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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.

Public and private universities keep facilitating knowledge transfer and sharing within academic institutions. Multiple factors have been investigated to strengthen the infrastructure of these universities; however, the researchers have always been trying to explore the best one. Therefore, the purpose of this study is to investigate the influence of academic supports on motivation to learn (MTL) and transfer, in turn, influence transfer of training (TOT). Interestingly, the sector (i.e. public or private universities) unveils TOT to see whether the public sector has best practices or private.

This study adopts valid measurement instruments from the literature studies. This study pretests the validity and reliability of the instruments. This study administers the designed survey questionnaire among the faculty members of both public and private universities. This study uses a convenient sampling approach using a quantitative research method. By applying Smart partial least square (PLS) 3.3.3, this study uses structural equation modeling.

This study supports that organization, supervisor and peer support significantly and positively influence TOT. Additionally, MTL and motivation to transfer (MTT) significantly and positively mediate the link between TOT and organizational, supervisor and peer support. MTL also significantly and positively influences MTT. Most interestingly, the sector significantly and positively moderates the link between TOT and organizational, supervisor and peer support, MTL and transfer.

The results support the public and private universities that they should develop the infrastructure containing learning motivation and transfer for easy TOT. This would be more effective if the in higher educational institutions (HEIs) follow research findings.

This study empirically tests the impacts of academic supports on MTL and transfer, which boosts the TOT. The novelty of the research can be implemented in HEIs’ rules and regulations.

This study aims to explore how transformational leaders enhance public service employees’ growth satisfaction in the job and mitigate job stress and burnout, incorporating follower interpersonal communication satisfaction with the leader (IPCSL) as a mediator.

On the basis of the survey data collected from the Indian public service employees, regression analysis, bootstrapping and SOBEL test are used to test the proposed research model.

The findings highlighted a partial mediation of follower interpersonal communication satisfaction with leader between transformational leadership (TL) and public service employees’ growth satisfaction in the job. Although there was no significant direct effect of TL on job stress and burnout, the results underlined a significant indirect effect of follower IPCSL.

By examining the important role of follower IPCSL, this study unravels the precise intervening mechanism between TL and follower affective outcomes like growth satisfaction in job, job stress and burnout among public service employees.

This study aims to explore whether Chat Generative Pre-training Transformer (ChatGPT) can produce quantitative data sets for researchers who could behave unethically through data fabrication.

A two-stage case study related to the field of tourism was conducted, and ChatGPT (v.3.5.) was asked to respond to the first questionnaire on behalf of 400 participants and the second on behalf of 800 participants. The artificial intelligence (AI)-generated data sets’ quality was statistically tested via descriptive statistics, correlation analysis, exploratory factor analysis, confirmatory factor analysis and Harman's single-factor test.

The results revealed that ChatGPT could respond to the questionnaires as the number of participants at the desired sample size level and could present the generated data sets in a table format ready for analysis. It was also observed that ChatGPT's responses were systematical, and it created a statistically ideal data set. However, it was noted that the data produced high correlations among the observed variables, the measurement model did not achieve sufficient goodness of fit and the issue of common method bias emerged. The conclusion reached is that ChatGPT does not or cannot yet generate data of suitable quality for advanced-level statistical analyses.

This study shows that ChatGPT can provide quantitative data to researchers attempting to fabricate data sets unethically. Therefore, it offers a new and significant argument to the ongoing debates about the unethical use of ChatGPT. Besides, a quantitative data set generated by AI was statistically examined for the first time in this study. The results proved that the data produced by ChatGPT is problematic in certain aspects, shedding light on several points that journal editors should consider during the editorial processes.

本研究旨在探讨ChatGPT是否能够为那些可能通过数据伪造行为不道德的研究人员生成定量数据集。

本研究进行了与旅游领域相关的两阶段案例研究, 并要求ChatGPT（v.3.5.）代表400名参与者回答第一个问卷, 以及代表800名参与者回答第二个问卷。通过描述统计、相关分析、探索性因子分析、验证性因子分析和哈曼的单因素测试对人工智能生成的数据集的质量进行了统计测试。

结果显示, ChatGPT能够按照所需的样本大小水平回答问卷, 并以表格格式呈现生成的数据集, 以便进行分析。还观察到ChatGPT的回答是系统性的, 并且它创建了一个在统计上理想的数据集。然而, 本研究注意到所产生的数据在观察变量之间存在较高的相关性, 测量模型未能达到足够的拟合度, 并出现了共同方法偏差的问题。本研究得出的结论是, ChatGPT目前不能生成适用于高级统计分析的数据, 或者说不适合这样做。

本研究表明, ChatGPT可以为试图不道德地伪造数据集的研究人员提供定量数据。因此, 它为关于ChatGPT不道德使用的持续争论提供了一个新而重要的论点。此外, 在本研究中首次对由人工智能生成的定量数据集进行了统计检验。结果表明, ChatGPT生成的数据在某些方面存在问题, 为期刊编辑在编辑过程中考虑的几个要点提供了启示。

This study explores how lesson study (LS) can promote elementary Science, Technology, Engineering, and Mathematics (STEM) teachers’ professional development (TPD) in terms of new pedagogical practices, attitudes and beliefs in the maker education (ME) context.

This is a case study of a LS conducted in China involving four primary school teachers, 20 grade-4 students, and one researcher who also acted as a facilitator. This study adopted an integrated model that combined the unique characteristics of Chinese LS (CLS) with the Dutch LS (LSNL) model.

This study revealed that LS participation facilitates teachers’ integration of new ME pedagogical practices in their classrooms, while their attitudes and beliefs regarding teaching and learning are increasingly aligned with ME principles. However, challenges such as time constraints, lack of research skills, and insufficient learning resources have also been identified.

This was a small-scale study, which may limit the generalizability of the findings.

This study expands the use of LS in the ME context by highlighting its effectiveness in enhancing teachers’ PD in terms of new pedagogical practices, attitudes, and beliefs. It also recommends incorporating diverse international LS models to address the limitations associated with localized models of TPD.

The originality of this study lies in its adoption of an integrated LS model to enhance STEM teachers’ PD in an ME context. The findings of this study further strengthen evidence supporting the positive impact of LS on teachers’ PD.

To explore how motivational interviewing (MI) training might benefit the practice of COVID-19 contact tracers.

Following co-production of a MI training package, with a United Kingdom (UK) track and trace organisation, training was delivered virtually to 101 volunteer participants involved in contact tracing. Data were captured via an online survey, incorporating questions from recognised measures of occupational self-efficacy and workplace wellbeing, prior to the training. Open data fields were used to gather feedback about participants' reasons for attending, and views about the training afterwards.

Although the contact tracers reported high occupational self-efficacy and workplace wellbeing, both quantitative and qualitative data suggested participants saw practitioner value and utility in MI.

The sample was self-selecting and typically involved contact tracers from UK local authorities. The study did not measure impact on compliance with self-isolation guidance and/or providing details of contacts, and larger-scale research would be needed to establish this. This was not a pre-post-test evaluation study, and measures of occupational self-efficacy and workplace wellbeing were gathered to give insight into the sample and to test the feasibility of using this survey for a future large-scale study. The research was conducted during the height of the pandemic. While UK COVID-19 contact tracing services have since been reduced, there are potential implications for infection control more generally.

MI is potentially a useful approach for enhancing contact tracing practice. However, implementation factors should be carefully considered, to ensure effective and sustainable practice.

Improved practice in contact tracing could have potential benefits in infection control, through improving compliance with central guidance, although this requires more widespread investigation.

This is the first empirical study to investigate how MI training could benefit COVID-19 contact tracing practice.

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.

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.

The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.

First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”

This article aims at a Unified Theory of Acceptance and Use of Technology (UTAUT) model framework that was used to investigate the impact of a 16-h smartphone training program on the correlations among different constructs of smartphone use in a sample of older adults.

A total of 208 participants aged 60–78 (mean: 65.4) years completed a questionnaire that collected information on demographic variables and the frequency and duration of smartphone use as well as the answers to questions on the six UTAUT constructs of performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention and usage behavior. The data were analyzed using partial least squares analysis.

This study was the first to compare post-training changes in the correlations among UTAUT constructs. The results revealed significant post-training changes in all construct correlations. Behavioral intention and facilitating conditions were shown to significantly impact usage behavior both before and after training and performance expectancy was shown to impact behavioral intention before training. After training, both effort expectancy and social influence were found to impact behavioral intention significantly. Moreover, the impact of facilitating conditions on usage behavior was significantly increased after training.

To date, no study published in the literature has investigated the impact of technological training on the technology-use intentions and behaviors of older adults. The findings of this study suggest that, for older adults, the results of the acceptance and use model for smartphones change significantly and positively between pre-smartphone training and post-smartphone training time points. The findings support that technology training has a positive impact on smartphone use in older adults.