Role In Decision Research Articles

Social learning and the acquisition of information and knowledge—a network approach for the case of technology adoption

Abstract The complexity of agricultural innovations and heterogeneity of circumstances of technology application, outcomes, and social network structures have often led to obstacles in social learning and suboptimal adoption. In this article, we examine technology diffusion by focusing on the roles of peer adoption decision, peer experience, and network structures, using survey data from 500 farm households in Northern Ghana to generate social network contacts. Our results reveal that learning from peer experiences matter more in the diffusion process than just peer adoption decisions alone. We also find that social learning and the likelihood of adoption are higher when peers are central nodes, and particularly when they belong to cohesive subgroups, but lower when they are in highly segregated networks. Our results shed new light on the role of central agents, since highly cohesive neighbourhoods seem to promote diffusion more in high-modularity networks than central nodes.

Exploring the role of the pharmacist within the Hospital@Home team at a tertiary centre in England

Abstract Introduction Hospital@Home (H@H) is an emerging healthcare model providing hospital-level care within patients’ homes (1). Despite its global presence for three decades, H@H adoption remains limited in the United Kingdom. The COVID-19 pandemic prompted National Health Service (NHS) England to promote virtual wards, including H@H, as an innovative care alternative (2). Among the pivotal functions in H@H are prescribing, administering, or stopping medications, where pharmacists play a critical role, undertaking these tasks along with the multidisciplinary team (MDT). However, there has been little research that investigates pharmacists’ scope of practice in H@H. Aim To explore the role of the pharmacist within the H@H team at a tertiary centre in England. Methods A qualitive study was conducted using 1) online (via Microsoft Teams) or in-person audio-recorded semi-structured interviews with pharmacists working in the tertiary centre’s H@H team, and 2) non-participant observations of purposively sampled MDT H@H team meetings; heart failure, general care and antibiotic. Pharmacists’ collaboration with MDT members, tasks they performed and how the MDT made decisions were the focus of the observations. Interview transcripts and observation field notes were concurrently analysed inductively to generate key themes. All three H@H pharmacists were approached to take part in the study. All members of the respective MDTs were recruited to take part in the observations. All participants gave informed consent. Results All three H@H pharmacists participated in interviews. Six MDT meetings were observed, attended by doctors (n=7), nurses (n=8) and pharmacists (n=3). Interviews lasted approximately 45 minutes each and MDT meetings lasted 30-120 minutes. Four main themes were generated from the analysis: 1. Scope of practice extends beyond a “traditional” pharmacist. 2. Being open-minded to a changing scope of practice. 3. Active collaboration with MDT members underpins and expands role. 4. Is intentional about, and advocates for the provision of patient-centered care. Pharmacists’ varied scope of practice (e.g. participating/co-leading MDT meetings and conducting home visits) placed them in pivotal roles in patient care coordination, treatment planning, and decision-making. Pharmacists were open-minded and proactive in expanding the scope of H@H care, and their role. They continuously acquired new skills as they encounter and provide diverse care scenarios. Their specialised knowledge and skillsets were viewed by the MDT as an asset. Pharmacists were reflective practitioners, saw patients as complex individuals with specific needs to care for and advocated for their needs. Conclusion To our knowledge, this is the first study exploring the role of the H@H pharmacist in England, adding to the knowledge base in this emerging service delivery; pharmacists undertake non-traditional clinical roles in the team, work collaboratively with others to expand the service and their own skillset, to advocate for the need of patients. Although only three pharmacists were interviewed, this was the entire available sample. The study did not include home visit observations, which could provide insights into pharmacists’ interactions with patients in their homes. Future research can focus on complementing the findings of this study with in-depth exploration of the pharmacist’s role in H@H settings.

On Some Demographic Structures and Economic Growth in Nigeria (2003–2022)

Effective development planning necessitates the integration of sound social and economic planning strategies. In Nigeria, demographic and population data play a crucial role in shaping these plans. This research investigates the impact of demographic structure on the country's economy, with a specific focus on key characteristics such as population size, birth rate, mortality rate, growth rate, GDP growth rate, and fertility rate. The study analyzes demographic features alongside GDP growth rate data spanning from 2003 to 2022. The findings reveal a robust correlation between birth rates and GDP growth, indicating that population dynamics significantly influence economic development. Additionally, positive relationships are identified between death rates and GDP growth, underscoring the impact of mortality issues on economic outcomes. The regression model demonstrates that demographic factors collectively mold Nigeria's economic growth trajectory, shedding light on the intricate dynamics between demography and economic development. Through time series analysis, the model exhibits low errors and high explanatory power. However, residual analysis highlights serial association, emphasizing the temporal demographic effects on economic growth. Ultimately, the study underscores the potential contributions of demographic characteristics to Nigeria's economy. Policymakers and economists keen on leveraging demographic shifts for economic advancement will find valuable insights in this research. As demographic considerations increasingly play a pivotal role in Nigeria's economic growth, strategic planning and decision-making must take these factors into account.

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study

IntroductionDifferentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases. MethodsTwenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3rdV/ID). Machine learning (ML) models with Logistic Regression (LR), Random Forest (RF), and XGBoost algorithms based on 3rdV/ID and serum Nf-L levels were tested in distinguishing among PSP, PD and HC. ResultsPSP patients showed higher serum Nf-L levels and larger 3rdV/ID ratio in comparison with both PD and HC groups (p < 0.005). All ML algorithms (LR, RF and XGBoost) showed that the combination of MRI and blood biomarkers had excellent classification performances in differentiating PSP from PD (AUC ≥0.92), outperforming each biomarker used alone (AUC: 0.85–0.90). Among the different algorithms, XGBoost was slightly more powerful than LR and RF in distinguishing PSP from PD patients, reaching AUC of 0.94 ± 0.04. ConclusionOur findings highlight the usefulness of combining blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD patients. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions in these neurodegenerative diseases.

Incorporating diverse values of nature in decision-making-theory and practice.

Values play a significant role in decision-making, especially regarding nature. Decisions impact people and nature in complex ways and understanding which values are prioritised, and which are left out is an important task for improving the equity and effectiveness of decision-making. Based on work done for the IPBES Values Assessment, this paper develops a framework to support analyses of how decision-making influences nature as well as whose values get prioritised. The framework is used to analyse key areas of environmental policy: a) the present model for nature protection in market economies, b) the role of valuation in bringing nature values into decisions, and c) values embedded in environmental policy instruments, exemplified by protected areas for nature conservation and payments for ecosystem services. The analyses show that environmental policies have been established as mere additions to decision-making structures that foster economic expansion, which undermines a wide range of nature's values. Moreover, environmental policies themselves are also focused on a limited set of nature's diverse values. This article is part of the theme issue 'Bringing nature into decision-making'.

Exploring Symphysio-Fundal Height and Abdominal Girth in Predicting Fetal Weight: Lessons from Bugando Medical Centre, Mwanza, Tanzania

Background: Estimating fetal weight plays a crucial role in decision-making, particularly in high-risk pregnancies when determining the timing and method of delivery. In resource-limited settings where ultrasound availability is limited, Symphysio-Fundal Height and Abdominal Girth (clinical method) have been utilized as a substitute for predicting fetal weight. However, the accuracy of this clinical method has not been assessed locally. This study seeks to evaluate the precision of clinical method in estimating fetal weight and its correlation with the actual birth weight at Bugando Medical Centre. Methods: A hospital-based cross-sectional study was conducted in the Lake Zone area of Tanzania at Bugando Medical Centre. The study conveniently enrolled 400 mothers with singleton pregnancies who were admitted for normal vaginal delivery, elective cesarean section, or induction of labor. Fetal weight was estimated using clinical methods and compared with the birth weight at delivery. Statistical analysis was performed using SPSS version 20, employing descriptive statistics such as frequency, mean, and standard deviation. The relationship between estimated fetal weight, and birth weight were examined using the Chi-square and Pearson's correlation coefficient. A P-value of 0.05 was considered statistically significant. Results: The study participants had an average age of 29.13 years (± 5.346). Our analysis revealed that the mean estimated fetal weight slightly exceeded the actual birth weight, with values of 3495 grams and 3250 grams, respectively. Notably, this difference was statistically significant with a P-value &lt; 0.001, indicating the clinical method's tendency to overestimate fetal weight. Furthermore, the clinical method showed a strong positive correlation with actual birth weight (correlation coefficient = 0.7309, p-value &lt; 0.001), demonstrating its reliability in fetal weight estimation. The positive linear correlation between clinical fetal weight ...........

Review of trends regarding artificial intelligence and its prospects for procedural decisions during criminal proceeding

Discussions on the advantages and risks of artificial intelligence (AI) in various fields are among the most relevant today. It is evident that proponents and opponents of its implementation have existed, exist, and will continue to exist. However, it is undeniable that with each scientific and technological advancement, humanity has the opportunity to improve or worsen the quality of its existence. Therefore, it is crucial to establish rules for developers and users who must be aware of the existing threats posed by the use of a particular tool, despite or in defiance of these limitations. This overview aims to explore how various aspects of our lives, including the legal sphere, are changing due to the widespread availability and increasing accessibility of artificial intelligence (AI). It seeks to clarify the role of the state as a regulator and implementer of relevant functions, the rules developers and users must adhere to, the aspects of AI usage already accessible to the legal sector, and how AI can assist in decision-making during criminal proceedings (while still leaving the leading role in decision-making and choice of alternatives to humans). It also discusses the prospects ahead and the myths that need to be overcome for progress. Through analysis and synthesis, it has been observed that threats from harmful AI are present, especially when it is deployed not for socially beneficial or human-centric purposes. Regulation of the AI sphere is crucial in all its aspects, starting from ethical considerations and ensuring an anthropocentric approach to AI development and usage, and ending with cutting-edge developments, including AI applications in various fields, such as the creation of new product designs like confectionery. Despite potential risks and threats, the use of artificial intelligence can have advantages, but careful regulation of this area is necessary. It is also noted that regulating AI to the detriment of progressive development should be avoided. It is argued that in the legal and law enforcement sectors, AI assistance can bring many benefits, but its use must be accompanied by a full awareness of the axiom that “AI systems for justice and law enforcement are considered ‘high risks’.” Therefore, before actively applying AI, especially in this realm, it is essential to establish clear boundaries and rules, and importantly, accountability for any harm caused. Such sensitive aspects as the integrity of human privacy, respect for human rights, and the like should prevail in this mechanism.

Analysis of the Impact of Equity Incentives on Corporate Performance

Equity incentive is a long-term incentive mechanism implemented by enterprises to motivate and retain core talents, and is one of the most commonly used methods to motivate employees. Equity incentives have been developed from the beginning of the twentieth century to the present day to form a relatively complete and mature system, and their dynamic evolution and key role in corporate governance (employee loyalty), executive compensation, and investor decision-making make them of far-reaching research significance. Also, the equity incentives of enterprises play a crucial role in their development. The purpose of this paper is to comprehensively summarize and analyze the development and classification of these incentives. In addition, this paper focuses on the equity incentive policies of Tesla and Xiaomi and explores how and why incentivizing two target groups, namely executives and employees, affects corporate performance, as well as making a side-by-side comparison, and gives some suggestions for future equity incentives.

Women's Knowledge of Genomic Testing and Precision Medicine in Breast Cancer Treatment Decision-Making.

To understand awareness of genetic and genomic testing, as well as decision-making, in women diagnosed with breast cancer. 29 African American/Black and Latina/Hispanic women diagnosed with breast cancer. A semistructured interview guide was used in focus groups conducted via videoconference. Transcripts were analyzed using thematic analysis. Many of the women understood the concept of genetic testing to identify the BRCA1/BRCA2 variant, but none of them were aware of genomic testing and its implications for personalized medicine. Participants discussed provider and patient roles in treatment decision-making, identifying roles that the physician might play in treatment planning, from primary decision-maker to collaborator. As the number of precision cancer treatments expands, patients must be able to comprehend the information provided to make informed decisions about their treatment. Providers should do a better job of explaining potential treatments so that patients feel they are part of the decision-making process. Addressing gaps in treatment access and uptake requires providers to prioritize patient engagement and understanding.

A complex methodology for the development of mathematical modeling skills in engineering education

AbstractToday, the role of humans is changing rapidly in both industrial production activities and services. Mediocre, easy-to-learn activities can be performed more efficiently by machines; mediocre knowledge is being devalued while the importance of high-level skills is increasing. As a result, in all sectors of the economy, and especially in engineering, new approaches to expert training are needed; people must learn to hand over certain decision-making roles and to control the processes supported by AI rather than compete with it. STEM education has a responsibility to achieve these goals and must develop appropriate tools for engineering education. This paper presents a complex didactic methodology for competency-based education in engineering bachelor programs. An important element is the mathematical competency map, which shows the importance and place of mathematical and algorithmic (coding) knowledge in engineering topics. Another element is the systematic testing of mathematical knowledge in non-mathematical contexts in engineering courses. We provide an overview of our achievements in applying the developed toolset and improving the efficiency of mathematics teaching in engineering bachelor programs.

Makkah healthcare cluster response, challenges, and interventions during COVID-19 pandemic: A qualitative study

BackgroundThe global COVID-19 crisis has underscored the critical role of effective decision-making in healthcare systems. Saudi Arabia has shown resilience by implementing comprehensive testing, tracing, and vaccination measures. Given the unique cultural and religious characteristics of Makkah, specific challenges have prompted efforts to uncover local pandemic responses. This qualitative assessment aims to delineate the challenges faced by decision-makers during COVID-19 in Makkah and identify key interventions implemented by the Makkah healthcare cluster to manage and coordinate care. MethodUtilizing a purposive sampling approach, executive leaders within the Makkah Healthcare Cluster were invited for semi-structured interviews during the COVID-19 period. Thematic analysis was carried out in five steps, ensuring rigor and trustworthiness through multiple checks, and employing a critical and collaborative approach. ResultThroughout the COVID-19 outbreak in Makkah, participants revealed Several factors that have hindered healthcare organizations' ability to effectively manage the pandemic, including testing difficulties, resource shortages, vaccination misconceptions, continuity of care issues, infections among healthcare workers, and the need for consistent protocols. The participant leaders in Makkah's healthcare cluster specified implemented strategies that helped in overcoming the encountered challenges, such as adopting new technologies, enhancing communication, managing supply and demand, and improving workforce adaptability and development. Their experience in managing Hajj and Umrah provided valuable insights for handling the pandemic effectively. ConclusionThe study emphasizes the significance of technology adoption, effective communication, supply management, workforce development, and lessons from managing religious events. Its findings have implications for healthcare systems globally, emphasizing the importance of preparedness, response, and resilience in diverse cultural contexts.

Exploring Neuro Management: Bridging Science and Leadership – An Overview

Purpose: To develop a comprehensive overview of Neuro Management, by bridging the gap between scientific insights and effective leadership within organizational contexts based on exploring Neuro Management concepts, applications, and their implications for managerial practices. Procedure: The overview begins by delineating the fundamental concepts and applications of Neuro Management, elucidating its relevance in leveraging neuroscience principles for enhancing leadership effectiveness. It then provides an extensive overview of key themes and contributions within the field, identifying foundational works and highlighting significant research directions. A critical analysis of the evolution of Neuro Management concepts is presented, identifying existing research gaps and tracing the evolutionary trajectory of the field. Furthermore, a conceptual framework is developed, integrating neuroscience principles with leadership practices, and delineating a structured approach for applying neuroscience insights in leadership contexts. Analysis: The paper discusses the neural mechanisms underlying various aspects related to Neuro Management, exploring decision-making processes, emotional intelligence, and their neural correlates. Subsequently, it analyzes how Neuro Management concepts bridge the gap between scientific insights and effective leadership, highlighting the translation of neuroscience findings into practical leadership strategies. Moreover, the implications of Neuro Management for organizational practices, decision-making, team dynamics, and productivity are expounded upon, emphasizing its transformative impact on enhancing organizational performance. The study assesses the connection between human productivity and Neuro Management, focusing on how Neuro Management strategies affect organizational effectiveness. The study also critically analyzes leadership and neuro management from a managerial perspective, evaluating their effects on organizational outcomes and their roles in decision-making. Additionally, it explores how different Neuro Management models that are helpful in decision science might be applied to create efficient decision-making processes. Furthermore, the paper adopts an ABCD listing framework, analyzing Neuro Management from Managers' Points of View, evaluating its Advantages, Benefits, Constraints, and Disadvantages. Finally, it suggests postulates for future research and practical applications in Neuro Management, emphasizing areas warranting further exploration and implementation. Originality/Value: This comprehensive overview aims to serve as a foundational resource in the expanding field of Neuro Management, offering insights, analyses, and frameworks for advancing leadership practices informed by neuroscience principles. Type of Paper: Conceptual exploratory research.

CEO overconfidence and investor sentiment in M&A decisions

This study investigates the interactive impact of CEO overconfidence and investor sentiment on merger behavior in U.S. firms. We find that the influence of CEO overconfidence on merger frequency is stronger during periods of high investor sentiment, diminishing in low sentiment times. It suggests that investor sentiment is a more dominant determinant than CEO overconfidence in shaping merger frequency. Furthermore, a bullish market trend motivates overconfident CEOs to pursue diversifying mergers, while a bearish market trend encourages cash payments. Overall, overconfident CEOs adapt their merger behavior based on the economic environment, with investor sentiment playing a dominant role in decision-making.

A Multiagent Cooperative Learning System With Evolution of Social Roles

Recent developments in reinforcement learning have been able to derive optimal policies for sophisticated and capable agents, and shown to achieve human-level performance on a number of challenging tasks. Unfortunately, when it comes to multi-agent systems (MASs), complexities such as non-stationarity and partial observability bring new challenges to the field. Building a flexible and efficient multi-agent reinforcement learning algorithm capable of handling complex tasks has to date remained an open challenge. This paper presents an Multi-Agent learning system with the evolution of Social Roles (eSRMA). The main interest is placed on solving the key issues in the definition and evolution of suitable roles, and optimizing the policies accompanied by social roles in MAS efficiently. Specifically, eSRMA incorporates and cultivates role division awareness of agents to improve the ability to deal with complex cooperative tasks. Each agent is assigned a role module, which can dynamically generate roles based on the individuals’ local observations. A novel multi-agent reinforcement learning algorithm (MARL) is designed as the principal driving force that governs the role-policy learning process by a role-attention credit assignment mechanism. Moreover, a role evolution process is developed to help agents dynamically choose appropriate roles in decision-making. Comprehensive experiments on the StarCraft II micromanagement benchmark have demonstrated that eSRMA exhibits superiority in achieving higher learning capability and efficiency for multiple agents compared to the state-of-the-art MARL methods.

A Study of Microeconomics Based on Market Behavior Theory

In microeconomics, market behavior theory plays an important role. This article applies in-depth research on the application of market behavior theory in microeconomics, exploring market prices, consumer analysis, and market structure, fully highlighting the role of economic subject behavior decision-making, and exploring the value of this theory in policy design and application.

Generating a Set of Consistent Pairwise Comparison Test Matrices in AHP using Particle Swarm Optimization

Pairwise Comparison Matrix (PCM) play a crucial role in multi-criteria decision-making, especially within the framework of the Analytic Hierarchy Process (AHP). It is essential for an expert to systematically assess and compare alternatives, considering various criteria as part of the decision-making process. The AHP relies on consistency to ensure the accuracy of pairwise comparisons made by decision makers, increasing the overall integrity of the decision-making process. The aim of this paper is to present an approach based on Particle Swarm Optimization (PSO) as a metaheuristic method to generate a set of consistent pairwise comparison matrices. Numerical results are presented with different sizes of matrices showing the effectiveness of our algorithm in producing acceptable matrices for the benefit of expets.

Exploring the Influence of Behavioral Biases on Decision-making in Digital Finance: Implications for Financial Inclusion and Consumer Protection

This research paper investigates the influence of socioeconomic factors on financial behaviors among millennials. Through an analysis of variance (ANOVA) approach, the study examines the significance of socioeconomic variables on various aspects of financial decision-making, including online banking usage, saving priorities, comfort with investment decisions, and reliance on digital financial resources. The findings reveal minimal to modest effects of socioeconomic factors on these financial behaviors among millennials. While some variables such as familiarity with digital financial resources show slightly higher effect sizes, overall, socioeconomic factors explain only a small proportion of the variance in financial behaviors. These results suggest that other factors beyond socioeconomic status may also play significant roles in shaping financial decision-making among millennials. The study contributes to the existing literature by highlighting the nuanced relationship between socioeconomic factors and financial behaviors among millennials. It underscores the importance of considering multiple determinants when analyzing financial decision-making processes. Future research could explore additional factors such as personality traits, cultural influences, and technological advancements to provide a more comprehensive understanding of millennial financial behaviors. Moreover, longitudinal studies could offer insights into the dynamic nature of financial decision-making and its evolution over time. Understanding these factors is crucial for policymakers, financial institutions, and educators to develop targeted interventions and strategies aimed at promoting financial well-being among millennials in an increasingly complex financial landscape.

The relationship between the quality of big data marketing analytics and marketing agility of firms: the impact of the decision-making role

The relationship between the quality of big data marketing analytics and marketing agility of firms: the impact of the decision-making role

Analytical Study of the Implementation of AAOIFI’s Ethical Standards in the Algerian Islamic Banking System: Assessment and Recommendations

Purpose ‒ The application of ethical requirements is a key way for all stakeholders in the Islamic financial industry to build trust and promote integrity. This study aims to assess the extent to which the Algerian banking system has adopted the ethical standards for Islamic financial transactions issued by the Accounting and Auditing Organisation for Islamic Financial Institutions (AAOIFI). Design/Methodology/Approach ‒ This study conducted an analytical reading. It utilised the descriptive approach to present the study's variables and employed the analytical approach to discuss the various concepts and legal regulations that govern the banking sector and to provide recommendations that would help to build a solid foundation for the banking system. Findings ‒ The findings revealed that the Algerian monetary authority is committed to gradually promoting Islamic banking regulations in a thoughtful manner. However, the study also found that there is a lack of clarity regarding the adoption of regulatory laws in Algeria for the ethical standards issued by AAOIFI, even within the 2023 Monetary and Banking Act. Originality/Value ‒ To the best of the authors’ knowledge, this paper is the first study to assess the extent to which the Algerian banking system has adopted the ethical standards for Islamic financial transactions issued by AAOIFI. Research Limitations/Implications ‒ The primary limitation of this research is its exclusive focus on the Algerian banking system and its reliance on secondary data. Practical Implications ‒ This research offers valuable insights for those in decision-making roles. It recommends the establishment of a committee of experts to develop standards tailored to the Algerian Islamic banking environment. The committee should be financially independent and under the supervision of the monetary authority. Additionally, the study suggests the issuance of a code of ethics to govern the professional conduct of Islamic banks (IBs) and Islamic financial institutions (IFIs) in accordance with Sharīʿah.

Rheumatoid arthritis patients' perspective on the use of prediction models in clinical decision-making.

A rapidly expanding number of prediction models is being developed aiming to improve rheumatoid arthritis (RA) diagnosis and treatment. However, few are actually implemented in clinical practice. This study explores factors influencing the acceptance of prediction models in clinical decision-making by RA patients. A qualitative study design was used with thematic analysis of semi-structured interviews. Purposive sampling was applied to capture a complete overview of influencing factors. The interview topic list was based on pilot data. Data saturation was reached after 12 interviews. Patients were generally positive about the use of prediction models in clinical decision-making. Six key themes were identified from the interviews. First, patients have the need for information on prediction models. Second, factors influencing trust in model-supported treatment are described. Third, patients envision the model to have a supportive role in clinical decision-making. Fourth, patients hope to personally benefit from model-supported treatment in various ways. Fifth, patients are willing to contribute time and effort to contribute to model input. And lastly, we discuss the theme on effects of the relationship with the caregiver in model-supported treatment. Within this study RA patients were generally positive about the use of prediction models in their treatment given some conditions were met and concerns addressed. The results of this study can be used during the development and implementation in RA care of prediction models in order to enhance patient acceptability.