Antecedents and Consequences of Digital Human Resource Management: An Exploratory Meta-Analytic Structural Equation Modeling (E-MASEM) Approach to a Multifaceted Phenomenon

This thesis presents a research framework that explores the multifaceted phenomenon of Digital Human Resource Management (DHRM) from various perspectives. It employs an exploratory four-step meta-analytic structural equation modeling (E-MASEM) approach to address domain-specific challenges and identify 32 constructs associated with DHRM usage. Findings reveal that user perceptions, expectations, attitudes, and intentions are key predictors of DHRM usage, while HRM service quality and user satisfaction play a significant role in explaining other DHRM consequences. The thesis also provides practical insights for practitioners regarding the factors that facilitate DHRM adoption and measure DHRM success. Additionally, it contributes to the MASEM methodology by outlining a new approach to summarize statistical inferences from multiple moderator tests.

Format: Paperback / softback
Length: 292 pages
Publication date: 19 August 2021
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Over the past few decades, extensive research has been conducted to understand the concept of Digital Human Resource Management (DHRM). However, a comprehensive evaluation of DHRM antecedents and outcomes in relation to potential contextual factors remains lacking. To address this gap, this thesis presents a comprehensive research framework that explores the multifaceted nature of DHRM from various perspectives.

An exploratory four-step meta-analytic structural equation modeling (E-MASEM) approach is introduced and applied to address the unique challenges of DHRM. This approach enables a rigorous analysis of the data, allowing for the identification of 32 constructs associated with the DHRM usage phenomenon. These constructs are categorized into DHRM antecedents and DHRM consequences.

The findings reveal that user perceptions, expectations, attitudes, and intentions play a vital role in predicting DHRM usage. Additionally, HRM service quality and user satisfaction are identified as crucial factors in explaining other DHRM consequences. Practitioners are provided with insights into the relative importance of factors that facilitate DHRM adoption and measure DHRM success.

Furthermore, this thesis contributes to the MASEM methodology by outlining a novel approach to summarize statistical inferences from multiple moderator tests.

In conclusion, this thesis provides a valuable contribution to the field of DHRM by offering a comprehensive research framework, employing an innovative methodology, and shedding light on the antecedents, consequences, and measurement of DHRM in various contexts.

Weight: 415g
Dimension: 210 x 148 (mm)
ISBN-13: 9783658351151
Edition number: 1st ed. 2021