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The current study examines the effect of fraud training on auditors' ability to identify fraud risk factors. This is important because most auditors have little or no direct experience with fraud; thus, research that investigates the potential effect of indirect experience through training is vitally important to fraud detection and audit quality. A total of 369 experienced auditors completed a complex audit simulation task that involved 15 seeded fraud risk red flags. A total of 143 auditors participated in a 30-minute training session focused specifically on fraud risk, while the remaining 226 auditors learned about general internal control risk during this time block. The results indicate that auditors with fraud training identified significantly more red flags and obtained greater knowledge about fraud risk than auditors who did not receive the training. Considering that the fraud training consumed only 30 minutes out of a 64-hour training session, the findings suggest that even modest exposure to fraud training is quite effective.

This chapter reviews the safety of railway operation in Europe particularly by examining the causes of fatalities over periods of up to three decades ending in 2017. Fatalities are examined to passengers, staff, level crossing users, and trespassers, together with a brief look at suicides. The accidents that attract most attention are fatal train collisions and derailments because they can result in multiple fatalities, and are in most cases wholly the responsibility of the railway operators. However, train accidents are infrequent, and account for only about 1% of all railway fatalities if suicides are included, or 3% if they are not. The fatal train accident rate per train-kilometre fell at a rate of 5.4% per year between 1990 and 2017 and was 77% lower in 2017 than it had been in 1990. This chapter goes on to discuss level crossings, which account for far more fatalities than train accidents, personal accidents, accidents to trespassers, and suicides. This chapter ends with a brief look at the evidence of the effect of rail restructuring on safety.

Organizational diversity training is designed to enhance employees' skills and competencies regarding diversity and its management. The question of its effectiveness, and the conditions under which it thrives, remains a matter of debate. Unlike previous studies that have predominantly focused on the perspective of training participants, this study shifts the lens to the viewpoints of diversity training providers in Germany – a country where the formal requirement for diversity management was implemented relatively recently. The primary objective is to ascertain the critical factors influencing training effectiveness from the providers' perspective.

This research draws upon case studies based on document analysis and qualitative interviews with diversity training providers across Germany.

The investigation reveals that the effectiveness of diversity training, as perceived by providers, hinges on several key factors: the organizational environment (including the widespread recognition of diversity issues and the presence of an organizational diversity framework), the attributes of diversity trainers (notably their personal familiarity with diversity) and the setting and design of the training (such as venue, duration and a blend of instructional approaches). A notable barrier to achieving effective training outcomes is the lack of supportive conditions within client companies, exemplified by limited training budgets, which impedes the accurate assessment of training effectiveness.

This study marks a novel contribution to the field by explicitly focusing on the perspective of diversity training providers in Germany. It provides new insights into the importance of the organizational context surrounding diversity education within the private sector.

This study aims to investigate the influence of two perceived organizational ethics systems (perceived ethics training quality and integrity-based climate) on the ethical decision-making (EDM) of tax accountants in Ghana. The study also examines the moderating role of the decision-makers’ financial situation on the quality ethics training–EDM relationship.

Survey data from 356 tax accountants were analyzed using the partial least squares structural equation modeling technique.

The results show that the two ethics systems influence EDM, but their extent of influence varies across the stages of EDM. Specifically, quality ethics training is a better predictor of EDM at the ethical issue recognition stage, whereas integrity-based climate is a better predictor of EDM at the ethical intention stage. The study also found that decision-makers’ financial situation predicts the ethical recognition stage of EDM but does not moderate the quality ethics training–EDM relationship.

This study recommends the concurrent deployment of quality ethics training and an integrity-based work climate to improve ethical behavior. Policymakers should also emphasize a work climate that promotes honesty, conscientiousness and ethical principles (integrity-based climate) to improve ethical intentions.

This study applied the interactionist theory by capturing the relative effects of two organizational ethics systems and an individual-level situational factor in a single model. To the best of the authors’ knowledge, this is the first study that tests the moderation effect of decision-makers’ financial situation on the ethics training–EDM relationship in a developing country context.

This study aims to contribute to the enrichment of the literature by examining the impact of cross-cultural training on expatriates’ adjustment of Nigerian bankers on expatriate assignments in other African countries.

Data on cross-cultural adjustment from expatriate employees in five banks that collectively accounted for over 80 per cent of Nigerian banks with subsidiaries in other African were systematically selected for the investigation. This data was collected quantitatively via a survey instrument. Independent sample t-test, analysis of variance (ANOVA) and regression analysis were deployed in analyzing the data.

The study found that cross-cultural adjustment varied significantly across the different categories of gender, age, marital status, previous expatriate training, previous expatriate experience, and duration of expatriation. The study concludes that in order to attain higher levels of adjustment African banks and other organizations should provide a more comprehensive cross-cultural training program that mirrors the needs of employees following a detailed needs analysis. Also, the training must be sequential and not a one-off approach.

The literature though still nascent is largely focused on expatriate preparation and adjustment for expatriates moving from Western-to-western contexts and very little exists in the literature on how multinationals from Non-Western contexts like Africa prepare their staff for expatriation and its consequent impact on their adjustment. This study aims to enrich the literature by examining the impact of cross-cultural training on expatriates’ adjustment of Nigerian bankers on expatriate assignments in other African countries.

The combination of action and process has remained the attention of the psychology of entrepreneurship research. Moreover, special attention has also been paid to the whens and hows of entrepreneurial psychological training. Keeping this in view the current study has tested the impact of individual entrepreneurial orientation (IEO) training on small business entrepreneurial career resilience. Using action regulation theory (ART), a serial mediation model of IEO behaviour and entrepreneurial career adaptability is hypothesised.

This study used a longitudinal randomised controlled field experiment in which the data were collected at four points. A total of 546 small business owners from Lahore, Pakistan, participated in this research (training group = 265, control group = 281). The data were analysed using ANCOVAs and PROCESS Model 6.

The research has found that IEO training impacts the entrepreneurial career resilience of small business owners through the development of IEO behaviour and career adaptability.

This experimental inquiry is a novel attempt to extend the career-related outcomes of IEO training (career resilience) based on ART through the mediating role of IEO behaviour and career adaptability. In addition, it has tested the IEO training in the developing country of Pakistan, which is a relevant context for enhancing its socio-economic growth.

This study aims to investigate the impact of job rotation on employee performance and explores the mediating role of human resources (HR) strategy and training effectiveness on this relationship, within the petrochemical industry, which represents a highly specialist and hazardous industrial context.

Data was collected through a questionnaire which was distributed among the experts working in an Iranian petrochemical organization. Previously validated scales were used to measure job rotation, employee performance, HR strategy and training effectiveness, and partial least squares structural equation modeling was used for hypothesis testing.

The research findings indicated that job rotation had a negative effect on employee performance, while training effectiveness and HR strategy positively mediated the relationship between job rotation and employee performance. This highlights the importance of ensuring effective training and a HR strategy to support job rotation of skilled and specialist employees.

Managers of employees in specialist and hazardous industries, such as petrochemical workers, interested in job rotation to support employee career development, should be mindful of potential negative implications on employee performance. To support and improve employee performance, job rotation should be considered alongside HR strategy and training.

Previous research has largely focused on the value of job rotation to develop managers’ organizational understanding and to reduce injury within blue-collar work, which has led to a paucity of research into job rotation within highly skilled and specialist industrial roles. It is highlighted within the literature that it remains unclear what supports effective job rotation. This study addresses this lacuna by investigating how job rotation affects employee performance in a highly skilled and specialized industry and how strategy and training effectiveness mediate this effect.

Apprenticeships in England, while defined by level and typical duration, are not quantified regarding the number of learning hours required to achieve the outcomes specified, as with other regulated qualifications and accredited programmes. This paper proposes an approach to ascribe credit to apprenticeships recognising both on-and-off-the-job learning to remove some of the existing barriers to accessing higher education (HE) and the professions.

A mixed methodological approach resulting in a total learning hours/credit value was proposed.

There is significant HE-wide confusion regarding the amount of learning/training that is required to complete apprenticeships in England. Whilst sector guidance made it clear that there was no prescribed method to ascribe credit to qualifications, programmes, modules, units or apprenticeships by drawing out the core principles within current practice, a key outcome of this project was the development of a method to ascribe a credit value to apprenticeships.

There is potential to support further research into the recognition of prior learning as a specialised pedagogy and for reflecting on apprenticeship practice in other roles and sectors.

Whilst the project underpinning this paper focused on the healthcare sector, the method used to ascribe credit to the level-3 healthcare support worker apprenticeship was not sector specific and can therefore be applied to apprenticeships within other contexts providing more widespread benefits to workforce development.

Policy makers must ensure that employers and providers are clear that the minimum statutory off-the-job hours constitute an apprentice employment entitlement, which must not be conflated with total apprenticeship learning hours requirements. This recommended policy clarification could assist in simplifying the process required for ascribing credit to apprenticeships and at the same time support a move towards better and more consistent recognition of the value of apprenticeship learning.

It is a first attempt to ascribe a credit value to an apprenticeship in England for the specific purpose of facilitating progression to HE.

This research paper investigates the theoretical frameworks encompassing a nuanced analysis of the digital divide in the Indian educational context, recognising that it is not merely a matter of technology access but also the ability to effectively use the resource for enhancing learning outcomes. This research provides valuable insights for fostering a more equitable and digitally inclusive learning environment by integrating conceptual insights with empirical evidence. The New Education Policy (NEP), India 2020 firmly emphasises the appropriate integration of technology into the teaching-learning process to develop relevant competencies. The pertinent question is, for India to conquer the second digital divide challenge, is the pace of technology accessibility and skill development sufficient?

The paper is a desk research, using secondary data from the Unified District Information System for Education (UDISE+), the Indian Government database of schools. A structured dataset has been created for all years, where states are grouped in descending ranking order of availability of infrastructure and teachers trained. A colour key segregates the States into three zones demonstrating their different levels of performance – high (green), moderate (blue) and low (yellow). The purpose is to identify state/s that have moved from one zone to another and, thereafter, analyse the reasons behind the movement.

Almost all states remained in the same digital resource availability zone for the four years studied, except for a limited few. Despite government interventions through higher budget allocation and targeted policies, growth rates of teacher training in computer usage slowed down post-COVID-19. A high positive correlation between Teachers' training in computer usage and the availability of computer and Internet facilities in schools indicates that an increase in digital infrastructure in schools is highly linked to teachers' training in computer usage and would ultimately translate into better use of digital resources to impart equitable education opportunities.

Primary data collection through interviews might have added to the critical findings. Therefore, researchers are encouraged to test the proposed propositions further on a case-by-case basis for any state under consideration.

Enhancing digital infrastructure in schools and building digital competence in teachers must be understood in the context of the learning organisation and the beneficiaries' attitudes at the meso-level to expand stakeholder motivation towards digital internalisation. This requires continuous engagement with education institutions as professional learning organisations, which will thereby help develop a decentralised context for teacher competency building. Collaboration, continuous monitoring of the outcomes of professional development programs, and sharing best practices are crucial in improving teacher readiness for digital education.

Access to tangible resources, such as computers, Internet connectivity and educational software, and developing intangible resources, such as teacher digital competencies, will play a pivotal role in shaping students' learning experiences. By studying the discrepancies in digital resource accessibility and teacher technology adoption, this research endeavours to add to the efforts towards enhancing the educational landscape.

This paper seeks to address a critical issue in the Indian education system and contribute to the ongoing effort to prevent the widening of the second and third digital divide in schools, and help achieve UN SDG Goals 4 and 10.

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.