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      Examining the slow acceptance of HR analytics in the Indian engineering and construction industry: a SEM-ANN-based approach

      , , ,
      Engineering, Construction and Architectural Management
      Emerald

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          Abstract

          Purpose

          Despite the extensive benefits of human resource (HR) analytics, the intention to adopt such technology is still a matter of concern in the engineering and construction sectors. This study aims to examine the slow adoption of HR analytics among HR professionals in the engineering and construction sector.

          Design/methodology/approach

          A cross-sectional online survey including 376 HR executives working in Indian-based engineering and construction firms was conducted. Hierarchal regression, structural equation modeling and artificial neural networks (ANN) were applied to evaluate the relative importance of HR analytics predictors.

          Findings

          The results reveal that hedonic motivation (HM), data availability (DA) and performance expectancy (PE) influence the behavioral intention (BI) to use HR analytics, whereas effort expectancy (EE), quantitative self-efficacy (QSE), habit (HA) and social influence (SI) act as barriers to its adoption. Moreover, PE was the most influential predictor of BI.

          Practical implications

          Based on the findings of this study, engineering and construction industry managers can formulate strategies for the implementation and promotion of HR analytics to enhance organizational performance.

          Originality/value

          This study draws attention to evidence-based decision-making, emphasizing barriers to the adoption of HR analytics. This study also emphasizes the concept of DA and QSE to enhance adoption among HR professionals, specifically in the engineering and construction industry.

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          Most cited references57

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          Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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            Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

            G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
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              User Acceptance of Information Technology: Toward a Unified View

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Engineering, Construction and Architectural Management
                ECAM
                Emerald
                0969-9988
                December 22 2022
                December 22 2022
                Article
                10.1108/ECAM-09-2021-0795
                08e2c146-3962-49c2-9db2-03c83b5882ef
                © 2022

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