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Purpose: Previous research identified a measurement gap in the individual assessment of social misconduct in the workplace related to gender. This gap implies that women respond to comparable self-reported acts of bullying or sexual discrimination slightly more often than men with the self-labeling as “bullied” or “sexually discriminated and/or harassed.” This study tests this hypothesis for women and men in the scientific workplace and explores patterns of gender-related differences in self-reporting behavior.

Basic design: The hypotheses on the connection between gender and the threshold for self-labeling as having been bullied or sexually discriminated against were tested based on a sample from a large German research organization. The sample includes 5,831 responses on bullying and 6,987 on sexual discrimination (coverage of 24.5 resp. 29.4 percentage of all employees). Due to a large number of cases and the associated high statistical power, this sample for the first time allows a detailed analysis of the “gender-related measurement gap.” The research questions formulated in this study were addressed using two hierarchical regression models to predict the mean values of persons who self-labeled as having been bullied or sexually discriminated against. The status of the respondents as scientific or non-scientific employees was included as a control variable.

Results: According to a self-labeling approach, women reported both bullying and sexual discrimination more frequently. This difference between women and men disappeared for sexual discrimination when, in addition to the gender of a person, self-reported behavioral items were considered in the prediction of self-labeling. For bullying, the difference between the two genders remained even in this extended prediction. No statistically significant relationship was found between the frequency of self-reported items and the effect size of their interaction with gender for either bullying or sexual discrimination. When comparing bullying and sexual discrimination, it should be emphasized that, on average, women report experiencing a larger number of different behavioral items than men.

Interpretation and relevance: The results of the study support the current state of research. However, they also show how volatile the measurement instruments for bullying and sexual discrimination are. For example, the gender-related measurement gap is considerably influenced by single items in the Negative Acts Questionnaire and Sexual Experience Questionnaire. The results suggest that women are generally more likely than men to report having experienced bullying and sexual discrimination. While an unexplained “gender gap” in the understanding of bullying was found for bullying, this was not the case for sexual discrimination.

This paper examines whether the successful bid rate of the OnBid public auction, published by Korea Asset Management Corporation, can identify and forecast the Korea business-cycle expansion and contraction regimes characterized by the OECD reference turning points. We use logistic regression and support vector machine in performing the OECD regime classification and predicting three-month-ahead regime. We find that the OnBid auction rate conveys important information for detecting the coincident and future regimes because this information might be closely related to deleveraging regarding default on debt obligations. This finding suggests that corporate managers and investors could use the auction information to gauge the regime position in their decision-making. This research has an academic significance that reveals the relationship between the auction market and the business-cycle regimes.

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

This research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides decision makers an assessment of a proposed station location’s ability to support a unit’s personnel requirements from the local population.

This research first develops an allocation method to overcome challenges caused by overlapping unit boundaries to prevent over-counting the population. Once populations are accurately allocated to each location, we then then develop and compare the performance of statistical models to estimate a location’s likelihood of meeting staffing requirements.

This research finds that local demographic factors prove essential to a location’s ability to meet staffing requirements. We recommend that the USAR and US Army Recruiting Command (USAREC) use the logistic regression model developed here to support USAR unit stationing decisions; this should improve the ability of units to achieve required staffing levels.

This research meets a direct request from the USAREC, in conjunction with the USAR, for assistance in developing models to aid decision makers during the unit stationing process.

This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.

This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.

The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.

The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.

The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

The purpose of this study is to estimate the determinants of household food insecurity in the Gauteng City-Region, South Africa. This is motivated by the fact that food insecurity remains a key challenge at the household level in South Africa. Furthermore, the Gauteng Province has been rapidly urbanising due to a migrant influx, both locally and internationally. The findings will assist the country in achieving its mandate on the local economic development policy, Agenda 2063 and the Sustainable Development Goals 1 and 2.

The study adopted a quantitative cross-section design, utilising the binary logistic regression technique, drawing on the Gauteng City-Region Observatory Quality of Life 2020/2021 data, consisting of 13,616 observations, randomly drawn from nine municipalities in Gauteng City-Region.

The main findings of the study highlight unemployment, health status, education, household size, indigency and income as the main determinants of food insecurity in Gauteng City-Region. Policies towards sustainable urban agriculture, improving access to education, increasing employment and income, and health for all can help improve the food insecurity status of households in the Gauteng City-Region.

Further studies would require an in-depth assessment of household coping mechanisms, as well as the influence of household income (notably government social grants) and access to credit on household food security status, to better understand the dynamics of food security in the Gauteng City-Region.

Determinants of food insecurity should be considered when developing and implementing policies to reduce food insecurity in urban municipalities.

The study is of interest as it interdicts food insecurity issues, which have an effect on socio-economic well-being.

The study adds value by providing evidence on the determinants of food insecurity in an urban setting in a developing country. Gauteng is the richest of all provinces in South Africa and is also at the receiving end of internal and international migration. Factors affecting food insecurity have changed in the nine cities. This compromises nutrition safety and calls for targeted policy interventions.

The purpose of this paper is to show how data analytics can be used to identify areas of potential cost savings for category managers of installation-level services. Using integrated solid waste management (ISWM) as a test case, the authors also examine the impact of small business set-asides on price and contractor performance.

The authors use data analytics, specifically sequential regression, the Wilcoxon rank-sum test and ordered logistic regression to investigate the influence of service- and contracting-related variables on price and contractor performance.

The authors find that service- and contracting-related variables influence price. Specifically, they identify that a service-related variable, number of containers, significantly affects price, and that two contracting-related variables, one type of small business set-aside and the number of offers received, also significantly affect price. The authors quantify the price premiums paid for using various types of small business set-asides.

Although the findings were significant, the authors believe that the robustness of the conclusions could be enhanced if the Air Force captured more data. Additional observations would increase the generalizability of the results.

This empirical experiment demonstrates that detailed analyses are required to gain insights into services’ price drivers to craft more appropriate category management strategies for installation-level services.

This empirical study shows how historical data can be used to assess price drivers of installation-level services. It is also one of the first to quantify the impact that small business set-asides have on price.

The purpose of this study is to investigate benefits tourist seek when visiting a nature-based tourism destination to develop a benefit segmentation framework.

The study used quantitative research methods, with 400 self-administered survey administered to a sample of 400 tourists visiting the Kruger, Panorama, and Lowveld areas in Mpumalanga.

Cluster analysis produced two benefit segments. Binary logistic regression benefits that emerged from the cluster analysis were statistically significant predictors of the attractions tourists visited and the activities in which they participated during their stays in Mpumalanga. Factor-cluster analysis and binary logistic regression results were used to develop a benefit segmentation framework as a marketing planning tool.

The study was only based on Mpumalanga Province and therefore, the results cannot be generalised. The study was conducted over one season, the Easter period

The proposed benefit segmentation framework provides a tool that destination management organisations can use to plan effectively for marketing.

Effective marketing may lead to increased tourism growth which can have a multiplier effect on the destination.

This article is based on a master’s study conducted in Mpumalanga and results are presented on this paper.

The practice of consanguinity has been culturally preferred in most Arab countries, including Morocco. This behavior leads to an increase in genetic abnormalities, such as hypertension and diabetes. This paper examines the prevalence and determinants of first-cousin marriages and their impact on diabetes among offspring.

Data on 882 couples were collected through face-to-face interview via a pre-established questionnaire based on the variables selected within the objectives of this study. The authors used the multiple logistic regression modeling procedure in this study.

The results of the study indicate that the prevalence of first-cousin marriages were 15% among students’ parents. From the multiple logistic regression modeling, the authors found a significant effect of paternal and maternal grandparents’ first-cousins marriage on that of parents (aOR = 3.27 and aOR = 3.36, respectively). However, an 11-fold higher risk of first relative marriages among parents once the paternal and maternal grandparents were first-cousins and the father was illiterate (aOR = 11.01). Moreover, the authors reported a diabetes risk of more than 14 times when the effects of first-cousin maternal grandparents and parents and the hypertension among mother or her sibling were combined (aOR = 14.48) or when the effects of first-cousins maternal grandparents, first-cousin parents and mother’s age at marriage between 21 and 29 years were combined (aOR = 14.56).

First-cousin marriage depends on the father’s illiteracy and the consanguinity of grandparents’ factors. The cumulative effect of first-cousin marriage among grandparents, parents and a family history of hypertension among mother or her sibling increase the risk of diabetes among these mothers.