AI-augmented HRM: Antecedents, assimilation and multilevel consequences

IF 8.2 1区管理学 Q1 MANAGEMENT

Human Resource Management Review Pub Date : 2023-03-01 DOI:10.1016/j.hrmr.2021.100860

Verma Prikshat , Ashish Malik , Pawan Budhwar

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引用次数: 29

Abstract

The current literature on the use of disruptive innovative technologies, such as artificial intelligence (AI) for human resource management (HRM) function, lacks a theoretical basis for understanding. Further, the adoption and implementation of AI-augmented HRM, which holds promise for delivering several operational, relational and transformational benefits, is at best patchy and incomplete. Integrating the technology, organisation and people (TOP) framework with core elements of the theory of innovation assimilation and its impact on a range of AI-Augmented HRM outcomes, or what we refer to as (HRM^(AI)), this paper develops a coherent and integrated theoretical framework of HRM^(AI) assimilation. Such a framework is timely as several post-adoption challenges, such as the dark side of processual factors in innovation assimilation and system-level factors, which, if unattended, can lead to the opacity of AI applications, thereby affecting the success of any HRM^(AI). Our model proposes several testable future research propositions for advancing scholarship in this area. We conclude with implications for theory and practice.

查看原文本刊更多论文

人工智能增强的人力资源管理:前因、同化和多层次后果

目前关于将颠覆性创新技术(如人工智能(AI)用于人力资源管理(HRM)功能)的文献缺乏理解的理论基础。此外，人工智能增强的人力资源管理的采用和实施有望带来一些运营、关系和转型方面的好处，但充其量是不完整和不完整的。将技术、组织和人员(TOP)框架与创新同化理论的核心要素及其对一系列人工智能增强的人力资源管理结果的影响(或我们所说的(HRM(AI))相结合，本文开发了一个连贯和集成的人力资源管理(AI)同化理论框架。这样一个框架是及时的，因为一些采用后的挑战，如创新同化过程因素的阴暗面和系统级因素，如果不加以注意，可能导致人工智能应用的不透明，从而影响任何人力资源管理(人工智能)的成功。我们的模型提出了几个可测试的未来研究命题，以推进该领域的学术研究。最后，我们提出了理论和实践的启示。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Human Resource Management Review MANAGEMENT-

CiteScore

20.20

自引率

7.00%

发文量

审稿时长

48 days

期刊介绍： The Human Resource Management Review (HRMR) is a quarterly academic journal dedicated to publishing scholarly conceptual and theoretical articles in the field of human resource management and related disciplines such as industrial/organizational psychology, human capital, labor relations, and organizational behavior. HRMR encourages manuscripts that address micro-, macro-, or multi-level phenomena concerning the function and processes of human resource management. The journal publishes articles that offer fresh insights to inspire future theory development and empirical research. Critical evaluations of existing concepts, theories, models, and frameworks are also encouraged, as well as quantitative meta-analytical reviews that contribute to conceptual and theoretical understanding. Subject areas appropriate for HRMR include (but are not limited to) Strategic Human Resource Management, International Human Resource Management, the nature and role of the human resource function in organizations, any specific Human Resource function or activity (e.g., Job Analysis, Job Design, Workforce Planning, Recruitment, Selection and Placement, Performance and Talent Management, Reward Systems, Training, Development, Careers, Safety and Health, Diversity, Fairness, Discrimination, Employment Law, Employee Relations, Labor Relations, Workforce Metrics, HR Analytics, HRM and Technology, Social issues and HRM, Separation and Retention), topics that influence or are influenced by human resource management activities (e.g., Climate, Culture, Change, Leadership and Power, Groups and Teams, Employee Attitudes and Behavior, Individual, team, and/or Organizational Performance), and HRM Research Methods.