controversies, identify the actors, itemize them, and examine their relationships. Therefore, we recognize and analyze a network of actors in this field and their relationships through the Actor-Network Theory. The data of this research, which is related to the period of 1393 to 1400, was collected based on the Cartography of Controversies tool and completed the data with targeted interviews. The results show three cold disputes raised: market entry permits, prohibition of activity in some cities, illegal activity and one hot controversy: dynamic pricing in the regulatory process of platform businesses which have 62, 22, 18 and 17 actors respectively. Furthermore, creating the virtual business union, notification of the regulations, developing the relevant guidelines and issuing the permits have been resolved.

It has long been recognized that the role of futures studies in order to appropriate policies for unknown and uncertain futures is undeniable. But the weak link of this knowledge with policy-making is one of the biggest challenges that researchers face in the futures studies. Therefore, the current research seeks to provide a career approach in order to apply futures studies in the contextual environment. Since policy analysis is an important step in the policy-making process and on the other hand, based on reliable sources, not using futures studies in the policy-making process is due to the weak link between policy analysis and futures studies, so in this research, improving the link between futures studies and policy analysis is considered. In order to achieve this goal, using library studies, ten factors improving the link of futures studies and policy analysis were identified, and then, using the opinion of the focus group, eight main factors were confirmed and their alternatives were extracted. In the following, using the scenario planning method and scenario wizard software, four consistent scenarios based on uncertainties: "type of view and judgment towards the future", "type of policy analysis", "conditions of the policy environment towards the future", "actors' approach towards the future" , "understanding the system in the face of future issues", "institutional conditions of openness to alternatives", "time focus" and "uncertainty level", with the names "Futurology", "Forecasting", "Foresight" and "Futures research" and Seven intermediate scenarios close to the real space were obtained with the titles of "Interpretive Futurology", "Time series Forecasting", "Cause & Effect Forecasting", "Judgmental Forecasting", "Model-Based Foresight", "Dynamic Foresight" and "Open Foresight". These scenarios enable policy makers to conduct more accurate policy analysis and policy analysis in different decision-making coordinates, and to improve their strategies regarding future threats and opportunities.

A given technology goes through a unique path in the process of its diffusion. Factors residing in a socio-technical environment either inhibit or strengthen the path and speed of technology diffusion. In this regard, the aim of this research analyzes the diffusion curve of unstructured supplementary service data (USSD) technology in Iran. The scope of this paper includes mobile phone subscribers who have adopted USSD technology from 2010 to 2019. By adopting Roger’s technology diffusion framework, this article depicts and analyzes the diffusion curves of different applications of USSD technology through the historical data of USSD technology subscribers in the Hamrahe Avval operator in Iran. To better understand and explain the diffusion and the influential factors in the diffusion in the context of Iran, 14 semi-structured interviews were conducted with USSD experts affiliated to the regulatory bodies, policymakers, and mobile phone operators. Our findings reveal that USSD was mostly used in the operator and banking services while its adoption in other survives was been limited. We also found that relative advantage, complexity and Trialability have the highest strengthening effect on the diffusion of USSD; in the same vein price, regulations, and the interaction of actors are the key barriers to the diffusion of USSD in Iran. The findings of this paper provide policy and management implications for regulation and business activities of financial technologies and in particular USSD. To this end, the significant influence of regulation needs to be taken into account which could be a barrier to financial technologies diffusion as the case of USSD in Iran indicates.

Artificial intelligence will be one of the main drivers of human life at least in the next decade. Therefore, many governments are striving to plan and utilize the benefits of this technology. To this end, one of the focus areas has been the analysis of ecosystems and providing the groundwork for the development of this technology and wealth creation. However, research findings show that the artificial intelligence ecosystem in the country has not yet taken shape in the true sense, and it is in the early stages of formation, in terms of maturity. There is still no relative consensus among actors for dividing tasks and missions. In this article, using qualitative research methods and based on library studies, structured interviews, and expert panels, the country's artificial intelligence ecosystem has been analyzed, and a proposed model for redesigning it has been mapped out. The proposed model consists of 16 functions and seven main actor groups that must interact with each other. In the proposed model, there are gaps in institutions that currently do not exist or have not played a significant role, which include: governance gap, awareness-raising gap, standardization gap, ethical institution gap, innovation development gap, data governance gap, consortia, business cluster gaps, and the network institution gap. In addition to these, in some of the functions, institutions or organizations have been considered for them, but for various reasons, the desired and suitable output for advancing that function is not seen in the artificial intelligence ecosystem. These functional weaknesses are mainly related to monitoring, research and development, technical infrastructure provision, and the provision of other technical services.

In recent years, the issue of the health of entrepreneurial ecosystems has gained significant attention among researchers. The health of ecosystems refers to the robustness of ecosystems and maintaining their structure and function over time in the face of internal and external disturbances. It enhances the health level of an ecosystem, the guaranteed survival, and the better performance it demonstrates for entrepreneurs. However, adopting appropriate policies to enhance the health level of entrepreneurial ecosystems has received less attention from policymakers and researchers in this field. This research aims to identify and classify policy instruments to enhance the health level of entrepreneurial ecosystems in Iran. In this study, efforts have been made to identify policy instruments suitable for enhancing the health level of Iranian entrepreneurial ecosystems using interview and questionnaire tools. The final list of policy instruments has been extracted through expert opinions and statistical analyses. Subsequently, through the analysis of interrelationships and clustering of policy instruments, different classifications including policy instruments related to "Strengthening infrastructures" including 4 policy instruments to reform and stabilize social and macroeconomic policies, "Development of actors in the entrepreneurial ecosystem" including 4 policy instruments for the quantitative development of the entities and actors of the entrepreneurial ecosystem, "Diversification of the entrepreneurial ecosystem" "including 6 policy instruments to diversify business domains in the entrepreneurial ecosystem and "demand development" including 5 distinct policy instruments to strengthen finance of the entrepreneurial ecosystem can affect the enhancement of the health level of Iran's entrepreneurial ecosystem. The results of this research indicate that these distinct policy classifications can impact enhancing the health level of Iranian entrepreneurial ecosystems.

The growth of knowledge-based industries based on new technologies has marked extensive changes in advanced and emerging economies. Various factors inside and outside the company affect this growth. One of the most important factors inside the company, is technological learning. The purpose of this article is to identify technological learning modes and factors affecting them in nanotechnology knowledge-based companies. This research is applied in terms of purpose, and in terms of data type, it is a qualitative research based on a case study. The participants were experts in the various departments of knowledge-based nanotechnology companies, which were selected by purposeful sampling. The sample size was determined based on the principle of theoretical saturation. Qualitative content analysis has been used in the process of analyzing texts and documents. In science-based learning, scientific promotion of the company, internal R&D, and joint research activities have been the most important modes of technological learning. In experience-based learning, the most important learning modes are individual employee communication, company interactions with the outside, work experience, technical and skill training, teamwork, organizational flexibility, and knowledge and experience sharing. The innovation of these learnings also includes product innovation, process and technology innovation, market innovation and obtaining approvals and certificates. Factors affecting technological learning are R&D capability, production capability and learning culture as internal drivers and internal and international restrictions, legal capacities and government interventions, technological changes and competition intensity as external drivers.