Decision-making Behavior Research Articles

Energy transition in the retail sector: Revealing decision-making behaviours for energy efficiency retrofits of Dutch shopping centres

Shopping centres hold significant energy retrofit potential in the retail sector. However, their complex multi-stakeholder governance structure complicates decision-making for Energy Efficiency Retrofits (EER). Although literature identifies some barriers linked to EER, they are often scattered and not identified within a process perspective that considers their contextual complexities. This study addressed this gap by examining stakeholders’ behaviours, barriers, and relations during EER decision-making processes, to detect stakeholders’ needs to catalyse the energy transition. Qualitative research was conducted on three representative Dutch shopping centres case studies. The cross-case analysis uncovered the decision-making process for EER and new barriers attributed to this building typology. Amongst other, detected barriers can be attributed to limitations with governmental and internal regulations, lifecycle conflicts, proposed interventions being out of scope, and existing technical challenges. The study also revealed causal relationships among stakeholders, showcasing varying interpretations of barriers and highlighting the roles of owners, property managers, and public authorities in overcoming them. Furthermore, the findings indicate the key role that different contextual variables play in the EER decision-making process, such as ownership type, governance structure, and leasing structure. This study offers insights and recommendations for shopping centre owners, property managers, and policymakers, to support them in navigating the energy transition of the retail building stock. Key recommendations include decentralizing decision-making, optimizing governance structure, streamlining sustainability advisors, and acknowledging the collaborative roles of property managers and policymakers in providing effective and holistic solutions.

Evolutionary Game Analysis of Government Regulation on Green Innovation Behavior Decision-Making of Energy Enterprises

Encouraging environmentally friendly innovation in energy companies is an essential way to stop global warming. Through ingenious integration of reputation and fairness preference, this research develops an evolutionary game model between the government and energy companies. This research investigates the dynamic evolution of green innovation strategy selection by energy firms operating under government supervision, using an evolutionary game model as a basis. This study examines how government regulations, including their subsidies and penalties, reputation, and fairness preference, affect the green innovation behavior of energy enterprises. The research shows that without considering the fairness preference, the subsidy and punishment of government regulation can improve the tendency of energy enterprises to choose green innovation behavior. At the same time, considering the reputation of energy enterprises to assume social responsibility can improve the tendency of energy enterprises to choose green innovation behavior. In the case of considering fairness preference, energy companies with strong fairness preference are more likely not to adopt green innovation and need more subsidies and penalties to choose green innovation; energy enterprises with weak fairness preference are more likely to adopt green innovation; green innovation will take place with fewer subsidies and penalties; reputation plays a stronger role in energy companies with weak fairness preferences. The study can give the government a theoretical foundation on which to build precise regulatory plans for various energy firms and encourage green innovation in those enterprises.

Impact of fairness concerns on resource-sharing decisions: A comparative analysis using evolutionary game models in manufacturing enterprises

In recent years, the resource-sharing model has developed rapidly, and manufacturing enterprises have paid more attention to profit distribution and resource investment. Studying the role of fairness concern behaviour in enterprise resource-sharing decisions can provide strategic references for enterprises to implement resource-sharing. This article considers the fairness concern behaviour of resource-sharing enterprises and constructs two symmetric evolutionary game models for resource-sharing. Based on the resource-sharing strategy when the enterprise is fair and neutral, a comparative study is conducted on the impact of fairness concern behaviour on the resource-sharing equilibrium strategy. MATLAB is used for numerical simulation. The analysis results indicate that: (1) the system cost coefficient, information level coefficient, information loss coefficient, resource conversion ability coefficient, resource collaboration ability coefficient, and penalty factor all have an impact on the strategic evolution results of both parties in the game enterprise, (2) fairness concerns include heterogeneity, directionality, and reinforcement, which affect the resource-sharing strategies of enterprises. This study provides a reference for shared decision-making in manufacturing enterprises and provides recommendations for their business development.

HERDING BEHAVIOUR MARKET INFORMATION AND INDIVIDUAL INVESTOR DECISION-MAKING AT THE NAIROBI SECURITIES EXCHANGE

This paper examined the relationship between herding behaviour and individual investor decision-making at the Nairobi Securities Exchange. In addition, the study assessed the moderating effect of market information on the relationship between herding behaviour and individual investor decision-making at the Nairobi Securities Exchange. The study was anchored on behavioural finance theory and was guided by a correlational research design. The study's target population was 2.03 million individual investors who traded at the Nairobi Securities Exchange through the 17 licensed brokerage firms in Kenya. A structured questionnaire was used to collect data and Internal consistency of the instruments was measured using Cronbach’s alpha coefficient, where a coefficient of 0.865 was obtained. The obtained data was analyzed descriptively using frequencies, means, and standard deviation and inferentially by correlation and multiple regression models. The findings revealed that herding behaviour (r = 0.235; β =0.180 p<0.05) had a positive and significant relationship with individual investor decision-making. Jointly, the study established that herding behaviour explains a 59.6% change in individual investor decision-making. In addition, the study established that market information had a positive significant moderating effect on the relationship between herding behaviour and individual investor decision-making as the moderator increased the R2 from 59.6% to 61.0%, which is an R-square change of 1.4%. The study concluded that herding behaviour had a significant relationship with individual investor decision-making at the Nairobi Securities Exchange. The study recommended that investors understand market trends, financial statements, and economic indicators and continue seeking advice from financial experts. NSE should also provide regular market analysis, ensure transparency in market operations to build investor confidence and introduce investment products that cater to different risk profiles, such as low-risk mutual funds. The findings of the study could be of significance to policymakers, financial experts, academicians, scholars, and theoretical developments.

The role of fatigue in attentional processing in multiple sclerosis: data from event-related potentials.

Fatigue is an extremely common symptom in in people with multiple sclerosis (PwMS) and has a severe impact on quality of life. The purpose of the present study was to verify whether fatigue in PwMS is associated with a selective covert attention impairment, as measured by event-related potentials and to assess whether it is more associated with an impairment of top-down or bottom-up attentional control. Twenty-two PwMS and fatigue-MSF, 17 without fatigue-MSnF and 35 healthy volunteers underwent a three-stimulus P300 novelty task that elicits both the P3a and the P3b components. P3b latency was comparable between groups, but PwMS, independently from the presence of fatigue displayed significantly greater P3b amplitudes. P3a latency was significantly prolonged in MSF alone, while P3a amplitude in MSnF group was greater than controls. MSF were able to categorize the task-relevant target stimulus but the orienting response to a novel salient stimulus was delayed, indicating an impairment in bottom-up attentional control mechanism related to ventral attention network. Fatigue is selectively associated with a covert attentional deficit related to the ability to reallocate attentional resources to salient stimuli, a crucial function of adaptive decision-making behaviour.

Federations of academic departments: performance-based funding and academic field-level expenditures at Russell group universities

ABSTRACT The widespread adoption of performance-based funding has been a global trend in higher education policy over the past 40 years. While considerable attention has been devoted to studying the relationship between performance-based funding and institutional behavior at the university level, little research has explored the relationship between performance-based funding and institutional decision-making behavior within universities. Conceptualizing universities as federations of academic departments, I used results from the 2014 Research Excellence Framework (REF) exercise to explore the relationship between those results and subsequent spending patterns within Russell Group universities. The most important finding is that this relationship varies based upon both the academic field and the university’s rank in the Times Higher Education World University Ranking (THE-WUR). It also has implications for policymakers around the world as it underscores that policy decisions can have differential impacts not only across universities, but also within universities.

Hypothalamic representation of the imminence of predator threat detected by the vomeronasal organ in mice.

Animals have the innate ability to select optimal defensive behaviors with appropriate intensity in response to predator threats within specific contexts. Such innate behavioral decisions are thought to be computed in the medial hypothalamic nuclei, which contain neural populations that directly control defensive behavioral outputs. The vomeronasal organ (VNO) serves as a primary sensory channel for detecting predator cues by relaying signals to the medial hypothalamic nuclei, particularly the ventromedial hypothalamus (VMH), via the medial amygdala (MeA) and bed nucleus of the stria terminalis (BNST). Here, we demonstrate that cat saliva contains predator cues that signal the imminence of predator threat and modulate the intensity of freezing behavior through the VNO in mice. Cat saliva activates neurons expressing the V2R-A4 subfamily of sensory receptors, suggesting that specific receptor groups are responsible for inducing the freezing behavior. The number of VNO neurons activated in response to saliva correlates with both the freshness of saliva and the intensity of freezing behavior. In contrast, the downstream neurons in the accessory olfactory bulb (AOB) and the defensive behavioral circuit are activated to a similar extent by fresh and old saliva. Strikingly, however, the number of VMH neurons activated by fresh, but not old, saliva positively correlates with the intensity of freezing behavior. Detailed analysis of the spatial distribution of neurons responding to fresh and old saliva, as well as the overlap of those activated within the same individual mice, revealed that fresh and old saliva predominantly activate distinct neuronal populations within the VMH. Collectively, this study suggests that there is an accessory olfactory circuit in mice that is specifically tuned to time-sensitive components of cat saliva, which optimizes their defensive behavior to maximize their chance of survival according to the imminence of threat.

Evaluating risk of IoT adoption in the food supply chain using an integrated interval-valued spherical fuzzy generalised TODIM method

The existing literature has shown that the Internet of Things (IoT) has proven to be beneficial for managing operations in the food supply chain. However, the potential failures resulting from these new technologies have not been thoroughly examined. Therefore, the aim of this study is to create a new framework for analyzing the risks associated with implementing IoT in the food supply chain. To achieve this, we introduce an extended failure mode and effect analysis framework using an integrated generalised Toma de Decisão Iterativa Multicritério (TODIM) method within the interval-valued spherical fuzzy setting. Initially, we developed a risk structure consisting of four dimensions and sixteen sub-factors to facilitate the implementation of IoT technology in the food supply chain. We then introduced an interval-valued spherical fuzzy-weighted Bonferroni mean operator to gather expert opinions and create a group risk matrix considering the interaction between the input risk data. Subsequently, we proposed a newly developed interval-valued spherical fuzzy TODIM method based on the Logarithmic percentage change-driven objective weighting (LOPCOW) method to evaluate and rank the risks associated with IoT application in the food supply chain. This method can capture the bounded rational decision behavior of experts and the uncertain preference of risk parameters. Based on the identified risk factors, we implemented a proposed risk analysis framework in line with the preferences of the experts. The results indicate that "human resource risk" and "uncertainty in technology adoption" are the most and least significant risks, respectively. We also conducted sensitivity and comparison studies to test the reliability and rationality of the proposed framework. The main findings can assist policymakers and managers in mitigating and minimizing the risks connected with IoT applications in the food supply chain. Additionally, these outcomes can aid stakeholders in making well-informed decisions about resource allocation for risk management.

The Information Cocoons in Military Decision Making and Strategies for Coping

As we all know, the information cocoon has a broad impact on people's basic cognitive abilities such as memory, decision-making, and judgment. This article first extends the concept of information cocoons to the field of command and decision-making behavior, and studies the harm and response measures of information cocoons to command and decision-making. The paper first elaborates on the causes of information cocoons, including technical factors, organizational structure factors, and psychological factors. Secondly, the harm of information cocoons to command decision-making was analyzed, including hindering accurate judgment of the battlefield situation, increasing battlefield risks, and increasing the likelihood of battlefield losses.Then, corresponding measures were proposed, including the application of technical means and optimization of organizational structure and decision-making processes. Finally, the importance of improving psychological resilience and leadership abilities was emphasized. The research conclusion of this article is helpful for command decision-makers to effectively overcome the challenges brought by the information cocoon to command decision-making and improve the correctness of decision-making.

Behavioral Economics: Understanding the Psychological Factors Driving Consumer Decisions

Behavioral economics is an interdisciplinary field that combines insights from psychology and economics to explain and predict consumer behavior in economic decision-making. This article provides a concise overview of the definition and importance of behavioral economics, exploring its core concepts such as bounded rationality, heuristics and biases, and prospect theory. It also examines the impact of emotional and social factors on consumer decisions and discusses practical applications of behavioral economics in marketing and public policy. Finally, the article summarizes key points and looks ahead to future directions in the field.

A homogeneous multi-vehicle cooperative group decision-making method in complicated mixed traffic scenarios

Connected and Automated Vehicles (CAVs) are expected to reshape the transportation system, and cooperative group intelligence of CAVs has great potential for improving transportation efficiency and safety. One challenge for CAV group driving is the decision-making under scenarios mixed with CAV and human-driven vehicles (HDV). Current studies mainly use methods based on single physical rules such as platoon driving or formation switch control, failing to reach a balanced and homogeneous state of optimal efficiency and risk in mixed traffic environments. In addition, most studies focus only on one specific type of scene, lacking the scene adaptability to various surrounding conditions. This paper proposes a homogeneous multi-vehicle cooperative group decision-making method targeting mixed traffic scenarios. A bi-level framework composed of behavior-level and trajectory-level decision-making is established to achieve balanced optimal cooperation. A region-driven behavioral decision mechanism is designed to decompose vehicle actions into a unified form of sequential target regions. Solutions are derived based on Cooperative Driving Safety Field, a risk assessment module inspired by field energy theory. The trajectory-level decision module takes the target regions as input and generates the control quantities of the CAVs through target point selection, conflict reconciliation, and dynamic constraint consideration. Experimental results on 19 various scenarios and continuous traffic flow scenes indicate that the proposed method significantly increases passing efficiency, reduces driving risk, and improves scene adaptability. In addition, experiments on multiple kinds of scenarios including intersections, ramps, bottlenecks, etc. prove that our method can adapt to various road topology structures. Feasibility is also verified through scaled physical platform validations and real-vehicle road tests.

Transitions in dynamical regime and neural mode underlie perceptual decision-making.

Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks 1,2 . However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use an unsupervised, deep learning-based method to discover decision-related dynamics from the simultaneous activity of neurons in frontal cortex and striatum of rats while they accumulate pulsatile auditory evidence. We found that trajectories evolved along two sequential regimes, the first dominated by sensory inputs, and the second dominated by the autonomous dynamics, with flow in a direction (i.e., "neural mode") largely orthogonal to that in the first regime. We propose that the second regime corresponds to decision commitment. We developed a simplified model that approximates the coupled transition in dynamics and neural mode and allows precise inference, from each trial's neural activity, of a putative internal decision commitment time in that trial. The simplified model captures diverse and complex single-neuron temporal profiles, such as ramping and stepping 3-5 . It also captures trial-averaged curved trajectories 6-8 , and reveals distinctions between brain regions. The putative neurally-inferred commitment times ("nTc") occurred at times broadly distributed across trials, and not time-locked to stimulus onset, offset, or response onset. Nevertheless, when trials were aligned to nTc, behavioral analysis showed that, as predicted by a decision commitment time, sensory evidence before nTc affected the subjects' decision, but evidence after nTc did not. Our results show that the formation of a perceptual choice involves a rapid, coordinated transition in both the dynamical regime and the neural mode of the decision process, and suggest the moment of commitment to be a useful entry point for dissecting mechanisms underlying rapid changes in internal state.

The Influence of Education 4.0 on Decision-making and Agripreneurship Start-up Behavior among Agriculture Students in Kenyan TVET Institutions

This study examined the impact of Education 4.0 on decision-making processes and agripreneurship among agriculture students in Kenyan TVET institutions. The researchers conducted a systematic literature review following PRISMA guidelines, utilizing databases such as PubMed, Scopus, and Google Scholar, resulting in a final sample of 54 articles. The selected studies explored Education 4.0's influence on agricultural education, decision-making skills and agripreneurship behavior among TVET students. Articles included in the review met specific inclusion criteria: focus on Education 4.0's impact, involvement of agriculture students, publication in peer-reviewed journals, written in English and inclusion of empirical research or theoretical papers. Findings highlight that Education 4.0 initiatives, including technological integration and agripreneurship skill development, enhance students' abilities to make informed decisions and pursue agripreneurship opportunities in agriculture. Technological tools such as drones, GIS, GPS and AI play a crucial role in improving crop and livestock management, thereby facilitating better decision-making in agricultural practices. In conclusion, Education 4.0 significantly shapes decision-making processes and agripreneurship among TVET agriculture students. Recommendations include prioritizing investments in digital infrastructure, supporting educator training, and fostering industry partnerships to maximize the benefits of Education 4.0 in agricultural education. The implications extend to various stakeholders in agricultural education and entrepreneurship, guiding educators, policymakers, and students in leveraging Education 4.0 for enhanced learning and agripreneurship development.

Behavioral Biases and Decision-Making in Investment among working individuals in the education sector

This study investigates the impact of behavioral biases on investment decisions among working individuals within the education sector. Behavioral finance theory serves as the theoretical foundation, emphasizing the role of psychological factors in shaping investment choices. The study employs a mixed-methods approach, combining surveys and in-depth interviews to gain insights into the investment behavior of educators, administrators, and support staff. Our findings reveal that a range of behavioral biases, including loss aversion, overconfidence, and herding, significantly influence investment decisions. Working professionals in the education sector often exhibit a strong preference for conservative investment strategies, even when market conditions may suggest otherwise. Psychological factors, such as fear of losses and a tendency to follow trends, play a vital role in shaping investment behavior. These biases are further exacerbated by a lack of financial literacy and investment education. The implications of this research suggest a need for targeted financial education programs to mitigate behavioral biases and enhance decision-making among working individuals in the education sector. Addressing these biases can lead to more informed and diversified investment strategies, potentially improving financial security and long-term wealth accumulation for this demographic. This abstract provides a brief overview of the research focus, methodology, key findings, and the potential implications of the study. It gives readers a clear understanding of the research's scope and significance.

“A quantitative analysis on the adoption of soil, water, and forest conservation technologies in the upper Gelana watershed, Northeast Ethiopian highlands”

Globally, the degradation of soil, water, and forests has had a significant impact on both livelihoods and the environment. This issue is particularly severe in developing countries, including Ethiopia. Despite extensive efforts to implement conservation measures for soil, water, and forests in the highlands of Ethiopia, there has been a lack of thorough evaluation and documentation regarding the adoption of these practices by rural households. It is crucial to have scientific and up-to-date information at various spatial scales in order to effectively monitor existing practices, scale up successful initiatives, and promote sustainable regional development. Therefore, this paper focuses on analyzing the adoption of soil, water, and forest conservation activities by households in the upper Gelana watershed, South Wollo zone, Amhara Regional State of Ethiopia. The field data collection for this study took place from January to March 2022, from 150 rural household heads. Data analysis was carried out using SPSS software version 23. Descriptive statistics, Pearson bivariate correlation, and multinomial logistic regression were used. The survey findings revealed that 69 % of the respondents had implemented various soil, water, and forest conservation measures at different stages. The Pearson correlation results indicated a positive relationship between the adoption of soil, water and forest conservation practices. The multinomial logistic regression analysis has revealed that age, gender, access to credit, and access to extension services, significantly influenced the households’ decision behaviour to adopt soil conservation practices. Age, access to extension service, and access to water resource were significant predictors of adoption of water conservation practices; whereas age, educational status, and access to extension service were significant predictors of adoption of forest conservation practices. This study underscores the significance of institutional factors in driving the adoption of technology in the research area. It further recommends policies that prioritize the dissemination of information on effective strategies, improvement of access to extension services, water resources, and credit facilities to promote sustainable watershed management. This study is exceptional in its innovative approach, which explores the convergence of these vital conservation domains within the distinct setting of the upper Gelana watershed. Studying the adoption of these technologies is crucial for informing policy-making and designing effective interventions that promote sustainable watershed practices. In this case, the Ministry of Agriculture, and development agents should scale up the adoption of these practices and take remedial actions for those not yet adopted.

Nephrology Provider Engagement in Comprehensive Shared Decision-Making Discussions With Older Adults: Results of a Cross-Sectional Survey.

Background: The largest patient group initiating dialysis for end-stage renal disease is persons over 75 years. Evidence indicates pre-dialysis shared decision-making discussions between nephrology providers and older adults are infrequent and often do not include an option for conservative management of symptoms without dialysis. Aims: To assess and compare behavioral determinants of nephrology providers' use of comprehensive pre-dialysis shared decision-making discussions with older adults. A secondary aim was to prepare the bases for tailored behavioral intervention(s) for nephrology providers by identifying the determinant(s) that should be targeted to elicit change in comprehensive shared decision-making intentions and the resulting behavior. Methods: A cross-sectional survey based on the Theory of Planned Behavior was completed by nephrologists from a state organization and nephrology advance practitioners from a national organization, either in person or online via Qualtrics. Results: Survey respondents included nephrologists (n=29), and nurse practitioners (n=37) and physician assistants (n=7), combined, were predominantly female (83%) and White American (67%). Spearman rho correlation results showed attitude, social norm, and perceived behavioral control were correlated with shared decision-making behavior for one or both groups. Significant differences were present between the two groups for determinants and behaviors. Conclusion: Based on the determinants predominantly influencing SDM behavior of nephrology advance practitioners, behavioral interventions targeting the determinants of Social Norm and Perceived Behavioral Control are warranted. All determinants can be utilized in behavioral interventions for nephrologists.Opportunity exists for collaboration between providers to develop consistent and effective methods of SDM discussion that fit into a typical workday.

Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior.

Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior.

Measuring the performance of Antarctic Treaty decision-making.

Agreements reached at the Antarctic Treaty Consultative Meetings (ATCMs) are among the primary means for addressing Antarctic conservation and environmental protection issues. However, according to contemporary scholars, Antarctic Treaty decision-making is becoming increasingly unresponsive to the rising environmental challenges in the region. We assessed the performance of Antarctic Treaty decision-making by measuring the rate and diversity of decision-making over the last 6 decades. To measure the rate, we counted the number of inputs and outputs of ATCMs and calculated the time taken for legally binding outputs to enter into force. To measure diversity, we calculated the range of topics addressed by the inputs and outputs of ATCMs. The average number of agreements reached per ATCM increased from 1961 to 2022. Although the diversity of Antarctic topics discussed at ATCMs remained consistently high, the diversity of topics on which legally binding agreements were adopted declined significantly. Antarctic issues-including those of highest priority-are now almost entirely dealt with through nonbinding, soft-law agreements. It is plausible that this move away from binding decisions reflects a dynamic governance institution evolving to respond to new pressures. However, we suggest that the change reveals a concerning shift in decision-making behavior and performance, unique to the treaty's history. Soft law is beneficial in some cases, but its overuse diminishes accountability and transparency, significantly reducing the parties' abilities to understand and measure their performance, including the outcomes and impacts of decisions. Although the rate and diversity of ATCM inputs and outputs provide only a partial view of decision-making performance, the exploration of these metrics provides a foundation for asking essential questions about the impacts of Antarctic Treaty governance on the region's environmental protection and conservation.

Consumer Segmentation and Decision: Explainable Machine Learning Insights

ABSTRACT The e-commerce market is experiencing a period of rapid growth, with online shopping becoming a prevalent consumer behavior. However, the challenge of identifying and segmenting high-value customer groups, and subsequently enhancing the conversion rate of purchases made on the shop’s platform, has remained a significant obstacle for merchants engaged in e-commerce. This study presents a novel approach that combines consumer involvement theory, quality signaling theory and consumer heterogeneity theory to develop a multi-algorithmic, interpretable machine learning model based on shapely additive explanations (SHAP). The results revealed the existence of four distinct consumer groups: the comprehensively involved, interactive, reading-oriented, and low-involvement groups. Comprehensively involved and interactive consumers have the highest purchase conversion rate and should be given priority attention. This study addresses the limitations of single theoretical perspective and “black box” problem of machine learning models for decision-making behavior, and can bring management insights for merchants to improve shop operation.

Of two minds: an experiment on how time scarcity shapes risk-taking behaviour

ABSTRACT Several studies report that the brain evaluates prospects and executes decisions as the outcome of two types of mental processes: one described as slow and reflective and the other as fast and intuitive. We investigate how these two mental processes affect risk-taking behaviour in a one-shot decision by using time pressure to establish an intuitive response. We observe that time constraints do not change risk attitudes. Furthermore, it is only when subjects are given ample time to decide and instructed to reflect that they show the well-documented shift of risk preferences across the domain of losses and gains. Our results suggest that framing effects are the result of cognitive processes that become operative when individuals are granted sufficient time to undertake a more comprehensive analysis of decision tasks.