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Despite the interest in leaders' identity work as a framework for leadership development, coaching psychology has yet to expose its active ingredients and outcomes.

To do so, the authors reconcile published systematic literature reviews (SLRs) in the field to arrive at a more thorough understanding of the role of identity work in coaching. A total of 60 eligible SLRs on identity work and coaching were identified between 2010 and 2022. Four were included in the data extraction after selecting and screening, and the full texts of 196 primary studies reported therein were analysed.

Amongst the coachee-related factors of effective coaching, the coachee’s motivation, general self-efficacy beliefs, personality traits and goal orientation were the most frequently reported active ingredients, and performance improvement, self-awareness and goal specificity were the most frequently supported outcomes. The analysis indicates that leaders' identity work, as an active ingredient, can be a moderator variable for transformative coaching interventions, while strengthening leadership role identity could be one of the lasting outcomes because coaching interventions facilitate, deconstruct and enhance leaders' identity work. Further research is needed to explore the characteristics of these individual, relational and collective processes.

This study adds value by synthesising SLRs that report coachee-related active ingredients and outcomes of executive coaching research. It demonstrates that the role of leaders' identity work is a neglected factor affecting coaching results and encourages coaching psychologists to apply identity framework in their executive coaching practice.

The study proposes to empirically test a model that illustrates how identical elements (IEs), transfer design and trainer performance as training predictors affect trainees' motivation to improve work through learning (MTIWL) and training transfer (TT) in the Indian context.

An online survey was conducted to validate the study model. The quantitative data collected from 360 executives and managers were analyzed using the covariance-based structural equation modeling (CB-SEM) technique.

The study finds that trainees' MTIWL has a full mediation impact between transfer design, trainer performance and TT. However, a partial mediating impact of MTIWL was found between IEs and TT.

This is the first study that empirically explores the mediating mechanism of MTIWL between IEs, transfer design, trainer performance and TT. This study extends the current understanding of trainees' MTIWL that links the cumulative influence of training predictors to TT.

In the construction sector, site excavation is one of the most dangerous and challenging activities. Proper training can be an effective way to mitigate excavation hazards. Virtual reality (VR) has been used as an effective training tool to enhance safety performance in various industries. However, little attention has been paid to the potential of this technology for construction excavation safety training.

This study proposes an immersive VR training system for excavation safety and hazard identification. The proposed VR training system was compared with a health and safety manual via a controlled experiment.

Results based on scores obtained immediately after training indicate that VR training significantly enhanced practical performance, knowledge acquisition and self-efficacy. Results also show that knowledge was retained four weeks after training. In addition, VR training outperformed health and safety manuals regarding knowledge retention.

This study measures the practical performance to evaluate the effectiveness of the proposed VR training system. Also, this study compares the VR training system with a traditional training method by measuring knowledge acquisition and retention. The results demonstrate the potential of VR as a training tool for excavation safety and hazards.

The purpose of this study is to increase the resilience capacity of residential health-care professionals to achieve intrapreneurial development in workers. Through training based on the development of emotional competencies and the application of mindfulness techniques, the aim is for the individual to become aware of his or her role, learn to manage emotions and reduce feelings of distress and anxiety.

The proposed programme has a duration of eight weeks. Every four days of training, mindfulness sessions are integrated, and at the beginning and end of the working day, 10 min are spent with superiors to give feedback on what happened during the day and the setting of new objectives. A control group is also established where they do not undergo such training. After the delivery of the programme, the results obtained after the application of the methodology to a sample of 91 residential health-care professionals are presented. By means of a validated resilience questionnaire composed of 25 items, the aim is to measure the resilience capacity of the participants before and after training and to observe the impact of the programme.

The results of this study show that the training has led to an improvement in the overall resilience capacity by 3.93% and has been able to reduce the existing gap between those over 45 years of age and younger people, although the age-related variable still represents a significant difference.

This study provides an innovative way of fostering entrepreneurship. While participants work on resilience management through mindfulness techniques, organisational commitment is achieved.

This paper aims to present a systematic literature review and bibliometric analysis of research papers published on chat generative pre-trained transformer (ChatGPT), an OpenAI-developed large-scale generative language model. The study’s objective is to provide a comprehensive assessment of the present status of research on ChatGPT and identify current trends and themes in the literature.

A total of 328 research article data was extracted from Scopus for bibliometric analysis, to investigate publishing trends, productive countries and keyword analysis around the topic and 34 relevant research publications were selected for an in-depth systematic literature review.

The findings indicate that ChatGPT research is still in its early stages, with the current emphasis on applications such as natural language processing and understanding, dialogue systems, speech processing and recognition, learning systems, chatbots and response generation. The USA is at the forefront of publishing on this topic and new keywords, e.g. “patient care”, “medical”, “higher education” and so on are emerging themes around the topic.

These findings underscore the importance of ongoing research and development to address these limitations and ensure that ChatGPT is used responsibly and ethically. While systematic review research on ChatGPT heralds exciting opportunities, it also demands a careful understanding of its nuances to harness its potential effectively.

Overall, this study provides a valuable resource for researchers and practitioners interested in ChatGPT at this early stage and helps to identify the grey areas around this topic.

As global issues such as climate change, economic growth, social equality and the wealth gap are widely discussed, education for sustainable development (ESD) allows every human being to acquire the knowledge, skills, attitudes and values necessary to shape a sustainable future. It also requires participatory teaching and learning methods that motivate and empower learners to change their behavior and take action for sustainable development. Teachers have begun rating pupils based on peer assessment for open evaluation. Peer assessment enables students to transition from passive to active feedback recipients. The assessors improve critical thinking and encourage introspection, resulting in more significant recommendations. However, the quality of peer assessment is variable, resulting in reviewers not recognizing the remarks of other reviewers, therefore the benefits of peer assessment cannot be fulfilled. In the past, researchers frequently employed post-event questionnaires to examine the effects of peer assessment on learning effectiveness, which did not accurately reflect the quality of peer assessment in real time.

This study employs a multi-label model and develops a self-feedback system in order to use the AIOLPA system in the classroom to enhance students' learning efficacy and the validity of peer assessment.

The research findings indicate that the better peer assessment through the rapid feedback system, for the evaluator, encourages more self-reflection and attempts to provide more ideas, so bringing the peer rating closer to the instructor rating and assisting the evaluator. Improve self-evaluation and critical thinking for the evaluator, peers make suggestions and comments to help improve the work and support the growth of students' learning effectiveness, which can lead to more suggestions and an increase in the work’s quality.

ESD consequently promotes competencies like critical thinking, imagining future scenarios and making decisions in a collaborative way. This study builds an online peer assessment system with a self-feedback mechanism capable of classifying peer comments, comparing them with scores in a consistent manner and providing prompt feedback to critics.

This study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.

The authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.

The empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.

The results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

The purpose of this paper is to identify the factors determining students’ attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model.

The recommended model was empirically tested with partial least squares structural equation modeling using 375 student survey responses.

The study revealed that students have a favorable view of the instructional use of ChatGPT. Usefulness, social presence and legitimacy of the tool, as well as enjoyment and motivation, contribute to a favorable attitude toward using this tool in a learning environment. However, perceived ease of use was not found to be a significant determinant in the adoption and utilization of ChatGPT by the students.

This research is intended to benefit enterprises, academic institutions and the global community by offering light on how students perceive the ChatGPT service in an educational setting. Furthermore, the application enhances confidence and interest among learners, leading to improved literacy and general awareness. Eventually, the outcome of this research will help AI developers to improve their product and service delivery, as well as benefit regulators in regulating the usage of AI-based bots.

Due to its novelty, the current research on AI-based ChatGPT usage in the education sector is rather restricted. This study provides the adoption aspects of ChatGPT, a new AI-based technology for students, thereby contributing significantly to the existing research on the adoption of advanced education technologies. In addition, the literature lacks research on the adoption of ChatGPT by students for educational purposes; this study addresses this gap by identifying adoption determinants of ChatGPT in education.

The contemporary business environment is characterised by an increasing reliance on artificial intelligence, automation, optimisation, efficient communication and data-driven decision making. Based on the limited academic literature that examines the managerial perspective on enterprise chatbots, the paper aims to explore organisational needs and expectations for enterprise chatbots from a managerial perspective, assesses the relationship between managerial knowledge and managerial opinion regarding enterprise chatbots, and delivers a framework for integrating chatbots into the digital workforce.

The paper presents a quantitative design. An online, self-administered survey yielded 111 valid responses from managers in service and manufacturing organisations based on convenience and snowball sampling strategies. Given the nature of the data and the research questions, the research was conducted using principal component analysis, parallel analysis, correlation, internal consistency and difference in means tests.

This research explores the managerial perspective on enterprise chatbots from multiple perspectives (i.e., adoption, suitability, development requirements, benefits, barriers, performance and implications), presents a heat map of the average level of chatbot need across industries and business units, highlights the urgent need for education and training initiatives targeted at decision makers, and provides a strategic framework for successful chatbot implementation.

This study equips managers and practitioners dealing with enterprise chatbots with knowledge to effectively leverage the expected benefits of investing in this technology for their organisations. It offers direction for developers in designing chatbots that align with organisational expectations, capabilities and skills.

Insights for managers, researchers and chatbot developers are provided. The work complements the few academic studies that examine enterprise chatbots from a managerial perspective and enriches related commercial studies with more rigourous statistical analysis. The paper contributes to the ongoing discourse on decision-making in the context of technology development, integration and education.

This study evaluated the impact of a faculty training program on student assessment using the Kirkpatrick model.

A self-reported survey assessed 111 Saudi and non-Saudi participants' satisfaction. Subjective and objective measures (self-reported measures, assessment literacy inventory and performance-based assessment tasks) gauged participants' learning level. Pre- and post-training data were collected from 2020 to 2022.

A highly significant effect on satisfaction (>80%) and learning levels was observed, as manifested by workplace practices of student assessment (>70%, the cut-off score). Pre- and post-training comparisons of participants' satisfaction and assessment literacy scores showed significant improvements following training. Multiple regression analyses showed no significant effects for gender and educational attainment but a substantial impact of academic cluster on participants' student assessment skills.

Long-term effects of training faculty on assessment practices and student achievement will be studied at the institutional level in future research.

The current study contributes to human capital investment via faculty training on student assessment, helping them comply with assessment best practices. This assures the quality, fairness and consistency of assessment processes across disciplines in higher education institutions, enhances assessment validity and trust in educational services and may support institutional accreditation.

This study provides opportunities for sharing best practices and helps establish a community of practice. It enhances learning outcomes achievement and empowers higher education graduates with attributes necessary to succeed in the labor market. The human capital investment may have a long-term impact on overall higher education quality.

This study contributes to the scarce literature investigating the impact of training faculty from different clusters on student assessment using subjective and objective measures. It provides developing and evaluating a long-term student assessment program following the Kirkpatrick model.