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There is currently a shortage of applications for the role of principal. There are a range of factors contributing to this, one of which may be the considerable levels of stress and burnout reported by principals and deputy principals. Distributed leadership may offer some solutions to this challenge. This study aimed to explore the lived experiences of distributed leadership from a role sustainability perspective of school principals and deputy principals.

This paper follows a qualitative interpretivist approach based upon 15 semi-structured interviews with principals and deputy principals working in Irish post-primary schools. Data were analysed via thematic analysis.

Results indicate challenges to the sustainability of the role of senior school leaders comprising administrative overload, policy proliferation and challenges due to the complexity and breadth of the role of these school leaders. It was reported that engagement with distributed leadership could aid the sustainability of participants in their roles and the importance of focusing on well-being practices was also highlighted.

Recommendations include the need to reconsider policy proliferation and the need to reconceptualise school leadership. Further consideration regarding how distributed leadership can aid the sustainability of the role of senior school leaders, without adversely contributing to the already busy role of schoolteachers is also recommended.

The findings of this study are valuable as they reflect previous findings relating to the current challenges to sustainable school leadership as well as highlight distributed leadership as a potential aid to mitigate against these challenges.

Most teacher preparation programs (TPP) provide little instruction on mitigating the stress-related consequences of teaching (Miller and Flint-Stipp, 2019). This study aims to provide empirical support for including a self-care unit in teacher preparation curricula to address the secondary trauma and stressors inherent to the teaching profession (Essential 2; NAPDS, 2021; Sutcher et al., 2019).

This investigation occurred in an elementary TPP at a private southeastern US university and spanned two years, utilizing a mixed methods approach.

Findings suggest that after experiencing a 5-week self-care unit, preservice teachers exhibited a statistically significant increase in well-being and a newfound recognition of the need to prioritize self-care for effective teaching, suggesting its potential effectiveness in reducing burnout and attrition.

While this study provided valuable insights into the implementation and impact of a self-care unit within the context of elementary education majors at a mid-sized private university in the USA, it is essential to acknowledge its limitations. One notable limitation is the relatively homogenous sample, primarily consisting of White female participants.

The implications of this study are critical for teacher education policy and practice, advocating for including self-care curricula to enhance teacher well-being and, by extension, prepare teachers with a skillset to support their career trajectory (Essential 3; NAPDS, 2021).

This recommendation underscores the collaborative efforts between TPPs and partnership schools to implement such initiatives effectively, representing a pivotal step toward better-preparing teachers to manage the demands of their profession while prioritizing their mental health (Essentials 4 & 5; NAPDS, 2021).

This paper aims to determine the impact of mentorship on the development of self-directedness among beginner teachers in their initial years of teaching.

The researcher adopted a positivist paradigm to explore the situation of concern. Quantitative research was conducted, involving the collection and analysis of numerical data. Two closed-ended structured questionnaires were utilised, derived from the 40-item self-directed learning readiness scale (SDLRS) developed by Fisher and King, and a pre-determined questionnaire by Glazerman focused on the first-year teaching experience, induction and mentoring of beginner teachers.

Beginner teachers merely relying on the knowledge obtained from their studies is insufficient to achieve a satisfactory level of self-directedness when starting a teaching career. Most beginner teachers faced significant challenges in their early years of teaching due to the absence of mentoring support. In addition, most indicated that they resume their teaching duties and rely on their district for general support, guidance and orientation. Finally, the results have shown that mentoring positively impacts beginner teachers’ self-directedness.

The first limitation was that this study was narrowed to one South Africa University part-time B.Ed honours students working as beginner teachers in different provinces at primary and secondary schools. As a result, the findings of this research might be interpreted by some critics as one-sided and not representative of the views of most beginner teachers in South Africa who are working. The second limitation of this study is the sample size. In this study, 222 responses were received. As a result, the findings of this research might be considered not representative of the target sample size.

The presence and effective implementation of mentoring programmes in schools can positively impact beginner teachers' professional development and retention during their first years of teaching.

We contend that our research holds significance for international readership as it aims to garner attention towards potential research endeavours in diverse settings concerning mentorship programs for beginner teachers, specifically promoting self-directed learning. Our research offers opportunities to compare our findings with studies conducted in more comprehensive, comparative contexts and foster research possibilities in broader, contrasting contexts.

Based on the findings of this research, the availability and effective use of mentoring programmes would significantly affect beginner teachers' self-directedness, improve their retention rate and alleviate their teaching challenges. This study was the first research on the perceptions of the influence of mentoring on the self-directedness of beginner teachers.

This paper shares findings from a multi-year literacy professional development partnership between a school district and university (2014–2019). We share this case of a Literacy Cohort initiative as an example of cross-institutional professional development situated within several of NAPDS’ nine essentials, including professional learning and leading, boundary-spanning roles and reflection and innovation (NAPDS, 2021).

We asked, “In what ways did the Cohort initiative create conditions for community and collaboration in the service of meaningful literacy reforms?” Drawing on social design methodology (Gutiérrez & Vossoughi, 2010), we sought to generate and examine the educational change associated with this multi-year initiative. Our data set included programmatic data, interviews (N = 30) and artifacts of literacy teaching, learning and leading.

Our findings reflect the emphasis areas that are important to educators in the partnership: diversity by design, building relationships through collaboration and rooting literacy reforms in teacher leadership. Our discussion explores threads of reciprocity, simultaneous renewal and boundary-spanning leadership and their role in sustaining partnerships over time.

This paper contributes to our understanding of building and sustaining a cohort model of multi-year professional development through the voices, perspectives and experiences of teachers, faculty and district administrators.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

This study aims to examines both accrual and real-based earnings management (EM) behavior of listed corporations in tax-free countries during different economic situations. It also addresses the link between firm- and country-level determinants of accrual and real-based EM and explores economic conditions' influence on these determinants.

The study examines 1,608 firm-years, covers sixteen years (2004–2019), clustered into three periods according to the global financial crisis (GFC): four years prior (2004–2007), two years during (2008–2009), and ten years post the GFC (2010–2019). We employ the modified Jones model (performance-matched) developed by Kothari et al. (2005) to measure the accrual-based EM (positive and negative discretionary accrual EM) and the three levels model for Dechow et al. (1998) to measure the real-based EM (cash flow from operating, discretionary expenses and abnormal production cost).

The study finds a significant increase in EM practices in the listed corporations in tax-free countries during the economic downturn. These corporations are found to understate their earnings during the economic stress period. Simultaneously, the firm-level determinants of EM practices were at the same level of significance during different economic conditions in accrual-based EM. In contrast, the country-level EM determinants vary based on the economic conditions.

Financial reports' users gain a deep understanding of the quality of financial reports in the context of tax-free country. And, the study outcomes inspire policymakers to develop relevant legislation to mitigate financial reports' risk and adequately protect the financial reports' users.

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

Drawing upon social exchange theory, this research investigates the interplay of corporate philanthropy, environmental marketing strategy, relationship quality, greenwashing, and customer citizenship behavior in the family-owned hotels of an emerging market.

A field survey questionnaire was used to gather the data from 394 hotel customers by randomly selecting three premium family-owned hotels in Lahore: Faletti’s, Avari, and Holiday Inn. The data was analyzed using the structural regression modeling (SRM) technique with the assistance of AMOS version 24.

The results show that corporate philanthropy and environmental marketing strategy positively influence relationship quality, and relationship quality positively influences customer citizenship behavior. Relationship quality partially mediates the association between corporate philanthropy and customer citizenship behavior, but we found that greenwashing does not have a moderating role.

This research has theoretical implications for marketing scholars and practical implications of family-owned hotels in emerging markets.

The study has contributed contextually by collecting a unique dataset from family-owned hotels in an emerging market. Theoretically, we have conceptualized a model through the Social Exchange Theory by recommending relationship quality as a mediator and greenwashing as a moderator.

This purpose of this study was to assess dietary diversity among women of reproductive age (WRA) and the associations between consumption of a diversified diet and overweight/obesity statuses in Ekiti State, Nigeria.

Cross-sectional study of 207 WRA from six local government areas in Ekiti State, Nigeria, was done. A validated interviewer-administered questionnaire was used to collect data on sociodemographic characteristics, dietary intake and anthropometry. Dietary intake was assessed with 24-h dietary recall to calculate the Minimum Dietary Diversity Score for Women (MDD-W). Data were analyzed using descriptive statistics, Spearman rank correlation and Chi square test at α_0.05.

Majority of the participants (65.2%) were aged between 19 and 34 years, 58.5% were married and 49.8% had high school as their highest level of education. Mean MDD-W and body mass index (BMI) were 3.8 ± 0.9 and 25.46 ± 6.4 kg/m², respectively. All participants (100%) consumed foods from the “grains, white roots and tubers” group and majority also from the “meat and poultry” group (79.7%) but ranked low in the consumption of foods from other food groups. Many were overweight (34.8%), obese (14.0%) and a few (1.9%) had morbid obesity. MDD-W was significantly associated with marital status (X² = 7.7, P = 0.022) and BMI (X² = 11.4, P = 0.023) and had a weak positive correlation with BMI (r = 0.189, P = 0.007).

Study shows that both undernutrition and overweight/obesity coexist in the population, indicating a case of double burden of malnutrition (DBM) at a population level. However, further studies may be needed to investigate the extent of DBM at individual levels. Although there was a positive correlation between MDD-W and BMI, it cannot be used to predict causality. Study further reveals that the micronutrient intake of the WRA population in Ekiti is inadequate. Considering the importance of the 10 food groups highlighted in MDD_W to nutrition and health, the promotion of the consumption of foods from these food groups with more attention to the micronutrient-rich ones needs to be heightened.

Diet of participants was not diverse enough, indicating micronutrient inadequacy. Promotion of the consumption of a diverse diet, particularly from the food groups rich in micronutrient, needs to be heightened, while food groups high in calorie should be minimally consumed to forestall DBM.

Although risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.

To convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.

The PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

The relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.