The change toward competence-based education has implications for teachers as well as school management. This study investigates which professional development activities teachers undertake related to this change and how these activities differ among schools with various human resource (HR) policies. Two types of HR policy were involved: (1) a government-enforced, national system of Integrated Personnel Management and (2) a voluntary, integrative approach of Schooling of teachers, Organizational development of schools and teacher training institutes, Action- and development-oriented research, and Professional development of teachers. Semi-structured interviews with 30 teachers in nine schools with different HR policies were held and analyzed both qualitatively and quantitatively. Findings show that teachers undertake professional development activities in five categories: maintaining knowledge base, applying and experimenting, reflection, collaboration, and activities indirectly related to teaching practice. Teachers' professional development activities were found to be relatively similar across schools with different HR policies. It is concluded that neither government-enforced nor voluntary HR policies seem to play much of a role in the participation by teachers in professional development activities. Implications for further research and school practice are discussed.
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Although the attention for neurodiversity in human resource management (HRM) is growing, neurodivergent individuals are still primarily supported from a deficit-oriented paradigm, which points towards individuals' deviation from neurotypical norms. Following the HRM process model, our study explored to what extent a strengths-based HRM approach to the identification, use, and development of strengths of neurodivergent groups is intended, implemented, and perceived in organizations. Thirty participants were interviewed, including HRM professionals (n=15), supervisors of neurodivergent employees (n=4), and neurodivergent employees (n=11). Our findings show that there is significant potential in embracing the strengths-based approach to promote neurodiversity-inclusion, for instance with the use of job crafting practices or (awareness) training to promote strengths use. Still, the acknowledgement of neurodivergent individuals' strengths in the workplace depends on the integration of the strengths-based approach into a supportive framework of HR practices related to strengths identification, use, and development. Here, particular attention should be dedicated to strengths development for neurodivergent employees (e.g., optimally balancing strengths use). By adopting the strengths-based HRM approach to neurodiversity as a means of challenging the ableist norms of organizations, we add to the HRM literature by contributing to the discussion on how both research and organizations can optimally support an increasingly diverse workforce by focusing on individual strengths
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This research focuses on exit choices within SMEs. In this study, “exit choice” refers to the decision to opt for either liquidation or sale of the firm. The predictions focus on human-capital and firm-resource variables. The hypotheses are tested on a set of 158 owners of small firms, the majority of which are micro-firms with 0–9 employees. The results of a series of binominal logistic regression analyses show that firm-resource characteristics (previous sales turnover, the firm’s independence from its owner, and firm size), together with one aspect of the owner’s specific human capital (the owner’s acquisition experience), predict exit choice. The conclusions have been made with caution, as the dataset is relatively small and the number of predictors is limited.
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The increasing use of AI in industry and society not only expects but demands that we build human-centred competencies into our AI education programmes. The computing education community needs to adapt, and while the adoption of standalone ethics modules into AI programmes or the inclusion of ethical content into traditional applied AI modules is progressing, it is not enough. To foster student competencies to create AI innovations that respect and support the protection of individual rights and society, a novel ground-up approach is needed. This panel presents on one such approach, the development of a Human-Centred AI Masters (HCAIM) as well as the insights and lessons learned from the process. In particular, we discuss the design decisions that have led to the multi-institutional master’s programme. Moreover, this panel allows for discussion on pedagogical and methodological approaches, content knowledge areas and the delivery of such a novel programme, along with challenges faced, to inform and learn from other educators that are considering developing such programmes.
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Human rights groups are increasingly calling for the protection of their right to privacy in relation to the bulk surveillance and interception of their personal communications. Some are advocating through strategic litigation. This advocacy tool is often chosen when there is weak political or public support for an issue. Nonetheless, as a strategy it remains a question if a lawsuit is strategic in the context of establishing accountability for indiscriminate bulk data interception. The chapter concludes that from a legal perspective the effect of the decision to litigate on the basis of the claim that a collective right to group privacy was violated has not (yet) resulted in significant change. Yet the case study, the British case of human rights groups versus the intelligence agencies, does seem to suggest that they have been able to create more public awareness about mass surveillance and interception programs and its side-effects
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This entry begins by reviewing the definitions of “human”, “environment” and “dichotomy”, consequently turning to the debates concerning the human–environment relationship. Synthesizing various studies, the capability of advanced tool use; language, hyper-sociality, advanced cognition, morality, civilization, technology, and free will are supposed to be distinctly human. However, other studies describe how nonhuman organisms share these same abilities. The biophysical or natural environment is often associated with all living and non-living things that occur naturally. The environment also refers to ecosystems or habitats, including all living organisms or species. The concepts of the biophysical or natural environment are often opposed to the concepts of built or modified environment, which is artificial - constructed or influenced by humans. The built or modified environment typically refers to structures or spaces from gardens to car parks. Today, one of the central questions in regard to human-environment dichotomies centres around the concept of sustainability. https://onlinelibrary.wiley.com/doi/book/10.1002/9781118924396 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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This longitudinal, quantitative study contributes to the debate on technology-based professional development by examining the extent to which a learning (LinkedIn) intervention in a university setting affects an individual’s social media use for professional development, and the extent to which this relates to self-reported employability. In addition, we investigated how this relationship is moderated by an individual’s motivation to communicate through social media (LinkedIn). Based on social capital theory and the conservation of resources theory, we developed a set of hypotheses that were tested based on longitudinal data collected from university employees (N = 101) in middle- and high-level jobs. First, in line with our expectations, social media use for professional development was significantly higher after the learning intervention than before. Second, partially in line with our expectations, social media use for professional development was positively related with the employability dimension anticipation and optimization. Third, contrary to our expectations, motivation to communicate through social media (LinkedIn) did not have a moderating role in this relationship. We concluded that the learning intervention has the potential to foster social media use for professional development, and in turn, can contribute to individuals’ human capital in terms of their employability. Hence, the intervention that forms the core of this empirical research can be a sustainable and promising human resource management (HRM) practice that fits the human capital agenda.
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This special issue (SI) aims to assemble a high-quality set of papers, which improve our understanding of how contextual factors impact the conceptualization, implementation and effectiveness of Talent Management. The context can be used several ways, e.g. to frame the relevance of the study, to interpret results or even by using theoretical frameworks in which the contextual factors and variables are incorporated.
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Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.
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