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The purpose of this study is to contribute to the literature on information elicitation. The authors investigated the impact of social influence strategies on eyewitness recall performance. Specifically, the authors examined the effect of social influence techniques (Cialdini, 2007) on recall performance (Experiment 1) and conducted a follow-up experiment to examine the incremental effect of social proof on the report everything cognitive interview mnemonic (Experiment 2).

Participants watched a video depicting vandalism (Experiment 1: N = 174) or a verbal altercation (Experiment 2: N = 128) and were asked to recall the witnessed event. Experiment 1: Participants were assigned randomly to one of six conditions: control (open-ended prompt), engage and explain (interview ground rules), consistency (signing an agreement to work diligently), reciprocity (given water and food), authority (told of interviewer’s training) and social proof (shown transcript from an exemplar participant). Experiment 2: The authors used a 2 (social proof: present, absent) × 2 (report everything: present, absent) between-participants design.

Across both experiments, participants exposed to the social proof tactic (i.e. compared to a model exemplar) spoke longer and recalled more correct details than participants not exposed to the social proof tactic. In Experiment 2, participants interviewed with the report everything mnemonic also spoke longer, recalled more correct details, more incorrect details and provided slightly more confabulations than those not interviewed with the report everything mnemonic.

The findings have practical value for police investigators and other professionals who conduct interviews (e.g. military personnel, doctors obtaining information from patients). Interviewers can incorporate social proof in their interviewing practices to help increase the amount and accuracy of information obtained.

Spare Parts Management (SPM) and Industry 4.0 has proven their importance. However, employment of Industry 4.0 solutions for SPM is at emerging stage. To address the issue, this article is aimed toward a systematic literature review on SPM in Industry 4.0 era and identification of research gaps in the field with prospects.

Research articles were reviewed and analyzed through a content-based analysis using four step process model. The proposed framework consists of five categories such as Inventory Management, Types of Spares, Circularity based on 6Rs, Performance Indicators and Strategic and Operational. Based on these categories, a total of 118 research articles published between 1998 and 2022 were reviewed.

The technological solutions of Industry 4.0 concepts have provided numerous opportunities for SPM. Industry 4.0 hi-tech solutions can enhance agility, operational efficiency, quality of product and service, customer satisfaction, sustainability and profitability.

The review of articles provides an integrated framework which recognizes implementation issues and challenges in the field. The proposed framework will support academia and practitioners toward implementation of technological solutions of Industry 4.0 in SPM. Implementation of Industry 4.0 in SPM may help in improving the triple bottom line aspect of sustainability which can make significant contribution to academia, practitioners and society.

The examination uncovered a scarcity of research in the intersection of SPM and Industry 4.0 concepts, suggesting a significant opportunity for additional investigative efforts.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

This study aimed to address a gap in subcontractor management by focusing on previously unexplored complexities surrounding subcontractor management in developing countries. While past studies concentrated on selection and relationships, this study delved into how effective subcontractor management impacts project success.

This study used the Bayesian Network analysis approach, through a meticulously developed questionnaire survey refined through a piloting stage involving experienced industry professionals. The survey was ultimately distributed among participants based in Accra, Ghana, resulting in a response rate of approximately 63%.

The research identified diverse components contributing to subcontractor disruptions, highlighted the necessity of a clear regulatory framework, emphasized the impact of financial and leadership assessments on performance, and underscored the crucial role of main contractors in Integrated Project and Labour Cost Management with Subcontractor Oversight and Coordination.

Previous studies have not considered the challenges subcontractors face in projects. This investigation bridges this gap from multiple perspectives, using Bayesian network analysis to enhance subcontractor management, thereby contributing to the successful completion of construction projects.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

Ticket sales are an essential source of income for football clubs and federations. Analyzing the determinants of fans' willingness-to-pay for tickets is therefore an important exercise. By knowing the match- and fan-related characteristics that influence how much a fan wants to pay for a ticket, as well as to what extent, football clubs and federations can modify their ticket offering and targeting in order to optimize this revenue stream.

Using a detailed discrete choice experiment, based on McFadden's random utility theory, this paper formulates a Bayesian hierarchical multinomial logit model. Such models are very common in the discrete choice modeling literature. The analysis identifies to what extent match and personal attributes influence fans' willingness-to-pay for games of the Belgian men's and women's football national teams.

The results show that the strength of the opponent, the type of competition, the location of the seats in the stadium, the day and kick-off time of the match and the ticket price exert an influence on the choice of the respondent. Fans are attracted most by competitive games against strong opponents. They prefer to sit along the sideline, and they have clear preferences for specific kick-off days and times. The authors also find substantial variation between socio-demographic groups, defined in terms of factors such as age, gender and family composition.

The authors use the results to estimate the willingness-to-pay for match tickets for different socio-demographic groups. Their findings are useful for football clubs and federations interested in optimizing the prices of their match tickets.

To the best of the authors' knowledge, no stated preference methods, such as discrete choice analysis, have been used to analyze the willingness-to-pay of sports fans. The advantage of discrete choice analysis is that options and variations in tickets that are not yet available in practice can be studied, allowing football organizations to increase revenues from new ticketing instruments.

This study aims to explore the relationship between promoter share pledging and the company’s dividend payout policy in India. Furthermore, this study also analyses the moderating impact of family involvement in business on the association between share pledging and dividend payout.

A sample of 236 companies from the S&P Bombay Stock Exchange Sensitive (BSE) 500 Index (2014–2023) has been analysed through fixed-effects panel data regression. For additional testing, robustness checks include alternative measures of dividend payout and promoter share pledging, as well as alternative methodologies such as Bayesian regression. Lastly, to address potential endogeneity, instrumental variables with a two-stage least squares (IV-2SLS) methodology have been implemented.

Upholding the agency perspective, a significantly negative impact of promoter share pledging on corporate dividend payouts in India has been uncovered. Moreover, family involvement in business moderates this relationship, highlighting that the negative association between promoter share pledging and dividend payouts is more pronounced in family companies. The findings are consistent throughout the robustness testing.

The present study represents a pioneering endeavour to empirically analyse the link between promoter share pledging and dividend payouts in India. It enhances the theoretical underpinnings of the agency relationship, particularly by substantiating the existence of Type II agency conflicts between majority and minority shareholders. The findings of this research bear significant implications for investors, researchers and policymakers, particularly in light of the widespread prevalence of promoter-controlled entities in India.

Despite representing a relatively small portion of the population, those who experience repeat victimization make up a significant share of all sexual and violent crimes, implying that perpetrators target them repeatedly. Indeed, research reveals specific traits (e.g. submissiveness) and behaviors (e.g. gait) related to past victimization or vulnerability. The purpose of this study is to explore the link between personality traits, self-assessed vulnerability and nonverbal cues.

In all, 40 undergraduate Canadian women were videotaped while recording a dating profile. Self-report measures of assertiveness, personality traits and vulnerability ratings for future sexual or violent victimization were obtained following the video-recording. The videotape was coded for nonverbal behaviors that have been related to assertiveness or submissiveness.

Self-perceived sexual vulnerability correlated with reduced assertiveness and dominance and increased emotionality (e.g. fear and anxiety). Additionally, nonverbal behaviors differed based on personality traits: self-touch was linked to lower assertiveness, dominance and extraversion and higher submissiveness, emotionality and warm-agreeableness.

To the best of the authors’ knowledge, this is the first study of its kind to consider the relationships between personality, self-perceived vulnerability and nonverbal behaviors among college-aged women. Potential implications, including enhancing autonomy and self-efficacy, are discussed.