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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.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

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 uses biosocial gender theory to describe successful entrepreneurial behavior in conflict zones. Specifically, the authors investigate how the reliance on agentic (assertive, individual focused) behavior and communal (facilitative and friendly) behavior lead to differential outcomes depending on the physical gender of the entrepreneur exhibiting the behavior.

The authors developed a conceptual framework based on extant literature. To test the framework, the authors gathered survey data from Iraqi-Kurdish entrepreneurs who have been living in a state of war since the late 1980s and use a novel analytical method to deal with the limitations inherent in gathering survey data in conflict zones. Qualitative data is presented to generate a better understanding of the survey results.

The findings indicate females who are successful in taking on the traditional male role of entrepreneur in conflict zones engage in lower levels of agentic behavior compared to their male counterparts. Successful entrepreneurs (male and female) rely extensively on communal behavior in their ventures. When it comes to community development, male entrepreneurs engaging in agentic behavior, seem to mentor aspiring entrepreneurs more than females. Females relying on communal behavior engage in more mentoring of aspiring entrepreneurs than males.

An understanding of the unique gender dynamics underlying entrepreneurial behavior in conflict zones remains incomplete. The study introduces evidence that gender differences, as well as social factors, combine with the unique characteristics of conflict zones resulting in different behavioral paths to entrepreneurial success. The analytical method introduces some statistical tools to scholars attempting to understand the unique conflict zone context. As such, the study provides guidance for scholars working in this context, as well as NGO’s and other institutions seeking to train entrepreneurs and improve economic conditions in conflict zones.

This paper aims to investigate the relationship of female participation in labor force with the cybersecurity maturity of nations and the enabling role of e-government development in moderating the same.

The authors have conducted fixed-effects regression using archival data for 149 countries taken from secondary sources. Furthermore, the authors have grouped the sample countries into four levels of cybersecurity maturity (unprepared, reactive, anticipatory and innovative) using clustering techniques, and studied the influence of their interest variables for individual groups.

Results show that female participation in labor force positively influences national cybersecurity maturity, and e-government development positively moderates the said relationship, thereby enabling the empowerment of women.

Encouraging broader participation of women in the labor force and prioritizing investments in e-government development are essential steps that organizations and governments may take to enhance a country’s cybersecurity maturity level.

This study empirically demonstrates the impact of the nuanced interplay between female participation in labor force and the e-government development of a nation on its cybersecurity maturity.

Since the first definition of Total Quality Management (TQM) was established, its inseparable relationship with Human Resource Management (HRM) has been undeniable. However, many years have passed since this initial definition was made. During this time, not only have TQM and HRM evolved, but their relationship has also adapted in order to accompany and follow the environmental and technological changes that have ultimately changed the ways the authors work and how the authors perceive quality. This study provides a detailed map of the knowledge evolution of the TQM–HRM relationship. It also identifies interesting gaps for future researchers to consider to enable us to better understand emerging challenges and identify the next steps in the progression of TQM literature.

The study adopted a bibliometric analysis approach, using a systematic literature review of a data set that included 132 peer-reviewed articles published between 1991 and 2021 to identify thematic topics. In exploring each of these, the authors created a framework to spotlight future explorative and exploitative research questions.

The results show that literature on the relationship between HRM and TQM focuses on six topics: a different performance appraisal (PA); a different role for the human resources (HR) department; HRM practices, such as different bridges between JIT and TQM; the integration of job satisfaction into TQM goals; different TQM healers; and the different effects of HRM practices on firm performance. This paper provides clear interpretations of these topics and offers some recommendations for how HRM studies could contribute towards the development of TQM research, thus forming a clear agenda for the future of TQM–HRM research.

This study represents the first attempt to systematize, analyze and critically interpret TQM–HRM studies, promoting a collective reflection on the state of the art and stimulating further discussions on this topic.

Sustainable tourism is becoming more popular all over the world. Eco-friendly (green) hotels are properties that are friendly to the environment and are becoming increasingly popular among green travellers. Electronic word-of-mouth is a technique of communicating with consumers in order to share their experiences, and it is a significant marketing tool for hotels. This paper aims to identify the main themes shared in online reviews by tourists visiting eco-friendly hotels, and which of these themes were associated with satisfaction and dissatisfaction ratings.

The research used qualitative content analyses to analyse 1,202 user-generated content of the top 10 hotels in UK shared by guests on an online platform.

The analyses revealed nine themes in descriptions of airline travel experiences. These are “hotel amenities”, “services”, “location”, “staff”, “eco” (eco-friendly activities), “value” and “recommend/revisit” (intentions). Negative comments are associated with the “bathroom”, “mattress”, “water”, “bed”, “price”, “shower”, “Wi-Fi” and “restaurant” concepts.

This study differs from previous research in which it aims to address a void in the literature on the shortcomings of research focused on finding the dominant themes expressed in online reviews by tourists visiting eco-friendly hotels, and it does so using data mining approach.

Drawing on COR theory and based on a person-centered approach, this study aims to explore profiles of both leadership behavior (transformational leadership, abusive supervision) and well-being indicators (cognitive irritation, emotional exhaustion). Additionally, we consider whether certain resource-draining (work intensification) and resource-creating factors (leader autonomy, psychological contract fulfillment) from the leaders' work context are related to profile membership.

The profiles are built using LPA on data from 153 leaders and their 1,077 followers. The relationship between profile membership and correlates from the leaders' work context is examined using multinomial logistic regression analyses.

LPA results in an interpretable four-profile solution with the profiles named (1) Good health – constructive leading, (2) Average health – inconsistent leading, (3) Impaired health – constructive leading and (4) Impaired health – destructive leading. The two groups with the highest sample share – Profiles 1 and 3 – both show highly constructive leadership behavior but differ significantly in their well-being indicators. The regression analyses show that work intensification and psychological contract fulfillment are significantly related to profile membership.

The person-centered approach provides a more nuanced view of the leadership behavior – leader well-being relationship, which can address inconsistencies in previous research. In terms of practical relevance, the person-centered approach allows for the identification of risk groups among leaders for whom organizations can provide additional resources and health-promoting interventions.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.