مطالب

Evidence-based policy-making is a continuous process of collecting valid and ethical information, intelligently analyzing it, and effectively communicating the findings to policymakers at the operational level in a controlled manner. This approach enables policymakers to take a proactive stance toward the consequences of science and technology (S&T) and develop ethical policies to achieve favorable economic and social outcomes in the S&T field. The purpose of this research is to describe how the evidence-based approach contributes to ethical policy-making in science and technology (S&T). To achieve this goal of explaining the evidence-based policy-making approach, the concept of strategic intelligence in policy-making has been utilized to elucidate the theoretical foundations of the research. The ethical components of evidence-based policy-making are then identified from relevant resources and documents through a documentary study. To classify the ethical components of evidence-based policy-making, identify the relationships between them, and formulate the framework for evidence-based ethical policy-making, a thematic analysis method inspired by the 7-step approach of Noblit and Hare (1988) has been used. Based on the results of this research, the steps of evidence-based ethical policy-making include the identification of information sources, the use of analytical tools for strategic intelligence in policy-making, policy intelligence, and ethical policy formulation. Also, in all these steps, policymakers must adhere to ethical principles of politics, which include participation, objectivity, methodicality, and learning.

Today, businesses are considered the main factor in improving the country's economy and creating employment through the implementation of policies that foster entrepreneurship and the establishment of supportive institutions. Therefore, the current research seeks to identify factors that affect the development of IT-based platform services. In this research, a questionnaire based on the fuzzy Delphi method was used to identify the factors. The statistical population of this research consists of 20 managers, professors, specialists, and experts who are associated with start-up companies. Due to the small size of the population, the entire population was considered as a sample. In this research, the influential factors were identified using the fuzzy Delphi method. The results of the research showed that providing access to financial resources is the most important factor affecting the development of IT-based platform services. On the other hand, improving the entrepreneurial environment through the establishment of laws is considered the least important factor in this area.

In recent years, ecological metaphors have gained popularity in the field of innovation. Such excessive preoccupation of researchers with common ecological metaphors, such as ecosystems, and a lack of due attention to the development of other ecological principles, challenges the systematic conceptual framework of the field. This raises the metaphorical concept of a "boundary object," which could hinder the connection between theoretical discussions on innovation ecosystems and their practical applications and policy development. Therefore, there is a need for an evidence-based approach to define and map the innovation ecosystem metaphorically. The concepts deployed would form a part of the conceptual network of the innovation ecosystem. As part of an effort to implement evidence-based practice in the field of innovation ecosystems, this study employs Scaringella's and Radziwon's (2019) systematic review framework and the comparative-qualitative content analysis method. The analysis is based on a structured matrix that is rooted in metaphor mapping. Upon analyzing the content of the selected articles, the ecological metaphors grounded in innovation ecosystems can be classified into two general categories: metaphors related to actors (species, population, and community) and metaphors related to the environment (landscape, biome, and ecotone). The developed framework serves as a tool for innovation policymakers, enabling them to make informed decisions by gaining a comprehensive understanding of the seven hierarchical metaphorical layers within innovation ecosystems. Consequently, policymakers should not restrict their governance to the suggestions offered by the prevailing literature on ecosystems. Instead, they should also give equal attention to the various dimensions of innovation ecosystems, including actors, the environment, and the relationships between them, from an ecological perspective.

This paper discusses the application of the Technology Development Envelope (TDE) as a road mapping tool in combination with the Delphi method and the Analytic Hierarchy Process (AHP). The method helps policy and decision-makers in organizations strategically manage emerging technologies by mapping their anticipated development path. The paper focused on creating a Technology Development Envelope (TDE) for artificial intelligence (AI) technologies in the food industry. In the first step, an expert panel identified a list of AI technologies applicable to the food industry. A hierarchical decision-making model, comprising 5 criteria and 24 sub-criteria for technology evaluation, was extracted from the literature and validated using the Delphi method. The Analytic Hierarchy Process (AHP) was used to determine the relative importance of criteria and sub-criteria for evaluating the identified technologies. At this stage, the most important criteria considered were food safety and social and political acceptance of the technology. The expected value of each technology was calculated over different periods, resulting in the development of the Technology Development Evaluation (TDE) for AI technologies in the food industry within a ten-year horizon. The analysis of the future indicates that technologies such as computer vision, robotics, industrial automation, and machine learning will undergo continuous improvements over time and eventually become dominant technologies in the food industry. These advancements can lead to an increase in quality and efficiency within the food industry. Finally, sensitivity analysis was conducted to test the model's sensitivity and explore how policymakers' extreme strategies affect the model's outputs.