fuzzy cognitive map

Digitalization has become a crucial factor driving innovation in companies, research centers, and governmental organizations. This study aimed to identify the drivers of digital innovation, map the relationships between them, and analyze the effective routes of these drivers within governmental hospitals in Rafsanjan.

This research is pragmatic in its purpose and employs a descriptive survey for data collection, conducted in 1402. The study's population comprised experts from governmental hospitals in Rafsanjan, with a sample of 12 experts selected through the snowball method. The data's validity was confirmed using the content validity ratio index. Data analysis was performed using the fuzzy cognitive mapping method, supported by Fcmapper, Pajek, and Excel software.

The study identified 12 key factors driving digital innovation in governmental hospitals. Fuzzy cognitive map analysis revealed that senior management support, with a coefficient of 1.57, exerted the most influence. Meanwhile, the organization's digital capacity, with a coefficient of 3.24, was found to have the greatest influence. Additionally, the organization's digital capacity, with a coefficient of 3.89, exhibited the highest centrality. This research contributes by identifying key digital innovation factors, establishing causal relationships among these factors, and determining the primary factors influencing others through various digital innovation scenarios within governmental hospitals.

Based on scenario analysis findings encompassing 12 drivers and understanding the causal relationships among them, it is recommended that hospital authorities prioritize the "needs of citizens" driver to foster digital innovation.

Health promotion is a crucial task for policymakers. Therefore, this study aims to identify the key factors that determine population health in Iran and illustrate the causal relationships between these factors.

The research was conducted using a mixed-method approach in three stages. First, macro social determinants of health were identified by analyzing available resources from 2000 to 2019. Screening of factors was carried out through the fuzzy Delphi method and a survey involving 15 experts. Finally, the causal relationships between the social determinants of health were depicted using a fuzzy cognitive map.

Initially, 96 health determinants were identified at the national and global levels. After the screening, 8 factors that had less significance in population health were excluded. In terms of causal relationships, the economic system emerged as the most important and influential factor, with a centrality degree of 21.06 and an out-degree of 10.91. Following that, governance and policy-making, as well as national policies, were ranked with centrality degrees of 20.25 and 20.19, respectively.

Irrespective of the causal relationships, the health system holds the utmost importance in population health. However, when considering the causal relationships, the economic system takes precedence as the most critical factor, while the health system does not rank among the top three factors. Therefore, the analysis of fuzzy cognitive maps assists policymakers in understanding priorities and the interconnectedness between sectors in developing and implementing health-oriented policies across all sectors, enabling the adoption of the most effective policies.

The readiness of health centers, especially hospitals for management challenges ahead, has a significant impact on reducing mortality in addition to physical and psychological damage due to the prevalence of viral or infectious diseases with high pandemic potential, including the COVID-19. Given the importance of this issue, this study aimed to identify the challenges of managing health centers and prioritizing them during the COVID-19 pandemic.

In this research, first, by reviewing the literature, text mining and, using the opinions of experts, we identified the main challenges of managing health centers. Then, the Likert-scale questionnaire was used to determine the main challenges. The Best Worst Method was used to determine the importance of each challenge, and the Fuzzy Cognitive Map Approach was used to determine the impact of each challenge on other challenges.

The 20 identified challenges were classified into three categories: human resource challenges, organizational challenges, and external challenges. Nine main challenges were identified among them. According to the results, inadequate physical space, restrictive rules and regulations, and manpower shortages were the most important challenges, respectively, and inadequate physical space was the most effective of the challenges.

Due to the importance of managing health care centers and the need for immediate action during this period, more specialized personnel response, empowerment of medical personnel, and correction of laws which act as obstacles can be useful tools for improving the management of these centers in the short term.

Configuration of supply chain is considered as the highest level of decision-making, which is part of the strategic decision-making process. According to few studies on the service supply chain, the current study aimed at configuring the sustainable supply chain of health services using the fuzzy cognitive mapping method.
The current research was deigned based on mixed method (quantitative and quantitative). The qualitative statistical population of the study was the hospital experts of which 18 individuals were selected by the snowball sampling method for interviewing and extraction of effective concepts on the sustainable supply chain of hospitals. In a small part of all employees of hospitals in Kerman, Iran, 414 individuals were selected as the quantitative samples using random sampling method. The sustainable supply chain questionnaire in hospital was made by the author and exploratory factor analysis was used to identify the concepts of 15 main factors designed with fuzzy cognitive mapping method to quantitatively configure the sustainable supply chain. SPPS version 18, EXCEL, PAJEK, and FCMAPPER were employed in the current study.
The effective measure on the sustainable supply chain comprised of 60 concepts in the form of 15 main factors. Based on 15 concepts, the configuration of the sustainable supply chain was designed. The results showed that service delivery management with 4.19 degrees has the highest centrality and attention to the environment with 0.99 degrees has the lowest centrality among other factors.
Service delivery management has a high impact on sustainable supply chain in hospitals. Therefore, it is recommended that hospital officials focus on service delivery management factors and manage it to reach a more sustainable supply chain.

Nowadays, e-procurement systems have provided many opportunities to improve supply and procurement activities in healthcare industry through Internet-based electronic communications for supplier-buyer interactions. Considering the fact that the implementation of these systems is not successful in many cases, extensive preparation in relevant areas is necessary before implementation. This paper aims to present a framework of key readiness factors of hospitals for the adoption of e-procurement system.
This study was an applied descriptive survey. The framework of readiness factors was developed based on an extensive review of literature and e-readiness models as well as opinions of experts in the field of information technology, procurement, and healthcare systems. The proposed framework was applied in a case study of a specialty hospital located in Tehran in 2016. The data were collected using a questionnaire, and Fuzzy Cognitive Map (FCM) method was used for modeling and analysis of the identified factors.
In this research, a comprehensive framework of critical readiness factors of hospitals for e-procurement adoption was developed comprising five main components: management, technology, process, human resource, and environment. Environmental and managerial factors were identified as the most important readiness determinants of the case hospital, and among various factors, manager's expertise in IT, IT infrastructures of hospital, and continuous monitoring of system's performance are of higher priority.
This paper provides guidance for more accurate planning to improve readiness and successful adoption of e-procurement systems in hospitals by identifying the most important influential readiness factors.