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This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Secure children’s homes (SCHs) restrict the liberty of young people considered to be a danger to themselves or others. However, not all young people referred to SCHs find a placement, and little is known about the outcomes of the young person after an SCH or alternative placement. The purpose of this paper is to understand which characteristics most likely predict allocation to an SCH placement, and to explore the outcomes of the young people in the year after referral.

A retrospective electronic cohort study was conducted using linked social care data sets in England. The study population was all young people from England referred to SCHs for welfare reasons between 1st October 2016 to 31st March 2018 (n = 527). Logistic regression tested for differences in characteristics of SCH placement allocation and outcomes in the year after referral.

In total, 60% of young people referred to an SCH were allocated a place. Factors predicting successful or unsuccessful SCH allocation were previous placement in an SCH (OR = 2.12, p = 0.01); being female (OR = 2.26, p = 0.001); older age (OR = 0.75, p = 0.001); and a history of challenging behaviour (OR = 0.34, p = 0.01). In the year after referral, there were little differences in outcomes between young people placed in a SCH versus alternative accommodation.

The study raised concerns about the capacity of current services to recognise and meet the needs of this complex and vulnerable group of young people and highlights the necessity to explore and evaluate alternatives to SCHs.

Policing requires atypical work hours. The present study examined associations between shiftwork and pregnancy loss among female police officers.

Participants were 91 female officers with a prior history of at least one pregnancy. Shiftwork information was assessed using daily electronic payroll work records. Any prior pregnancy loss (due to miscarriage) was self-reported. Logistic regression estimated odds ratios (OR) and 95% confidence intervals (CI) for main associations.

On average, the officers were 42 years old, had 14 years of service, and 56% reported a prior pregnancy loss. Officers who worked dominantly on the afternoon or night shift during their career had 96% greater odds of pregnancy loss compared to those on day shift (OR = 1.96, 95% CI:0.71–5.42), but the result was not statistically significant. A 25% increase in percent of hours worked on night shift was associated with 87% increased odds of pregnancy loss (OR = 1.87, 95% CI:1.01–3.47). Associations were adjusted for demographic and lifestyle factors. Objective assessment of shiftwork via electronic records strengthened the study. Limitations include small sample size, cross-sectional design and lack of details on pregnancy loss or the timing of pregnancy loss with regard to shiftwork.

The present study is preliminary and cross-sectional.

With considerable further inquiry and findings into this topic, results may have an impact on police policy affecting shift work and pregnant police officers.

Implication on the health and welfare of police officers.

To our knowledge, there are no empirical studies which associate shiftwork and pregnancy loss among police officers. This preliminary study suggested an association between shiftwork and increased odds of pregnancy loss and points out the need for further study.

The outbreak of COVID-19 has caused a slowdown of economic activity across the globe, which has resulted in high levels of disruption to labour markets. This study seeks to examine how the outbreak of COVID-19 has impacted the search strategies of students seeking for an internship, and whether these have changed since the start of the pandemic. The study utilises the strength of weak ties hypothesis, social capital theory and status attainment theory to explore the changes in securing a position since the outbreak of COVID-19.

This study draws on data from two cohorts of MBA students seeking to secure internships: one before the outbreak and one during. A multinomial regression is employed to examine how students have used network ties to secure internships and how this has changed since the outbreak of COVID-19.

The multinomial regression results indicate that there was little difference in the strategies employed by students before the crisis compared to those that secured them during, potentially indicating that students are unwilling to deviate from typical job search strategies, especially in times of uncertainty.

This study provides insights into how network ties are used by job seekers during a period of economic and environmental uncertainty.

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

This paper aims to examine prospective graduate students' attitudes toward educational loan borrowing in an experimental setting.

Participants were randomly assigned to two treatment groups and one control group. Subjects in experimental group 1 received financial education: a short online course on the economic viability of getting a master's degree and how to finance it with a graduate student loan, while subjects in experimental group 2 received financial education along with information on the availability bias.

Relying on a control group in the assessment of financial literacy education intervention impacts, this research finds positive causal treatment effects on individuals’ attitudes toward debt-financed graduate education. In comparison to the control group, experimental subjects perceived the possibility of going into debt with a graduate loan to complete a master’s degree as less stressful and worrying.

This study has important educational policy implications to prevent students from stopping investing in human capital by perceiving educational loan debt as something stressful or worrying. The results can help potential (and current) grad students develop a feasible financial plan for graduate school by encouraging higher education institutions to implement educational loan information and financial education into university seminar courses for better graduate student loan decision-making.

Student attitudes toward debt have been analyzed in the context of higher education, but only a few researchers internationally have used an experimental design to study personal financial decision-making.

This study aims to analyze the effect of cognitive load and social value orientation on managers’ preferences when they face with two types of restructuring choices in financially distressed firms: the first belonging to the family of organizational restructuring (massive layoffs) and the second to the family of financial restructuring (debt increases).

The authors investigate experimentally the impact of managers’ cognitive load and social value orientation on the decision to restructure leveraged buyout (LBO) firms in financial distress by using either massive layoffs or debt increases.

By investigating the impact of managers’ cognitive load and social value orientation on the restructuring decision of an LBO firm in financial distress, the research reveals that, on average, cognitively loaded managers prefer massive layoffs over increased debt levels. The massive layoffs seemingly provide a relatively easier way to avoid conflict with influential, residual claimants. In contrast, social value–oriented managers actively avoid massive layoffs and prefer to increase debt.

These results imply that the performance mechanisms emphasized to improve agency relations, for example, in LBOs, have their own limitations during periods of financial distress. This study shows that one of these limits is related to cognitive distortions and personality traits.

In this research, the originality lies in understanding how managers’ internal factors affect their restructuring decision-making, in the case of LBO firms in financial distress.

This paper aims to examine the association between the dietary inflammatory index, the consumption of Enteral Nutrition Supplemented with probiotics with certain serum inflammation markers and gastrointestinal complications among individuals diagnosed with COVID-19.

This cross-sectional investigation involved 100 COVID-19 patients who were admitted to intensive care units in hospitals. These patients were administered two different types of Enteral Nutrition, so the dietary inflammatory index (DII), gastrointestinal complications and some serum inflammation markers have been compared between two groups.

The mean DII scores in all patients were significantly pro-inflammatory (probiotic formula 2.81 ± 0.01 vs usual formula group 2.93 ± 0.14 p = 0.19). The probiotic formula consumption had an inverse association with High-sensitivity C-reactive Protein concentration (coef = −3.19, 95% CI −1.25, −5.14 p = 0.001) and lead to a reduction of 2.14 mm/h in the serum level of Erythrocyte sedimentation rate compared to normal formula. The incidence of diarrhea, abdominal pain and vomiting in probiotic formula patients was respectively 94%, 14% and 86% less than in usual formula patients (p = 0.05).

In this cross-sectional study for the first time, the authors found that probiotic formula consumption was inversely associated with serum inflammation markers and gastrointestinal complications incidence. The high DII leads to more gastrointestinal complications incidence and inflammation markers. More studies are needed to prove this relationship.

This paper aims to examine the causation linking financial technology to economic growth in the East African Community states from 1997 to 2019.

Autoregressive distributed lag is used. Gross domestic product per capita proxies economic growth, automated teller machines, point of sale, debit card ownership and mobile banking measure financial technology.

The results unveil a significant relationship between financial technology and economic growth. The findings show bidirectional causality between automated teller machine and economic growth, with unidirectional causation from economic growth to point of sales and internet banking, mobile banking and government effectiveness to economic growth. The error correction term is negatively significant, demonstrating a long-term convergence between Fintech measures and economic growth.

The governments should effectively enact and implement policies that protect investments in financial technologies to boost economic growth in the East African Community countries. The government should reduce taxes on financial technology equipment and related services. The use of automated teller machine, debit card ownership and internet banking should be encouraged through cashless transactions. Financial institutions should adopt cashless operation policies to encourage the use of financial technologies.

Research results on the bond between financial technology and economic growth are not conclusive. These studies demonstrate that technological innovations are double edged-swords, with both positive and negative sides. The results are conflicting; some reveal positive relationships, while others show negative links. Hence, research is required to fill the lacuna.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.