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Enregistrement W4307283975 · doi:10.1108/978-1-80262-057-320221011

Index

2022· paratext· en· W4307283975 sur OpenAlexaboutno aff

Notice bibliographique

Revuenon disponible
Typeparatext
Langueen
DomaineBusiness, Management and Accounting
ThématiqueHuman Resource and Talent Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIndex (typography)Computer scienceWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Citation (2022), "Index", Schlosser, F. and McPhee, D.M. (Ed.) Global Talent Management During Times of Uncertainty (Talent Management), Emerald Publishing Limited, Bingley, pp. 139-144. https://doi.org/10.1108/978-1-80262-057-320221011 Publisher: Emerald Publishing Limited Copyright © 2023 Francine Schlosser and Deborah M. McPhee INDEX Academic experience as enabler for global employment, 75 length of, 75 as opportunity to study with multicultural groups, 74–75 as source of cultural knowledge, 73–74 Academic experiences, 4 Administration, 53 AI, 13 Airbnb, 89 Alphabet, 84 Amazon, 83–84 Amenities, 57 hypothesis, 59, 61 Annual bonus, 101 Anxiety, 52 Appearance, 43 Apple, 83–84 Art of doing, 89 Attracting, 14 Authoritative approach, 51–52 Barcelona Handball, 65 Big OE, 62–63 Black swan events, 43 Brain circulation, 5, 117–118 Brain drain, 5, 62–63, 109 considerations, 118–119 migration of talent, 110–116 strategies for sustainable global talent management, 116–118 Brain gain, 111, 118 Brand-related crises, 44 Brazil, 2, 5, 18, 115–116 Brazilian context, 115–116 Brexit campaign, 12 Burnout, 126–127 Business environment (BE), 2, 26–27 talent as product of, 29–31 Calling, 131 Canadian empirical studies, 63–65 Canadian military, 45 Career competencies, 4, 72–73 Career factors, 113 Chaos theory, 124, 132 of careers, 6, 124, 132 Chinese digital R&D center of Nordic multinational, 93 Chinese digital talents, 103–104 Chinese managers, 95 Chinese society, evolving changes in, 103–104 Cities and the Creative Class, 58 City branding, 58, 61–63 City of London, 60 City-regions, 58–59 Collectivism, 17 Communication, 52–53 Compensation, 130 Compensation, 95 Competition, 84, 88 Computational modelling, 35 Concentration of force, 50–51 Contextualisation, 26 Contingency theory, 28–29 Controls systems, 51 Conventional media outlets, 12 Cooperation, 52–53 Corporate communication, 13 Corporate disinformation, 13, 21 Corporate information by competition and location businesses, 20 Cosmopolitanism, 64 Counselling, 53 Country-branding, 61–63 Courage, 43, 48 COVID-19 pandemic, 11, 21, 93, 112, 115–116, 123–125 effects of, 1 global, 5 GTM, 2 socio-political ramifications, 4 Creative city, 58 Creative class theory, 63 Crimean military conflict, 17 Crisis events, 45 Cross-border academic education, 71 Cross-cultural training, 77 Cross-national lens, 15 GTM through, 16–17 Cultural density, 76 Cultural diversity, 76–77 Cultural embeddedness, 75 Cultural factors, 114 Cultural paradoxes, 73 Cultural-cognitive economy, 63 Curbside grocery pickup, 85 D-Day invasion, 17 Danish talent, 65 De-globalisation process, 60 Demand, 86–88 Developing countries, 6, 109, 115–116 Developing talents, 14 Differentiated talent management, 2, 4–5 Digital competencies, 96 Digital R&D center, 96 Digitalisation, 85 Direct harm, 13 Discomfort, 43 Disinformation, 11–12 campaigns, 20–21 as global phenomenon, 12–13 and global talent management, 19–21 managing talent in global context, 14–19 in mix, 19–20 as source of uncertainty to global talent management activities, 20–21 Distributed talent, 5 Distributive justice perceptions, 95 Dubai, 61 Economic factors, 113 Economic inequalities, 2, 5, 119 Economy of effort, 51 Edu-immigrants, 2, 4, 72, 75, 78 Effective leadership, 41 Employee benefits, 18 Employee morale, 47 Employee turnover, 95 Employer branding, 58 Employment model, 34 Energy, 43 Environmental scanning scholarship, 29–30 Ethnic diversity, 64 Experienced nursing professionals, 2, 6, 123–124 Extra-organisational (macro) elements, 15 Facebook, 84 False advertising, 13 Family, 114 Fear, 52 Feeling appreciated, 125–126 Financial rewards, 95 Flexibility, 51–52 Flexible working hours and leaves, 17 Fog of war, 43 Free-reign approach, 51–52 Freedom Convoy, The, 13 Gini coefficient, 104 Global high-tech talent, 2, 4–5 Global Migration Data Analysis Centre (GMDAC), 110 Global nursing shortage, 6, 128 Global staffing, 88 Global Talent Competitive Index (GTCI), 115 Global talent management (GTM), 1, 11–12, 14, 25–26, 42, 71 activities, 15 through cross-national lens, 16–17 disinformation and, 19–21 implications for, 77–78 impact of macrofactors impeding on micro-GTM activities across different nations, 18–19 practical contributions, 78 practices, 2, 5 ‘realised talent’ and ‘talent discovery’ in, 27–29 as system, 15 and uncertainty, 3–6 Global talent managers, 11–12 Global warming, 112 Global workforce, 109 Globalisation, 26, 109 Good living conditions, 57 Google, 83 Government for Science (GOS), 35 Government institutions, 117 Harm, 13 Healthcare HRM, 6, 132 Hierarchy and decision-making at work, 17 High-Tech Talent (HTT), 5, 85–86 global staffing, 88 recruitment, 5 supply, demand and mobility, 86–88 Higher education, 4, 64, 110, 115 Honesty, 43 HR process automation, 18–19 Human capital (HC), 32 employment model, 34 knowledge base, 32–33 priorities, 32 skills imbalances, 33–34 Human resource management (HRM), 42, 112, 129–131 (see also International Human Resource Management (IHRM)) IBM, 83 Inclusive approach, 128 Indirect harm, 13 Individual-level perceptions, 94 Industry-oriented perspective, 5 Inequality, 103 Information acquisition, 29 Information and communication technology (ICT), 84, 86 Information flow, 2–3, 11–12, 16, 19–20, 117 Information spread, 12 Initiative, 43 Integrity, 43 Intelligent career competencies, 71, 73–77 Intelligent career concept, lessons from, 72–73 Intentional political messaging, 16 Interconnectedness, 83 Internal inequity, 104 International academic experience, 73 breadth of, 76 International academic exposure, elements of, 73–77 International experience, 72–73 International exposure, 72 International Human Resource Management (IHRM), 1, 11 activities, 2 in organisations, 3 research and practice, 42 International Organization for Migration (IOM), 112, 116 International self, 73 International students, 4, 72, 74 Interviewing, 53 Intra-organisational (micro) elements, 15 IT talent, 86 ‘Johnson & Johnson’s response, 44 Justice perceptions, 93 data analysis, 97 data collection, 96–97 evolving changes in Chinese society, 103–104 findings, 97–101 implications for research and practice, 104–105 limitations, 105 literature review, 94–96 local justice perceptions regarding incentives, 101–102 single case study, 96–97 Knowing-how, 72, 78 Knowing-whom, 72, 78 Knowing-why, 72–73, 78 Knowledge, 110 Knowledge, skills, and abilities (KSAs), 88 Knowledge base, 32–33 for decisions, 27 Leaders, 41 relevance of uncertainty in leadership, 42–45 Ten Principle framework, 46–53 theoretical underpinnings, 45–46 Leadership, 41–42, 47 relevance of uncertainty in, 42–45 Leading through turbulence, 3 Local justice perceptions regarding incentives, 101–102 regarding salary, 101 Local socio-cultural contexts, 5 Low-skilled service employees, 60 Loyalty, 95 Luxembourg, 61 Macrodrivers of GTM, 1 Malaysia, 17 Management by objectives (MBO), 46 Mental fog, 43 Microdrivers of GTM, 1 Microsoft, 83–84 Migration, 5 aspects in cities debate, 59–60 Brazilian context, 115–116 factors, 112–115 flows of workers, 109 sustainability, 111–112 of talent, 110 Military leadership, 45 Military science, 42 Misinformation, 12 Mistrust, 52 MLcomp, 96–97, 101 R&D employees in, 101 Mobilising, 14 Mobility, 86–88 Monetary rewards, 5, 93, 102 Morale, maintenance of, 47 Multilateral organisations, 118 Multimedia disinformation, 13 Multinational Enterprises (MNEs), 2–3, 11, 93 operations, 17 talent management, 11 National Apprenticeship Act, 89 National business environment, 26 National level policy decisions, 5 New Zealand talent, 62–63 Newspapers, 12 Nordic multinational, 93 Nursing careers, 123 professionals, 124 talent re-attraction, 130–131 talent renewal, 131–132 talent retention, 129–130 Obedience, 95 Object approach, 26 Objectives, selection and maintenance of, 46–47 Offensive action, 48 One Child policy, 103 ‘Ordinary’ talent management, 5 Organisation for Economic Co-operation and Development (OECD), 123 Organisational talent management strategies, 118 Organisations, 13, 72 Oure Sports College, 64 Pandemic, 115 post-pandemic challenge, 124–133 restrictions, 84 Paris Saint German Handball (PSG Handball), 65 Participative approach, 51–52 Pay-for-performance practice, 95, 102 Pension regulations, 18 People, planet and profit (3Ps), 111 Perceived breach, 126 Perceived organisational justice, 95–96 Perceived quality of job websites, 18 Perception of reward and recognition, 17 PIEs, 73 Policy-makers, 117 Political city initiatives, 2, 4 Political factors, 113 Post-pandemic challenge feeling appreciated, 125–126 implications for research and practice, 132–133 nursing talent re-attraction, 130–131 nursing talent renewal, 131–132 nursing talent retention, 129–130 professional calling and skills development, 124–125 stress and burnout, 126–127 talent management literature, 127–128 Post-pandemic health-care, 125 ‘Post-pandemic’ approach, 1 Professional calling, 124–125 Public information about business environment, 20 Qualified professionals, 112 R&D employees in MLcomp, 101 Re-employment, 130 ‘Realised talent’ in GTM, 27–29 Remote work, 84 Repatriation, 14 Responsibility, 43 Retaining, 14 Retention, 94 Reverse retirees, 130 Rewards management, 94–95 in case company, 97–100 Richard Florida, 57–58 Robustness, 25 Russia, 2–3, 25–26, 33–34 analysis of HC in, 32 BE of, 31 Russia-Ukraine war, 33, 43–44 Russian labour market, 31 SARS-CoV-2 virus, 112 Scandinavian empirical studies, 63–65 Scanning, 30 Security, 48–50 Selective information processing, 19–20 Self-confidence, 43 Self-directed expatriation, 112 Self-efficacy, 49 Shared regional talent approach, 2, 4 Singapore, 61 Skills development, 124–125 Skills imbalances, 33–34 Skills misallocation, 33 Social factors, 113 Social media, 12–13 Socio-economic change, 4–6 Socio-political change, 3–4 Stakeholders, 2 Stress, 126–127 Subjective evaluation of information, 19 Supply, 86–88 Surprise, 50 Sustainability, 111–112 Sustainable approach to GTM, 5 Sustainable global talent management, strategies for, 116–118 Talent, 41 appreciation, 6, 123 assessment, 30, 35 contextualised talent insights, 31–32 contingencies, 130 flow, 5, 117 identification, 26 identity, 126 inflows and outflows, 115–116 leadership, 41 migration, 111 mobility, 4, 6, 60 planning, 14 as product of business environment, 29–31 reattraction, 131 retention strategies, 14 Talent discovery (TD), 26 in GTM, 27–29 human capital and, 32–34 Talent management (TM), 57, 93–94, 96, 123 (see also Global talent management (GTM)) Canadian and Scandinavian empirical studies, 63–65 city-and country-branding, 61–63 earlier studies on city-regions and talent, 58–59 global implications and nursing, 128 literature, 127–128 migration aspects in cities debate, 59–60 Talented employees, 57 Tech apprenticeships, 89 Technology, 18, 131 Tele-health, 85 Ten Principle framework, 42, 45–46 administration, 53 concentration of force, 50–51 cooperation, 52–53 economy of effort, 51 flexibility, 51–52 maintenance of morale, 47 offensive action, 48 security, 48–50 selection and maintenance of objectives, 46–47 surprise, 50 Third-party information, 20 Transformational leadership, 128 Transition from retirement to work, 6, 132 TV, 12 Twitter, 83 Tylenol, 44–45 Uncertainty, 1, 25, 41, 83 contextualised talent insights, 31–32 GTM and, 3–6 human capital, 32–34 influence, 6 ‘realised talent’ and ‘talent discovery’ in GTM, 27–29 relevance of uncertainty in leadership, 42–45 robustness, 25 talent as product of business environment, 29–31 talent discovery, 26 talent discovery, 32–34 Unemployment, 130 United Arab Emirates (UAE), 17 United Kingdom (UK), 18 United States (US), 61 Unretirement, 130 Vaccination efficacy, 21 Virtual customer relationships, 84 Vocation, 131 Voluntary employee turnover, 94 Voluntary workers flow, 118 Whistleblowing, 53 Wikipedia, 12 World Economic Forum (WEF), 31, 61 Zelensky, Volodymyr, 43–45, 48–49 Book Chapters Prelims Introduction Part I: Global Socio-political Change Chapter 1: Investigating the Role of Disinformation on GTM Activities Chapter 2: Reducing Uncertainty by Contextualising Talent Chapter 3: A Guiding Framework for Leaders in Uncertain Times: Learning from GTM in the Canadian Military Chapter 4: Talent Management at City Level: Past Experiences and Future Directions of Mobility? Part II: Global Socio-economic Change Chapter 5: Being There, Done That, Known This, and Known That: International Academic Experience and Intelligent Career Competencies Chapter 6: Global High-tech Talent in Times of Uncertainty Chapter 7: Chinese Digital Talents and Monetary Rewards: A Case Study of Justice Perceptions in a Nordic Multinational Chapter 8: Preventing Brain Drain: A Sustainable Perspective of Global Talent Management Chapter 9: The Post-pandemic Challenge of Retaining, Re-attracting, and Renewing Experienced Nursing Talent Index

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,422
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,6740,252

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,018
Tête enseignante GPT0,225
Écart entre enseignants0,207 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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