Decision-making Process Research Articles

Professional Competences of Emergency Department Nurses Working in the Earthquake Region on Triage Management in Children and the Difficulties Experienced by Nurses in the Decision-Making Process: A Qualitative Study

Limited resources in disasters may negatively affect the care of pediatric patients and cause complications to be prevented. The aim of this study was to investigate the professional competence of nurses regarding triage management in children during the Kahramanmaraş earthquake and the difficulties they experienced in the decision-making process. This study used a qualitative exploratory research design with a phenomenological approach. In-depth interviews were conducted with the participants individually and through focus group discussions using a semi-structured interview form. A total of 23 participants working in the emergency department during and after the earthquake were included in this study, which was conducted using a purposive sampling technique. Data were analyzed using content analysis procedures. Max Qualitative Data Analysis (2020) programme was used to code the data. As a result of data analysis, 4 main themes and 15 sub-themes emerged. The four main themes were “Professional Competencies of Nurses During Disasters,” “Triage Decision in the Emergency Department,” “Expectations of Nurses Working in a Disaster Area” and “Participation in Disaster Education”. The results of this study showed that nurses are exposed to difficulties arising from many individuals, psychological, professional, environmental, and educational variables in the early period of disaster. Participants' views revealed a strong relationship between being professionally competent, providing appropriate care to patients and emotional exhaustion.

Are innovative firms more circular when women hold upper positions?

The circular economy paradigm demands immediate and significant adjustments and changes in management and production business processes to maintain adequate to good ecosystem conditions for the use and development of human life. In this context, we analysed the impact of female leadership in the business environment and its relationship with the conservation of planetary health. This empowers women in the transition toward circular business model within the planet's capacity.For a panel data sample of the world's largest companies, we find that active female participation in high business positions leads to significant changes toward a model that protects the natural and endemic wealth of different ecosystems around de world. Additionally, we found that the active presence of women in the decision-making process concerning the protection of the environment, human development, and economic prosperity is strongly associated with the prospective knowledge that female independent directors possess, and to a greater extent, with the knowledge spillover that the presence of female executive directors entails.

The impact of social comparison on self-deception: An event-related potentials study.

Self-deception refers to an individual holding inflated beliefs about their abilities, and it plays a crucial role in human behavior and decision-making. The present study employed event-related potentials (ERPs) technique to explore the neural responses to the impacts of social comparison direction and comparison gap on self-deceptive behavior. They were instructed to predict their performance in the forward-looking paradigm. Behavioral responses and neural reactions during the decision-making process were documented. The behavioral results indicated that, in contrast to the downward comparison condition, participants engaged in upward comparison exhibited more occurrences of self-deception. However, within the context of upward comparison, participants demonstrated a higher frequency of self-deception in the large gap condition compared with the small gap condition. The ERP results showed that induced self-deception under conditions with a large comparative gap between participants and their paired counterparts stimulated larger P300 and smaller N400 amplitude than under conditions with a small gap. However, when participants were in the upward comparison situation, the late positive potential (LPP) amplitude induced by self-deception behavior in the condition of a large comparison gap between participants and paired opponents was larger than that in the condition of a small comparison gap. These results indicated that individuals in the large gap group feel strong unfairness and negative emotions. More importantly, the self-deception induced by the large gap group in the upward comparison situation used fewer cognitive resources than the small gap condition, whereas the individuals in the downward comparison situation did not show the difference in cognitive resources.

The transfer of social threat learning to decision making is robust to extinction.

Through traditional mass media and online social media, we are almost constantly exposed to second-hand experiences of trauma and violence, providing ample opportunities for us to learn about threats through social means. This social threat learning can influence instrumental decision making through a social learning to decision-making transfer process, resembling the so-called Pavlovian to instrumental transfer effect, resulting in consequences that can be maladaptive. Here, we assessed if this influence could be diminished by extinction learning, a procedure where a previously threatening stimulus is learned to be safe, and thereby mitigate possible maladaptive consequences. To this end, we recruited 251 participants to undergo a social threat learning procedure (where they observed someone else receive electric shocks to one out of two images), followed by either a social or direct extinction procedure (in which no shocks were given), before conducting an instrumental decision-making task to measure the strength of the transfer effect. Based on theoretical considerations and previous literature, we proposed two competing hypotheses: (a) extinction learning would diminish the transfer effect or (b) the transfer effect would be robust to extinction. Our results clearly demonstrate that the social to instrumental transfer effect is remarkedly robust to extinction, supporting the second hypotheses. Irrespective of whether extinction was carried out through direct experience or social means, learning about threats through second-hand aversive experiences strongly influence instrumental behavior, suggesting that potentially maladaptive effects of social threat learning are challenging to diminish. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

AUTOLYCUS: Exploiting Explainable Artificial Intelligence (XAI) for Model Extraction Attacks against Interpretable Models

Explainable Artificial Intelligence (XAI) aims to uncover the decision-making processes of AI models. However, the data used for such explanations can pose security and privacy risks. Existing literature identifies attacks on machine learning models, including membership inference, model inversion, and model extraction attacks. These attacks target either the model or the training data, depending on the settings and parties involved. XAI tools can increase the vulnerability of model extraction attacks, which is a concern when model owners prefer black-box access, thereby keeping model parameters and architecture private. To exploit this risk, we propose AUTOLYCUS, a novel retraining (learning) based model extraction attack framework against interpretable models under black-box settings. As XAI tools, we exploit Local Interpretable Model-Agnostic Explanations (LIME) and Shapley values (SHAP) to infer decision boundaries and create surrogate models that replicate the functionality of the target model. LIME and SHAP are mainly chosen for their realistic yet information-rich explanations, coupled with their extensive adoption, simplicity, and usability. We evaluate AUTOLYCUS on six machine learning datasets, measuring the accuracy and similarity of the surrogate model to the target model. The results show that AUTOLYCUS is highly effective, requiring significantly fewer queries compared to state-of-the-art attacks, while maintaining comparable accuracy and similarity. We validate its performance and transferability on multiple interpretable ML models, including decision trees, logistic regression, naive bayes, and k-nearest neighbor. Additionally, we show the resilience of AUTOLYCUS against proposed countermeasures.

No Country for Sick Men: The Political Determinants of Health Policy in Poland.

The objective of this article is to explain the political factors determining the relatively weak performance of the Polish health care (HC) sector. This can be treated as a critical case for several reasons. First, the Poles are among the most unsatisfied patients in the European Union, with one of the lowest life expectancy levels. Second, Poland spends one of the lowest shares of gross domestic product on HC-related expenditures among OECD countries. Third, the country is facing medical personnel shortages. The analysis is based on the mixed-methods approach. The authors rely on quantitative data outsourced from a survey, which is supplemented by the semistructured, in-depth interviews with selected key HC stakeholders representing patients' advocacy groups, medical personnel organizations, and high-level decision-makers. The Polish HC system remains weak due to the postcommunist legacy in terms of organization, a short-term approach by politicians, and weak decision-making processes. The HC policy inertia in Poland is determined by a group of interrelated political factors that effectively block the development of any positive reform.

Health care under the military government (1964-1985) and its legacy for the SUS: the public-private hybrid Unified Health System

This article analyzes the health care policy developed during the military government in Brazil (1964-1985), with a core focus on the relationship between the public and private sectors in the provision of health services, in the different modalities in which this occurred, and its repercussions. The objective, from an institutionalist and historical perspective, which considers the mechanisms by means of which prior policies affect the subsequent decision-making process and impose limits on the possibilities for change, is to identify the effects of this policy on the configuration of health policy reform, implemented in the democratization process, which led to the creation of a universal and comprehensive health system. Among these, the political effects on the configuration of the health area stand out; cognitive effects related to the perception of health policy, and effects on the government's capacity to provide and regulate services. The result was the continuation of a duality in the health system, resulting in the coexistence of a vigorous private sector with an allegedly universal public system which posed/poses difficulties for the SUS, but did not prevent its implementation and institutionalization. The article is taken from a previously published book.

Reliability Centered Modeling of an Automated Waste Sorting Robotic Arm System

In contemporary society, proper waste management is a critical issue that needs to be addressed. Waste management encompasses the collection, transportation, segregation and disposal of waste material to minimize health risks and environment impact. Manual segregation, along with other methods of waste segregation, is often inefficient and time consuming. To address this challenge, numerous industries have embraced automation in waste sorting to expedite the process through advanced technological interventions. Therefore, the optimal functioning of the components of these automated systems is crucial for rapid and accurate segregation of waste. This work is oriented toward the study of a system which is used to segregation of waste and known as waste sorting robotic arm system (WSRAS). Author used Markov decision process, along with mathematical modeling, to evaluate the different performance measure of WSRAS. Sensitivity analysis have also been done to understand the contribution of different components failure in overall performance of WSRAS.

Word Type and Frequency Effects on Lexical Decisions Are Process-dependent and Start Early.

When encountering letter strings, we rapidly determine whether they are words. The speed of such lexical decisions (LDs) is affected by word frequency. Apart from influencing late, decision-related, processing stages, frequency has also been shown to affect very early stages, and even the processing of nonwords. We developed a detailed account of the different frequency effects involved in LDs by (1) dividing LDs into processing stages using a combination of hidden semi-Markov models and multivariate pattern analysis applied to EEG data and (2) using generalized additive mixed models to investigate how the effect of continuous word and nonword frequency differs between these stages. We discovered six stages shared between word types, with the fifth stage consisting of two substages for pseudowords only. In the earliest stages, visual processing was completed faster for frequent words, but took longer for word-like nonwords. Later stages involved an orthographic familiarity assessment followed by an elaborate decision process, both affected differently by frequency. We therefore conclude that frequency indeed affects all processes involved in LDs and that the magnitude and direction of these effects differ both by process and word type.

Navigating the green beauty boom: Unveiling the influence of social media on consumer behavior in the organic cosmetics industry

It has been evident in the recent years that the natural and organic segment particularly in the beauty industry is steadily growing with people’s awareness of the health effects of using natural products. Today, social networks are the main driving force affecting consumer attitude and their behavior concerning this sector. This paper will seek to evaluate the utility of social media and the purchase pattern in the organic cosmetics sector. The ‘literature review outlines several that have led to what is commonly referred to as the ‘green beauty boom’ The theoretical framework entails theories from the marketing channel that regards consumer behavior within the social media and eWOM contexts. The research designed consisted of a written self- administered survey and face- to face interviews with fifteen consumers of organic beauty products within the age range of 18-35 years whereby data was gathered and analyzed qualitatively by the use of thematic analysis. Stylized facts also establish how the content on social media platforms and reviews influence distinct stages in the consumer decision process of organic cosmetics. Of all the mentioned evaluative resources, those commonly used by consumers include resources accessed from social media sites. Moreover, as to the communities of people, who are interested in organic lifestyles or natural products influencing the degree of positive attitudes in the usage of natural ingredients and environmental sources. Consequently, the author discusses the subsequent marketing implications for the brands based on the paper and provides ideas for the subsequent brand development, the opportunities, and the possible ethical issues that should be considered by brands in the context of this area. From this study further research can be made to contribute to the mass pool of data that helps in understanding new media that constitutes consumer culture today.

Use of Building Information Modeling (BIM) to Improve Construction Management in the USA

The construction industry in the United States faces ongoing challenges related to cost overruns, project delays, and inefficiencies in project management. Building Information Modeling (BIM) has emerged as a transformative technology with the potential to address these issues by enhancing the precision, collaboration, and decision-making processes in construction management. This paper provides a comprehensive review of the application of BIM in the U.S. construction sector, with a focus on its impact on improving construction management practices. The review explores the specific ways in which BIM contributes to project success, including its ability to streamline workflows, reduce errors, and facilitate real-time collaboration among stakeholders. Through an in-depth analysis of current literature, this paper highlights the specialized functions of BIM that are particularly beneficial for construction management in the U.S., such as clash detection, 4D scheduling, and cost estimation. The paper also examines case studies that demonstrate the successful integration of BIM in large-scale projects across the country, showcasing tangible improvements in project outcomes, including reduced project timelines and enhanced cost efficiency. Additionally, the paper delves into the challenges associated with BIM adoption in the U.S., including the need for skilled personnel, the integration of BIM with existing project management systems, and the legal and contractual implications of BIM-based collaboration. These challenges are discussed alongside strategies for overcoming them, emphasizing the importance of training, standardization, and cross-disciplinary collaboration. The findings of this review suggest that the widespread adoption of BIM in the U.S. construction industry holds significant promise for enhancing project management efficiency, improving stakeholder communication, and ultimately contributing to more successful project outcomes. The paper concludes by proposing best practices for BIM implementation in construction management, aiming to guide industry professionals in leveraging this technology to its fullest potential.

Harnessing the Power of AI in Salesforce: An Empirical Study on Enhanced Data Insights and Organizational Efficiency

Abstract: This article examines the integration of Artificial Intelligence (AI) in Salesforce platforms to enhance business analytics and decision-making processes. We explore how AI-powered solutions are revolutionizing data analysis, providing organizations with actionable insights, and improving market responsiveness. The article investigates key AI techniques implemented in Salesforce, including machine learning algorithms for pattern recognition and natural language processing for unstructured data analysis. We analyze the impact of these technologies on identifying trends, forecasting, threat detection, and opportunity identification for sustainable growth. Through case studies and empirical analysis, we demonstrate the effectiveness of AI-driven Salesforce solutions in streamlining data processing, enabling real-time market adaptation, and enhancing organizational efficiency. The article also addresses the technological infrastructure supporting these AI solutions, discussing cloud-based computing resources, integration challenges, and data security considerations. Our findings suggest that AIpowered Salesforce solutions significantly improve business analytics capabilities, allowing companies to make more informed decisions and maintain a competitive edge in rapidly evolving markets. This article contributes to the growing body of knowledge on AI applications in customer relationship management and business intelligence systems

Extraction of value and impact from IoT big data sets: A qualitative exploration of current methodologies and future directions

Integrating the Internet of Things (IoT) with big data analytics has created transformative opportunities across various domains, including smart cities, healthcare, and industrial automation. However, the challenges of extracting value from the vast and heterogeneous IoT data sets are significant. This study aims to explore methods for maximizing value extraction from IoT-generated big data and evaluate their impact on decision-making processes. A systematic literature review was conducted, and an exploratory qualitative methodology was employed to assess existing frameworks and propose improvements. The results highlight the importance of edge computing, machine learning algorithms, and data processing architectures in managing IoT data effectively. Additionally, the study identifies gaps in current research and suggests future directions to enhance the practical application of IoT big data analytics.

Adopting integrated project delivery (IPD) in oil and gas construction projects

This review explores the adoption of Integrated Project Delivery (IPD) in oil and gas construction projects, focusing on the potential benefits, challenges, and strategies for successful implementation. IPD is a collaborative project delivery method that brings together the owner, designer, contractor, and other key stakeholders early in the project lifecycle to optimize efficiency, reduce waste, and enhance communication. In the oil and gas sector, where projects are often complex, capital-intensive, and high-risk, adopting IPD can lead to significant improvements in project outcomes. The review highlights the core principles of IPD, including shared risk and reward, early involvement of key stakeholders, and the use of collaborative tools and technologies, such as Building Information Modeling (BIM). These elements of IPD can help to streamline decision-making processes, improve cost management, and reduce the likelihood of disputes. Furthermore, the integration of IPD with advanced technologies, such as digital twins and AI-driven analytics, can enhance real-time monitoring and predictive maintenance, leading to better project performance. However, the review also discusses the challenges of adopting IPD in the oil and gas industry, including resistance to change, the need for cultural shifts within organizations, and the complexity of aligning multiple parties with differing interests. It suggests strategies for overcoming these challenges, such as providing training on IPD principles, fostering a culture of collaboration, and developing clear contractual frameworks that support IPD objectives. The review concludes by emphasizing the potential of IPD to transform oil and gas construction projects, leading to more efficient, cost-effective, and sustainable project outcomes. It calls for industry-wide efforts to embrace IPD as a standard practice, supported by appropriate policies and guidelines that facilitate its adoption and implementation.

Digital transformation impact on business decision-making

The objective of this study was to assess the different ways in which digital transformation tends to affect business decision-making processes. To accomplish this objective, a mixed research design was used in this study. Accordingly, both quantitative and qualitative methods were used, in the form of secondary research and pilot testing. The findings obtained from the study revealed that companies continue to face challenges related to their need to integrate digital transformation and sustain in the incessantly changing business environment.

Privacy-preserving data analysis

With the ever-increasing volume of data being generated and shared across various platforms, the challenge of maintaining privacy while extracting value from this data has become paramount. This paper delves into the realm of Privacy-Preserving Data Analysis (PPDA), examining its current landscape and the pivotal techniques shaping it. Using datasets from diverse domains, we evaluated four leading PPDA techniques—Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computation (SMPC), and Data Obfuscation—to discern their efficacy and trade-offs in terms of data utility and privacy breach risk. Our findings underscore the strengths and constraints of each method, guiding researchers and practitioners in choosing the optimal approach for specific scenarios. As data continues to be an invaluable asset in the digital age, the tools and techniques to analyze it privately will play a critical role in shaping future data-driven decision-making processes.

Unveiling the drivers of sustainable housing choices among millennials in emerging markets

Purpose The purpose of this study is to explore the complex interplay between technology, personal norms and emotional factors in shaping the sustainable housing choices of millennials in emerging economies. It integrates the model of goal-directed behavior, technology acceptance model and norm activation model, incorporating both self-interest and prosocial motivations. Key adaptations involve replacing perceived behavioral control with financial self-efficacy and substituting hedonic motivation for anticipated positive emotions. Moreover, it introduces location as a practical anchor. Design/methodology/approach A quantitative, cross-sectional and descriptive research design was used in this study. Data were gathered from a sample of 610 millennial residential real estate investors across Indian smart cities. A multistage stratified sampling technique was used to ensure a representative sample. For data analysis, partial least squares structural equation modeling was used. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess the significance and magnitude of these relationships, respectively. Findings One of the key findings of this study was the establishment of significant positive relationships between awareness of consequences, ascription of responsibility and personal norms with behavioral intentions. This underscored the importance of personal ethical considerations in shaping intentions. Perceived usefulness and ease of use were found to significantly influence attitudes positively, highlighting the relevance of these factors in forming favorable attitudes toward behaviors. Attitude, subjective norms, financial self-efficacy and location played significant positive roles. However, negative anticipated emotions decreased desire. This illustrated the complex role emotions play in motivational processes. The study also revealed that subjective norms did not significantly contribute to shaping personal norms. This indicated a potential decoupling of societal expectations from personal ethical obligations in the decision-making process. Practical implications This study offers actionable insights for both policymakers and real estate developers. For policymakers, the findings highlight the need to craft initiatives that go beyond mere awareness, instead fostering a deep sense of personal responsibility and environmental stewardship among potential homebuyers. For real estate developers, the emphasis on financial self-efficacy and location suggests a strategy shift toward designing sustainable homes that not only meet environmental standards but also align with buyers’ financial confidence and geographic preferences. Together, these strategies can drive a more widespread adoption of sustainable housing, making sustainability a tangible and appealing choice for millennials. Originality/value To the best of the authors’ knowledge, this empirical research study was one of the first studies that contributed to the literature by integrating the model of goal-directed behavior, technology acceptance model and norm activation model. This study thus offered a nuanced understanding of the interplay between normative influences, usability perceptions, ethical considerations and emotions in the context of behavioral intentions.

‘Leveraging on CSR as a tool of brand communication based on consumer’s perception with extrapolation from a novel 3-factor model

This study is to establish that consumers’ perceptions can trigger value creation from commitment, and pursuit of CSR by an organization can bring leverage and advantage by adopting the same as a brand, when fused to lifestyle and culture, while an extrapolation has been made from a novel 3-factor model of consumer responses as presented and newly introduced and adapted (draft study, Adewole 2023). Consumer decision-making process reflects the “black box models from cognitions, cognitive pathway, some intrinsically inner hidden pictures, and rational choices”; strategically, a smart and well-communicated brand can impact financial performance, thus emphasizing the significance of the brand from the consumer side. It can be inferred that culture and traditional behavior play significant roles in brand perception, which can generate and create other leads, such as preferences, intentions, and repeats, considering the complexes, unpredictable trends, or patterns associated with consumers’ expressions and behavior in the context of a black box, rational and complex mixes, even justified and vividly affirmed by the result of the ‘hypothesis testing and verification of the composite attributes and evident from the ‘ANOVA and multiple comparison, which gave a p-value exceeding (>0.05).

Exploring key metrics of sterilization services using cost analysis

ABSTRACT Sterilization services not only impact service delivery processes directly but also account for a substantial portion of total costs. Therefore, accurate resource measurement becomes imperative for efficient resource utilization. This research aims to draw a framework for calculating the package and autoclave unit cost within a sterilization unit and to pinpoint sterilization unit-specific findings distinct from those of other service provider departments. To achieve this, we collected and analyzed cost data from a public hospital in Trabzon, Türkiye, for 2021 and 2022. Subsequently, the unit cost of the package and autoclave of the sterilization unit was calculated based on the collected data. Our analysis considered various cost components, such as equipment maintenance, personnel salaries, and utility expenses. Through our research, we identified key factors influencing the sterilization process. The findings guided decision-makers in prioritizing areas for efficient and sustainable service provision. Furthermore, we emphasize the importance of our developed framework in cost analysis, highlighting its potential impact on resource management practices in healthcare settings. Additionally, insights into decision-making processes regarding in-house versus outsourced provision of sterilization services can inform managerial decisions and resource allocation strategies more effectively.

Decision dynamics and landscape approach by individuals and collective groups: the case of Japanese rural land use in agricultural, forest, and boundary areas in an era of population decline

Establishing a decision-making scheme for multiple land use types in a shrinking society is an urgent and persistent task. In particular, residents and governments face the complexity of cross-sectoral visions over concerned landscapes. This study examined the preference of residents for future visions and the effects of group deliberation in a Japanese rural community to enhance the current understanding of decision and learning processes on cross-sectoral land use types (i.e., agricultural land, forest land, and boundary areas) across ages and groups. This study posed the following questions: (i) what types of interactions do residents find among options on cross-sectoral land uses? and (ii) how do group deliberation influence the individual preferences of residents for land use? In the survey, the residents disclosed their preferences for the prepared visions over three land use types. The survey also enabled the comparison of choices before and after group deliberation. Regarding question (i), the residents’ choice prior to the deliberation demonstrated thematic coherence through land use categories. Smart technology and potential productivity improvement were selected across agriculture and forests. However, regarding question (ii), thematic coherence decreased after the group deliberation. The residents could communicate disagreements during group deliberation and consider the issues relatively independently over land use types, which separately views agriculture lands and forests. The choice after group discussions became increasingly embedded and influenced by residential areas and land ownership of residents. These findings provide unique insights into the group discussion on how individual decisions are affected. In summary, the preferences of cross-sectoral land use types did not converge but tended to diverge.