Arora, M; Prakash, A; Mittal, A and Singh, S (2024) Examining the slow acceptance of HR analytics in the Indian engineering and construction industry: A SEM-ann-based approach. Engineering, Construction and Architectural Management, 31(5), pp. 1973-1993. ISSN 0969-9988
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural network; data availability; HR analytics; quantitative self-efficacy; structural equation modeling; technology adoption |
| Index terms: | technology adoption, implementation, self-efficacy, construction industry, organizational performance, executive, promotion, habit, construction sector, evidence, human resource, decision-making, methodology, motivation, artificial neural network, survey, construction firm, strategy, structural equation modelling, relative importance, manager |
| Subjects: | research methods, performance measurement, management, decision analysis, statistical analysis, industry analysis, organization, risk assessment, evaluation and assessment methods, data collection methods, practitioner, human factors and perception, health behaviours and lifestyles, modelling and simulation, psychology, contractual arrangements, innovation and technology management |
| Topics: | Research Practice, Engineering Principles, Business Strategy, Procurement, Roles and Professions, Risk Management, Human Resources, Quality Management |
| Descriptive scope: | 5 PCTEA |
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