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The purpose of this paper is to provide a comprehensive framework that identifies thematic clusters and their interconnections within Global Talent Management (GTM), global careers and talent management (TM).

In this paper, this study conducted a co-citation analysis using bibliographic data to unveil the intellectual connections and relationships among thematic articles related to GTM sourced from the Web of Science.

This review highlights three key research themes: experiences working abroad, TM approaches and the complex nature of GTM as a living system.

The main limitation of this research is the sample itself. Content analysis based on the co-citation method resulted in some more recent releases being omitted.

The practical implications of the paper include providing a structured framework for understanding the complexities of GTM.

Research into the academic literature in this area is divided into various clusters, empirically demonstrating how GTM and global mobility are intertwined, revealing the need for us to more thoroughly comprehend the social ramifications of GTM practices and activities and the need to further analyse the influencing social aspects in a GTM strategy, like diversity, increased mobility and virtual reality.

The analysis revealed the emergence of three distinct thematic groups: (1) global work experiences, (2) TM approaches and (3) GTM.

Today, as we hurtle towards imminent planetary destruction in the age of the Anthropocene, we believe it may be instructive to try and understand if the ancient science of spirituality can prove useful in humankind's ability to change course, even at this late hour. We argue that such a paradigm shift is critically essential for human survival and that without the inner transformation proposed by this science, it may prove impossible to build a society based on the principles of liberty, equality and fraternity. This chapter draws from foundational texts and authoritative sources across multiple religious traditions, based upon which it outlines a brief sketch of the ancient science of spirituality. We begin with an account of the differentia specifica of this science, where we delve into what kind of science this is. Since it is centrally concerned with inner transformation, we briefly outline the theory of change embedded in this science and the kind of rejuvenation it enables, which makes it possible for us to clearly perceive the key elements and the structure of reality. We then spell out the impact this has on the nature of human action, continually teasing out implications for policy and practice in our time. We provide a few concrete illustrations of the same. Inter alia, we also show how many of these insights can be found even within modern scientific and philosophical traditions, thereby indicating possibilities of convergence and synthesis between ancient and modern science, following thereby the guidance of genuine spirituality.

The challenges facing 21st-century society are becoming increasingly complex, requiring the development of new citizen competencies. This study aims to validate an educational model focused on developing complex thinking in higher education students. Current educational models lack future-ready competencies, necessitating the emergence of new models to guide future generations toward the common good.

This was an adaptation of the causal-layered analysis (CLA) applied to 415 participants from higher education institutions in Mexico, Panama and Spain. Sessions were designed to present the proposed educational model and explore participants’ perceptions of its significance and contributions to future education.

Key findings include the following: participants perceived complexity as difficult and challenging; causes of problems were linked to outdated educational models requiring replacement by those that develop students’ competencies; participants envisioned changes that would develop individuals capable of understanding and transforming society; and participants recognized the model’s transformative potential, offering a novel proposal for 21st-century education.

This research sought to gather opinions from different stakeholders using the CLA methodology, providing a deep understanding of participants’ perspectives on the proposed solution.

This study aims to use a theoretical-based case study of two distinct ownership groups of the Jack Daniel’s brand to explore how rhetorical history (i.e. malleability of the past for strategic goals) may evoke and capitalize on different forms of nostalgia. Within, the authors configure four forms of nostalgia (i.e. personal, historical, collective and cultural) from the individual or collective interaction and level of direct experience one has with the past as lived or happened.

This study uses an historical research approach which involved the identification of primary and secondary sources, facility tour, source criticism and triangulation to create themes of rhetorical history infused with nostalgic narratives using compelling evidence through rich description of this fusion.

The findings reveal how nostalgia-driven narratives reflecting different collective longing for the re-creation of an American Paradise Lost used by Jack Daniel (i.e. the man) and later but differently by Brown-Forman. This study uncovers how the company’s inherited past was used rhetorically throughout its history, beginning with the nostalgic story of Jack Daniel and the distillery’s nostalgically choreographed location in Lynchburg, Tennessee. This study delves into this setting to highlight the importance of symbols, details, emotional appeals and communications for collective memory and identity development and to showcase the ways in which they are influenced by different types and forms of nostalgia.

This study adds to a limited number of studies focused on understanding the impact of founders on an organization’s brand and how that is malleable. This study responds to scholarly calls to study the influence of sequenced historical rhetoric on an organization and highlight the relevance of social emotions such as nostalgia for rhetorical history. Finally, the theoretical contribution involves the advancing and construction of a theory typology of nostalgia previously proposed by Havlena and Holak in 1996.

Strategic decisions leaders make involving organizational changes such as mergers and acquisitions (M&A), divestitures, and downsizing, which can influence and/or interact with other organizational factors. For example, within the context of M&A, changes impact financial performance, firm behaviors, and organizational culture. In addition, strategic decisions for these types of change can also interrelate with other more intrapersonal factors, including both leaders’ and employees’ health and well-being. Employee stress, also referred to as “merger syndrome,” outlines individual negative impacts of the changes including, but not limited to, cynicism and distrust, change wariness, and burnout, all accumulating to psychological effects including increases in detachment to work, stress, and sick leave. In this chapter, the authors outline the different impacts M&A phases have on stress and well-being and how they interrelate with the strategic decisions leaders make. The authors also outline future research opportunities and practical implications for how leaders and employees could better manage future major changes such as M&A activities.

This viewpoint article explores the transformative capabilities of large language models (LLMs) like the Chat Generative Pre-training Transformer (ChatGPT) within the property valuation industry. It particularly accentuates the pivotal role of prompt engineering in facilitating valuation reporting and advocates for adopting the “Red Book” compliance Chain-of-thought (COT) prompt engineering as a gold standard for generating AI-facilitated valuation reports.

The article offers a high-level examination of the application of LLMs in real estate research, highlighting the essential role of prompt engineering for future advancements in generative AI. It explores the collaborative dynamic between valuers and AI advancements, emphasising the importance of precise instructions and contextual cues in directing LLMs to generate accurate and reproducible valuation outcomes.

Integrating LLMs into property valuation processes paves the way for efficiency improvements and task automation, such as generating reports and drafting contracts. AI-facilitated reports offer unprecedented transparency and elevate client experiences. The fusion of valuer expertise with prompt engineering ensures the reliability and interpretability of valuation reports.

Delineating the types and versions of LLMs used in AI-generated valuation reports encourage the adoption of transparency best practices within the industry. Valuers, as expert prompt engineers, can harness the potential of AI to enhance efficiency, accuracy and transparency in the valuation process, delivering significant benefits to a broad array of stakeholders.

The article elucidates the substantial impact of prompt engineering in leveraging LLMs within the property industry. It underscores the importance of valuers training their unique GPT models, enabling customisation and reproducibility of valuation outputs. The symbiotic relationship between valuers and LLMs is identified as a key driver shaping the future of property valuations.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

This study aims to elucidate the process and internal mechanism of place identity construction in traditional villages under the impact of tourism by taking Cuandixia village as a case. The research methods comprise participatory observation and in-depth interviews with the residents. The main results are as follows: the impact of tourism on traditional villages is mainly reflected in space reconstruction, livelihood change, social relations restructuring and culture change; under the impact of tourism, the representation of residents’ identity construction shows complexity, with positive and negative effects; and the place identity construction of residents affects their perception of and attitudes toward tourism. Moreover, self-esteem and self-efficacy principles play a key role in their perception of tourism. This study provides some reference for further investigation of the tourism development model and the mental mechanism of residents in traditional villages.

This paper aims to explore the evolutionary dynamics of innovation ecosystems in the food industry by adopting both open innovation and convergence approaches to derive practical policy implications to develop impactful innovation ecosystems to promote food production sustainably.

Starting from the cutting-edge case of Foodvalley in the Netherlands, the study adopts a backcasting approach to reach a future vision of food ecosystems from a sustainable food production perspective. The authors set the backcasting analysis in four steps: (1) description of the present and trends analysis, (2) selection of trustworthy criteria and goals, (3) development of future images and (4) analysis of how to reach the images.

The trends analysis has identified three existing innovation fields – protein shift, circular agrifood and food and health – and two strategic directions – convergence and localness decrease. The study reports how a long-term commitment may lead the valley toward a best future scenario.

The study suggests that policymakers and stakeholders can promote innovation strategies in sustainable food production ecosystems by encouraging collaboration between different sectors, reducing regulatory barriers, attracting innovative actors, and investing in education and training programs. To achieve measurable environmental and social impact outcomes, policies should promote entrepreneurship and create an enabling environment that encourages innovation and risk-taking.

The originality of this paper concerns the backcasting perspective applied to the analysis of the food ecosystem. This approach facilitates the identification of a path direction for successfully managing open innovation and industry convergence toward a desirable future of sustainable food production.

Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.

Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.

A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.

The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.