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The present study purports to unravel the mechanism in relationship among leadership behaviour integrity, organisational career development and employee engagement. Further, it also aims to understand if the employee feedback self-efficacy has any moderating influence on the relationship between leader behavioural integrity and organisational career development.

Pre-existing questionnaires are used for collecting data from a total of 417 employees working in the information technology industry operating within India. Analysis of the data is done using structural equation modelling technique.

Results of the study show that organisational career development partially mediates the relationship between leadership behavioural integrity and employee engagement. It is also found that feedback self-efficacy plays a moderating role in the relationship between leadership behavioural integrity and organisational career development.

The study helps to understand the mechanism of the relationship between leadership behavioural integrity and employee engagement through organisational career development with the support of ethical theory and social exchange theory. It also shows the moderating role played by feedback self-efficacy in the relationship between leadership behavioural integrity and organisational career development using social learning perspective.

Engaged employees assure organizational competitiveness and sustainability. The purpose of this study is to explore the relationship between job resources and employee turnover intentions, with employee engagement as a mediating variable.

Data were collected from 934 employees of eight wholly-owned pharmaceutical industries. The proposed model and hypotheses were evaluated using structural equation modeling. Construct reliability and validity was established through confirmatory factor analysis.

Data supported the hypothesized relationship. The results show that job autonomy and employee engagement were significantly associated. Supervisory support and employee engagement were significantly associated. However, performance feedback and employee engagement were nonsignificantly associated. Employee engagement had a significant influence on employee turnover intentions. The results further show that employee engagement mediates the association between job resources and employee turnover intentions.

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

The study’s findings will serve as valuable pointers for stakeholders and decision-makers in the pharmacuetical industry to develop a proactive and well-articulated employee engagement intervention to ensure organizational effectiveness, innovativeness and competitiveness.

By empirically demonstrating that employee engagement mediates the nexus of job resources and employee turnover intentions, the study adds to the corpus of literature.

This study aims at investigating how hybrid intelligence might enhance employee engagement in breakthrough innovation. Specifically, it empirically examines the mediating role of self-extinction and moderating role of social intelligence.

This study, using the lens of socio-technical system (STS) theory, collected data from 317 employees through cross-sectional survey. The hypotheses were tested using MPlus 8.3 by applying Structural Equation Modelling (SEM).

The results support the proposed model, suggesting that hybrid intelligence fosters employees' breakthrough innovation engagement and such a relationship is fully mediated by self-extinction. Besides, the findings provide support for the positive moderating impact of social intelligence on such indirect relationships in a way that high social intelligence will further strengthen the relationship.

As a pioneering contribution, the study uncovers the social mechanism that underlies hybrid intelligence–breakthrough innovation engagement relationship via self-extinction. The research suggests managers leveraging employees' social intelligence for playing a critical role in countering the negative impact of self-extinction by enhancing the employees' engagement in the breakthrough innovation process.

Drawing on affective events theory, the purpose of this paper was to investigate direct and indirect relationships between perceived organizational politics and workplace cyberbullying (WCB) perpetration mediated through anger, as well as to examine the moderating role of gender in these relationships.

The sample comprised 534 white-collar employees who were employed in a variety of service industries, including banking, higher education, telecommunications, health care and insurance in Islamabad, Pakistan. Data were analyzed using the structural equation modeling technique in Amos.

Results demonstrated that perceived organizational politics has a direct positive effect on WCB perpetration. Moreover, results indicated that perceived organizational politics evokes anger among employees that, in turn, triggers WCB perpetration. Results of a multigroup analysis revealed that the positive effect of perceived organizational politics on WCB perpetration was not significantly different between men and women. However, the positive relationship between perceived organizational politics and anger was significantly stronger for men than for women. Likewise, this study found a significantly stronger relationship for men than for women between anger and WCB perpetration. Anger partially mediated the relationship between perceived organizational politics and WCB perpetration only among men.

This study contributes to the literature by demonstrating that perceived organizational politics triggers WCB perpetration directly and indirectly through its impact on anger. Moreover, this study identified gender differences in the experience and expression of anger in response to perceived organizational politics.

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.

The purpose of this study is to examine the relationship between positive emotion, self-efficacy, job satisfaction and turnover intention in the context of resource building during the socialization process of new faculty members, particularly in the context of the coronavirus disease 2019 (COVID-19) pandemic.

The study utilizes a quantitative research design and employs purposive sampling to obtain 554 valid questionnaires. The study analyzes the relationship between positive emotion, self-efficacy, job satisfaction and turnover intention and examines the influence of strategic human resource management (SHRM) on these variables.

The results of the study reveal that SHRM positively influences positive emotion and self-efficacy, which, in turn, positively impact job satisfaction. However, positive emotion is negatively related to turnover intention.

This study contributes to the existing literature on human resource management (HRM) by examining the impact of strategic HRM on the socialization process of new faculty members. The findings of the study have significant practical implications for the implementation of HRM in research-oriented universities.

The purpose of this study is to explore how the risk of management motives for fraud can be assessed in external audits.

Semi-structured interviews were conducted with 26 experienced external auditors to explore their perspectives on the methods they employ to assess the risk of management motives for fraud.

The study identifies six methods external auditors can use to assess management motives for fraud. It emphasises that assessing management motives requires auditors to go beyond understanding these motives and necessitates a sceptical and analytical mindset. Auditors need to identify the accounts most vulnerable to management manipulations, observe management attitudes and assess the credibility of management assertions. The auditors in this study highlight specific accounts frequently manipulated by management. Still, manual year-end journal entries are the most vulnerable to management manipulations as they are subject to fewer controls. They recommend increasing the sample size to 100% and assigning more experienced staff, particularly, those with qualifications in fraud examination or anti-fraud training, to audit these vulnerable accounts thoroughly. They also provided examples of how auditors can identify management motives for fraud, observe management attitudes and assess the credibility of management assertions.

Audit standards (e.g. ISA 240, SAS99) lack explicit guidance on assessing management motives for fraud, but auditors are required to consider it in fraud risk assessment. This study proposes guidance recommendations to improve auditors' ability to assess this risk, which could be integrated into professional audit standards and training materials to improve auditors' professional scepticism, ability to challenge management and skills in fraud risk assessment.

Assessing the risk of management motives for fraud in external audits has received limited attention in the literature. To the best of the authors’ knowledge, this study is the first to address this knowledge gap.

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.

Higher education institutions (HEIs) offer courses and programmes focusing on sustainability in economics, as courses on sustainable development (SD), which examine the economic, social and environmental dimensions of SD. This paper aims to examine sustainability integration in economics degree programmes.

Through an extensive literature review in Web of Science (WoS) and information search in Google, conducting to 28 relevant case studies, this paper elucidates the emphasis given to sustainability as part of economics degree programmes in HEIs.

The results suggest that, whereas the inclusion of sustainability components in this field is a growing trend, much still needs to be done to ensure that matters related to SD are part of the routine of university students studying economics.

It is worth noting that the literature review conducted in WoS was primarily aimed at assisting in the selection of university case studies. The 28 university case studies scrutinised in this study may lack sufficient representation from numerous developing countries.

This study highlights challenges in integrating the SD into economics degree programmes, suggesting the need for curriculum adjustments as underscoring operational issues, acting as barriers. The inclusion of sustainability in economics programmes must navigate operational issues stemming from packed timetables and busy schedules, requiring innovative solutions.

As far as the authors are aware, this study holds substantial importance in its emphasis on implementing sustainability within HEIs’ economics programmes, assisting in pursuing SD.

The novelty of this study lies in addressing sustainability with the specific economics focus programmes within the HEIs context.