Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation
Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources...
Ausführliche Beschreibung
Autor*in: |
Basu, Shubhabrata [verfasserIn] Majumdar, Bishakha [verfasserIn] Mukherjee, Kajari [verfasserIn] Munjal, Surender [verfasserIn] Palaksha, Chandan [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Schlagwörter: | |
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Schlagwörter: |
Übergeordnetes Werk: |
Enthalten in: Human resource management review - Amsterdam [u.a.] : Elsevier Science, 1991, 33 |
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Übergeordnetes Werk: |
volume:33 |
DOI / URN: |
10.1016/j.hrmr.2022.100893 |
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Katalog-ID: |
ELV008928673 |
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520 | |a Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process. | ||
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650 | 4 | |a Artificial intelligence | |
650 | 4 | |a HRM | |
650 | 4 | |a Systematic review | |
650 | 4 | |a Thematic causal configurations | |
650 | 4 | |a Fuzzy set qualitative comparative analysis | |
700 | 1 | |a Majumdar, Bishakha |e verfasserin |4 aut | |
700 | 1 | |a Mukherjee, Kajari |e verfasserin |4 aut | |
700 | 1 | |a Munjal, Surender |e verfasserin |4 aut | |
700 | 1 | |a Palaksha, Chandan |e verfasserin |4 aut | |
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allfields |
10.1016/j.hrmr.2022.100893 doi (DE-627)ELV008928673 (ELSEVIER)S1053-4822(22)00001-8 DE-627 ger DE-627 rda eng 85.05 bkl 85.63 bkl Basu, Shubhabrata verfasserin aut Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process. 1.1\x Personalmanagement (DE-2867)12567-2 stw 1.2\x Theorie (DE-2867)19073-6 stw Artificial intelligence HRM Systematic review Thematic causal configurations Fuzzy set qualitative comparative analysis Majumdar, Bishakha verfasserin aut Mukherjee, Kajari verfasserin aut Munjal, Surender verfasserin aut Palaksha, Chandan verfasserin aut Enthalten in Human resource management review Amsterdam [u.a.] : Elsevier Science, 1991 33 Online-Ressource (DE-627)320052060 (DE-600)2026359-4 (DE-576)252902564 1053-4822 nnns volume:33 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.05 Betriebssoziologie Betriebspsychologie 85.63 Arbeitsbeziehungen BIZ-04001 SKW AR 33 |
spelling |
10.1016/j.hrmr.2022.100893 doi (DE-627)ELV008928673 (ELSEVIER)S1053-4822(22)00001-8 DE-627 ger DE-627 rda eng 85.05 bkl 85.63 bkl Basu, Shubhabrata verfasserin aut Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process. 1.1\x Personalmanagement (DE-2867)12567-2 stw 1.2\x Theorie (DE-2867)19073-6 stw Artificial intelligence HRM Systematic review Thematic causal configurations Fuzzy set qualitative comparative analysis Majumdar, Bishakha verfasserin aut Mukherjee, Kajari verfasserin aut Munjal, Surender verfasserin aut Palaksha, Chandan verfasserin aut Enthalten in Human resource management review Amsterdam [u.a.] : Elsevier Science, 1991 33 Online-Ressource (DE-627)320052060 (DE-600)2026359-4 (DE-576)252902564 1053-4822 nnns volume:33 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.05 Betriebssoziologie Betriebspsychologie 85.63 Arbeitsbeziehungen BIZ-04001 SKW AR 33 |
allfields_unstemmed |
10.1016/j.hrmr.2022.100893 doi (DE-627)ELV008928673 (ELSEVIER)S1053-4822(22)00001-8 DE-627 ger DE-627 rda eng 85.05 bkl 85.63 bkl Basu, Shubhabrata verfasserin aut Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process. 1.1\x Personalmanagement (DE-2867)12567-2 stw 1.2\x Theorie (DE-2867)19073-6 stw Artificial intelligence HRM Systematic review Thematic causal configurations Fuzzy set qualitative comparative analysis Majumdar, Bishakha verfasserin aut Mukherjee, Kajari verfasserin aut Munjal, Surender verfasserin aut Palaksha, Chandan verfasserin aut Enthalten in Human resource management review Amsterdam [u.a.] : Elsevier Science, 1991 33 Online-Ressource (DE-627)320052060 (DE-600)2026359-4 (DE-576)252902564 1053-4822 nnns volume:33 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.05 Betriebssoziologie Betriebspsychologie 85.63 Arbeitsbeziehungen BIZ-04001 SKW AR 33 |
allfieldsGer |
10.1016/j.hrmr.2022.100893 doi (DE-627)ELV008928673 (ELSEVIER)S1053-4822(22)00001-8 DE-627 ger DE-627 rda eng 85.05 bkl 85.63 bkl Basu, Shubhabrata verfasserin aut Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process. 1.1\x Personalmanagement (DE-2867)12567-2 stw 1.2\x Theorie (DE-2867)19073-6 stw Artificial intelligence HRM Systematic review Thematic causal configurations Fuzzy set qualitative comparative analysis Majumdar, Bishakha verfasserin aut Mukherjee, Kajari verfasserin aut Munjal, Surender verfasserin aut Palaksha, Chandan verfasserin aut Enthalten in Human resource management review Amsterdam [u.a.] : Elsevier Science, 1991 33 Online-Ressource (DE-627)320052060 (DE-600)2026359-4 (DE-576)252902564 1053-4822 nnns volume:33 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.05 Betriebssoziologie Betriebspsychologie 85.63 Arbeitsbeziehungen BIZ-04001 SKW AR 33 |
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Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation |
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artificial intelligence–hrm interactions and outcomes: a systematic review and causal configurational explanation |
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Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation |
abstract |
Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process. |
abstractGer |
Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process. |
abstract_unstemmed |
Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process. |
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Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation |
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