Level Of Uncertainty Research Articles

Exploring peer-to-peer lending: key influences of firm uncertainty, loan features and venture quality

PurposeThe purpose of this study is to identify the determinants of success in peer-to-peer (P2P) lending campaigns, especially amid global financial disruptions like the COVID-19 pandemic. Addressing a notable gap in current research, we explore how factors such as firm uncertainty, loan characteristics (interest rates and maturity) and venture quality (human, social and intellectual capital) influence P2P lending effectiveness. Using multiple regression analysis on data from 523 projects on the October platform, our study aims to enhance the understanding and operational efficiency of P2P platforms, contributing to a more resilient financial ecosystem.Design/methodology/approachThis study employs a quantitative research design using multiple regression analysis to examine the impact of specific variables on the success of P2P lending campaigns. Data were collected from 523 concluded P2P lending projects on the October platform, spanning from 2015 to 2021. Variables of interest include the level of uncertainty of the firm, loan characteristics such as interest rate and maturity and the quality of the venture assessed through human, social and intellectual capital. This method allows for a robust analysis of the factors contributing to the success of P2P lending within a dynamic financial context.FindingsThe findings of this study reveal that the success of P2P lending campaigns is significantly influenced by the level of uncertainty of the firm, the interest rate of the loan and the quality of the venture. Specifically, higher uncertainty in firms correlates negatively with campaign success, while competitive interest rates positively impact funding outcomes. Furthermore, ventures that demonstrate robust human capital, particularly those with management teams that possess diverse skills and high qualifications, tend to attract more funding. These results underscore the critical role of strategic financial and human resource planning in enhancing the effectiveness of P2P lending platforms.Originality/valueThis study contributes uniquely to the literature by integrating multiple variables – firm uncertainty, loan characteristics and venture quality – into a comprehensive analysis of success factors in P2P lending. It addresses the scarcity of research examining the combined effects of these factors, particularly in the context of global financial disruptions like the COVID-19 pandemic. By focusing on a specific European platform during a dynamic period, this research provides new insights into how P2P lending can adapt to and thrive amid financial crises. The findings offer valuable guidance for both practitioners and policymakers aiming to optimize P2P lending practices in uncertain economic landscapes.

Relationship Between Emotional Intelligence, Educational Achievement and Academic Stress in College Students: An Observational Study

Background and Objectives: In today’s rapidly changing and interconnected world, where individuals often face high levels of stress, uncertainty, and interpersonal dynamics, Alpas’s framework suggests that emotional intelligence plays a crucial role in helping people thrive. By developing emotional intelligence skills, such as self-awareness, self-regulation, empathy, and social skills, individuals can better manage stress, communicate effectively, build strong relationships, and make sound decisions even in challenging circumstances. Methods: A cross-sectional study was conducted among college students. Participants who were willing to take part in the study were included, considering both males and females. A total sample of 134 students was taken from the NIMS College of Physiotherapy. Emotional intelligence was assessed using the WONG scale, while educational achievement was measured by the total percentage obtained in the last semester. Academic stress was evaluated using the Student Inventory Scale. A Google Form was created, and only those willing to participate in the study filled out the form. Results: There was a negative correlation between emotional intelligence and academic stress (r=1.000), academic stress and percentage (r=-0.62), and percentage and emotional intelligence (r=-0.67). Conclusion: The present study concludes that emotional intelligence, academic stress, and educational achievements are negatively correlated with each other. It has been observed that students experiencing stress tend to exhibit lower emotional intelligence, which ultimately leads to poorer academic performance.

An Integrated Stochastic Optimization and Simulation Approach to SERU vs. Assembly Line Manufacturing Systems

This research compares SERU manufacturing systems to traditional assembly lines, focusing on the impact of uncertainty in task processing time on production output. The study considers worker skill levels and team identity, using a stochastic mixed integer linear programming approach to model uncertainty and optimize workforce allocation. Discrete event simulation is then integrated to evaluate performance using five key performance indicators (KPIs). Results show that SERU systems outperform traditional lines in terms of throughput when uncertainty is considered. The integrated approach provides more reliable performance data than deterministic optimization alone. The study also highlights the advantages of SERU systems when worker skill levels and team identity are factored in. This research fills a gap in the literature by proposing a stochastic optimization approach that considers uncertainty and worker skill levels, and by integrating stochastic optimization with simulation for comprehensive analysis. This approach provides valuable guidance for production managers in optimizing production systems.

Social capital, psychological contract fulfillment and buyer-supplier collaboration: the moderating roles of guanxi orientation and market uncertainty

Purpose Drawing on signaling theory and social capital theory, this study aims to examine the underlying mechanisms and contingencies of the relationships between social capital (SC) and collaboration in buyer-supplier relationships (BSRs). This is achieved by evaluating the mediating effect of psychological contract fulfillment (PCF) and the moderating roles of guanxi orientation and market uncertainty. Design/methodology/approach The current study used a survey method to collect data from 271 buyers in China. Structural equation modeling (SEM) and moderated regression analysis were applied to examine the hypotheses. Findings The positive effect of structural and relational SC on buyers’ collaborative behaviors is partially mediated by buyers’ PCF. In contrast, the positive effect of cognitive SC on collaboration is fully mediated by buyers’ PCF. Guanxi orientation strengthens the indirect effect of buyers’ PCF on the cognitive SC-collaboration relationship and relational SC-collaboration relationship. Market uncertainty amplifies the relational SC-collaboration relationship. Originality/value Prior studies have presented mixed evidence of the effect of SC on collaboration and have paid little attention to the underlying mechanisms and conditions moderating the effect. This research proposes a theoretical model that integrates signaling theory and social capital theory to explore how three dimensions of SC can enhance buyer collaboration through buyers’ PCF under different levels of guanxi orientation and market uncertainty.

Uncertainty-Aware Prediction of Remaining Useful Life in Complex Systems

Accurate prediction of the remaining useful life (RUL) of industrial systems is critical to ensuring smooth operation and safety. Various prognostic methods have been developed, but significant challenges remain for field applications. While many methods may achieve high accuracy, they often fall short in quantifying the uncertainty of their predictions. Without uncertainty quantification, it is difficult to assess the confidence level of the prognostic results. Therefore, it is essential to transparently present the uncertainty levels in the predicted results. This Ph.D. project aims to develop novel uncertainty-aware methods for RUL prediction of complex systems. The project will address the following situations where it is more and more uncertain: (a) propose a general framework for data-driven RUL methods to quantify uncertainty and generate adaptive confidence intervals under a single fault mode and a single operating condition; (b) consider both epistemic and aleatoric uncertainties in scenarios with multiple fault modes and multiple operating conditions and then calibrate uncertainty to enhance their accuracy; (c) explore how to predict RUL and quantify uncertainty when there are no run-to-failure data and RUL labels in practice; (d) handle uncertainty propagation from the component level to the system level. Through this research, the project will provide more reliable and comprehensive solutions for RUL prediction in complex systems.

How do analyst recommendations on banks respond to monetary policy news? An application to the Eurozone

ABSTRACT We assess the extent to which stock analysts consider European Central Bank monetary policy decisions when formulating their investment recommendations on European banks from 2002 to 2020. To identify monetary policy shocks, we extract monetary policy factors from high-frequency rate movements surrounding monetary policy announcements. Our findings indicate that stock analysts are more concerned with non-standard (or unconventional) monetary policy measures than standard policy measures. We also demonstrate that analysts interpret restrictive (accommodative) monetary surprises as positive (negative) news for bank financial health, particularly during periods characterized by high levels of financial uncertainty. Finally, we emphasize that the reaction of stock recommendations to rate- and forward guidance-related decisions is primarily influenced by the implicit disclosure of information on the economic outlook by the ECB along with its monetary policy decision.

Does the positioning of the patient in the EOS machine affect the validity of cervical and head parameters calculated on whole-spine radiographs?

The aim was to study the validity of free-standing position using modifiers for assessing cervical spine alignment. A total of 26 asymptomatic subjects (45 ± 11years) and 13 with spinal deformities (30 ± 21years) underwent lateral radiographs using EOS®, while adopting 3 different positions, in 2 sequences, without mirror and then with mirror (m) placed in front of them: first, the neutral standing position (upper extremities positioned on the sides of the body, P1 and P1m), then free-standing (P2 and P2m) and modified free-standing (P3 and P3m) positions (shoulders flexed at 90° and fingers placed on clavicles then cheeks, respectively). Participants were asked not to move their trunk and shoulders when moving from P1 to P2 and then P3. Eighteen cervical radiographic parameters were calculated from the 6 radiographs. The effects of shoulder flexion, mirror placement, and finger positioning on cervical alignment were studied using a repeated-measures ANCOVA. Mirror placement had no effect on cervical alignment. Concerning the effect of shoulder flexion, C2–C7 and T1 slopes were significantly decreased in P2 and P2m (C2–C7 = 10° and 8°; T1 slope = 26° and 24°, respectively) and P3 and P3m (C2–C7 = 9° and 9°; T1 slope = 25° and 26°, respectively) compared to P1 and P1m (C2–C7 = 14°and 14°; T1 slope = 29°and 30°, respectively), without exceeding the uncertainty level. Assessment of cervical alignment is valid on whole-spine radiographs when the shoulders are flexed (free-standing position). Clear instructions regarding the shoulder position at 90°, without moving the trunk, neck, and head before placing the fingers on the clavicles or cheeks, can ensure a cervical alignment comparable to that obtained with the neutral position, which is considered the most physiological one.

Three diverse motives for information sharing

Knowledge is distributed over many individuals. Thus, humans are tasked with informing one another for the betterment of all. But as information can alter people’s action, affect and cognition in both positive and negative ways, deciding whether to share information can be a particularly difficult problem. Here, we examine how people integrate potentially conflicting consequences of knowledge, to decide whether to inform others. We show that participants (Exp1: N = 114, Pre-registered replication: N = 102) use their own information-seeking preferences to solve complex information-sharing decisions. In particular, when deciding whether to inform others, participants consider the usefulness of information in directing action, its valence and the receiver’s uncertainty level, and integrate these assessments into a calculation of the value of information that explains information sharing decisions. A cluster analysis revealed that participants were clustered into groups based on the different weights they assign to these three factors. Within individuals, the relative influence of each of these factors was stable across information-seeking and information-sharing decisions. These results suggest that people put themselves in a receiver position to determine whether to inform others and can help predict when people will share information.

Talking about strategy: the role of epic plot structures in international contracting

Emerging international markets are frequently characterized by high levels of economic and political uncertainty. Such inherent uncertainties render established ideas of competitive positioning problematic. An alternative perspective is developed which focuses on the day-to-day enactment of strategy through the medium of narrative. Narrative interviews with senior managers from Turkish international contracting firms provide insights into the contested storylines of strategy making. The plot structures mobilized are seen to comprise strategy making “in flight”. Senior managers are further held continuously to test emergent narratives against different audiences for the purposes of enhancing their persuasiveness. The aim is to instil an overall sense of coherence and direction among those with whom they interact. The temporal narratives mobilize epic plot structures from the past for the purposes of ascribing Turkish contractors with heroic identities in their quest for a desirable future. Yet the shared narratives also serve as a means of identity work through which the narrators strive to position themselves within their networks as acknowledged strategy makers. The espoused plot structures are further seen to coalesce to form a narrative infrastructure with direct implications for the future enactment of strategy. The research focus on the essential temporality of strategy narratives has important implications beyond the specific context of enquiry.

THE USE OF METAPHORICAL ASSOCIATIVE CARDS FOR WORK WITH BUSINESS DURING TRANSFORMATION

The article aims to analyze the possibilities of using metaphoric associative cards for working with businesses and organizations. Metaphoric associative cards could be used as an additional tool in counseling to provide the ability to work simultaneously within the emotional, mental, and physical dimensions of the client, creating a secure space for reflections during the team sessions and enhancing creativity during brainstorming. Enterprise organizations and small and medium businesses overcoming market challenges, and financial changes should adapt to any scenarios and transform rapidly by representing innovative solutions (new products, services, processes, opening new markets, going through M&A, etc.). Such transformations bring anxiety for an organization and all employees as the level of uncertainty is high and bringing clear communications to the company culture, developing space for reflection is critical (Giernalczyk, Lohmer, 2012). To discover potential scenarios of strategic development, working with a portrait of the customers for improvement for customer journeys, discovering blockers or potential root causes of new project initiations working with metaphors through the session with MAC could be helpful. Practical techniques that could be used during the work on requests of organizations described.

Direct CKM determination from W decays at future lepton colliders

We project the reach of future lepton colliders for measuring CKM elements from direct observations of W decays. We focus our attention to |Vcs| and |Vcb| determinations, using FCC-ee as case study. We employ state-of-the-art jet flavor taggers to obtain the projected sensitivity, and scan over tagger performances to show their effect. We conclude that future lepton collider can sizeably improve the sensitivity on |Vcs| and |Vcb|, albeit the achievable reach will strongly depend on the level of systematic uncertainties on tagger parameters.

Permutation Entropy for Signal Analysis

Shannon Entropy is the preeminent tool for measuring the level of uncertainty (and conversely, information content) in a random variable. In the field of communications, entropy can be used to express the information content of given signals (represented as time series) by considering random variables which sample from specified subsequences. In this paper, we will discuss how an entropy variant, the \textit{permutation entropy} can be used to study and classify radio frequency signals in a noisy environment. The permutation entropy is the entropy of the random variable which samples occurrences of permutation patterns from time series given a fixed window length, making it a function of the distribution of permutation patterns. Since the permutation entropy is a function of the relative order of data, it is (global) amplitude agnostic and thus allows for comparison between signals at different scales. This article is intended to describe a permutation patterns approach to a data driven problem in radio frequency communications research, and includes a primer on all non-permutation pattern specific background. An empirical analysis of the methods herein on radio frequency data is included. No prior knowledge of signals analysis is assumed, and permutation pattern specific notation will be included. This article serves as a self-contained introduction to the relationship between permutation patterns, entropy, and signals analysis for studying radio frequency signals and includes results on a classification task.

Uncertainty analysis of digital twin model of mine ventilation system

The digital twin model of mine ventilation system (DTMVS) plays an important role in intelligent safety management. However, the uncertainty of the ventilation resistance coefficient, which is the core parameter of the model, makes it challenging to accurately construct a DTMVS. In this study, Latin Hypercube Sampling (LHS) and ventilation resistance coefficient estimation models (VRCEMs) are used to analyze the uncertainty. First, the LHS method was used to explore the effect of uncertainty in the simulated airflow by continuously increasing the level of uncertainty in the ventilation resistance coefficients. Subsequently, the ventilation resistance coefficients were estimated using the VRCEMs, and the uncertainty of the ventilation resistance coefficient and the simulated airflow was analyzed. The results showed that the ventilation resistance coefficients with a 5% coefficient of variation can cause the DTMVS to lose 34% of the real airflow data points. The degree of uncertainty in the ventilation resistance coefficients estimated by the VRCEM-GA (VRCEM using genetic algorithm) and VRCEM-DE (VRCEM using differential evolution algorithm) methods was enhanced by 27.4% and 4.4%, respectively, compared with VRCEM-ES (VRCEM using evolutionary strategy algorithm). The VRCEM-ES model had the least influence on the uncertainty of the simulated airflow of DTMVS. The simulated airflow of the DTMVS constructed based on VRCEMs fluctuated normally within the confidence interval. VRCEMs had a higher sensitivity to the ventilation resistance coefficients of branches with low coefficients of variation.

When does group chat promote cooperation in shared resource governance?

When people use shared resources, overextraction can occur. While deliberation tends to mitigate shared resource exploitation problems, the question remains: under what conditions does group chat improve cooperation in shared resource dilemmas? This study analyzes chat and game data from about 1500 rounds of gameplay involving 143 groups across 4 resource types using Sentiment Analysis and Structural Topic Model. We find that, despite their fundamental differences, the 4 games tend to have similar discussions, including strategizing actions, coordinating choices, and socialization, but they differ in which topics explain cooperation within each game. Discussion topics promoting cooperation include coordination in the foraging game (FOR) and long-term goals in the groundwater game (GG). However, discussion topics negatively associated with cooperation include off-topic/socialization in FOR and the irrigation game (IRR) and crop choice affirmation in GG. We suggest that the context in which communication occurs matters, and biophysical characteristics, rules of the game, and levels of uncertainty explain some variations of our findings.

Cognitive ability in matching with strategic uncertainty: An experimental study

Strategyproof mechanisms have become the predominant choice for educational institutions. However, both laboratory experiments and empirical evidence highlight the persistent occurrence of strategic misreporting, leading to adverse consequences. This underscores the need to examine the reporting strategies of students, particularly those with varying cognitive abilities, across different decision environments. We present an experimental comprehension test of reporting strategies using computerized opponents to precisely control the levels of strategic uncertainty. The results reveal that removing strategic uncertainty does not significantly affect truth-telling rates. However, subjects with median cognitive ability are more truthful under strategic uncertainty, especially when information is incomplete. Additionally, providing information about priorities has a negative and significant impact on truth-telling rates. The findings of this research demonstrate that constructing a simplified market environment using computerized agents and providing relevant feedback to students can effectively enhance their understanding of the matching mechanism and nudge them to make optimal strategy choices.

Application of Multi-criteria Decision-making Methods for Forensic Analysis of Mechanical Parts in Vehicle Accidents using q-Rung Orthopair Fuzzy Numbers

This study explores the application of a Multi-Criteria Decision-Making (MCDM) approach based on q-rung orthopair fuzzy numbers to identify the origin of mechanical parts found at vehicle accident scenes. The primary objective is to determine the most likely vehicle to which these parts belong by evaluating key criteria such as compatibility, damage level, serial number matching, and color compatibility. Q-rung orthopair fuzzy numbers offer an advanced method to handle the inherent uncertainty and vagueness associated with forensic evaluations, particularly in scenarios where data is incomplete or imprecise. The proposed methodology involves defining the criteria, assigning membership and non-membership degrees using q-rung orthopair fuzzy sets, and applying an aggregation process to effectively rank alternatives. This approach facilitates flexible decision-making by accommodating different levels of confidence and uncertainty, making it particularly suitable for forensic applications where evidence is often ambiguous. The findings demonstrate that integrating q-rung orthopair fuzzy numbers into the MCDM framework significantly enhances the accuracy and reliability of identifying vehicle components involved in accidents. The proposed methodology provides a systematic tool to support forensic investigations, aiding in the determination of liability and contributing to more robust outcomes in mechanical evidence analysis.

STRUCTURAL BREAKS AND VOLATILITY PERSISTENCE IN HOTEL OCCUPANCY RATE

Purpose: Although Singapore has been one of the most attractive destinations for international tourists over the past 20 years, little evidence exists on the dynamics of its hotel occupancy rate. Our study aims to extend this scarce literature Design/Methodology/Approach: The present study employs time-varying volatility processes (e.g., GARCH process) and models the uncertainty associated with the hotel occupancy rate in Singapore. Findings: The main results indicate that shocks to the occupancy rate have a long run persistence and that there are asymmetric impacts in the occupancy rate. Further analysis suggests that structural breaks emanating from extreme news or shocks exert substantial impacts on the level of uncertainty for hotel occupancy rate. Originality/Value: Managers in the hotel industry could benefit from our findings while adopting active measures to reduce risk in tourism demand and hotel occupancy and attract more international tourists.

Optimizing Thermoplastic Starch Film with Heteroscedastic Gaussian Processes in Bayesian Experimental Design Framework

The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently failing to identify optimal material formulations. As an alternative, adaptive experimental design methods based on Bayesian optimization (BO) principles have been recently proposed to streamline material development by iteratively refining experiments based on prior results. However, most implementations are not suited to manage the heteroscedastic noise inherently present in physical experiments. This work introduces a heteroscedastic Gaussian process (HGP) model within the BO framework to account for varying levels of uncertainty in the data, improve the accuracy of the predictions, and increase the overall experimental efficiency. The aim is to find the optimal TPS film composition that maximizes its elongation at break and tensile strength. To demonstrate the effectiveness of this approach, TPS films were prepared by mixing potato starch, distilled water, glycerol as a plasticizer, and acetic acid as a catalyst. After gelation, the mixture was degassed via centrifugation and molded into films, which were dried at room temperature. Tensile tests were conducted according to ASTM D638 standards. After five iterations and 30 experiments, the films containing 4.5 wt% plasticizer and 2.0 wt% starch exhibited the highest elongation at break (M = 96.7%, SD = 5.6%), while the films with 0.5 wt% plasticizer and 7.0 wt% starch demonstrated the highest tensile strength (M = 2.77 MPa, SD = 1.54 MPa). These results demonstrate the potential of the HGP model within a BO framework to improve material development efficiency and performance in TPS film and other potential material formulations.

Reassessing the proper motions of M31/M33 with Gaia DR3. Unravelling systematic uncertainties

We provide an updated inference of the proper motion of M31 using the Gaia DR3 proper motions of bright stars from the disc of M31. By refining the motion of the quasar reference frame, and statistically accounting for the variations in the inferred proper motions obtained across different regions of M31, we demonstrate that these inconsistencies most likely arise from systematic uncertainties. Our updated favoured values for the proper motion of M31 are $46.9 stat sys in the right ascension direction and $-29.1 stat sys in the declination direction, the systematics being determined at a 90<!PCT!> confidence level (the values for M33 are given in the paper). This clearly highlights that the systematics are the dominant source of uncertainty, their magnitudes being comparable to the proper motion of M31 itself. The analysis conducted using Gaia DR2 instead of DR3 revealed that a net reduction in these systematic uncertainties occurred between the two data releases. If similar progress is made with the upcoming DR4, the future Gaia -based estimates could match the uncertainty level of HST, and could be used to refine the dynamics and history of M31 and M33.

Sequential binary classification of lithofacies from well-log data and their uncertainty quantification

Understanding and identifying the composition of various lithofacies in the subsurface is essential for successful reservoir characterization in hydrocarbon exploration. However, conventional methods such as core sampling and manual well-log interpretation are labor-intensive. As a result, many scientists are conducting research to use machine learning to study lithofacies more effectively and efficiently. However, as researchers are becoming more dependent on machine learning, an uncertainty analysis of machine-learning models is crucial to determine the reliability of the prediction results. Machine-learning algorithms that use ensemble methods provide an easy method for an uncertainty analysis but algorithms that do not use ensemble methods have difficulty in quantifying the level of uncertainty. In our research, we introduce a method known as sequential binary classification (SBC), which helps not only classify lithofacies but also to quantify and visualize the regions of uncertainty of the machine-learning models. SBC provides a method for using any classification algorithm of the user’s choice to construct an ensemble, which allows the user to quantify uncertainty readily. Our research uses the SBC algorithm to classify and quantify the uncertainty from the well-log data obtained from the North Sea near Norway. The results show that most of the lithofacies that exist in the region of interest share similar characteristics, which results in high uncertainty among the various lithofacies, and SBC helps to visualize these high uncertainties. We additionally demonstrate how SBC alleviates the class imbalance issue among the various lithofacies in the area, which is a very common problem in well-log data analytics.