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The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method.

The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly.

The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable.

The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels.

The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud.

This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.

Higher education institutions have undergone a transformation over the past few decades, from loosely coupled systems to more centrally managed organizations. Central to this ongoing development is the increasing competition for resources and reputation, driving higher education institutions to rationalize their structures and practices. In our study, we focused on changes in job advertisements for professorships in Germany from 1990 to 2010. Findings showed that the requirements stipulated by universities for professorial positions have become increasingly differentiated (and measurable) over time. In this context, competitive aspects, such as third-party funding, international orientation, or publications, have particularly come to the fore and grown significantly in importance. We discuss these findings in light of an increasing managerialization of higher education institutions, which has a direct effect on collegiality. We argue that the differentiation of professorial job profiles leads to even more formalized appointment processes and may push collegial governance into the background.

Emphasis on quality and reducing costs has led many health-care organizations to reconfigure their management, process, and quality control infrastructures. Many are lean, a management philosophy with roots in manufacturing industries that emphasizes elimination of waste. Successful lean implementation requires systemic change and strong leadership. Despite the importance of leadership to successful lean implementation, few researchers have probed the question of ideal leadership attributes to achieve lean thinking in health care. The purpose of this paper is to provide insight into applicable attributes for lean leaders in health care.

The authors systematically reviewed the literature on principles of leadership and, using Dombrowski and Mielke’s (2013) conceptual model of lean leadership, developed a parallel theoretical model for lean leadership in health care.

This work contributes to the development of a new framework for describing leadership attributes within lean management of health care.

The summary of attributes can provide a model for health-care leaders to apply lean in their organizations.

Optimal application and commitment toward financial management practices enhance organization performance. This study aims to assess the influence of financial management practices on organizational performance of small- and medium-scale enterprises.

Data were collected from 45 small-sized and 72 medium-sized firms. Data supported the hypothesized relationships. Construct reliability and validity were established through confirmatory factor analysis. The conceptual model and hypotheses were evaluated by using structural equation modeling.

The results indicate that working capital significantly influenced organizational performance. Capital budget management significantly influenced organizational performance. A non-significant influence of asset management on organizational performance was observed.

The generalizability of the findings will be constrained due to the research’s SMEs focus and cross-sectional data.

The study’s findings will serve as valuable pointers for stakeholders and decision-makers of SMEs in the development of well-articulated and proactive financial management systems to ensure competitiveness, sustainability, viability and financial competences.

The study adds to the corpus of literature by evidencing empirically that financial management practices significantly influenced SMEs’ performance.

This research seeks to explain the mediating function of workplace happiness in enhancing employee engagement through the drivers of employee engagement among the IT sector employees of India.

Quantitative data were acquired from 104 respondents from the Indian IT industry via an online survey utilizing Google Forms, employing a stratified random sample method. The study hypotheses were tested using PLS-SEM.

Results indicated that workplace happiness positively mediates employee engagement and drivers of employee engagement.

The current study followed a cross-sectional analysis where establishing causality is difficult; however, there is a scope to do longitudinal study on the same phenomenon. Research data are produced through online surveys. Possible sources of bias may be selective memory, attribution and/or exaggeration. This study covers specific variables of workplace happiness and drivers of employee engagement other variables remain unaddressed, such as COVID-19-related impacts. The nature of the industry and sample size were limited.

This study shows that workplace happiness has a mediating effect on both drivers of employee engagement and employee engagement; as a result, organizations should consider the function of workplace happiness as a mediating factor when implementing drivers of employee engagement and employee engagement.

On the social level, this research will help organizations to understand the drivers for employee engagement and linkages between workplace happiness and employee engagement. It hopes to create more happy workplaces and have good social impact.

This is the first study to look into the function of workplace happiness as a mediator between the drivers of work engagement and work engagement.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Previous research suggests that the compensation offered to customers after a service failure has to be substantial to make customer satisfaction surpass that of an error-free service. However, with the right service recovery strategy, it might be possible to reduce compensation size while maintaining happy customers. The aim of the current study is to test whether an anchoring technique can be used to achieve this goal.

After experiencing a service failure, participants were told that there is a standard size of the compensation for service failures. The size of this standard was different depending on condition. Thereafter, participants were asked how much they would demand to be satisfied with their customer experience.

The compensation demand was relatively high on average (1,000–1,400 SEK, ≈ $120). However, telling the participants that customers typically receive 200 SEK as compensation reduced their demand to about 800 SEK (Experiment 1)—an anchoring effect. Moreover, a precise anchoring point (a typical compensation of 247 SEK) generated a lower demand than rounded anchoring points, even when the rounded anchoring point was lower (200 SEK) than the precise counterpart (Experiment 2)—a precision effect.

Setting a low compensation standard—yet allowing customers to actually receive compensations above the standard—can make customers more satisfied while also saving resources in demand-what-you-want service recovery situations, in particular when the compensation standard is a precise value.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

It is widely recognized that interior office space can affect health in several ways. Strategic and evidence-based design, including explicit design objectives, well-chosen design solutions and evaluation of results, aid realization of desired health effects. Therefore, this paper aims to identify possibly effective interior design strategies and accompanying design solutions and to provide examples of effectiveness measures.

A literature sample of 59 peer-reviewed papers published across disciplines was used to collect examples of workplace design features that have positively influenced workers’ well-being. The papers were grouped by their health objective and design scope successively and their theoretical assumptions, measures and findings were analyzed.

Four main workplace design strategies were identified. Design for comfort aims at reducing or preventing health complaints, discomfort and stress, following a pathogenic approach. It has the longest tradition and is the most frequently addressed in the included papers. The other three take a salutogenic approach, promoting health by increasing resources for coping with demands through positive design. Design for restoration supports physical and mental recovery through connections with nature. Design for social well-being facilitates social cohesion and feelings of belonging. Design for healthy behavior aims at nudging physical activity in the workplace.

By drawing complementary perspectives and offering examples of design solutions and effectiveness measures, this paper encourages workplace designers, managers and researchers to take a transdisciplinary and evidence-based approach to healthy workplaces. It also serves as a starting point for future empirical research.

This study aims to examine the most influential drivers, both product-specific and consumer-specific, affecting Asian consumers' willingness to pay (WTP) for organic olive oil.

To individuate the most influential drivers of WTP for organic products and to assess their effect, in terms of configurational paths and consumer profiles, this study sequentially employs explorative factor analysis approach and a fuzzy-set qualitative comparative analysis method. The survey is carried out in different areas of Asia (e.g. Pakistan, Vietnam and China).

The results suggest that Asian consumers' WTP for organic products is described by consumer-specific drivers (gender, occupation and household size) as well as product-specific drivers (product authenticity and sustainability, consumer ethnocentrism and food fraud risk perception).

The findings of the study permit the identification of different drivers that move consumers' WTP for organic olive oil. The study contributes to setting the ground for companies to propose and implement efficacious marketing strategies for organic olive oil in importing countries, such as Asia.