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Open Access
Article
Publication date: 23 January 2024

Vince Szekely, Lilith A. Whiley, Halley Pontes and Almuth McDowall

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Journal of Work-Applied Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2205-2062

Keywords

Open Access
Article
Publication date: 16 August 2022

Doreen Bredenkamp, Yvonne Botma and Champion N. Nyoni

There is a need for higher education to produce graduates who are motivated to transfer learning into the workplace. Motivated graduates are work-ready and associated with…

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Abstract

Purpose

There is a need for higher education to produce graduates who are motivated to transfer learning into the workplace. Motivated graduates are work-ready and associated with increased performance. Presently, the research field around motivation to transfer learning by students in higher education is not clear and is inconsistent.

Design/methodology/approach

This scoping review provides an overview of the characteristics of the literature, including key concepts, recommendations and gaps based on eight published articles on the motivation of students in higher education to transfer learning.

Findings

The results reflected a research field, which focused primarily on the influence of specific factors, namely student characteristics, educational design, the workplace environment, and on higher education students' motivation to transfer learning. The lack of a shared conceptual definition of motivation to transfer learning in higher education appears to influence the description of the results from the included studies. Most of the previous studies applied rigorous research designs.

Originality/value

This seemingly stunted research field related to higher education students' motivation to transfer learning needs to be amplified to influence the development of work-ready graduates from higher education. Approaches towards including all elements of motivation, expanding to other fields in higher education, including low-income countries, may be a proximal step in enhancing the trajectory of this research field.

Details

Higher Education, Skills and Work-Based Learning, vol. 13 no. 1
Type: Research Article
ISSN: 2042-3896

Keywords

Open Access
Article
Publication date: 15 August 2019

Yi-Ling Lai and Stephen Palmer

The purpose of this paper is to identify essential psychological-informed executive coaching approaches that enhance the organisational learning and development process and…

15395

Abstract

Purpose

The purpose of this paper is to identify essential psychological-informed executive coaching approaches that enhance the organisational learning and development process and outcomes through integrating existing research evidence. Since coaching has been widely used in leadership development related areas and previous studies confirmed that this generates positive effects on individual-level learning in the organisational setting. The identified frameworks and influential factors outlined in this paper can serve as explicit guidelines for the organisation and management team when setting selection and evaluation benchmarks for employing executive coaches.

Design/methodology/approach

An integrated review approach was applied to narratively synthesise 234 (k=234) identified peer-review articles between 1995 and 2018. This review followed a rigorous protocol that the authors consulted ten (n=10) experts in the field. Both qualitative and quantitative psychological-focused research evidence was included in this study.

Findings

First, certain psychological approaches, such as cognitive behavioural, solution-focused, GROW and strength-based approaches, were highlighted in current research evidence. Second, the essential factors and skills, for instance, building trust, transparency and rapport, and facilitating learning were identified. Third, the main organisational learning and development outcome evaluation methods were outlined in this review, such as the self-efficacy scale, organisational commitment, workplace psychological well-being, 360-degree feedback and the Multifactor Leadership Questionnaire.

Research limitations/implications

It is always challenging to integrate research evidence on coaching because of the diversity of theoretical disciplines upon which coaching interventions draw. Therefore, it is difficult to generate a meta-analytic review which can generate statistical results. This review also reveals room for improvement in the quality of existing coaching evidence in accordance with the criteria for evidence-based management or practice (Briner et al., 2009), such as research methodology and evaluation design. Moreover, there is a lack of evidence on this reflective process which helps professional coaches to ensure the quality of their practice and organisational support.

Practical implications

This review offers a new perspective on the role psychology plays in the organisational learning and development practices. The identified coaching approaches, influential interpersonal skills and outcome evaluation methods can serve as practical guidelines when applying external coaching to facilitate a better organisational learning and development process and outcome.

Originality/value

This is the first literature review to focus on contemporary psychological-informed coaching evidence (between 1995 and 2018) in the workplace setting. Despite the rapid growth in demand for professional coaching practitioners (International Coach Federation, 2016), there is a lack of research-informed evidence to overcome the challenges faced by organisations when employing external coaches, such as what selection criteria or evaluation benchmarks to use. This review takes a practical perspective to identify essential body of knowledge and behavioural indicators required for an executive coach to facilitate an effective learning and development outcome.

Details

Journal of Work-Applied Management, vol. 11 no. 2
Type: Research Article
ISSN: 2205-2062

Keywords

Open Access
Article
Publication date: 4 August 2020

Alessandra Lumini, Loris Nanni and Gianluca Maguolo

In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning…

2257

Abstract

In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional Neural Network (CNN) models, fine-tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning CNNs for underwater imagery analysis, the opportunity of using different datasets for pre-training models, the possibility to design an ensemble using the same architecture with small variations in the training procedure.

Our experiments, performed on 5 well-known datasets (3 plankton and 2 coral datasets) show that the combination of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report the performance of both the single CNN and the ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model. The MATLAB source code is freely link provided in title page.

Details

Applied Computing and Informatics, vol. 19 no. 3/4
Type: Research Article
ISSN: 2634-1964

Keywords

Open Access
Article
Publication date: 14 June 2019

Jaswant Kaur Bajwa, Bobby Bajwa and Taras Gula

The purpose of this paper is to describe the components, structure and theoretical underpinnings of a cognitive remediation intervention that was delivered within a supported…

1568

Abstract

Purpose

The purpose of this paper is to describe the components, structure and theoretical underpinnings of a cognitive remediation intervention that was delivered within a supported education program for mental health survivors.

Design/methodology/approach

In total, 21 participants enrolled in the course Strengthening Memory, Concentration and Learning (PREP 1033 at George Brown College (GBC)) with the diagnosis of depression, anxiety, PTSD, ED and substance use disorder were included in the research. After a baseline assessment, participants completed 14 week cognitive remediation training (CRT) protocol that included six essential components that were integrated and implemented within the course structure of the supported education program at GBC. This was followed by a post-training assessment.

Findings

Analysis of the participants’ performance on CRT protocol using computerized games showed little significant progress. However, the research found a positive change in the self-esteem of the participants that was statistically significant and the findings also aligned with the social and emotional learning framework.

Research limitations/implications

One of the limitations in the research was the use of computer-assisted cognitive remediation in the form of the HappyNeuron software. The value and relevance of computer assisted needs are to be further examined. It seems that the implementation of the course that explicitly address cognitive challenges creates a supportive environment can be helpful.

Practical implications

Despite the mixed results and the few limitations associated with the CRT intervention reported in the research, the study offers reminders of the complexity of cognitive remediation and all the factors involved that need to be taken into consideration.

Social implications

This research created explicit space for addressing some of the implicit assumptions about the cognitive abilities when in post-secondary education.

Originality/value

This work is based on author’s previous work on cognitive remediation research within the supported education setting.

Details

Journal of Research in Innovative Teaching & Learning, vol. 12 no. 2
Type: Research Article
ISSN: 2397-7604

Keywords

Open Access
Article
Publication date: 19 December 2023

Qinxu Ding, Ding Ding, Yue Wang, Chong Guan and Bosheng Ding

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive…

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Abstract

Purpose

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Design/methodology/approach

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

Findings

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

Practical implications

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

Originality/value

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

Details

Journal of Electronic Business & Digital Economics, vol. 3 no. 1
Type: Research Article
ISSN: 2754-4214

Keywords

Open Access
Article
Publication date: 4 June 2019

Aideen Ruttledge and John Cathcart

At present, there is no research to support teachers’ use of sensory interventions in the classroom. This study aims to investigate the extent to how participation in a sensory…

5159

Abstract

Purpose

At present, there is no research to support teachers’ use of sensory interventions in the classroom. This study aims to investigate the extent to how participation in a sensory processing training session would improve teachers’ competence, confidence and practice towards supporting children with autism.

Design/methodology/approach

A pilot study design with mixed qualitative and quantitative methods was used to evaluate the impact of sensory processing training on six teachers who taught at least one child with autism in a mainstream school. The Autism Education Trust Competency Framework and face-to-face semi-structured interviews were completed with participants both pre (Time 1) and post (Time 2) training session.

Findings

Quantitative findings presented statistically significant differences (p < 0.05) in results with large effect sizes in the areas of confidence, knowledge, implementing sensory strategies, adjusting sensory environments, reviewing and reflecting. Qualitative data provided by participants corroborated this and indicated a need for further and more detailed training in the area. There was no change in the practice of teachers consulting with pupils about their sensory needs.

Practical implications

This study found that the attendance of teachers at sensory processing training is justified and the promotion of sensory processing training is therefore warranted.

Originality/value

Findings of this pilot study indicate that sensory processing training for teachers does improve competence, confidence and practice towards supporting children with autism. Review of the session to allow more detail, including consulting with the children themselves, is recommended.

Details

Irish Journal of Occupational Therapy, vol. 47 no. 1
Type: Research Article
ISSN: 2398-8819

Keywords

Open Access
Article
Publication date: 1 July 2021

Marissa Orlowski

The purpose of this mixed-methods explanatory sequential study was to assess the effects of an external wine education and certification program on trainee reactions, learning…

1196

Abstract

Purpose

The purpose of this mixed-methods explanatory sequential study was to assess the effects of an external wine education and certification program on trainee reactions, learning, transfer and financial results.

Design/methodology/approach

The quantitative phase was a mixed experimental design in which the training intervention was between-subjects and time was within-subjects. The sample comprises 91 employees (NTraining = 43; NControl = 48) from 12 units of a fine dining restaurant group. The qualitative phase, comprised of semi-structured interviews with training group participants (N = 12), was implemented after the experiment.

Findings

Training group participants reported high scores for attitude toward training content, instructional satisfaction and transfer motivation. Financial metrics, tracked up to 60 days post-training, demonstrated the wine education program was effective in increasing wine knowledge but not wine sales. Four themes emerged from the qualitative data: sense of accomplishment, enhanced guest interaction, tips and gratuities and defeat. Integrated findings revealed increased wine knowledge led to personal financial impact (increased tips) rather than organizational impact.

Originality/value

This research builds on existing training literature and human capital theory by examining external training programs. Further, the use of a mixed-methods design and integration of the quantitative and qualitative findings offers a previously unidentified explanation for why wine training, although effective in facilitating positive reactions and learning, fails to result in transfer behaviors which generate increased wine sales.

Details

International Hospitality Review, vol. 36 no. 2
Type: Research Article
ISSN: 2516-8142

Keywords

Open Access
Article
Publication date: 19 August 2022

Marlon Santiago Viñán-Ludeña and Luis M. de Campos

The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative…

3043

Abstract

Purpose

The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users.

Design/methodology/approach

The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models.

Findings

The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated.

Research limitations/implications

The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place.

Practical implications

The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions.

Originality/value

This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.

研究目的

这项工作的主要目的是使用情感分析技术和来自 Twitter 和 Instagram 的数据来分析旅游目的地, 以便找到最具代表性的实体(或地点)和用户的感知(或方面)。

研究设计/方法/途径

我们使用 90,725 个 Instagram 帖子和 235,755 个 Twitter 推文来分析格拉纳达(西班牙)的旅游业, 以确定旅行者在两个社交媒体网站上提到的重要地点和看法。我们使用了几种方法对英语和西班牙语文本进行情感分类, 包括深度学习模型。

研究发现

测试集中的最佳结果是使用来自Transformers (BERT) 模型的双向编码器表示 (BERT) 用于西班牙语文本和Tweeteval 用于英语文本, 这些结果随后用于分析我们的数据集。然后可以确定最重要的实体和方面, 这反过来又为研究人员、从业人员、旅行者和旅游管理者提供了有趣的见解, 从而可以改进服务并制定更好的营销策略。

研究局限性

我们提出了一个用于执行情感分类的西班牙旅游 BERT 模型, 以及通过主题标签找到地点并揭示每个地点的重要负面方面的过程。

实践意义

该研究使管理人员和从业人员能够使用我们发布的西班牙旅游数据集实施西班牙-BERT 模型, 以便在应用程序中采用该数据集, 以找到正面和负面的看法。

研究原创性

本研究提出了一种如何在旅游领域应用情感分析的新方法。首先, 介绍了评估不同现有模型和工具的方法; 其次, 使用 BERT(深度学习模型)训练模型; 第三, 提出了如何通过标签识别目的地地点的接受度的方法, 最后通过实体和方面的识别来评估用户表达积极性(消极性)的原因。

Details

Journal of Hospitality and Tourism Technology, vol. 13 no. 5
Type: Research Article
ISSN: 1757-9880

Keywords

Open Access
Article
Publication date: 27 June 2023

Dawid Booyse and Caren Brenda Scheepers

While artificial intelligence (AI) has shown its promise in assisting human decision, there exist barriers to adopting AI for decision-making. This study aims to identify barriers…

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Abstract

Purpose

While artificial intelligence (AI) has shown its promise in assisting human decision, there exist barriers to adopting AI for decision-making. This study aims to identify barriers in the adoption of AI for automated organisational decision-making. AI plays a key role, not only by automating routine tasks but also by moving into the realm of automating decisions traditionally made by knowledge or skilled workers. The study, therefore, selected respondents who experienced the adoption of AI for decision-making.

Design/methodology/approach

The study applied an interpretive paradigm and conducted exploratory research through qualitative interviews with 13 senior managers in South Africa from organisations involved in AI adoption to identify potential barriers to using AI in automated decision-making processes. A thematic analysis was conducted, and AI coding of transcripts was conducted and compared to the manual thematic coding of transcripts with insights into computer vs human-generated coding. A conceptual framework was created based on the findings.

Findings

Barriers to AI adoption in decision-making include human social dynamics, restrictive regulations, creative work environments, lack of trust and transparency, dynamic business environments, loss of power and control, as well as ethical considerations.

Originality/value

The study uniquely applied the adaptive structuration theory (AST) model to AI decision-making adoption, illustrated the dimensions relevant to AI implementations and made recommendations to overcome barriers to AI adoption. The AST offered a deeper understanding of the dynamic interaction between technological and social dimensions.

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