Decision-making Process Research Articles

“I think we should wait and see”: A qualitative study of call-takers’ decision-making in consultations with patients suffering unrecognized myocardial infarction

ObjectivesCall-takers face a complex situation when assessing medical problems in emergency medical services calls. Patients with myocardial infarction experiencing atypical symptoms risk misinterpretation. We examined development in call-takers' decision-making process in telephone consultations with patients having imminent myocardial infarction. MethodsRecording of 38 calls among 19 patients (two per patient) who contacted Copenhagen Emergency Medical Services (Denmark) at least twice within one week before myocardial infarction diagnosis. The penultimate and last call were compared using qualitative content analysis. ResultsCall-takers’ assessment of the condition changed from symptom picture and dismissal of heart disease in penultimate call to severe condition, not heart-related, and possible heart disease in last call. Call-takers recommended watchful waiting in the penultimate call. Both calls involved response negotiation, while caution regarding misinterpretation was only seen in the penultimate call. ConclusionCall-takers used different decision-making approaches when the caller’s symptom descriptions appeared unclear and not corresponding with the medical understanding of severe conditions. Call-takers did not negotiate the condition's assessment but engaged in discussions about the response choice. Practice implicationsA protocol to negotiate response choice with callers having unclear clinical conditions should be developed. Clarifying watchful waiting as a recommendation may assist call-takers’ decision-making.

Opt-out vaccination in school and daycare: Reconciling parental authority and obligations.

An increasing vaccine hesitancy among parents, which has resulted in insufficient rates of immunization, provides reason to reconsider childhood vaccination practices. Studies suggest that parents' decision-making process concerning whether to vaccinate their child is highly influenced by cognitive biases. These biases can be utilized to increase vaccination uptake via changes in the choice context. This article considers childhood vaccination programmes, which involve children being vaccinated in school or daycare unless their parents actively 'opt out'. We suggest that such programmes reconcile parents' decisional authority and vaccination duties. First, opt-out childhood vaccination based in schools or daycare centres are not disrespectful of parental authority. Second, the programme aligns the default setting with a moral obligation to vaccinate one's child that most parents have.

Stop procrastinating, start renovating! Exploring the decision-making process for household energy efficiency renovations

A tricky contributor to the “energy efficiency gap” is homeowners who fail to finalize a planned Energy Efficiency Renovation (EER) project, even after having received an offer from a contractor. However, there is a lack of evidence on drivers and barriers of homeowners' EER in the final stage of the decision process, including the role of the energy consultation and homeowners' perceptions of barriers and impediments that can potentially lead to procrastination, that is, to the postponement of tasks. This study contributes to filling this gap based on a survey of 609 Danish homeowners in the final stage of an EER decision-making processes. Through a comparative analysis of adopters and non-adopters, we find that procrastination is a common reason for non-adoption. First, we identify potential predictors that differ between adopters and non-adopters. Next, these variables are included in a structural equation path model (SEM) to estimate their direct and indirect effects on EER adoption. We find that homeowners' final implementation of EER decisions is complex and far from a simple weighing of monetary costs and benefits. Psychological factors such as expected hassle and interpersonal aspects of the consultation, as well as perceived difficulties and impediments, specifically related to how easy an EER offer is to understand and ultimately accept, are identified as core antecedents of EER adoption or procrastination. Implications for interventions to reduce procrastination in EER decisions are discussed, including financial support measures and interventions designed to make it easier for homeowners to implement their EER decisions.

Article: A Hamiltonian Moment for EU Taxation?

This contribution explores the possibility of a ‘Hamiltonian moment’ in the European Union (EU) public finances, drawing a parallel with the historical experience of the United States (US) in the late eighteenth and early nineteenth centuries. It examines the different theoretical positions on the existence and scope of the EU’s authority to tax, as well as the legal and political challenges that such an authority would entail. It argues that the EU does have the power to levy taxes for funding its budget, based on Article 311 of the Treaty on the Functioning of the EU (TFEU), but that this power is constrained by the principles stemming from the common constitutional traditions, the respect for national identities, and the need for democratic legitimation. It suggests that a ‘Hamiltonian moment’ in the EU would require a qualitative change in the structure and size of the EU budget, as well as a stronger involvement of the European Parliament and the national parliaments in the decision-making process on the system of own resources.

Getting Acceptance for Tough Decisions

This article discusses research-based strategies that organizational leaders can use to effectively implement unpopular decisions and gain acceptance of necessary changes from employees and stakeholders. It outlines the importance of clear communication to explain the rationale and context behind tough choices. Leaders must be transparent in their decision-making process and involve stakeholders to address concerns and build ownership of outcomes. Addressing the negative emotions that arise and exploring alternative options or ways to mitigate impacts can help address psychological and practical issues. Enlisting champions and coalitions of supporters to reinforce the reasoning for decisions can boost consensus. Continued communication on outcomes and progress reinforces that the difficult period was necessary. Ultimately, navigating tough times requires combining these communication strategies with empathetic and honest leadership that cultivates trust and shared commitment to organizational goals and vision despite short-term hardship.

Real-time segmentation and classification of whole-slide images for tumor biomarker scoring

Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

Article: Generative Artificial Intelligence and Legal Decisionmaking

This article explores the transformative impact of generative artificial intelligence (GenAI) on legal decision-making processes. It also examines disclosure obligations and the challenges AI-assisted decisions pose in litigation and arbitration.A case study using ChatGPT as arbitrators in the Willem C. Vis International Commercial Arbitration Moot provides practical insights into AI’s potential and limitations in arbitral decision-making. The experiment highlights key learnings about technology constraints, due process concerns, and the enforceability of AI-assisted decisions.The article concludes with reflections on integrating AI into legal frameworks, emphasizing the need for updated regulations and best practices to ensure transparency, fairness, and accountability in AI applications within the legal domain.

Long-term outcome of invasive pure micropapillary breast cancer compared with invasive mixed micropapillary and invasive ductal breast cancer: a matched retrospective study.

Data on the prognostic impact of the micropapillary component in breast cancer are limited. The purpose of this study was to investigate the clinicopathological characteristics and long-term outcomes of pure and mixed invasive micropapillary breast cancer (IMPC) patients compared to invasive ductal cancer (IDC) patients. This retrospective study analysed all IMPC and IDC patients treated at the European Institute of Oncology (IEO) between 1997 and 2019. The overall cohort of IMPC patients was divided in two groups, pure and mixed IMPC. Each patient with mixed or pure IMPC was matched with one patient with IDC, based on year of surgery, age, pT, pN, and molecular subtype. A total of 30,115 IDC, 120 pure IMPC and 150 mixed IMPC patients were considered eligible. Compared to IDC, pure and mixed IMPC patients presented a higher rate of locally advanced disease (pT2-T3, pN2-N3), vascular invasion, and Luminal B subtype. After matching, pure and mixed IMPC showed a significant higher rate of vascular invasion compared to IDC patients (p < 0.001). Invasive disease-free survival was better in IDC compared to pure IMPC patients (p = 0.11). Long-term overall survival was significantly worse in pure IMPC group compared to IDC group (p = 0.004), being instead similar between mixed IMPC vs matched IDC (p = 0.07). These real-world data reported the worse prognosis of pure IMPC compared to IDC, highlighting the peculiar prognostic value of the micropapillary subtype itself in the decision-making process of IMPC management. An accurate pre-surgical diagnostic evaluation and a multidisciplinary approach are pivotal to best personalize its treatment.

Multi-objective decision optimization design for building energy-saving retrofitting design based on improved grasshopper optimization algorithm

With the national emphasis on building energy efficiency planning, energy efficiency optimization in existing buildings requires renovation measures based on multi-objective factors. In order to get the optimal solution in the multi-objective decision-making of renovation, the study proposes a class of improved grasshopper optimization algorithms. The process employs a systematic methodology to identify an optimal energy renovation method, taking into account the specific characteristics of the building environment. It then classifies and formulates the energy reduction substitution items for building renovation, and finally, it synchronizes the cost of the renovation project as a measure for decision-making. The elite inverse strategy approach enhances the grasshopper optimization algorithm to facilitate the multi-objective decision-making process associated with building renovation measures. The results showed that the improved grasshopper optimization algorithm could achieve a decision accuracy of 98.8% for the test samples, which was 5.5% higher than the accuracy of the particle swarm optimization algorithm. Repeated run tests of the research algorithm for multi-objective decision making yielded a mean decision fitness value of 2.34×104 and a data extreme value of 0.38×104. Compared to other algorithms improved grasshopper optimization algorithm converged in a lower range of fitness values, which indicated that the algorithm worked well for multi-objective optimization and the model repeatability was good. The research algorithm was used to decide the energy efficient renovation planning of the building and the power consumption of the renovated power supply system was reduced by 23.7%-49.6%. This indicates that the renovated building has better energy efficiency and can provide a reliable technical direction for decision-making optimization of building energy efficiency renovation.

Panel-based assessment of ecosystem condition as a platform for adaptive and knowledge driven management.

Ecosystems are subjected to increasing exposure to multiple anthropogenic drivers. This has led to the development of national and international accounting systems describing the condition of ecosystems, often based on few, highly aggregated indicators. Such accounting systems would benefit from a stronger theoretical and empirical underpinning of ecosystem dynamics. Operational tools for ecosystem management require understanding of natural ecosystem dynamics, consideration of uncertainty at all levels, means for quantifying driver-response relationships behind observed and anticipated future trajectories of change, and an efficient and transparent synthesis to inform knowledge-driven decision processes. There is hence a gap between highly aggregated indicator-based accounting tools and the need for explicit understanding and assessment of the links between multiple drivers and ecosystem condition as a foundation for informed and adaptive ecosystem management. We describe here an approach termed PAEC (Panel-based Assessment of Ecosystem Condition) for combining quantitative and qualitative elements of evidence and uncertainties into an integrated assessment of ecosystem condition at spatial scales relevant to management and monitoring. The PAEC protocol is founded on explicit predictions, termed phenomena, of how components of ecosystem structure and functions are changing as a result of acting drivers. The protocol tests these predictions with observations and combines these tests to assess the change in the condition of the ecosystem as a whole. PAEC includes explicit, quantitative or qualitative, assessments of uncertainty at different levels and integrates these in the final assessment. As proofs-of-concept we summarize the application of the PAEC protocol to a marine and a terrestrial ecosystem in Norway.

Enhancing Predictive Maintenance of Power Transformers Through Machine Learning Approaches

This paper focuses on the use of machine learning algorithms to assist in predictive maintenance, aiming to reduce downtime and associated costs for electrical equipment. It introduces two machine learning predictive indicators: the Chromatographic Assay Indicator (CAI) and the Electrical Failure Risk Indicator (EFRI), which leverage chromatographic and sensor data, respectively. The CAI indicator was trained on external data, and its generalization capability was assessed using company-specific chromatographic data with expert assistance. The evaluation involved two heuristics, both focusing on keyword identification in free-text diagnoses. Results showed over 90% accuracy in predicting failures in reactors and power transformers, and an AUC of nearly 85% for reactors. Additionally, CAI outperformed classical Dissolved Gas Analysis (DGA) methods. On the other hand, the EFRI indicator was trained using company’s internal data from a monitoring system of operational sensors and maintaining power transformer data. The classification model achieved over 95% accuracy and an AUC of almost 90% on the test set. These results indicate that both indicators can be integrated into a solution to support maintenance specialists in their decision-making processes.

Exploring financial graph literacy: determinants and influence on financial behavior

Purpose Graphs are widely used in the banking and finance domain to support consumers’ decision-making process, but subjects differ in their ability to understand them. This study aims to detect the determinants of the ability to read and process financial information conveyed in the graphical format, i.e. financial graph literacy (FGL) and the relationship between FGL and subjects’ actual financial behavior (FB). Design/methodology/approach Data are collected by administering a structured questionnaire to the Italian adult population (n = 502). The survey includes different sections aimed at collecting information about sociodemographic and socioeconomic variables, financial literacy and FB. The econometric analyses are developed using OLS and Poisson regressions. Findings The results show that gender, geographical area, education, marital status and income are crucial determinants of FGL. Moreover, the analysis reveals that an increase in the FGL indicator is associated with a higher propensity for individuals to purchase banking or financial products or actively manage financial resources; results are robust, even controlling for financial knowledge. Originality/value Although previous research investigates the impact of graphs in financial decision-making, no studies measure the ability of consumers to read and interpret financial information conveyed in the graphical format. This study is the first to investigate the determinants of FGL and link it to actual FB. Implications for policymakers, regulatory and supervisory authorities and financial intermediaries are discussed.

Conceptual framework for enhancing decision-making in higher education through data-driven governance

In an increasingly complex and competitive landscape, higher education institutions must leverage data-driven governance to enhance decision-making processes. This paper presents a conceptual framework aimed at integrating data analytics into the governance structures of higher education, facilitating informed decision-making, and improving institutional effectiveness. The framework emphasizes the importance of collecting, analyzing, and utilizing data across various dimensions, including academic performance, student engagement, financial management, and operational efficiency. By adopting a data-driven approach, institutions can identify trends, forecast outcomes, and make strategic decisions that align with their mission and goals. The study outlines key components of the framework, including data governance, analytics capabilities, stakeholder engagement, and institutional culture. Data governance ensures that data quality, integrity, and security are maintained, while analytics capabilities empower decision-makers with actionable insights. Engaging stakeholders—faculty, administrators, students, and external partners—fosters a collaborative environment where data-driven decisions are embraced and supported. Furthermore, the framework addresses the challenges institutions face when implementing data-driven governance, such as resistance to change, data silos, and inadequate technological infrastructure. Strategies for overcoming these challenges include investing in professional development, enhancing data literacy, and promoting a culture of continuous improvement. The framework also highlights the significance of ethical considerations and transparency in data usage, ensuring that decision-making processes are equitable and accountable. The findings indicate that by embracing data-driven governance, higher education institutions can enhance their responsiveness to changing demands, optimize resource allocation, and improve overall institutional performance. Ultimately, the conceptual framework serves as a guide for higher education leaders seeking to harness the power of data in their decision-making processes, fostering a proactive and adaptive institutional environment.

Navigating ethical challenges of explainable ai in autonomous systems

The rapid integration of autonomous systems, such as vehicles, drones, and robots, into various sectors brings forth significant ethical challenges concerning their decision-making processes. This paper examines the role of Explainable AI (XAI) in addressing these challenges, particularly regarding accountability in the event of accidents and the necessity of human oversight in automated environments. We discuss the critical ethical implications of transparency, emphasizing how XAI can bridge the gap between complex algorithmic decision-making and public understanding, thereby fostering trust in these technologies. The paper also outlines the current regulatory frameworks for AI safety, analysing their effectiveness in promoting responsible innovation. Furthermore, we investigate the consequences of opaque algorithms, especially in life-critical applications where the stakes are exceptionally high. Through an analysis of case studies, we showcase how organizations have successfully implemented XAI to enhance safety measures and uphold ethical responsibility in their autonomous systems. Ultimately, this study advocates for the integration of XAI as a vital component in developing responsible autonomous technologies, ensuring accountability, and safeguarding public trust in an era increasingly defined by automation.

Impact of behavioral finance on financial decision making

This paper highlights the disproportionate effect of behavioral finance on investor’s investing decisions. While making any financial decisions, one easily gets influenced by psychological and emotional factors. Past individual experiences, tendency or opportunity for herd behavior, personal judgment, overconfidence in one's abilities, aversion to feelings of regret, reliance on advice from experts, and last but not least, something known as the disposition effect—by which investors tend to hold on to losing investments and professionally sell profitable ones too early. The impact of these factors on the investment decision-making process varies; on the one hand, they may enhance the quality of the decision-making process by aligning investments with risk tolerance and financial goals. However, on the other hand, behavioral factors can be a cause of inefficiency if investment decisions are based more on emotions than objective analysis. In our recent research, we questioned 187 investors, using a questionnaire from whom we acquired crucial information regarding the impact of behavioral finance on financial decisions. The primary objective of the questioning was to determine the effect of behavioral finance on financial decision-making. The secondary objective was to suggest some methods for making one aware of the repercussions of impulsive or emotional decision-making and making prudent investments.

Data analytics as a driver of digital transformation in financial institutions

Data analytics plays a pivotal role in driving digital transformation within financial institutions, enabling organizations to leverage insights from vast amounts of data to enhance decision-making and optimize operational performance. This paper explores the multifaceted impact of data analytics on the financial services sector, highlighting how it can improve customer experience, streamline processes, and foster innovation. By implementing advanced analytics techniques, financial institutions can better understand customer behaviour, identify market trends, and personalize services to meet evolving customer needs. The study discusses effective strategies for integrating data analytics into decision-making processes and operational workflows, emphasizing the importance of creating a data-driven culture that encourages collaboration across departments. Case studies of leading financial institutions that have successfully integrated data analytics into their operations illustrate the transformative outcomes, including improved customer satisfaction and increased revenue growth. Additionally, the paper addresses challenges such as data silos, regulatory compliance, and the need for skilled personnel to analyse and interpret data effectively. To overcome these hurdles, best practices are outlined, including investment in robust data infrastructure, fostering cross-functional teams, and ensuring continuous training for employees. The findings indicate that a strategic approach to data analytics not only enhances performance but also positions financial institutions to adapt to rapidly changing market conditions. Ultimately, this paper underscores the critical role of data analytics in shaping the future of financial services and achieving sustainable growth in an increasingly competitive landscape.

AI-driven business analytics and decision making

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

Optimizing talent management in creative industries: Theoretical insights into effective database utilization

This review paper explores the optimization of talent management in creative industries through the utilization of advanced database systems. Traditional talent management methods often lack the structure and objectivity provided by modern database-driven approaches, which leverage comprehensive talent profiles, advanced search algorithms, and robust analytics to enhance decision-making processes. Case studies from industry leaders like Pixar, Wieden+Kennedy, and Ubisoft illustrate the practical benefits of these technologies in recruitment, development, and overall talent optimization. The paper also discusses challenges such as data quality, privacy concerns, and resistance to change, providing practical recommendations for overcoming these obstacles. Creative industries can significantly improve their talent management practices by fostering a culture of data-driven decision-making and investing in comprehensive talent databases. Future research directions include the impact of AI and machine learning, integration of emerging technologies, and longitudinal studies on the effectiveness of database-driven approaches.

AI and blockchain technology for secure and transparent financial transactions

The integration of Artificial Intelligence (AI) and blockchain technology presents a transformative approach to enhancing the security and transparency of financial transactions. Blockchain’s decentralized and immutable ledger system ensures that transaction records are tamper-proof and transparent, significantly reducing the risk of fraud and errors. AI, with its advanced data analytics and machine learning capabilities, further augments this ecosystem by providing real-time monitoring, predictive analytics, and automated decision-making processes. Together, these technologies enable more efficient and secure financial operations, from cross-border payments to trade finance and asset management. This paper explores the synergistic potential of AI and blockchain in revolutionizing the financial sector, highlighting key applications, benefits, and challenges. By leveraging the strengths of both technologies, financial institutions can achieve unprecedented levels of security, efficiency, and trust in their operations.