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Fabrics, which are one of the raw materials of the clothing industry, constitute approximately 40–45% of the total cost of an apparel product. Due to the labor-intensive nature of this industry and failure to apply scientific methods along with the manufacturing processes, the wastes in the raw materials, including fabrics, become higher. Besides, quality deficiencies are encountered due to the same reasons. This study aims to determine the optimum total fabric layer height based on the fabric type during the cutting process with a straight knife cutting machine, which provided a decrease in the cutting errors.

Frequently used fabric types in an enterprise operating in organic cotton knitwear were listed. During the cutting tests, the straight knife cutting machine was used as the cutting device. The weight and thickness values of the fabrics were obtained to provide a comparison basis. Two different algorithms were created to evaluate the defective pieces according to fabric type, cutting height and error placement. Cutting resistances of these fabrics were also determined to evaluate the defect reasons. In the end, optimum total fabric layer count and total cutting height suggestions were proposed for each fabric type for a minimum cutting error.

At the end of this study, the error-free layers were identified per fabric type. At the same time, the optimum cutting height was suggested for each fabric basis. For 40/1 single jersey fabrics, the cutting height should be between 2.10 cm and 10.40 cm; for 30/1 single jersey fabrics, between 1.65 cm and 5.70 cm; for 20/1 single jersey fabrics, between 1.83 cm and 6.70 cm; for two-thread fleece fabrics, between 2.13 cm and 4.70 cm; and for three-thread fleece fabrics, between 0 cm and 4.90 cm.

Within the scope of the study, since the products made of knitted fabric were produced more frequently and in large quantities, the study was carried out with 15 different types of knitted fabrics at 10 different layers. The same methods should be applied for woven, denim and nonwoven fabric types, which would shed light on the following studies.

Due to scarce research carried out on the cutting procedure of the clothing industry in regards to sustainability, this study aims to contribute to this area. The main difference between this study and the studies that mostly make mathematical predictions about the cutting procedure is that it is practice-oriented.

Knowledge sharing (KS) helps employees learn from errors, but not much research has highlighted how sharing practices develop and take place in networked organizations. This study aims to explore how the professionals in a service triad develop and execute KS practices to learn from error.

A case study approach was adopted that focused on professionals working in a US-based company that was part of a health insurance service triad. The organization (“CaseCo”) processed the insurance claims filed by hospitals and doctors. The authors gathered qualitative data by conducting nine focus group discussions (FGDs) among CaseCo’s professionals. The FGDs involved a total of 51 professionals (17 women and 34 men) working in three centres of CaseCo in India.

The analyses revealed that error-related knowledge sharing (ERKS) practices emerge in a professional service triad (PST) through a culture of situated learning. They occur in ways that involves the use of repositories on the one hand, and connections between individuals on the other, both within and across the PST’s organizations. Such practices represent a dynamic system of knowledge stocks and flows in the PST.

To the best of authors’ knowledge, this is the first study that brings to the fore how ERKS practices develop and are executed in a professional organization in a triadic network structure.

The paper aims to discuss error detection and correction in Kashmiri carpet weaving (KCW), mediated by cryptographic code, Talim which is held to guarantee accurate information transference from designing to weaving, even after hundred years. Yet, carpets often show errors on completion.

Human factors analysis revealed error emergence, detection and correction in this practice whose task domains are distributed over large geographies (from in-premises to several kilometers) and timescales (from days to decades). Using prospective observation method, production process of two research carpets from their design, coding and weaving was observed while noting the errors made, identified and corrected by actors in each phase.

The errors were found to emerge, identified and corrected during different phases of designing, coding and weaving while giving rise to fresh errors in each phase, due to actors’ normal work routines.

In view of this, usual branding of “weaver-error” behind flawed carpet turns out to be misplaced value judgment passed in hindsight.

Patient safety focuses on minimizing risks that might occur to patients during provision of healthcare. The purpose of this study was to explore healthcare practitioners’ attitudes towards patient safety inside different hospital settings in Jeddah, Kingdom of Saudi Arabia.

A descriptive, cross-sectional study was conducted on a sample of healthcare practitioners in main hospitals in Jeddah. Two main hospitals (one governmental and one private) were selected from each region of Jeddah (east, west, north and south), with a total number of eight out of thirty hospitals. Data were collected through the Attitudes to Patient Safety Questionnaire III that was distributed online. The questionnaire used a 5-point scale. Descriptive statistics were used. Comparisons were made by independent t-test and ANOVA. The statistical significance level was set at p < 0.05.

The study included 341 healthcare practitioners of different sexes and specialties in eight major governmental and private hospitals in Jeddah. “Working hours as error cause” subscale had the highest mean score (4.03 ± 0.89), while “Professional incompetence as error cause” had the lowest mean score (3.49 ± 0.97). The total questionnaire had a moderate average score (3.74 ± 0.63). Weak correlations between the average score of the questionnaire and sex, occupation and workplace were found (−0.119, −0.018 and −0.088, respectively).

Hospitals need to develop targeted interventions, including continuing professional development programs, to enhance patient safety culture and practices. Moreover, patient safety training is required at the undergraduate education level, which necessitates health professions education institutions to give more attention to patient safety education in their curricula.

The study contributed to the existing literature on patient safety culture in hospital settings in Jeddah, Saudi Arabia. The insights generated by the study can inform targeted interventions to enhance patient safety culture in hospitals and improve patient outcomes.

The process performed during a financial statement audit is critical but is unobservable to external stakeholders. This can create challenges in assessing the quality of individual audit engagements. This study’s objective is to introduce and investigate an archival measure based on publicly available information that proxies for audit process ineffectiveness.

We proxy for audit process ineffectiveness using errors in the audit report. We examine audit reports to identify errors because the audit report represents the auditor’s primary communication with financial statement users and is subject to rigorous preparation and review. We first examine if typical factors influencing audit process ineffectiveness are associated with audit report errors. We then examine whether audit reports containing errors are associated with audit quality measures.

We find that errors are more likely to be present in audit reports when time pressure exists and less likely when auditors exert more effort and when audit engagement risk is higher. Results also show that errors in audit reports are positively associated with financial reporting misstatements, measured by subsequently disclosed Big R restatements and out-of-period adjustments.

Collectively, our evidence suggests that an audit report containing an error is a suitable proxy for audit process ineffectiveness. This proxy has audit quality implications because inattentiveness in one area of the audit process could indicate inattentiveness in another area.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

The author aims to walk beside the singular privileged class of White women’s suffrage feminist origin story to (re)construct plausible feminist fragmented threads as antenarratives in the context of business management education. To accomplish this (re)assembling of threads, the author examined two North American business trade publications created and used within two business schools, Harvard University’s Harvard Business Review (HBR), established in 1922, and Western University’s The Quarterly Review of Commerce (The Quarterly), established in 1933.

The author carefully reviewed almost 4,000 articles from HBR and The Quarterly, focusing on 308 articles that addressed the experiences of complex women. With this subset of collected articles, the author highlighted overlooked details, accidents and errors, generating interest and curiosity about the emergence of these fragmented and paradoxical origins that align with Foucault's histories of errors. By grouping these narrative fragments into themes and conducting a critical discourse analysis that incorporated influences from the external environment, the author reconstructed plural feminist origins antenarratives.

The themes discovered, including women as consumers, explicit working women concerns, women as authors/coauthors, diversity and social justice initiatives, and women in higher education/training, are not merely descriptive observations. They are the building blocks for identifying and analyzing the power relations circulating among feminist origins antenarratives within management education circles. These antenarratives include shedding light on women working in capitalist contexts, the educational needs of business women, and men and naming (but not breaking) the “mythologies” of women at work. These findings are transformative to the understanding of plural feminist origins.

The uniqueness of this work lies in its threefold contributions: moving away from the notion of a singular feminist origin story and instead embracing the complexity of multiple, paradoxical and incomplete origins; shedding light on the spectrum of power relations – ranging from productive to oppressive – that shaped the experiences of women in two management educational circles during the first half of the 20th century; and introducing the concept of inflection points, which underscores the fluidity of knowledge.

This paper aims to assess the influence of nursing leadership style on error management culture (EMC).

This scoping review was conducted following the integrative review methodology of the Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). PubMed, CINAHL, Scopus, Web of Science, Embase and EBSCO databases were systematically searched to identify studies on nursing leadership, error management and measurement, and error management culture. The studies’ methodological quality was then assessed using the JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies.

Thirteen manuscripts were included for review. The analysis confirmed that nursing leadership plays an important role in EMC and nurses’ intention to report errors. Three emerging themes were identified: 1) leadership and EMC; 2) leadership and the intention to report errors; and 3) leadership and error rate.

A major limitation of the studies is that errors are often analyzed in a transversal way and associated with patient safety, and not as a single concept.

Healthcare managers should promote training dedicated to head nurses and their leadership style, for creating a good work environment in which nurses feel free and empowered to report errors, learn from them and prevent their reoccurrence in the future.

There is a positive relationship between nursing leadership and error management in terms of reduced errors and increased benefits. Positive nursing leadership leads to improvements in the caring quality.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

Skewed pricing is a typical tactic used by tenderers in unit price projects to gain additional advantages at the expense of the owner or other competing tenderers. This paper aims to describe the development of a model for detecting skewed pricing in competitive tendering for unit price contracts.

The model evaluates how much the offered unit rates for work items deviate from the reasonable rate identified from the item’s submitted unit rates. Item rate deviations are integrated into a total deviation score for each submitted tender based on the relative weight of the work item to the total project amount. The model allows for assigning higher weights to work items that are more prone to skewed pricing, such as those that are performed early and those that are expected to experience quantity fluctuations.

The paper presents a detection model that uses only the submitted prices of the competing tenderers to perform the needed calculations, which reduces subjectivity in identifying skewed tenders. Two examples are given to demonstrate how the model may be used to detect skewed tenders.

The model supports tendering officials in the challenging task of identifying skewed tenders, which is required by rules and regulations governing public procurement. The model’s ease of use is expected to make it more widely used as a decision-support tool during the tender evaluation stage of real-world projects.