Decision Maker's Preferences Research Articles

A behavioral signature for quantifying the social value of interpersonal relationships with specific others

The idea that individuals ascribe value to social phenomena, broadly construed, is well-established. Despite the ubiquity of this concept, defining social value in the context of interpersonal relationships remains elusive. This is notable because while prominent theories of human social behavior acknowledge the role of value-based processes, they mostly emphasize the value of individual actions an agent may choose to take in a given environment. Comparatively little is known about how humans value their interpersonal relationships. To address this, we devised a method for engineering a behavioral signature of social value in several independent samples (total N = 1111). Incorporating the concept of opportunity cost from economics and data-driven quantitative methods, we derived this signature by sourcing and weighting a range of social behaviors based on how likely individuals are to prioritize them in the face of limited resources. We examined how strongly the signature was expressed in self-reported social behaviors with specific relationship partners (a parent, close friend, and acquaintance). Social value scores track with other aspects of these relationships (e.g., relationship quality, aversion to losing relationship partners), are predictive of decision preferences on a range of tasks, and display good psychometric properties. These results provide greater mechanistic specificity in delineating human value-based behavior in social contexts and help parse the motivational relevance of the different facets that comprise interpersonal relationships.

Insights into patient awareness and preferences in medical imaging procedures involving ionizing radiation

Abstract Introduction: Advancements in technology have revolutionized the landscape of medical imaging, offering a multitude of modalities. However, this progress is not always fully traceable to patients, necessitating studies in this area to bridge the gap between technological advancements and patients’ insights. This study delves into patient awareness, preferences, and understanding concerning radiation risk in medical imaging procedures using ionizing radiation. Material and methods: An anonymous survey was conducted among 152 patients in Bulgaria who underwent X-ray procedures to gauge their insights into medical imaging. Data analysis utilized SPSS version 20.0, computing frequencies and percentages for categorical variables like gender, age, and education. Results: Significant correlations were found between gender and the need for knowledge related to radiation risks (χ2 = 4.622, p = 0.032), as well as patient understanding of radiation dose information and decision-making preferences (χ2 = 10.800; p = 0.020). Female participants showed higher awareness levels in these areas compared to male participants. Younger patients, notably aged 36-50 (52.6%), comprised a significant portion undergoing X-ray imaging, diverging from global trends. Age correlated with the frequency of X-ray examinations (χ2 = 171.256; p = 0.000) and decision-making preferences (χ2 = 8.324; p = 0.016), with older patients favoring informed decisions with physician input. Education levels significantly influenced radiation risk awareness (χ2 = 7.084, p = 0.008) and understanding of radiation dose information (χ2 = 14.752, p = 0.001). Patients with higher education levels demonstrated a significantly better understanding of absorbed radiation doses compared to those with secondary education. These findings highlight the influence of demographic factors such as gender, age, and education on patient awareness, knowledge acquisition, and decision-making processes regarding medical imaging and radiation risks. Conclusions: Tailored communication strategies, informed consent practices, and ongoing specialist education are crucial to enhancing patient awareness, optimizing decision-making processes, and ensuring radiation safety in medical imaging. Implementation of electronic health records can further improve radiation exposure monitoring, aligning with evolving technological standards and patient needs.

An optimization framework for achieving optimal hydrocyclone's performance aligning with decision-makers' preferences

Previous hydrocyclone optimization often neglected interactions among key performance objectives, which limits hydrocyclone wide applications to meet increasingly diverse industry demands. This study proposes an optimization framework to identify the most suitable hydrocyclone design and operating conditions with conflicting key performance objectives. An advanced multi-objective evolutionary algorithm (PICEA-g) is employed to generate Pareto-optimal solutions that capture trade-offs among multiple conflicting objectives. A novel data-driven predictive algorithm, INFO-ELM, is introduced to establish nonlinear relationships between key variables and performance objectives, thereby accelerating the search for Pareto-optimal solutions by PICEA-g. Furthermore, a multi-criteria decision-making method (TOPSIS) is utilized to determine the optimal solution based on decision-makers' preferences, ensuring consistency between preference information and decision outcomes. The framework's effectiveness is validated across various separation scenarios using two decision-making strategies. This study offers a comprehensive approach to address trade-offs in hydrocyclone optimization, widening its application in diverse separation scenarios.

Empowering Voices: Understanding Children's Roles and Preferences in Shared Decision-Making

Introduction: Shared decision-making (SDM) in paediatric healthcare presents a unique dynamic involving children, parents, and healthcare professionals. While previous research has focused on the roles of parents and healthcare professionals in this process, little attention has been given to the roles adopted by children themselves. This study aimed to explore the nature of children’s participation and their preferred roles in the SDM process during hospitalisation. Method: Using a genP6 qualitative approach, data was collected from 26 hospitalised children aged 7 to 12 years, their parents, and 48 healthcare professionals across three teaching hospitals in Malaysia. Data collection involved participant observation and a single interview with nine of the study’s child participants. Results: Children assumed either passive or active roles in the SDM process, with their roles fluctuating depending on the circumstances and adult involvement in communication. While some children were content with passive participation, others expressed a desire for greater involvement. Those who actively participated appreciated their role. Conclusion: The study highlights the challenge of determining the most appropriate role for children, as their preferences may not consistently align with their actual level of participation. Those involved in SDM with children must possess the ability to thoroughly understand their desires and communication needs, ensuring that their active involvement is both meaningful and fulfilling.

Dynamic optimization design of open-pit mine full-boundary slope considering uncertainty of rock mass strength

Rock and soil strength profoundly influences the stability of open-pit mine slopes. Traditional slope design, based on limited drilling data, often disregards inherent uncertainties. Effectively utilizing new sample information from mining operations poses a challenge, hindering dynamic and differentiated design for the entire perimeter slope. To address this, we propose a dynamic optimization method considering rock mass strength uncertainty for the entire perimeter slope. Our approach involves designing slope angles separately in different zones, while thoroughly considering decision-makers' preferences. Furthermore, we delegate the final adjustment authority of slope angles within the safety permissible range to on-site decision-makers. Compared to traditional methods, our dynamic design method incorporates rock mass strength uncertainty into slope evaluation while also accounting for decision-makers' safety and economic preferences. Through a case study of a specific open-pit mine, our proposed dynamic design method increases the overall slope angle by approximately 2.5°, fully accommodating the influence of on-site decision-making preferences on slope design. This article introduces a new method of dynamic optimization of open-pit mine slope based on simplified observation method, which improves the flexibility of decision-making and realizes the differential design of the whole surrounding slope.

Carbon dioxide removal through ecosystem restoration: Public perceptions and political participation.

We compare public perceptions of restoring different ecosystems to increase CO2 uptake in Germany, through focus groups and a general population survey. Among focus group participants forests were highly popular, peatlands evoked negative associations, and seagrass was largely unknown. Nevertheless, the restoration of all ecosystems was viewed positively. We contrast these reactions to those of survey respondents who had not received additional information on restoration. They voiced narrower, less diverse opinions centering around afforestation. Further, focus group participants preferred expert-led restoration decisions, citing low trust in politicians' technical competence. Contrary to common policy recommendations, also beyond the German context, participants did not emphasize the need of citizen participation and were not strongly concerned about land use conflicts or compensation of affected user groups. The results imply that the public underestimates the political complexity of negotiation processes in ecosystem governance, which are becoming increasingly relevant in the international policy landscape.

The interplay between the proposer's role model and moral behavior modulates proposal processing in the Ultimatum Game: An ERP study

Economic decision-making plays a paramount role in both individual and national interests. Individuals have fairness preferences in economic decision-making, but a proposer's moral-related information may affect fairness considerations. In prior ERP studies, researchers have suggested moral identity influences fairness preferences in the Ultimatum Game (UG), but there are discrepancies in the results. Furthermore, whether role models (individuals whom someone else looks to help decide suitable behaviors), who can modulate people's moral standards, can affect fairness concerns in UG is still understudied. To address the questions, we selected the moral-related statements by eliminating those with illegal information and employed the ERP technique to explore whether the interplay of the proposer's role model and moral-related behavior influenced fairness processing in the modified UG and the corresponding neural mechanisms. We mainly found that the aforementioned interaction effect on proposal considerations in UG could be mirrored in both rejection rates and P300 variations. The results demonstrate that the interaction between the proposer's role model and moral behavior can modulate fairness concerns in UG. Our current work provides new avenues for elucidating the time course of the influencing mechanism of fair distributions in complicated social environments.

Co-Evolutionary Algorithm for Two-Stage Hybrid Flow Shop Scheduling Problem with Suspension Shifts

Demand fluctuates in actual production. When manufacturers face demand under their maximum capacity, suspension shifts are crucial for cost reduction and on-time delivery. In this case, suspension shifts are needed to minimize idle time and prevent inventory buildup. Thus, it is essential to integrate suspension shifts with scheduling under an uncertain production environment. This paper addresses the two-stage hybrid flow shop scheduling problem (THFSP) with suspension shifts under uncertain processing times, aiming to minimize the weighted sum of earliness and tardiness. We develop a stochastic integer programming model and validate it using the Gurobi solver. Additionally, we propose a dual-space co-evolutionary biased random key genetic algorithm (DCE-BRKGA) with parallel evolution of solutions and scenarios. Considering decision-makers’ risk preferences, we use both average and pessimistic criteria for fitness evaluation, generating two types of solutions and scenario populations. Testing with 28 datasets, we use the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) to quantify benefits. Compared to the average scenario, the VSS shows that the proposed algorithm achieves additional value gains of 0.9% to 69.9%. Furthermore, the EVPI indicates that after eliminating uncertainty, the algorithm yields potential improvements of 2.4% to 20.3%. These findings indicate that DCE-BRKGA effectively supports varying decision-making risk preferences, providing robust solutions even without known processing time distributions.

An interactive reference-point-based method for incorporating user preferences in multi-objective structural optimization problems

Although multi-objective evolutionary algorithms (MOEAs) are often used to solve multi-objective structural optimization problems (MOSOPs), the incorporation of decision-maker (DM) preferences into MOEAs to search for regions of interest from the Pareto front has received limited attention in these problems. Recently, three MOEAs have shown promising results in solving MOSOPs incorporating DM’s preferences based on a priori reference points. However, providing this information can be challenging as the DM may not know an initial reference point for the MOSOP. Therefore, this study analyzes three interactive multi-objective optimization (IMO) algorithms, called I-NSGA-II, I-GDE3, and I-GDE3+APM, in which the reference points are progressively incorporated during the search process. These IMOs are applied to benchmark MOSOPs and evaluated using well-known performance indicators. The results demonstrate that I-NSGA-II outperforms I-GDE3 and I-GDE3+APM, which have similar overall performances. Additionally, the IMOs considered here outperformed their respective a priori approaches in MOSOPs where the reference points are located at the central sector of the Pareto front. Also, we identified the performance indicators in which the approaches considered in this work performed better than their corresponding a priori ones. Furthermore, the research findings suggest that these IMOs can generate good results without defining a priori reference points, making them a promising approach for solving MOSOPs and enabling DMs to make better and more reliable decisions.

Promoting the sustainable development of CCUS projects: A multi-source data-driven location decision optimization framework

Carbon Capture, Utilization, and Storage (CCUS) technology is vital for achieving global carbon reduction targets. However, the uncertainties in technology and economic viability are influenced by location. To promote the sustainable development of CCUS technology, the study proposes a data-driven framework for optimizing location decisions. Firstly, the framework considers multiple factors, including geospatial data on resources, risks, power production, transportation, and environment. It also evaluates qualitative and quantitative data across economic, social, environmental, and technological dimensions. Secondly, the two-stage model is conducted as follows: Using Geographic Information System (GIS) technology, the first stage identifies suitable regions for CCUS projects, while the second stage prioritizes these regions using the TODIM method. Further, validated in China, the Junggar Basin, Tarim Basin, Ordos Basin, Sichuan Basin, and Bohai Rim Basin are identified as suitable for CCUS deployment. The Huaneng Luohuang Power Plant is the most conducive location for CCUS projects as pilot demonstrations. Final sensitivity analysis, scenario analysis, and comparative analysis have respectively affirmed the stability, dynamism, and reliability of the model. These analyses have also been instrumental in elucidating the final preferred outcomes under various decision-making preferences and strategic orientations. The framework for decision-making and data-driven priority model for CCUS projects layout proposed in the study can provide technical support and practical evidence for decision-makers in planning CCUS projects and formulating supportive policies.

Multi-objective preventive maintenance strategy and optimization considering unavailability and cost: A case study on VOBC

The Vehicle On-Board Controller (VOBC) is a core component of urban rail transit trains, with high maintenance costs and serious consequences in case of failure. Ensuring long-term VOBC operation while minimizing maintenance costs is of paramount importance. This paper proposes a multi-objective preventive maintenance (PM) strategy and optimization considering system unavailability and maintenance costs. Firstly, we provide a statement of the problem and analyze the impact of different maintenance actions on VOBC. Next, the decreasing process parameter is introduced to adjust the PM interval dynamically. The mathematical models of system unavailability and maintenance costs are derived to construct a multi-objective PM strategy. Then, we develop a hybrid heuristic algorithm including multi-objective particle swarm optimization and genetic algorithm (MOPSO-GA), which is used to obtain the optimal Pareto frontier for maintenance strategies. In the study, we select maintenance strategies by introducing an aversion factor to fulfill the preferences of different decision-makers. A comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the multi-objective PM strategy. The case study on Beijing subway’s VOBC is also executed. The outcomes indicate that the proposed method can dynamically adjust to match the preferences of diverse decision-makers and the MOPSO-GA algorithm has excellent robustness on most parameters. This approach allows for creating field maintenance strategies catering to the varied maintenance requirements of urban rail transit VOBC.

The influence of externality in the graph model for conflict resolution under fuzzy preferences

The Graph Model for Conflict Resolution (GMCR) is a flexible and comprehensive methodology for systematically investigating strategic conflicts. However, most existing studies solely focus on internal conflicts among decision makers (DMs), often disregarding the influence of external stakeholders (ESs) on DMs’ preferences. Consequently, this leads to insufficient stability in equilibrium outcomes. The objective of this study is to address the conflict issue faced by DMs with subjective uncertain preferences who are influenced by externality. Building upon the framework of GMCR, this study proposes the graph model for conflict resolution with fuzzy preference based on externality (FEGMCR) which introduces fuzzy numbers to measure both uncertain preferences of DMs and uncertain expectations of ESs. Furthermore, four forms of stability are extended-- Fuzzy Nash Stability based on externality (FER), Fuzzy General Metarationality based on externality (FEGMR), Fuzzy Symmetric Metarationality based on externality (FESMR), and Fuzzy Sequential Stability based on externality (FESEQ). The optimal equilibrium is achieved by dynamically adjusting the weight of the impact of externalities. Finally, the method is applied to the case of plastic ban in Hainan. It is found that the expectations of ESs do change the preferences of DMs and affect the final equilibrium, which provides a sustainable solution for the issue.

Should the pricing or advertising decision come first in a supply chain with a network externality?

Purpose This paper aims to study how the timing of these decisions affects the total profit and the individual profits of the two agents. Design/methodology/approach This paper study a supply chain for a network good where there is a manufacturer and a retailer. The manufacturer determines its wholesale price and its share in the retailer’s advertising cost while the retailer decides the retail price and the advertising cost. Findings This paper finds that a stronger network externality leads to higher prices and higher advertising efforts. This increases the profits of both manufacturer and retailer, but the manufacturer’s share of advertising costs depends on the order in which the supply chain enterprise make their decisions, the strength of network externality and the effect of advertising determines which decision timeline results in a higher price and greater advertising effort. The manufacturer prefers the price decision to be made before the advertising decision, while the retailer prefers these decisions to be made simultaneously. Research limitations/implications Although this paper studies the price and advertising decision-making order preferences of channel members based on network externalities, this research can also be expanded from the following aspects based on network effects. First, network externality affects advertising cooperation between both parties in the situation such that the pricing power of retail prices is transferred from the retailer to the manufacturer and the retailer relies on revenue sharing (revenue sharing contract, nonwholesale price contract. Second, the manufacturer dominates the issues in the supply chain, but in reality, a retailer can also be the dominator or there are no dominators (Nash equilibrium). Finally, it is possible to consider pricing and advertising decisions in situations where two manufacturers or retailers compete. Practical implications When the price is reasonable, advertising investment is the main determinant of product sales. The greater the intensity of network externalities the more retailers will be willing to invest in advertising. An increase in the intensity of network externalities may not necessarily enhance manufacturers’ motivation or cooperative advertising, but it depends on the decision-making sequence. The strength of network externalities determines the decision-making sequence preferences of supply chain channel members whose preferences vary leading to conflicts of interest. Originality/value The impact of cooperative advertising or decision sequence on corporate decision-making has not been considered. To fill this gap, the paper integrates network externality and supply chain cooperative advertising models, focusing on the impact of network externality on pricing and advertising decisions, as well as on the sequence of decisions.

Assessment of Iranian airlines using network cross-efficiency DEA and the regret theory

Network Data Envelopment Analysis (NDEA) has been extensively applied to evaluate the air transportation sector. NDEA provides a tool for evaluating the internal processes of Decision-Making Units (DMUs). Optimistic Network Cross-Efficiency (ONCE) has recently been extended to the basic two-stage system. However, there are still two main shortcomings that need to be addressed. First, the ONCE evaluates DMUs based only on the optimistic viewpoint, neglecting the pessimistic viewpoint. The optimistic viewpoint assumes that there is only one set of reference points, which includes the best practice DMUs. The first contribution of this study is to develop a new Pessimistic Network Cross-Efficiency (PNCE) method. This method is based on a new set of reference points, which includes the worst-performing DMUs. The PNCE is developed as an extension of the ONCE. Second, both the ONCE and newly developed PNCE methods may lead to unrealistic results because they neglect the subjective preferences of Decision Makers (DMs). These NDEA models employ the Arithmetic Mean (AM) as the cross-evaluation aggregation method, which not only underestimates the importance of self-evaluation but also overestimates the importance of peer evaluations. Consequently, ONCE and PNCE may lead to biased efficiency results. To address this drawback, the second contribution of this study is to develop a new Aggregation method based on the Regret theory and Consensus (ARC). This method aims to reflect the psychological preferences of DMs when estimating cross-evaluation weights. To achieve this goal, we obtained new optimistic and pessimistic efficiencies by utilizing the newly developed ONCE-ARC and PNCE-ARC methods. Subsequently, a Double-Frontier Network Cross-Efficiency with ARC (DFNCE-ARC) is developed as a more comprehensive NDEA. Finally, a practical application is conducted to assess the performance of a set of Iranian airlines, demonstrating the usefulness and applicability of DFNCE-ARC.

Managing flexible linguistic expressions with subjective preferences and objective information in group decision-making: A perspective based on personalized individual semantics

Flexible linguistic expression (FLE) is a complex linguistic representation that almost generalizes all distributed representations, posing the following challenges for the personalized individual semantics (PIS) issues in group decision-making (GDM) with FLEs. (1) The available measurement methods for FLEs are limited and suffer from some unreasonable or poorly discriminated deficiencies. (2) The existing research on FLEs has failed to fully utilize decision information incorporating subjective preferences and objective information, and the format of FLEs may be altered when optimizing PISs. To address these challenges, this paper focuses on the GDM problems under subjective preferences and objective information, both of which are characterized by FLEs, and introduces a novel FLE-based GDM method with PIS. Firstly, a new similarity measure for FLEs is proposed, considering both external and internal characteristics, namely semantics, elements, and symbolic proportions of linguistic term subsets. Secondly, by utilizing the similarity between the decision-maker's preference matrix and decision matrix, as well as the consistency of the preference matrix, a PIS optimization model is constructed to determine the personalized semantics of individual decision-makers. Subsequently, an optimization model is constructed, focused on maximizing the consensus level to determine the differentiated weights of decision-makers. Furthermore, individual evaluation information is aggregated into group evaluation information, a personalized semantic model is constructed to determine the personalized semantics of the group, and the selection process for alternatives is implemented. Finally, the effectiveness and advantages of our method are illustrated through its application in airline service quality assessment and by comparing it with alternative approaches.

Translation and cultural adaption of the control preference scale across various care settings in a Danish hospital

BackgroundIn recent decades, there has been a growing emphasis on involving patients in healthcare decision-making, driven by political, ethical, and research considerations. Although patient involvement is associated with improved health outcomes, understanding patient preferences regarding their role in decision-making is crucial for effective interventions. The Control Preferences Scale (CPS) measures patient preferences along a continuum from passive to active participation. However, its application in Denmark necessitates translation and cultural adaptation.MethodologyThis study aimed to translate and culturally adapt the CPS for Danish use across diverse healthcare settings: acute care, cancer care, elective surgery, chronic medical treatment, and parental involvement in pediatric care. Following a cross-sectional design, the translation process was systematically planned and executed using Beaton’s guidelines, including the five stages: forward and back translation, synthesis, expert review, and pre-testing.ResultsThe translation and adaption process was carried out successfully. Few linguistic challenges were identified and resolved by the expert review. The findings of the pre-testing indicated high acceptability and usability of the adapted CPS among 152 Danish patients and parents. The collaborative role emerged as the most preferred across settings (69.8%), with passive roles more prevalent among cancer patients (30%) and parents waiting with their child to see a pediatrician (23.3%). Notable, more women preferred collaborative or active roles (83.9%) than men (73.9%). The content validity assessment yielded positive feedback, affirming the relevance and comprehensiveness of the CPS.ConclusionsIn summary, the adaptation and validation of the CPS for Danish use proved successful, providing a valuable tool for assessing patient’s role preferences in healthcare decision-making. However, future studies are recommended to ensure construct validity and reliability through psychometric testing.

Evaluating Taiwan’s Geothermal Sites: A Bounded Rationality Data Envelopment Analysis Approach

Amid rising global demand for renewable energy, geothermal power emerges as a vital, low-carbon solution to enhance energy security and sustainability. Taiwan, strategically located on the seismically active Pacific Ring of Fire, possesses an untapped geothermal potential that is underutilized due to complex site selection challenges. This study specifically addresses the need for a more precise and psychologically attuned site selection process, aiming to optimize the development of geothermal resources in regions with complex geological settings. Utilizing the Modified Bounded Rationality Data Envelopment Analysis (MB-DEA) model, this research integrates traditional DEA with bounded rationality to factor in the risk preferences of decision-makers, offering a novel approach that enhances accuracy in evaluating geothermal sites. This study addresses the critical challenge of accurately selecting geothermal energy sites in geologically complex regions like Taiwan, where traditional methods fall short, aiming to significantly boost the efficiency and effectiveness of geothermal energy exploitation as part of Taiwan’s transition to renewable energy sources. Applied to 30 potential sites across Taiwan, our model provides a detailed assessment based on technical, economic, and psychological criteria, revealing variations in site suitability influenced by stakeholder risk attitudes. Key locations such as Datun Mountain, Maoxing, and Taolin consistently rank highly, confirming their robust potential irrespective of risk preferences. At the same time, other sites show marked sensitivity to shifts in decision-making attitudes. This work significantly advances the methodology of renewable energy site selection by demonstrating the utility of incorporating psychological factors into analytical models, which not only refines decision-making processes but also aligns with Taiwan’s strategic energy planning goals. This study also underscores the importance of accurate geographical data in complex terrains, suggesting further refinement and dynamic integration of bounded rationality for future research.

Anything but Representative Democracy: Explaining Conspiracy Believers’ Support for Direct Democracy and Technocracy

Conspiracy theories gained considerable attention during the Covid-19 pandemic. Although studies have extensively explored their (mostly) negative impacts on various political and social aspects, like participation, health-related behavior, and violence, their influence on support for democracy remains relatively unexplored. The few existing studies offer conflicting findings, prompting my focus to shift from assessing generic support for democracy to examining preferences for alternative decision-making models. To address some limitations of prior research on alternative models of decision-making, I combine a trade-off item with a ranking methodology: respondents were prompted to indicate their first and second preferences for different democratic and non-democratic models over representative democracy. The study is based on data from a representative survey in Germany (July/August 2022; N = 2,536). My findings confirm that the belief in conspiracy theories is positively associated with a preference for direct democratic decision-making. However, conspiracy believers also favor expert-based decision-making over elected politicians—but direct democracy would be their primary choice. Although the evidence for a preference for autocracy over representative democracy is associated with a higher degree of uncertainty, it does suggest that conspiracy believers tend to favor “anything but” representative democracy. These findings contribute to the broader discourse on the impact of conspiracy beliefs on democratic systems.

Decision on the introduction of blockchain technology in green supply chains considering altruistic preferences

Purpose This paper aims to examine the blockchain introduction and altruistic preference decisions of the supplier in agricultural food supply chains and discuss how the supplier decisions are influenced by blockchain technology and altruistic preference levels. Design/methodology/approach The author considers a single period two-level supply chain model to describe the supplier’s decisions. The supplier, as the leader of the game, decides whether to introduce blockchain technology and his own level of altruistic preferences. Consumers have environmental awareness and heterogeneity in green trust. Supply chain members determine their own product pricing and green effort level under Stackelberg game. Findings The results reveal the negative impact of unit verification cost in the technology introduction process on the supply chain. In addition, the supplier can adjust their profits by adjusting their altruistic preferences after introducing blockchain to offset the impact of blockchain through the influence of altruistic preferences as discussed by the author. Originality/value This paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.

Exploring the Relationship Between Psychological Constructs and Decision-Making Preferences in Psychiatric Outpatients.

This study aimed to elucidate the relationships among health locus of control, psychological reactance, attitudes toward psychiatric treatment, and patient decision-making preferences within a psychiatric outpatient population. A total of 200 consecutive psychiatric outpatients from a community mental health center in Tenerife, Spain, were approached for participation between September 2023 and March 2024. Of these, 151 patients consented to participate in this cross-sectional study. Participants were selected based on their willingness to participate and were provided with informed consent forms. Data were collected using the Patient's Health Belief Questionnaire on Psychiatric Treatment (PHBQPT) and the Control Preferences Scale (CPS). The PHBQPT evaluates health beliefs impacting adherence to psychiatric treatment, while the CPS assesses the preferred level of involvement in medical decision-making. Sociodemographic data were also collected to contextualize the findings. Significant correlations were found between patients' control preferences and their attitudes towards medication, compliance with psychiatric advice, and perceptions of treatment control. A collaborative control preference was notably associated with positive attitudes toward medication and trust in the psychiatrist. These findings suggest that tailored treatment approaches prioritizing patient involvement could enhance adherence and outcomes. The study underscores the importance of considering psychological constructs in psychiatric care to foster a holistic, patient-centered approach. Recognizing and integrating patients' control preferences, attitudes towards medication, and psychological reactance can improve the therapeutic relationship and treatment adherence. Future research should explore longitudinal and interventional studies to further understand the impact of aligning treatment approaches with patient preferences and psychological profiles.