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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.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

This study aims to reflect on the future of work dimensions through the new concept of inclusive organizational behaviour (IOB) and its practices.

This work is a reflective concept development paper that focuses on new dimensions of organizational behaviour (OB) exploring IOB practices in the new workplace which can broaden the concept of OB that fits into the future of work.

The IOB practices will adapt and help in adapting to the new work dynamics that can create more humane and stimulating workplaces, thereby benefiting society at large. Individual positive psychological traits, team dynamics and a fusion of digital corporate culture with a human-centric approach and sustainability are highlighted in the redefined IOB, expanding the concept of OB from the three levels of analysis (individual, group and the entire organization’s behaviour) in the new normal post-COVID situation.

Limited research studies are being conducted to investigate the future of work dynamics in the new standard post-COVID environment, which is dominated by digitization. The lack of literature and the changing situations that impact OB are the limitations.

Corporate houses, policymakers and leaders who understand the workplace dynamics in the post-COVID scenario can effectively leverage the insights from this work and may chalk out a road map for future work through IOB practices.

This research extends knowledge pertaining to IOB practices and the changing dynamics that need to be followed in the future OB practices.

This study aims to explore the evolution of corporate governance through a 100-year-old Italian Barilla pasta family business from its founding to 1971. The study builds on prior research which has applied the three-circle model of family business systems in a historic context.

Using legal records, five phases in the history of Barilla are noted. Annual reports and other sources have allowed for some more insights into business events and developments. Then, drawing on the three-circle model of family business, the corporate governance regime is mapped to the model and the family actors.

The findings here support extant literature in that the systems in the three-circle model are found to overlap more in a historic setting. Challenges with the three-circle model are also noted, specifically, when corporate governance is considered across a century of an organisation’s history.

This study supports prior use of three-circle model of a family business in an historic context, providing further evidence the model is not static over time. Contrary to the original three-circle model, this study suggests that family actors can potentially occupy more than one location in the model if the non-human actor of corporate governance and its effect on human actors is also considered.

This study aims to analyse the differences between professional interaction mediated by video conferencing and direct professional interaction. The research identifies diverging interests of office workers for the purpose of addressing work ethical and business ethical issues of professional collaboration, competition, and power in future hybrid work models.

Based on 28 qualitative interviews conducted between November 2020 and June 2021, and through the theoretical lens of phenomenology, the study develops explanatory hypotheses conceptualising four basic intentions of professional interaction and their corresponding preferences for video conferences and working on site.

The four intentions developed on the basis of the interviews are: the need for physical proximity; the challenge of collective creativity; the will to influence; and control of communication. This conceptual framework qualifies a moral ambivalence of professional interaction. The authors identify a connectivity paradox of professional interaction where the personal dimension remains unarticulated for the purpose of maintaining professionality. This tacit human connectivity is intertwined with latent power relations. This plasticity of both connectivity and power in direct interaction can be diminished by transferring the interaction to video conferencing.

The application of phenomenology to a collection of qualitative interviews has enabled the identification of underlying intention structures and the system in which they affect each other. This research identifies conflicts of interests between workers relative to their different self-perceived abilities to persevere in competitive professional interaction. It is therefore able to address consequences of future hybrid work models at an existential and societal level.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

The study's primary purpose is to establish the direct and indirect roles of word-of-mouth communication (WOM) in initiating and maintaining consumer loyalty in the bottom of pyramid (BOP) markets in the Indian context. In addition, the study seeks to evaluate the conditions (viz. extent of media usage, brand distribution intensity and brand social connections) under which WOM leads to the initiation and maintenance of consumer brand loyalty.

The study hypotheses were formulated following the social identity theory. Later, a questionnaire-based survey was conducted among 898 rural BOP consumers. Structural equation modelling technique was applied to test the study hypotheses.

Results suggested a positive effect of WOM on brand credibility and self-brand connections-indicative of the initiation of strong cognitive and affective relationships respectively. Brand credibility and self-brand connections also mediated the paths between WOM and brand loyalty-indicative of the maintenance and continuation of strong affect-laden relationships. These indirect relationships were moderated by the extent of media usage, brand distribution intensity and brand social connections.

This is among the first studies that holistically evaluate the role of WOM in developing customer loyalty to rural BOP consumers against the backdrop of the systemic deficiencies in these markets.

This conceptual study examines the implications of the Internet of Behaviors (IoB) for tourism stakeholders in a hyper-connected and data-driven world.

Based on nudge theory, a literature review and empirical evidence from multidisciplinary research, this study explores the implications of the IoB for smart tourism.

This study reviews the literature, presents a conceptual framework and proposes a research agenda with areas for future research.

The research on the IoB is nascent. Therefore, it is critical to understand how data-driven nudging influences tourist behavior.

This study aims to explore the enabling and suppressing effects of leader affiliative and aggressive humor on employee knowledge sharing form the lens of emotional contagion process, which provides theoretical reference for the applications of different leader humor style, thereby enhancing employee knowledge sharing.

This study collected three waves of data and surveyed 379 employees in China. Regression analysis, bootstrapping and latent moderation structural equation were adopted to test the hypotheses.

Leader affiliative humor has a positive impact on employee knowledge sharing, whereas leader aggressive humor has a negative impact on employee knowledge sharing. Positive emotion plays a mediating role between leader affiliative humor and employee knowledge sharing, and negative emotion plays a mediating role between leader aggressive humor and employee knowledge sharing. Moreover, supervisor–subordinate Guanxi moderates the relationship between leader affiliative humor and positive emotion, and between leader aggressive humor and negative emotion, respectively.

This study not only adds to the knowledge sharing literature calling for the exploration of antecedents and mechanism of employee knowledge sharing, but also contributes to our comprehensive understanding of the suppressing and enabling effects of leader humor style on employee knowledge sharing. Besides, this study also unpacks the dual-path mechanism and boundary condition between leader humor style and employee knowledge sharing and augments the theoretical explanations of emotional contagion theory between leader humor style and employee knowledge sharing.