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Record Nr. |
UNINA9910495198703321 |
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Autore |
Theres Christian |
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Titolo |
Antecedents and Consequences of Digital Human Resource Management : An Exploratory Meta-Analytic Structural Equation Modeling (E-MASEM) Approach to a Multifaceted Phenomenon / / by Christian Theres |
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Pubbl/distr/stampa |
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Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Gabler, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (311 pages) |
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Collana |
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Gabler Theses, , 2731-3239 |
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Disciplina |
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Soggetti |
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Personnel management |
Human Resource Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction and Motivation -- DHRM: A Multifaceted Field of Research -- Methodology -- Data Collection and Findings -- Discussion, Implications, and Limitations. |
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Sommario/riassunto |
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During the last decades, a considerable amount of research has been directed towards explaining the concept of Digital Human Resource Management (DHRM). Yet, a holistic assessment of DHRM antecedents and consequences with respect to possible contextual contingencies is still missing. To this end, this thesis introduces a research framework illuminating the multifaceted phenomenon of DHRM from various perspectives. An exploratory four-step meta-analytic structural equation modelling (E-MASEM) approach tailored to address the domain-specific challenges of DHRM is introduced and applied. Results identify 32 constructs associated with the DHRM usage phenomenon which are categorized into DHRM antecedents and DHRM consequences. Findings reveal that user perceptions, expectations, attitudes, and intentions are essential in predicting DHRM usage while HRM service quality and user satisfaction are found crucial in explaining other DHRM consequences. Further, practitioners are informed about the relative importance of factors for both facilitating DHRM adoption and measuring DHRM success. Lastly, this thesis also |
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contributes to the MASEM methodology by outlining a new approach to summarize statistical inferences from multiple moderator tests. About the author Christian Theres is working as a researcher at the chair of management information systems at Saarland University. His focus is on digital human resource management. |
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