The intellectual structure of gender equality research in the business economics literature

Diéz‐Martín, Francisco; Miotto, Giorgia; Del-Castillo-Feito, Cristina

doi:10.1007/s11846-023-00671-8

Cited by 11 publications

(4 citation statements)

References 167 publications

(200 reference statements)

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“…Moreover, although the sample size is very small, the predictors are limited, and the test dataset is highly concentrated (almost all participants come from the academic environment), this experience illustrates how easy it is to use machine learning to personalize training proposals and course delivery based on students' interests. In analyzing motivation, future research may take into account the gender issue, contrasting differences between business students (Díez-Martín, et al 2023).…”

Section: Conclusion Discussion Limitationsmentioning

confidence: 99%

Motivation and personalization of teaching with machine learning

Gómez Martínez,

Medrano García,

Aznar Sánchez

2023

JMBE

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The motivation of the student causes the teaching experience to be more enjoyable for the student and results in better utilization of the teaching activity. The key is to identify where that motivation lies in order to adapt the content to the student's expectations. The objective of this work is to establish a method to identify the student's motivation regarding the training they are going to receive and be able to personalize the learning experience according to this motivation. To achieve this, we describe an experience in which a machine learning model of decision trees was trained using a voluntary survey generated through LinkedIn. By consulting the LinkedIn profiles of the respondents, a training dataset was created, which resulted in a model that achieved a 72% accuracy rate in a 10-fold stratified cross-validation. During the presentation of the students who enrolled in the activity, the necessary information was captured to generate a test dataset, which was used to validate the trained model. The accuracy rate of this validation was 100%. Although the sample size and predictors used are limited, we believe that this experience sufficiently illustrates the potential of artificial intelligence to identify student motivations and thus personalize the teaching experience, with the aim of increasing motivation and improving student performance.

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Section: Conclusion Discussion Limitationsmentioning

confidence: 99%

Motivation and personalization of teaching with machine learning

Gómez Martínez,

Medrano García,

Aznar Sánchez

2023

JMBE

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“…The final round aimed to 100% consensus, striving to resolve all discrepancies through comprehensive discussions. As previous research (Cruz-Suárez et al, 2020; Díez-Martín et al, 2023; Savin et al, 2022), we have analysed the common links between the papers of each cluster to distil their key aspects and enhance the robustness of the cluster labelling process. This procedure made it possible to summarise the essence of each research cluster.…”

Section: Methodsmentioning

confidence: 99%

The intellectual structure of destination image research in tourism (2001–2023): Background, pre-pandemic overview, shifts during COVID-19 and implications for the future

Torres-Pruñonosa,

Batlle,

De Esteban Curiel

et al. 2023

Journal of Vacation Marketing

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We aim to map the intellectual structure of destination image (DI) research and its theoretical development in two longitudinal periods: firstly, from 2001 and before the outbreak of Covid-19 and approval of vaccines (2001–2020); and secondly, during Covid-19 (2021–2023). This is the first article to use co-citation analysis that focuses solely on DI, identifying the main clusters, intellectual turning points, and citation burst papers in this field. Studying bibliometrics pre- and during Covid-19 can help to understand the impact of the pandemic on research output and the shift in research focus. This methodology expands tourism science by recognising the intrinsic nature of DI from an evaluative and relational point of view. Co-citation refers to the cited not the citing papers. The results in the first period show: 1) theoretical background on DI associated with branding from Destination Management Organisations (DMO); 2) the components of DI and their relationship with visitor behaviour; 3) how the Internet and User-Generated Content (UGC) have become the main sources to perceive DI. Whereas, in the second period, two main shifts have been identified: 1) the impact of the pandemic on tourism and perceived travel risk; 2) the emergence of a new approach focusing on the engagement of people with the destination through life experiences. The conclusions could help suppliers, DMOs, and policymakers to understand the components of DI before and during the pandemic, as well as provide valuable insights for the tourism industry to adapt to the new normal.

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“…This research is growing, and it is even more interesting if it involves school green leadership, green engagement of teachers and school employees, it is also even more interesting if it is examined in terms of gender equality as an implicator and driving force for the dissemination (Díez-Martín et al, 2023) of green knowledge to students in schools.…”

Section: Future Researchmentioning

confidence: 99%

The influence of green shared vision, education and training, and green recruitment, on organizational citizenship behavior for environment in elementary schools in Indonesia.

Nur Rahman,

Permana

2023

JMBE

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Managing human resources in schools as a basis for instilling knowledge becomes a strategic phenomenon. The importance of these human resources needs to start with a sustainable development plan, with green regulations and a green environment. Analysis of the influence of Green Shared Vision, Education and Training and Green Recruitment on Organizational Citizenship Behavior for the Environment is the main objective of this research. To achieve the research objectives, the object of this study were all elementary school employees, Yogyakarta Province - Indonesia, with a sample calculation using the slovin method of 163 workers in the school. Retrieval of data using purposive random sampling method and cluster sampling technique. With 23 indicators of 4 variables measured by simantec scale. The analysis tools used are validity and reliability, using Variance Based Structural Equation Modeling (SEM) analysis, with the SmartPLS version 4 software tool. The research results show that Green Shared Vision has no effect on Organizational Citizenship Behavior for Environment, Education and Training, Green Recruitment has a positive and significant effect on Organizational Citizenship Behavior for Environment.

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The intellectual structure of gender equality research in the business economics literature

Cited by 11 publications

References 167 publications

Motivation and personalization of teaching with machine learning

Motivation and personalization of teaching with machine learning

The intellectual structure of destination image research in tourism (2001–2023): Background, pre-pandemic overview, shifts during COVID-19 and implications for the future

The influence of green shared vision, education and training, and green recruitment, on organizational citizenship behavior for environment in elementary schools in Indonesia.

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