پژوهشنامه پردازش و مدیریت اطلاعات
سال سی و نهم شماره 2 (پیاپی 117، Spring 2024)

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.

Evidence-based policymaking stands at the forefront of contemporary governance, where data and evidence have emerged as indispensable allies in shaping effective and informed decisions. This article embarks on a comprehensive exploration of the challenges and critical issues encountered when data assumes the role of evidence in policy formulation. The foundation of this investigation is rooted in the extensive body of literature on evidence-based policy-making. We delve into the scholarly discourse, tracing the evolution of policy formulation from relying on intuition to being guided by empirical insights. As we navigate through this intellectual landscape, the crucial role of data as a catalyst for this transformation becomes apparent. Delving deeper, we will explore the intricacies of data and the rise of big data. Once regarded as mere numbers, data now represents the currency of the information age. Its volume, velocity, and variety characterize it, making it a powerful tool for generating evidence and formulating policies. As we explore its features, we uncover the potential of data to unlock unprecedented insights and inform governance with empirical precision. To conduct a comprehensive examination of the difficulties encountered when utilizing data as evidence in policy-making, we employ a rigorous scoping review methodology. Through meticulous screening, we have identified and analyzed 36 exemplary articles that offer invaluable insights into the multifaceted landscape of data-driven governance. These articles provide a comprehensive overview of the challenges, which can be grouped into three distinct clusters: technical challenges arising from data complexities, legal and privacy dilemmas intertwined with governance, and the formidable issues faced by policymakers. Our discussion unravels the intricate web of challenges, ranging from data quality and integration to confidentiality, ethics, and governance issues. We delve into the intricacies of data access, the fight against bias, and the challenges posed by data volume and complexity. Simultaneously, we explore the complex legal landscape of data ownership, security, sharing, and compliance. The challenges policymakers face in fostering data-driven cultures, navigating resource constraints, and communicating data-driven insights are brought to the forefront. In conclusion, our exploration sheds light on the complex challenges and crucial issues that underlie the use of data as evidence in evidence-based policy-making. This research underscores the transformative power of data in governance and emphasizes the challenges and pressing issues associated with using data as evidence in policymaking.

This paper aims to evaluate whether features of the public sphere are present in the activities of Iranian Twitter users. To achieve this objective, the paper provides a synthesis of computational social sciences, network analysis, and Habermas's approach to the public sphere. The data comes from all Iranian users, including those who are actively engaged in political issues on Twitter. This method of data collection focuses on the computational aspects of the users. Added information on how they conducted networking and facilitated public opinion, which resulted in increased user interaction, would complete the data-gathering process to align with the theoretical claims. It also indicates that Twitter was a platform on which Iranian users fulfilled the requirements of the public sphere. Some of the findings indicate that there were 48,076 users connected with 25,534,713 edges, and together, they formed three communities. While one of the communities focused solely on social issues, the other two engaged in a rival political challenge. It seems that this situation exemplified the main characteristic of the public sphere, namely, the freedom to express political views and the formation of diverse communities with competing ideas.

Enterprises employ information systems to carry out their day-to-day business operations. Organizations implement business policies to enhance their competitive edge through efficient process management. This paper aims to propose a method that combines two approaches: evidence-based policymaking and process mining, to facilitate process reengineering. While numerous evidence-based approaches utilizing process mining techniques have been employed to assess process performance through measurements, these methods often focus on individual process instances. This is in contrast to Business Process Redesign (BPR) assessments, which encompass more comprehensive performance measurements, including overall process performance. This study proposes a method for analyzing process execution time, which includes Cycle time, Lead time, and Activity time. The aim is to support evidence-based policymaking in information systems through the use of process mining. Several key performance indicators (KPIs) have been defined for evidence-based management of business processes to identify process bottlenecks. The results of this paper demonstrate the application of process mining in analyzing the execution time of business processes. Using a real-world dataset, the study identified time-consuming activities and provided key performance indicators (KPIs) to guide process optimization. These findings demonstrate the effectiveness of process mining in identifying bottlenecks and inefficiencies within operational processes, ultimately leading to improved process performance and efficiency.

Regional management and spatial planning of higher education in Iran is one of the important policy issues in the field of science, technology, and innovation. In February 2016, the Supreme Council of Cultural Revolution approved the country's spatial planning of higher education. Article 1 and 2 of this document directly refers to the regional management of higher education. In line with the implementation of the document, in October 2020, the Ministry of Science, Research and Technology approved the bylaw "Implementation of the National Spatial Planning of Higher Education - Provincial Management of Higher Education Institutions in the MSRT". In this research, by using the case study strategy, an attempt has been made to provide a policy analysis of the implementation of spatial planning of higher education documents, which led to the approval of the provincial management regulations of higher education institutions. The data used in this research are available documents, evidence, statistics, events, and narratives of 14 experts and policymakers. Based on the results of this research, the process of drafting documents, the commitment and expertise of the implementers of the document, and the integration of operational experiences and policy-making research during the implementation period have an important effect on the correct and faster implementation of national documents and macro policies related to them.

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.

Modern economic growth has been based on mass industrial production and consumption, which have heavily relied on fossil fuels and energy waste since the 18th century. Hence, current socio-technical systems are unsustainable in meeting humans' basic needs, such as energy and mobility. Fossil energy resources are non-renewable and, on the one hand, contribute to emissions that cause unreliable harm to the environment. In this research, the prime theory of Transformational Change illuminates how to use science and technology policy to meet social needs sustainably and inclusively in societies. This article answers questions regarding the essential policies and governance measures that states need to implement for the transition to electric autonomous vehicles (AVs) in the socio-technical system. Using meta-synthesis, followed by a case study and interviews with experts in the electric AV field, the article identifies state policies and governance measures to facilitate the transition of the sociotechnical system into electric AVs. The conceptualization of these roles determines that the state's role is influenced by policy, governance, and legal decisions, which are ultimately implemented through specific combinations of policies.

The present study investigates Peer Review Ethics (PRE) in scientific journals in Iran, with a specific focus on compiling the PRE statement for the Journal of Information Processing and Management (JIPM). Moreover, qualitative analysis was conducted on the PRE statements of the journals published by the Ministry of Science, Research, and Technology (MSRT). The review process and documents of the JIPM were then analyzed. This evidence-based case study was conducted using a mixed-method approach, which included three research methods researcher-based, data-based, and research-based. A focus group discussion (FGD) was also conducted to validate the research findings. The findings showed that PRE elements were classified such as "timeliness," "confidentiality," "bias," "conflict of interest," "research misconduct," "respectful and fair expressions," "constructive and objective feedback," and "accountability and responsibility." The analysis of ethical statements in MSRT journals found that only half of the journals published PRE statements. In addition, 102 instances of violations of PRE, accounting for 15% of each review, were identified in documents submitted for review in JIPM.

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.

Although there have been many publications on the subject of CKM, particularly in recent years, not much research has been conducted on the performance analysis and science maps of these studies. To overcome this constraint, a bibliometric study of the current body of academic literature on CKM is conducted the study's methodology involved conducting a bibliometric examination of publications indexed in the Web of Science (WoS) and Scopus from 1992 to 2022. The search strategy employed for this review was guided by the PRISMA standards, resulting in 255 papers being located during the search procedure. To visualize and quantitatively analyze bibliometric networks the VOS viewer tool and the bibliometric R-package were utilized. The descriptive statistics reveal a significant increase in CKM studies since 2011, with the majority of publications occurring in 2021, 2019, and 2013, respectively. China, Iran, and the United States are the countries with the highest number of publications, respectively. The “JOURNAL OF KNOWLEDGE MANAGEMENT” is a core journal in this field, and “Hussin A” is the most prolific author in this scientific domain. The results of the thematic analysis indicate that “co-creation” has gained popularity in recent years, and over the past seven years, “knowledge sharing” and “customer knowledge development” have replaced “market orientation” and “customer relations”, respectively.  Additionally, the co-word analysis of the author's keywords identified six clusters, including “Knowledge Management and Market Orientation,” “Knowledge Sharing and Co-Creation,” “CKM and Customer Engagement,” “CKM, Tools and Technique,” “Knowledge Management and CRM,” and “CRM.”By identifying and deriving key CKM themes, this study makes a significant contribution to the CKM literature. Researchers can utilize the scientific analysis of a variety of sectors, such as customer knowledge management, to understand the scientific boundaries and gain a general understanding of the scientific method. Another goal is to identify research goals for policymakers in many scientific disciplines and modify them to meet future societal requirements.

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.