Search results

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

The purpose of this study is to analyse the gender wage gap (GWG) in Spain adopting a labour market segmentation approach. Once we obtain the different labour segments (or idiosyncratic labour markets), we are able to decompose the GWG into its observed and unobserved heterogeneity components.

We use the data from the Continuous Sample of Working Lives for the year 2021 (matched employer–employee [EE] data). Contingency tables and clustering techniques are applied to employment data to identify idiosyncratic labour markets where men and/or women of different ages tend to match/associate with different sectors of activity and occupation groups. Once this “heatmap” of labour associations is known, we can analyse its hottest areas (the idiosyncratic labour markets) from the perspective of wage discrimination by gender (Oaxaca-Blinder model).

In Spain, in general, men are paid more than women, and this is not always justified by their respective attributes. Among our results, the fact stands out that women tend to move to those idiosyncratic markets (biclusters) where the GWG (in favour of men) is smaller.

It has not been possible to obtain remuneration data by job-placement, but an annual EE relationship is used. Future research should attempt to analyse the GWG across the wage distribution in the different idiosyncratic markets.

Our combination of methodologies can be adapted to other economies and variables and provides detailed information on the labour-matching process and gender wage discrimination in segmented labour markets.

Our contribution is very important for labour market policies, trying to reduce unfair inequalities.

The study of the GWG from a novel labour segmentation perspective can be interesting for other researchers, institutions and policy makers.

A significant number of criminal and deviant acts are investigated by nonpolice actors. These include private investigators who charge fees for their services, professional services firms such as firms of accountants who also charge fees, in-house investigators employed by private organisations and in-house investigators of public sector organisations who are not sworn police officers. Some of these investigators, such as private investigators, have been exposed in unethical activities such as illegal surveillance and blagging to name some. In this respect, this study aims to uncover the ethical orientations of investigators using cluster analysis.

This study is based upon an online survey of private investigators predominantly in the UK, i.e. investigators beyond the public police. An innovate statistical inferential analysis was used to investigate the sample which resulted in the development of three ethical orientations of such investigators.

Based upon a survey response from 331 of these types of investigators this study illustrates the extent they engage in unethical activities, showing a very small minority of largely private investigators who engage in such activities.

A unique feature of this study is the use of an innovative statistical approach using an unsupervised machine learning model, namely, TwoStep cluster analysis, to successfully group and classify respondents based on their ethical orientation. The model derived three types of ethical orientation: ethical, inbetweeners and risk takers.

This study aims to address the growing importance of online health information (OHI) and the associated uncertainty. Although previous research has explored factors influencing the credibility of OHI, results have been inconsistent. Therefore, this study aims to identify the essential factors that influence the perceived credibility of OHI by conducting a meta-analysis of articles published from 2010 to 2022. The study also aims to examine the moderating effects of demographic characteristics, study design and the platforms where health information is located.

Based on the Prominence-Interpretation Theory (PIT), a meta-analysis of 25 empirical studies was conducted to explore 12 factors related to information content and source, social interaction, individual and media affordance. Moderators such as age, education level, gender of participants, sample size, platforms and research design were also examined.

Results suggest that all factors, except social support, have significant effects on the credibility of OHI. Among them, argument quality had the strongest correlation with credibility and individual factors were also found to be relevant. Moderating effects indicate that social support was significantly moderated by age and education level. Different sample sizes may lead to variations in the role of social endorsement, while personal involvement was moderated by sample size, platform and study design.

This study enriches the application of PIT in the health domain and provides guidance for scholars to expand the scope of research on factors influencing OHI credibility.

This study aims to evaluate the health literacy level of the parents of children diagnosed with Down syndrome (DS) within one institution in Turkey.

A cross-sectional survey measuring demographics, information of the child and the parent and health literacy was administered to participants. The health literacy levels in the study were measured with the European Health Literacy Scale (EHLS), which consists of 47 questions.

Of the 65 participants who completed the questionnaire, 56.9% were mothers, 68.1% were diagnosed in the neonatal outpatient clinic examination after birth, and 58.5% stayed in the neonatal intensive care unit after birth. The mean score of the IHLS scale was 25.06 ± 6.59. Of the parents, 63.1% were found to be inadequate, 18.5% problematic-limited, and 18.5% adequate health literate. Any parent with excellent health literacy level was identified. High education level (p < 0.001), high income level (p < 0.001), living in the city center (p < 0.05), planned pregnancy (p < 0.05) and being a health worker (p < 0.001) were found to be statistically significant with a high EHSL score.

The presence of Down syndrome (DS) in a child also necessitates ongoing monitoring for a range of conditions, including eye diseases and heart disease. Some surgical procedures, such as heart or gastrointestinal surgeries, may also be required. Additionally, the child may require the administration of various medications. Finally, due to the potential lifelong need for assistance, the child may require the support of an adult throughout their lifetime. This is because of the child's inability to live independently due to their mental state. Therefore, parent education is the most important issue in the follow-up of the disease.

To the best of the authors’ knowledge, this is the first study to determine that parents of children diagnosed with DS have very limited knowledge of the disease and health literacy. Explanation of current diseases, treatments and training of parents should also be included in genetic counseling.

DS is a chromosomal disease that requires multidisciplinary care. Parents have to know the course of the disease and its complications.

The findings of this study indicate that parents of children with Down syndrome exhibit a profound lack of knowledge regarding the nature of their child's condition and the available healthcare options. It is therefore imperative that genetic counseling incorporates an explanation of the diagnosed diseases, treatments, and educational resources for parents.

Sharing patient health information (PHI) among hospitals has been much slower than the adoption of health record systems. This paper aims to investigate if privacy regulation (PR) or security measures (SMs) influence hospitals’ use of health information exchange (HIE) to share PHI with other providers (e.g. physicians, labs, hospitals). The study specifically focuses on how multiple PRs can impede and a strong national security infrastructure (NSI) can support HIE.

The study uses secondary data from a multi-national and multi-hospital survey administered by the European Union. The multi-level structure of the cross-sectional panel data is used to test the influence of both hospital-level (e.g. PR) and national-level variables (e.g. NSI) on HIE. A total of nine types of HIE, three types of PRs, nine SMs and other relevant control variables are considered. This study uses a two-level random intercept generalized linear model to test the hypothesis proposed in the study.

The study finds that national-level PRs (NLPR) have the strongest positive influence on HIE in comparison to regional (RLPR) and hospital-level (HLPR) PRs. Moreover, the study finds evidence that the presence of RLPR and HLPR, on average, decreases the positive impact of NLPR by 264%. The SMs also have a significant and positive impact on HIE. Adoption of an additional SM can increase the odds of engaging in a certain type of HIE between 21% and 61%. On the other hand, a strong NSI can also amplify the positive impact of SM on certain types of HIE.

This study extends prior research on the role of PRs in enabling HIE by considering the complexities brought up by adopting multiple PRs. NLPRs have the strongest impact on HIE in comparison to RLPRs or HLPRs. Moreover, public infrastructure initiatives such as those related to secure communications can also complement SMs adopted by the providers by encouraging HIE.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

The job satisfaction of university graduates can serve as an indicator of success in their professional development. At the same time, it can be a measure of higher education systems’ effectiveness. The purpose is to assess the relationship of university graduates’ socio-demographic characteristics, aspects of their degree program, experiences in the labor market and current working conditions and their job satisfaction.

The data were collected from a survey conducted at 11 Chilean universities with 534 graduates. An ordinal logistic regression model was fit to calculate job satisfaction probabilities for different graduate profiles.

The results show that sex, field of study, gross salary and horizontal match are related to graduates’ job satisfaction. Men and graduates in education and humanities are more likely to report being satisfied with their current job. Those graduates receiving higher salaries and those who are horizontally well-matched report higher levels of job satisfaction.

This study contributes to expanding knowledge about the job satisfaction of university graduates. Specifically, based on the results obtained, it introduces the idea of aspiration fulfillment as a possible determinant of job satisfaction in different fields of study. This can serve as a starting point for research that delves into differentiated expectations for graduates from different disciplines.

While service failures and recoveries (SFR) constitute a well-explored research domain, such negative consumption experiences have been understudied in the luxury context. The current study undertakes a systematic review to capture current knowledge and stimulate research in this vital domain.

This study adopts a systematic literature review methodology and employs the Theory-Context-Characteristics-Methods (TCCM) framework.

Guiding theories, industry and national contexts, relevant constructs and their relationships, and research methods employed in studying SFR in luxury consumption are identified and synthesized.

A comparative analysis of the luxury SFR with broader extant knowledge is offered. Further, hospitality is revealed as the dominant context, meriting its emphasis.

The purpose of this research is to examine the impact of audit committee financial experts on the risk of financial corruption in public companies.

A time-lagged, matched-pairs sample of 352 corporations was utilized to test the study's hypotheses (176 financially corrupt firms plus 176 compliant firms). To uncover financially corrupt firms, 2,895 Accounting and Auditing Enforcement Releases from the Securities and Exchange Commission were thoroughly evaluated.

The results show that financial experts on audit committees generally increased financial corruption. However, the impact was reversed when audit committees had three or more financial experts, showing that having at least three financial experts reduced financial corruption.

The study's findings call into question the long-held practice of appointing at least one financial expert to audit committees. This study offers a novel approach to improve corporate oversight and reduce financial corruption by having at least three financial experts on audit committees.