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Vocational education and entrepreneurship are constantly increasing in research fields. However, what is the current state of vocational education and entrepreneurial research? Where will the area go next? These questions are still unanswered; thus, this study tries to map the research landscape of the study area to get insights and provide directions for future research.

This research collected extant literature on vocational education and entrepreneurship using Scopus scientific database. Bibliometric analysis has been performed to extract insights from 175 documents published in the study area. Content analysis on the extant literature has also been committed to getting contextual information and developing an integrated research framework for future researchers.

The bibliometric analysis revealed that training, career choice, curriculum, self-employment, student psychology, better job opportunity, learning environment and innovation are the most discussed in the vocational education and entrepreneurship literature. Developed nation’s strong presence, indicated by the number of publications in the field.

This study significantly contributes to entrepreneurship by disclosing advances in the literature and some of the most active research fronts in this sector, delivering insights that have yet to be wholly appreciated or appraised. The study also developed an integrated framework that could benefit various vocations, education and entrepreneurship stakeholders.

The purpose of this study was to identify the relationship between training and financial performance in cooperative organizations.

To achieve this goal, the fixed-effect panel regression technique was used, from a single database containing hours and amounts invested in training by 35 large Brazilian agribusiness cooperatives over 10 years as the main independent variable of the econometric model. Financial performance was operationalized by the Net Margin and ROE.

It was possible to identify a positive relationship between expenditure on training and the future rate of return and profitability of the organizations in question. The results also indicate that this relationship grows stronger over the first three years after the investments are made and ceases to exist after this period. The findings are robust with regard to a series of alternative explanations and contribute to understanding the relationship between training and organizational performance in financial terms, considering the extent and duration of training.

The originality this study is justified by the pioneering spirit of presenting direct evidence linking investment in training and financial performance and the duration of this relationship. Thus, the study makes a significant contribution to the construction of knowledge on the subject.

Healthcare tribalism refers to the phenomenon through which different groups in a healthcare setting strictly adhere to their profession-based silo, within which they exhibit stereotypical behaviours. In turn, this can lead to deleterious downstream effects upon productivity and care delivered to patients. This study highlights a clinician-led governance model, implemented at a National Health Service (NHS) trust, to investigate whether it successfully overcame tribalism and helped drive innovation.

This was a convergent mixed-methods study including qualitative and quantitative data collected in parallel. Qualitative data included 27 semi-structured interviews with representatives from four professional groups. Quantitative data were collected through a verbally administered survey and scored on a 10-point scale.

The trust arranged its services under five autonomous business units, with a clinician and a manager sharing the leadership role at each unit. According to interviewees replies, this equivalent authority was cascaded down and enabled breaking down professional siloes, which in turn aided in the adoption of an innovative clinical model restructure.

This study contributes to the literature by characterizing a real-world example in which healthcare tribalism was mitigated while reflecting on the advantages yielded as a result.

Previous studies from all over the world identified major differences in the perspectives of different healthcare professional groups. In the United Kingdom, clinicians largely felt cut off from decision-making and dissatisfied with their managerial role. The study findings explain a governance model that allowed harmony and inclusion of different professions. Given the long-standing strains on healthcare systems worldwide, stakeholders can leverage the study findings for guidance in developing and implementing innovative managerial approaches.

In this chapter, we focus on specially designed instruction (SDI) as a core value for the field of specific learning disabilities (SLD). SDI is at the heart of special education, and the field of LD has been built on the core value that effective instruction improves student outcomes. We describe a two-step test and an extended example of what is and is not SDI for Matt, a student with an SLD. Finally, we discuss some of the confusion surrounding SDI and the need for the field to return to its core value of individualized, intentional, targeted, evidence- or high leverage practice–based, and systematic instruction for students with SLD.

The common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common techniques used in data mining to increase the accuracy of clustering. In this study, based on segmentation, selecting the best segments, and using ensemble clustering for selected segments, a multistep approach has been developed for the whole clustering of time series data.

First, this approach divides the time series dataset into equal segments. In the next step, using one or more internal clustering criteria, the best segments are selected, and then the selected segments are combined for final clustering. By using a loop and how to select the best segments for the final clustering (using one criterion or several criteria simultaneously), two algorithms have been developed in different settings. A logarithmic relationship limits the number of segments created in the loop.

According to Rand's external criteria and statistical tests, at first, the best setting of the two developed algorithms has been selected. Then this setting has been compared to different algorithms in the literature on clustering accuracy and execution time. The obtained results indicate more accuracy and less execution time for the proposed approach.

This paper proposed a fast and accurate approach for time series clustering in three main steps. This is the first work that uses a combination of segmentation and ensemble clustering. More accuracy and less execution time are the remarkable achievements of this study.

This study explores the influence of the coronavirus disease 2019 (COVID-19) career experience on the investment behavior and risk tolerance of chief executive officers (CEOs). Specifically, this study focuses on CEOs' abilities to allocate financial assets and maintain solvency.

This study adopts a comprehensive approach to analyze financial assets and asset-to-liability ratios. Financial data and individual information of CEOs from listed companies are collected from 2020Q1 to 2021Q4, along with statistics on confirmed COVID-19 cases. Instrumental and alternative variables are used to examine the robustness and endogeneity of the research, ensuring a thorough analysis.

A significant positive correlation is revealed between CEOs' COVID-19 career experience and their capacity to effectively allocate financial assets. However, COVID-19 has a negative effect on firm performance in terms of solvency. These findings contribute to the empirical evidence linking the pandemic to company performance, representing part of the initial research in this area.

The study suggests that the implementation of potential policy implications, such as loose monetary policies and tax and fee reduction measures, may alleviate the tax burden on listed companies.

Although commitment and employability are legitimized in the current world of work, they also have a dark side that has been ignored in the extant literature. To tackle this gap, the study developed and examined a comprehensive theoretical framework including learning, motivation, commitment, employability and self-exploitation. Limited research exists that explicitly examines this relationship or explores its potential implications. The author theorizes employability as a cultural fantasy that ends up in self-exploitation.

The study concretizes Lacan's (1977; 1981 and 1988) psychoanalysis, utilizing a sample of 658 subjects from eight industries. The hypothesized relationships were examined using structural equation modeling (SEM) in AMOS.

The findings provided support for the hypothesized relationships. Employability escorts to self-exploitation. Those employees who try to remain relevant to their firms continue to engage in employability activities end up being exploited in this process.

The study provides a new roadmap to scholars of employability who wish to explore the domain further.

The theoretical knowledge from this research will inform practice. It will influence managers and policymakers in the organization as well as politicians. Although the macroaspects of the organizational environment are beyond the control of an organization, the development efforts of the organization should be real and should not estrange individuals from their true nature. The real intent should be to unite the individual with its true nature. This way, it will be real development and will empower individuals rather than exploitation.

The finding that commitment is linked to self-exploitation via employability has implications for managers and policymakers. To avoid estrangement and exploitation, the organization should focus on employee real development. To have an ideal workplace, where employees unite with their nature, the organization should invest in employees, focus on their real needs, emphasize their career prospects and constantly provide them with learning and growth opportunities. In addition to material compensation, the organization should connect people with their true spirit. An organization that is concerned with people's real needs and real development will have a pool of human capital that will create real value for the organization and society as well.

The dark side of employability has been ignored in the extant literature. Limited research exists that explicitly examines this relationship or explores its potential implications. This study is an initiative for such debate.

Built environments are highly vulnerable to climatic disasters such as extreme floods, droughts and storms. Inaccurate decisions in adopting emerging construction technologies can result in missed opportunities to improve the resilience of built environments. Therefore, understanding the effectiveness of emerging construction technologies in improving built environment resilience can help in making better strategic decisions at the national and organizational levels. This study aims to evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience.

A list of Construction 4.0 technologies was adopted from a national strategic plan. Then, the data were collected using the fuzzy technique for order preference by similarity to ideal solution technique from selected built environment experts to determine the relative effectiveness of Construction 4.0 technologies in improving built environment resilience.

Six Construction 4.0 technologies are critical in improving built environment resilience (in rank order): building information modeling, autonomous construction, advanced building materials, big data and predictive analytics, internet of Things and prefabrication and modular construction. In addition, adopting Construction 4.0 technologies collectively is crucial, as moderate to strong connections exist among the technologies in improving built environment resilience.

To the best of the authors’ knowledge, this is one of the first papers that evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience. Industry professionals, researchers and policymakers can use the study findings to make well-informed decisions on selecting Construction 4.0 technologies that improve built environment resilience to climatic disasters.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.