Decision Maker's Preferences Research Articles

Creating false rewarding memories guides novel decision making.

When memories of past rewarding experiences are distorted, are relevant decision-making preferences impacted? Although recent research has demonstrated the important role of episodic memory in value-based decision making, very few have examined the role of false memory in guiding novel decision making. The current study combined the pictorial Deese/Roediger-McDermott false memory paradigm with a reward learning task, where participants learned that items from some related lists gained reward and items from other lists led to no reward. Later, participants' memories and decision-making preferences were tested. With three experiments conducted in three countries, we successfully created false memories of rewarding experiences in which participants falsely remembered seeing a nonpresented lure picture bring them reward thereby confirming our constructive association hypothesis. Such false memories led participants to prefer the lure pictures and respond faster in a follow-up decision-making task, and the more false memories they formed, the higher preferences for the lure items they displayed (Experiment 2). Finally, results were replicated with or without a memory test before the decision-making task, showing that the impact of false memory on decision making was not cued by a memory test (Experiment 3). Our data suggest that the reconstructive nature of memory enables individuals to create new memory episodes to guide decision making in novel situations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Competitive Analysis or Stakeholder Pre-Commitments? How Firms' Strategic Decision-Making Choices Mediate the Relationship Between its Strategic Posture and Digitalization

This article aims to understand the influence of strategic posture of a firm on its decision-making orientation related to competitors and partners, and evaluates if such decision-making orientation of firms mediate the relationship between a firm's strategic posture and digitalization. The article uses partial least squares based structural equation modeling technique on primary data from 100 enterprises belonging to manufacturing and electronics sector operating from India. Based on the analysis, the article empirically establishes the linkage between a firm's strategic posture and its decision-making preferences in the contexts related to engaging with competitors and partners, taking digitalization as context. Next, it extends available knowledge on firms’ decision-making literature by establishing that a firm's decision-making orientation related to its competitors and partners has direct influence on its degree of digitalization. Finally, it empirically deduces that in the context of digitalization of firms, market-based analysis is the dominant decision-making paradigm as against obtaining stakeholder partnerships or precommitments.

Disparities in Healthcare-Seeking Behavior and Decision Preference Among Hispanics: A Comparative Study Across Races/Ethnicities, SES, and Provider Types.

Hispanics, the largest minority in America, have increased risk of several medical issues and face noteworthy health disparities. This study compares care-seeking behaviors and choice experience among Hispanics, Asians, Blacks, and Whites, considering SES (income, education, and insurance status) and across five healthcare provider (HCP) types. Concurrent analysis provides a comprehensive view of how and where inequity manifests in healthcare. A cross-sectional online survey assessed 1485 adults (Hispanic=314, Asian=313, Black=316, White=542, recruited through a panel agency) of the frequency of visiting primary care providers, dentists, optometrists, gynecologists, and specialists for chronic conditions. Participants also rated the importance of self-selecting a HCP and difficulty in finding one. Whites visited each HCP most regularly. Compared to Asians, more Hispanics saw specialists regularly (45.1% vs 56.5%, p=0.042), and Blacks saw dentists less (47.0% vs 38.3%, p=0.028) and gynecologists more often (21.2% vs 33.1%, p=0.024). No other frequency differences were observed among minorities. Low-income participants across four races saw dentists and gynecologists with comparable infrequency. Hispanics and Asians assigned similarly significantly lower self-choice importance and experienced more difficulty relative to Whites or Blacks. Participants with lower income or education visited HCPs less regularly yet perceived the same choice importance as higher-SES peers (p>0.05). Notably, discrepancies in visit frequency between Whites and minorities were more pronounced in higher-SES than lower-SES group. Differences in experiencing care-seeking difficulty were associated with income (p=0.029) and insurance type (p=0.009) but not education (p>0.05). Higher income and education increase healthcare utilization; however, racial disparities persist, particularly among higher-SES groups. Despite similarities among minorities, the extent of disparities varied by SES and provider type. The findings help explain evident inequity in healthcare access and health outcomes. Tailored patient education, culturally-specific navigation support, and more inclusive services are needed to address barriers faced by minorities and disadvantaged populations.

Preferences for Ethical Intermediaries and Sustainable Investment Decisions in Micro-firms: the Role of Financial Literacy and Digital Financial Competencies

Preferences for Ethical Intermediaries and Sustainable Investment Decisions in Micro-firms: the Role of Financial Literacy and Digital Financial Competencies

Understanding the role of antibiotic-associated adverse events in influencing antibiotic decision-making.

To (1) understand the role of antibiotic-associated adverse events (ABX-AEs) on antibiotic decision-making, (2) understand clinician preferences for ABX-AE feedback, and (3) identify ABX-AEs of greatest clinical concern. Focus groups. Academic medical center. Medical and surgical house staff, attending physicians, and advanced practice practitioners. Focus groups were conducted from May 2022 to December 2022. Participants discussed the role of ABX-AEs in antibiotic decision-making and feedback preferences and evaluated the prespecified categorization of ABX-AEs based on degree of clinical concern. Thematic analysis was conducted using inductive coding. Four focus groups were conducted (n = 15). Six themes were identified. (1) ABX-AE risks during initial prescribing influence the antibiotic prescribed rather than the decision of whether to prescribe. (2) The occurrence of an ABX-AE leads to reassessment of the clinical indication for antibiotic therapy. (3) The impact of an ABX-AE on other management decisions is as important as the direct harm of the ABX-AE. (4) ABX-AEs may be overlooked because of limited feedback regarding the occurrence of ABX-AEs. (5) Clinicians are receptive to feedback regarding ABX-AEs but are concerned about it being punitive. (6) Feedback must be curated to prevent clinicians from being overwhelmed with data. Clinicians generally agreed with the prespecified categorizations of ABX-AEs by degree of clinical concern. The themes identified and assessment of ABX-AEs of greatest clinical concern may inform antibiotic stewardship initiatives that incorporate reporting of ABX-AEs as a strategy to reduce unnecessary antibiotic use.

What drives the performance of the SME sector? Evidence from selected European countries

Small and medium-sized enterprises (SME) are highly important for economic development of every nation. Recently, many experts and policymakers have been trying to measure the performance and efficiency of these businesses. However, the methods used so far are often idiosyncratic, subjective, or static. This paper suggests a new way to measure the success of these businesses using a data-driven method. More specifically, we combine decision-making techniques (namely, TOPSIS) and preference learning techniques. This innovative approach is flexible and can adapt to changes over time by iterative preference elicitation and TOPSIS calculation. As a part of the case-study of the proposed approach, we found that Ireland is a great example to follow and that the ?think-small-first? principle is the most important driver of the SME growth. The findings can help government officials and analysts, especially when they are planning policies for small and medium-sized enterprises.

MCClusteringSM: An approach for the multicriteria clustering problem based on a credibility similarity measure

Multicriteria clustering problem has been studied and applied scarcely. When a multicriteria clustering problem is tackled with an outranking approach, it is necessary to include preferences of decision makers on the raw dataset, e.g., weights and thresholds of the evaluation criteria. Then, it is necessary to conduct a process to obtain a comprehensive model of preferences represented in a fuzzy or crisp outranking relation. Subsequently, the model can be exploited to derive a multicriteria clustering. This work presents an exhaustive search approach using a credibility similarity measure to exploit a fuzzy outranking relation to derive a multicriteria clustering. The work includes two experimental designs to evaluate the performance of the algorithm. Results show that the proposed method has good performance exploiting fuzzy outranking relations to create the clusterings.

Science and technology resource allocation evaluation of research institutions considering the risk preference of decision makers

With the increasing emphasis on research institutions in China, there is a growing imperative to evaluate the efficacy of resource allocation in science and technology (S&T) investments. However, limited research has been conducted on the efficacy of resource allocation in research institutions while considering decision makers’ risk preference behavior. In order to address this issue, the present study employs a prospective cross-efficiency evaluation model that incorporates decision makers’ risk preferences. This model is utilized to assess the efficiency of S&T resource allocation in research institutions across 13 industries in China. After conducting empirical analysis, it has been observed that decision makers’ risk preferences significantly influence the efficiency outcomes of S&T resource allocation in research institutions. Finally, this study proposes appropriate measures and recommendations for S&T resource allocation in research institutions.

The high-end bias - A decision-maker preference for premium over economy innovations

Deciding which new product concepts to develop is an important strategic management decision. One part of it is to decide whether to develop “premium” products, priced above the average product on the market, or “economy” products, priced lower than the average product on the market. We hypothesize that, ceteris paribus, firms and individual decision makers prefer premium over economy innovation projects. Building on implicit attitude and status discrimination theories, we argue that the origin of the bias lies in the implicit decision-making system of the mind, such that decision-makers inherently prefer premium innovations and that this implicit high-end bias affects their explicit preferences. We use the results from one longitudinal set of archival sales data covering 2312 new product introductions and three experimental studies with decision makers, including practicing managers, to provide evidence for the high-end bias. With these findings, we extend status theory as well as discrimination theory from well researched personnel decisions to managerial decisions about inanimate objects such as product concepts. We further augment literature on low-end innovation by identifying an important constraint for managers and researchers who work on inclusive innovation, frugal innovation, social responsibility, and Base of the Pyramid innovation.

Developing Resilience Pathways for Interdependent Infrastructure Networks: A Simulation-Based Approach with Consideration to Risk Preferences of Decision-Makers

Developing Resilience Pathways for Interdependent Infrastructure Networks: A Simulation-Based Approach with Consideration to Risk Preferences of Decision-Makers

Research on the decision-making of work safety investment in industrial park enterprises: evidence from behavioral experiments.

Due to the clustering of risk factors, industrial park safety accidents can easily trigger a domino effect. Work safety investment is the foundation of enterprise work safety in industrial parks. Therefore, increasing the work safety investment of enterprises in industrial parks is the key condition to prevent accidents. However, due to the typical negative externalities of industrial park work safety accidents, the decision-making process of work safety in park enterprises is influenced by other enterprises within the park, including imitation behaviors. This makes the decision-making of work safety in park enterprises very specific. In order to clarify the influencing factors and effects of work safety investment in industrial park enterprises, this study uses a behavioral experiment method and conducts decision-making experiments using the experimental platform O-Tree. The study recruits 76 participants who play the role of decision-makers in park enterprises. This study uses a lottery price experiment and a dictator experiment to measure the risk preference and altruism preference of the participants, respectively. The study introduces the real background of work safety investment in industrial park enterprises and collects data on work safety investment by the participants in different experimental scenarios. The research results show that the safety attitudes of decision-makers, altruism preference, accident experience, government work safety supervision, park management measures, and safety benefits positively influence work safety investment in park enterprises. The risk preference of decision-makers and the resource capability of work safety negatively influence work safety investment in park enterprises. Work safety investment in park enterprises is influenced by the work safety investment of other enterprises within the park.

What Influences the Success of Career Exploration in School?

Introduction. Career exploration is an individual's efforts to gain a better understanding of career-related information, alternatives, and choices. Through career exploration, individuals develop self-awareness and knowledge about future work, which may contribute to forming a commitment to a career choice. Career exploration behavior encompasses self-assessment and external search activities that provide information to support career choice and adjustment. Aim. The aim of this study was to determine the factors that influence the success of career exploration in schools. Career exploration is one of the important stages in the process of making informed career decision, as it can be linked to difficulties in making career decisions due to a lack of maturity in career exploration. Methodology and research methods. This article presents a systematic literature review on the factors influencing career exploration in schools, using the Scopus and Web of Science databases. The research examines career exploration in schools between 2018 and 2023. A total of 137 articles were reviewed, and 36 were selected based on inclusion criteria. A bibliometric review was then conducted, involving empirical and theoretical analysis of the available data related to the phenomenon of career exploration in schools and the factors that influence it. Result. The bibliometric results and the influential factors related to career exploration in schools are presented. The largest number of affiliated journals and authors studying this phenomenon originate from the United States of America. The research findings indicate that the influential factors of career exploration can be categorized into four groups: in-depth self-exploration, extensive self-exploration, in-depth environmental exploration, and extensive environmental exploration. Scientific novelty. The scientific novelty of this research lies in the discovery of factors that influence the success of career exploration in schools. This includes the initial grand theory of career exploration trends, the distribution of best practices for successful career exploration across various countries representing different continents, and up-to-date literature from the last five years. Practical significance. This systematic literature review has addressed how career exploration activities are conducted in schools, highlighting the influencing factors and their correlations with other fields. The results of this research have implications for future studies, particularly regarding career exploration as an individual decision-making preference. For educators, the findings suggest a need for individualized planning services to enhance students' career exploration, making it more focused and measurable in the context of career decision-making.

The Influence of Academic Pressure on Risk Decision Preferences: An Experimental Study on a Malaysian University

The Influence of Academic Pressure on Risk Decision Preferences: An Experimental Study on a Malaysian University

Research on decision analysis with CVaR for supply chain finance based on partial credit guarantee and blockchain technology

AbstractConsidering the influence of decision maker's risk preference degree on decision making, this paper employs CVaR risk measure criterion to facilitate decision making. The optimal decision making of supply chain finance under partial credit guarantee and blockchain technology empowerment is investigated. In addition, the CVaR decision models for supply chain finance under two scenarios of partial credit guarantee and blockchain technology empowerment are constructed. The inverse solving is conducted with the help of the Stackelberg model to find out the optimal ordering quantity, wholesale price, and interest rate. The influencing factors of decision making are investigated. This study shows that, within a certain risk‐averse range, the ordering quantity of retailers under the partial credit guarantee model is larger than that under the blockchain technology‐enabled model. In contrast, the conditional risk value of suppliers under the partial credit guarantee model is much smaller than that under the blockchain technology‐enabled model. In conclusion, the blockchain technology is beneficial to reduce supply chain finance risks.

Optimized Development of Hohhot’s New Public Cultural Space Layout Construction Based on Genetic Algorithm under the Perspective of Urban Renewal

Abstract The rational layout and scientific configuration of public cultural space is an important criterion to measure the public service function of a city, whether it is perfect or not. In this paper, Hohhot Downtown is selected as the research object, and the spatial layout structure of the city is explored using the indicators of expansion intensity, compactness and fractal dimension. A layout optimization method based on a genetic algorithm is proposed to build a spatial layout model, set the spatial layout optimization constraints, complete the genetic algorithm optimization through the optimization coding process, and finally solve the Pareto optimization solution set by using the genetic algorithm of Matlab, which results in the optimization scheme of the new public cultural spatial layout of Hohhot City. The results show that six new public cultural space layout schemes of Hohhot are obtained after optimization, and the six layout scores are in the interval of [0.9, 1.0], all of which are excellent. Decision makers can choose the final layout scheme based on their actual situation and decision preferences.

A three-way conflict analysis model with decision makers’ varying preferences

The preferences of decision makers (DMs) over conflicting states have a significant impact on the stability of the conflict system. However, the DM’s preferences over conflicting states are generally unclear and variable, as they are influenced by the unpredictability of future situations of nature and the psychological behavior of DMs. To deal with multi-issue conflict problems, since the interval grey number and three-way decisions can reveal the mechanism of DMs’ grey and varying preferences in uncertain natural situations, we exploit grey system theory, conflict analysis, and three-way decisions to establish a novel conflict analysis model with varying decision makers’ preferences, and a conflict case of industry-university-research innovation alliance verifies the efficacy and rationality of the model.

A data-driven approach to predicting consumer preferences for product customization

Product customization is a complex task that relies heavily on consumer preferences. Eliciting these preferences can be challenging for firms looking to develop novel products and require significant investments of both time and effort. Prediction models can serve to replace traditional methods of understanding consumer preferences such as elicitation, focus groups or the designer’s intuition, while speeding up the production cycle and saving cost. Current prediction models generally focus on one specific product type and require large amounts of data or historical product data. The idea of predicting consumer preferences for products based on the product type and its features using a clustering approach has not been explored in literature. This paper presents a proof-of-concept consumer preference prediction and decision support model based on a data-driven approach to design for product customization. First, consumer preference information is crowdsourced using surveys with 307 individual responses that are collected for a data set of thirty-seven training products and three validation products. Second, clustering techniques are assessed for user-generated clustering variables along with features that are extracted with artificial intelligence (ChatGPT). A threshold metric is proposed to evaluate the accuracy of different clustering algorithms. Third, a recommendation model is developed for customization decisions, and it is validated with three different products with an average accuracy of 70%. Areas for future work to improve the accuracy and expand the scope of the model are discussed including the use of a larger training data set, different machine learning approaches, and the improved use of ChatGPT.

A multi-source transfer-based decision-making method with domain consistency and contributions

Large volumes of historical data that characterize the preferences of decision-makers are generated and recorded. Each decision-maker's historical data contain different knowledge and reflect different preferences. However, the learned decision models based on data-driven multi-criteria decision-making (D2MCDM) cannot help generate satisfactory decision solutions when the available data are insufficient. To solve this problem, the parameter-based transfer learning with multi-source domains (multi-SDs) is considered to be integrated with D2MCDM. Following the idea, this paper proposes a multi-source transfer-based decision-making (MTBDM) method with domain consistency and contributions to acquire the preferences of decision makers. The stability of criterion weights and that of decision makers’ individual consistency are constructed and combined with the difference between data distributions of multi-SDs and the target domain (TD) to develop the domain consistency. Based on the developed domain consistency, two optimization models are constructed to transfer criterion weights and individual consistency between multi-SDs and the TD, in which domain contributions of multi-SDs to the TD are considered based on domain consistency to avoid negative transfer. The advantages of the MTBDM method are demonstrated by its application to the auxiliary diagnosis of breast lesions. Its predictive performance is further validated by its comparison with the MTBDM method in four different situations and existing methods.

DEA cross-efficiency models with prospect theory and distance entropy: An empirical study on high-tech industries

Cross-efficiency evaluation with the data envelopment analysis (DEA) model is an effective way to assess performance and provide a complete ranking of decision-making units. However, it is generally assumed that decision makers are perfectly rational in the cross-efficiency model, which fails to consider the subjective preferences of decision-makers. Moreover, the arithmetic average method is usually adopted to aggregate efficiency scores in traditional cross-efficiency methods, which underestimates the importance of self-evaluation. To address these issues, we extend cross-efficiency with the DEA model by incorporating prospect theory and the distance entropy function. First, we calculate the prospect values of decision-making units to describe the non-rational subjective preferences under the risk of decision-makers. Second, based on prospect cross-efficiency, a new distance entropy function is developed to aggregate the ultimate prospect cross-efficiency values. More specifically, some traditional cross-efficiency evaluation models can be considered special cases of prospect cross-efficiency models with appropriate adjustments to the parameters. Finally, an empirical example is used to evaluate the prospect cross-efficiency results with the high-tech industries of 29 provinces in China to illustrate the applicability and effectiveness of the proposed model in ranking observations.

The Role of Language Concordant Care on Increasing Parental Engagement in Shared Decision Making

BACKGROUND: Shared decision making (SDM) allows patients and physicians to make decisions together by incorporating clinical expertise and patient preference. While SDM can improve patient satisfaction, preferences on the extent of involvement in SDM vary, particularly among different cultures and languages. We sought to describe preferences in decision making and degree of SDM among Spanish-speaking caregivers receiving pediatric Otolaryngology care. We hypothesized that language-concordant encounters would have higher SDM scores. METHODS: A cross-sectional study of Spanish-speaking caregivers undergoing consultation with pediatric otolaryngology was performed (May-August 2022). Care was provided in a language-concordant (LC) setting in which the physician and caregiver communicate in Spanish, or a language–discordant (LD) setting in which the physician communicates with interpreter support. Following their appointment, caregivers were invited to complete the Spanish Control Preferences Scale (CPS, select 2 of 5 options) to determine their two most preferred roles in medical decision making, and the Shared Decision-Making Questionnaire (SDM-Q-9; scored 0-100) to assess perceived SDM. Descriptive statistics, Fisher’s exact and the Mann-Whitney U test were used for analysis. RESULTS: Sixty-one caregivers were enrolled. CPS scores revealed the passive-collaborative medical decision making role was the most favored (59%), followed by an active-collaborative role (26%), and passive role (13%). Median SDM-Q-9 score for all caregivers was 100.00 (mean=93.70). The median SDMQ-9 score was 100 in LC visits, and 97.78 in LD visits (p = 0.428). No differences in SDM-Q-9 scores were detected for CPS categories between LC and LD visits. CONCLUSION: High levels of SDM were observed among Spanish-speaking caregivers using both LC and LD care. Most caregivers preferred a passive-collaborative role in medical decision making. Results may be influenced by positive response bias given the high scores observed. Further studies with larger sample sizes can further our understanding of the impact of LC care in SDM.