Value Theory Research Articles

Embracing new energy vehicles: An empirical examination of female consumer perspectives

New energy vehicles have emerged as a significant alternative in the ongoing effort to address the energy crisis. However, most consumers are still cautious about new energy vehicles, especially female consumers, whose increasing purchasing power has not been generally translated into recognition of new energy vehicles. How to increase female consumers' willingness to purchase new energy vehicles has become a focus of attention in the field of new energy management. This study integrates existing academic results and comprehensively explores the determinants of female consumers' willingness to purchase new energy vehicles by integrating the Unified Theory of Acceptance and Use of Technology, the Perceived Value Theory, and the Trust Theory. Specifically, this study uses structural equation modeling to analyze questionnaire data from 610 female consumers in Beijing. It was found that all five factors of the Unified Theory (performance expectation, effort expectation, social influence, facilitating condition, and hedonic motivation) significantly affect female consumers' purchase intentions for new energy vehicles. Meanwhile, female consumers' perceived value and trust act as effective mediators in purchase decisions, motivating them to choose new energy vehicles. This study contributes to the theoretical literature on new energy vehicles through a theory-driven methodology. In addition, this study delves into the factors influencing female consumers' propensity to purchase new energy vehicles, which helps us better understand female consumers' decision-making process for vehicle purchases. Finally, this study also extends the theoretical and empirical research on the marketing of new energy vehicles and provides valuable information for automakers and marketers.

A Credibility Framework for Extreme Value-at-Risk

Value-at-risk estimates derived from extreme value data by fitting fat-tailed distributions can be so large that their validity is open to question. In this paper, an objective criterion, and a framework from which it was developed, are presented in order to decide whether or not a fitted distribution is inappropriate for the purpose of value-at-risk calculation. That criterion is based on established extreme value theory (principally the Pickands-Balkema-deHaan Theorem), which is used to calculate a sequence of reference value-at-risk estimates using Generalised Pareto distributions. Those estimates are used to develop a closed-form formula for calculating a theoretical ’maximum’ value-at-risk. The method is validated by generating 100 random data sets and testing them against the framework for varying input parameter values. Approximately 75% of those cases passed the validation test.

Optimal dynamic climate adaptation pathways: a case study of New York City

AbstractAssessing climate risk and its potential impacts on our cities and economies is of fundamental importance. Extreme weather events, such as hurricanes, floods, and storm surges can lead to catastrophic damages. We propose a flexible approach based on real options analysis and extreme value theory, which enables the selection of optimal adaptation pathways for a portfolio of climate adaptation projects. We model the severity of extreme sea level events using the block maxima approach from extreme value theory, and then develop a real options framework, factoring in climate change, sea level rise uncertainty, and the growth in asset exposure. We then apply the proposed framework to a real-world problem, considering sea level data as well as different adaptation investment options for New York City. Our research can assist governments and policy makers in taking informed decisions about optimal adaptation pathways and more specifically about reducing flood and storm surge risk in a dynamic settings.

System reliability analysis of Cable-stayed bridge using pair Copula and ICLHS with unequal weights under seismic loading

Considering the correlation between different components, quantifying the failure probability of a high-level hyper-static structure under non-stationary seismic loading is of great significance in guiding earthquake-resistant design and fortification of bridges. In this study, a high-pier and large-span railway cable-stayed bridge in the mountainous region were employed to evaluate the seismic performance. First, the basic theoretical methods of the non-stationary random seismic motion simulation, equivalent extreme value theory based on extreme value distribution (EVD), improved Latin hypercube sampling (ICLHS) with unequal weighted fractional moment estimation and Pair Copula technique are presented. Second, the finite element analysis model of the cable-stayed bridge was established based on the OpenSees platform, the seismic response samples of the bridge were calculated under three levels of seismic ground motion to determine the first passage failure probability of the main components of the bridge under the design failure modes. Finally, the Pair Copula function was introduced to estimate the seismic system reliability of the cable-stayed bridge. The optimal Copula type that can describe the correlation between the bridge components was selected through goodness-of-fit tests such as AIC, BIC, and the minimum distance method. The failure probability of the entire system was obtained by calculating the selected Copula function type under seismic loading.

The role of political orientation and value framing in carbon pricing acceptance: Evidence from a representative sample

Carbon pricing is a policy instrument for mitigating climate change. For such climate policies to be enforceable and effective, citizen support is crucial. We examined how values and political orientation relate to people's acceptance of carbon pricing using Schwartz's theory of basic human values and a representative online survey of 4091 Austrian residents. The results showed that liberals were more in favor of carbon pricing than conservatives. This effect can partly be explained by the value of universalism (i.e., the value of the welfare of all people and nature) which is more important to liberals than to conservatives. However, presenting messages that emphasized the need for carbon pricing and were framed in accordance with values that fit participants' political orientation did not influence how much people accepted carbon pricing. We discuss the theoretical and practical implications of our findings and suggest directions for future research.

Extreme value analysis of the number of student absences in Jiangsu, China: Based on extreme value theory.

Attendance absences have a substantial impact on student's future physical and mental health as well as academic progress. Numerous personal, familial, and social issues are among the causes of student absences. Any kind of absence from school should be minimized. Extremely high rates of student absences may indicate the abrupt commencement of a serious school health crisis or public health crisis, such as the spread of tuberculosis or COVID-19, which provides school health professionals with an early warning. We take the extreme values in absence data as the object and attempt to apply the extreme value theory (EVT) to describe the distribution of extreme values. This study aims to predict extreme instances of student absences. School health professionals can take preventative measures to reduce future excessive absences, according to the predicted results. Five statistical distributions were applied to individually characterize the extreme values. Our findings suggest that EVT is a useful tool for predicting extreme student absences, thereby aiding preventative measures in public health.

CMIP6 Models Rarely Simulate Antarctic Winter Sea‐Ice Anomalies as Large as Observed in 2023

AbstractIn 2023, Antarctic sea‐ice extent (SIE) reached record lows, with winter SIE falling to 2.5Mkm2 below the satellite era average. With this multi‐model study, we investigate the occurrence of anomalies of this magnitude in latest‐generation global climate models. When these anomalies occur, SIE takes decades to recover: this indicates that SIE may transition to a new, lower, state over the next few decades. Under internal variability alone, models are extremely unlikely to simulate these anomalies, with return period >1000 years for most models. The only models with return period <1000 years for these anomalies have likely unrealistically large interannual variability. Based on extreme value theory, the return period is reduced from 2650 years under internal variability to 580 years under a strong climate change forcing scenario.

We are Living on the Edge: Managing Extreme‑Severity Claims Using Extreme Value Theory

Claims with high severity and low probability constitute a high risk for the insurance market. One tool to deal with this kind of event is the Extreme Value Theory (EVT). The main goal of this article is to apply the EVT to Actuarial Science using a different estimator for parameters, allowing the calculation of pure reinsurance premiums and the choice for the retention limit for insurance companies. The execution was split into two parts: (i) comparing the estimators through simulations, and; (ii) using data from 5 SUSEP lines of business with different natures, intending to estimate some extreme value statistics. In simulation studies, the Pickands estimator was very promising, but the limited amount of data resulted in a great variance when applied to real cases. Lastly, we concluded that the EVT is a powerful tool for insurance, capturing the behavior of extreme clams amount better than traditional models

Extreme Treatment Effect: Extrapolating Dose-Response Function into Extreme Treatment Domain

The potential outcomes framework serves as a fundamental tool for quantifying causal effects. The average dose–response function μ(t) (also called the effect curve) is typically of interest when dealing with a continuous treatment variable (exposure). The focus of this work is to determine the impact of an extreme level of treatment, potentially beyond the range of observed values—that is, estimating μ(t) for very large t. Our approach is grounded in the field of statistics known as extreme value theory. We outline key assumptions for the identifiability of the extreme treatment effect. Additionally, we present a novel and consistent estimation procedure that can potentially reduce the dimension of the confounders to at most 3. This is a significant result since typically, the estimation of μ(t) is very challenging due to high-dimensional confounders. In practical applications, our framework proves valuable when assessing the effects of scenarios such as drug overdoses, extreme river discharges, or extremely high temperatures on a variable of interest.

Is checkworthiness generalizable? Evaluating task and domain generalization of datasets for claim detection

The spread of misinformation has reached a level at which neither research nor fact-checkers can monitor it only manually anymore. Accordingly, there has been much research on models and datasets for detecting checkworthy claims. However, the research in NLP is mostly detached from findings in communication science on misinformation and fact-checking. Checkworthiness is a notoriously vague concept whose meaning is contested among different stakeholders. Against the background of news value theory, i.e., the study of factors that make an event relevant for journalistic reporting, this is not surprising. It is argued that this vagueness leads to inconsistencies and poor generalization across different datasets and domains. For the experiments, models are trained on one dataset, tested on the remaining, and evaluated against the results on the original performance, against a random baseline, and against the scores when the models are not trained at all. The study finds that there is a drastic reduction in comparison with the performance on the original dataset. Moreover, often the models are outperformed by the random baseline and training on one dataset has no or even a negative impact on the performance on the other datasets. This paper proposes that future research should abandon this task design and instead take inspiration from research in communication science. In the style of news values, Claim Detection should focus on factors that are relevant for fact-checkers and misinformation.

Exploring competency: Corporate framework, learning theories, and a cognitive development model

This review paper delves into the cognitive aspects of corporate learning and competency development, focusing on various theories and models explaining neurocognitive and behavioral psychology. This paper explores “Maslow's hierarchy of needs”, “Affective, Behavioral, and Cognitive (ABC) Model of Attitude”, “Expectancy Value Theory”, “NLP Communication Model”, “Social Cognitive Learning Theory”, “Knowles' theory of Andragogy”, “NLP Neurological Levels”, “Theory of Planned Behavior”, "Cognitive Load Theory", "Cognitive Learning Principles Underlying the 5E Model", and "Constructivism Theory". While these theories individually address specific aspects of human behavior, they have limitations in their individual scope when applied to the corporate competency development process. This paper constructs the "Cognitive Competency Development Model (CCDM)", which integrates the examined theories and models, providing a cohesive framework for understanding competency development within organizations. This paper briefly discusses the meaning of competency and a typical competency development framework used by corporates. The CCDM emphasizes the multifaceted nature of competency development. It considers the intricate interplay of individual, social, and environmental factors in shaping competency development. It begins with the mental filtering of learning stimuli by various expectancies, neurological factors, and hierarchies of needs. This filtered information forms a perception and knowledge construct, leading to competency development. Competency may affect attitudes and intentions, resulting in behavioral change and better performance at work. The paper concludes by highlighting the potential of the model to inform the design of more effective learning and development interventions. By understanding how the brain influences learning and development, organizations can improve their training programs and create a culture of continuous learning.

Knowledge-Production, Digitalization and the Appropriation of Surplus-Knowledge

ABSTRACT According to some commentators, the introduction of digital technologies into the sphere of knowledge-production and the consequent digitalization and computerization of this field have radically changed the nature of knowledge-producing activity and the produced knowledge. At the social level, such a change has allegedly caused the irrelevance of grand narratives and emergence of paralogies or the obsolescence of the labour theory of value, which in its turn signifies the end of capitalism. It is argued that such accounts simply disregard the social form of production of knowledge. The real revolutionary effect of digitalization, in resemblance to the introduction of machinery in large-scale industrial production, is intensification of knowledge-producing labour, which in its turn signifies the various forms of subsumption of knowledge-producing activity under capital and the appropriation of the commonly produced surplus-knowledge by it.

Identification of critical conflicts and its implementation in microsimulation for effective safety assessment in unsignalised intersections

Traffic safety assessment through microsimulation using Surrogate Safety Measures (SSM) has gained significant attention. Despite researchers proposing effective approaches for critical conflict identification in manual safety assessment, integrating these approaches into microsimulation is yet to be explored. This study provides a methodology for implementing critical conflict identification into microsimulation and calibrating and validating the models with selected SSM’s distribution as Measure of Effectiveness (MoE). Post Encroachment Time was used to assess the safety of crossing and merging traffic in unsignalized intersections. Genetic Algorithm was used to calibrate the model with PET distribution of the identified critical conflicts as MoE. The calibrated models were used to investigate the effectiveness of various preventive measures after validation. Roundabout was found to be reasonably effective in terms of crash frequencies estimated using Extreme Value Theory as it reduces the speed of the crossing and merging traffic and channelises the traffic in the study location.

Customer switching intention from home delivery to smart locker delivery: Evidence from Vietnam

The purpose of this study is to uncover evidence on the factors influencing switching intention from home delivery to smart lockers in the last-mile delivery service. The research model is constructed based on the Pull-Push-Mooring Theory and the Customer Perceived Value Theory using structural equation modelling to analyze data collected from 557 smart locker users in Vietnam. The results indicated a significantly positive influence of pull factors (convenience, environmental friendliness, and security) and push factors (delivery failure experience and risk), and confirmed that mooring factors (habit and switching cost) negatively impacted customers’ intention to switch from home delivery to smart lockers. The study also revealed that mooring factors moderate the relationship between pull factors and the intention to switch. Moreover, gender, age and frequency of shopping online are significant to switching intention, and usefulness mediates between them and switching intention. Several managerial implications were suggested for stakeholders in order to enhance customers’ switching intentions to use smart lockers, thereby improving the quality, efficiency, and sustainability of the last-mile delivery service in the future.

Investigating and modeling motorcycle crash rates at urban unsignalized intersections in India

Objective The present study focuses on understanding the behavior of motorized 2-wheeler (MTW) riders at urban unsignalized intersections in India. In the Indian context, over 60% of road crash fatalities are attributed to vulnerable road users, with MTWs serving as the predominant contributors, accounting for 44% of total fatalities. Notably, unsignalized intersections have emerged as critical sites for accidents involving vulnerable road users. Methods Postencroachment time is used to assess traffic conflicts of MTW users. Furthermore, the study employs the exceedance property of extreme value theory to calculate crash probabilities. Tobit and grouped random parameters Tobit regression models are developed to model crash probabilities, incorporating variables such as traffic volume, traffic composition, gap acceptance time, intersection characteristics, and intersection conflict area at 4 urban unsignalized intersections in Surat, India. Results MTW riders have the lowest gap acceptance time among vehicles in the traffic stream. Cars and other heavy vehicles readily accept gaps when MTWs are in the conflicting stream at unsignalized intersections, which increases traffic conflicts. MTWs have the highest crash rates in the traffic stream. Among the developed models, the grouped random parameters Tobit regression captures the spatial unobserved heterogeneity of the study sites and outperforms the simple Tobit regression model. The results also indicate that MTW riders are exposed to a higher risk of crashes while turning at unsignalized intersections. The presence of a central traffic island has varied implications; it raises crash rates at 3-legged intersections but lowers them at 4-legged intersections for 2-wheelers. Conclusion The study concludes that MTW crash rates are influenced by traffic and intersectional factors. Increased gap acceptance time correlates with lower crash rates. Countermeasure selections require detailed investigations, because it was observed that the presence of central traffic islands has varied effects on crash rates at 3-legged and 4-legged unsignalized intersections.

Extreme value theory applied to long-term sunspot areas

Extreme value theory applied to long-term sunspot areas

Exploring the Impact of Online Teaching Factors on International Students’ Control-Value Appraisals and Achievement Emotions in a Foreign Language Context

Drawing upon Pekrun’s (2006) control–value theory of achievement emotions, the present study explores the relations between online teaching factors (i.e., teachers’ information and communications technology (ICT) competence and provision of structure) and students’ control–value appraisals and achievement emotions (i.e., enjoyment, boredom, and anxiety) in an online foreign language learning context. Data were collected through semi-structured interviews with 12 international students and 6 teachers who were involved in a Chinese foreign language course delivered online during the coronavirus pandemic. Results from the thematic analysis showed that both students’ and teachers’ positive evaluations of teachers’ ICT competence and provision of structure were associated with students’ increased control beliefs and intrinsic value, but not necessarily with utility value. According to the participants, the two increased appraisals related to students’ greater enjoyment and reduced feelings of boredom and anxiety during the online language learning process. Findings are discussed in light of the role of online teaching factors in shaping students’ appraisals and emotional experiences in online environments, particularly during the coronavirus pandemic.

“Tell me who your friends are, and I will tell you who you are:” The contribution of peers to adolescents' values

AbstractValues serve as guiding principles, motivating specific behaviors, and actions. Peers spend a considerable amount of time together, thus offering a unique platform for the acquisition and development of values. During adolescence, a stage of life when youth are actively defining their identities, peers emerge as vital social agents, contributing significantly to the process of value formation. In this article, I examine the ways peers contribute to the development of youth's values, with a focus on basic personal values theory. This theory sheds light on how and why peers play a role in shaping the values of adolescents. It examines the phenomenon of similarity in peer values (values similarity) and explores how factors such as gender, age, and culture act as potential moderators in the relation between peers' and youth's values similarity. I conclude by summarizing key insights and proposing directions for research.

Paradigms and conceptions of theories of social justice, equity, and values

The new times, convulsive, characterized by an international crisis and other factors that aggravate social stability and in fact equitable and just coexistence for society. They make the human mind evolve and transform in search of well-being and quality of life according to the satisfaction of their needs, they need more knowledge to help them survive these ills that affect them. It is here that there is a proliferation of studies to increase the knowledge of social justice, equity and values because of their impact on the life of man and the importance of his understanding and comprehension. Respect for diversity, equity, the scarcity of resources, the protection of animals, women's rights, and the need to protect the most vulnerable groups call for a deeper understanding of these issues from a scientific perspective and to form a comprehensive culture that positions them in better circumstances in defence of their rights. The essential objective of social justice is to achieve equal opportunities for all, based on taking into account diversity and needs, both individual and common. The objective of this article is to carry out a theoretical and descriptive analysis that shows the different paradigms and conceptions of the theories of social justice, equity and values, which society demands so much.

Multivariate Extreme Value Theory Based Channel Modeling for Ultra-Reliable Communications

Multivariate Extreme Value Theory Based Channel Modeling for Ultra-Reliable Communications