The human resource architecture model: A twenty-year review and future research directions

Luo, Ben Nanfeng; Sun, Tuwei; Lin, Caihui; Luo, Dongying; Ge, Qi; Pan, Jingzhou

doi:10.4324/9781003344544-2

Cited by 3 publications

(6 citation statements)

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“…Improving the efciency of human resource management through the use of AI technology has become a major trend in the future growth of human resource management. How to realize enterprise HRM through digital technology has become an inevitable problem for enterprises and human resource managers [3].…”

Section: Introductionmentioning

confidence: 99%

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Jiaping

2022

Computational Intelligence and Neuroscience

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Artificial intelligence (AI) is a potentially transformative force that is likely to change the role of management and organizational practices. AI is revolutionizing corporate decision-making and changing management structures. The visible effects of AI can be observed in key competencies and corporate processes such as knowledge management, as well as consumer outcomes including service quality perceptions and satisfaction. This study aims to optimize the human resource management (HRM) process, reduce the workload of human resource managers, and improve work efficiency. Based on AI digitization technology, a salary prediction model (SPM) is designed using a backpropagation neural network (BPNN), and the Nesterov and Adaptive Moment Estimation (Nadam) algorithms are integrated to optimize the model. Next, the content information of the resumes are used to predict the hiring salary of the candidates and validate the model. Results show that compared with other optimization algorithms, the final predicted result score of the Nadam optimization algorithm is 0.75%, and the training period is 186 s, providing the best optimization effect and the fastest convergence speed. Moreover, the BPNN-based SPM optimized by Nadam has good performance in the learning process and the accuracy rate can reach 79.4%, which verifies the validity of the SPM. The outcomes of this study can provide a reference for HRM systems based on data mining technology.

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Section: Introductionmentioning

confidence: 99%

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Jiaping

2022

Computational Intelligence and Neuroscience

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“…The above three methods are used to forecast the total talent demand of the group company. Although the workload increases, it can be calculated from different angles, and the results can be evaluated [9]. This is conducive to improving prediction accuracy.…”

Section: Forecast Of the Total Labor Force In The Non-transportation ...mentioning

confidence: 99%

Human Resource Management Model of Large Companies Based on Mathematical Statistics Equations

Feng

Yousif

2023

Applied Mathematics and Nonlinear Sciences

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The article applies mathematical statistics to the dynamic analysis of the demand for talents in large companies. Prediction of the overall demand for talents through the use of deep learning models. We use the theory of diminishing marginal utility to solve the degree of enterprise human resource investment under each index. Based on the analysis of traditional human resource management methods, this paper uses the three-dimensional fuzzy model to construct a new three-dimensional fuzzy model of human resource management model. The research found that the algorithm proposed in this paper can improve the quality of data training and improve the matching of people and posts.

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“…Summarizing, the HR architecture proposes a configurational approach to the management of human capital where alignment is sought among human capital characteristics (i.e., value and uniqueness), employment modes (i.e., employment vs. contracting), and HRM practices (i.e., coherent bundles; Kang et al, 2007; Lepak & Snell, 1999; Luo et al, 2021). Following assumptions about the ease and efficiency of monitoring how human capital is deployed by workers (Williamson, 1975), the internal employment mode is only favored when human capital is seen as valuable.…”

Section: The Hr Architecture: Core Assumptionsmentioning

confidence: 99%

“…Theoretically, the hybrid nature of HRM approaches combining compliance‐, commitment‐, and productivity‐based practices to manage contractors in OLPs is at odds with the HR architecture model where consistency, coherence, and clarity in bundles of mutually reinforcing HRM practices aimed at particular cohorts of workers is emphasized (Lepak & Snell, 1999; Luo et al, 2021). Accordingly, we propose the following:Proposition Gig workers are subject to hybrid bundles of HRM activities that are traditionally part of commitment‐, productivity‐ and compliance‐based HRM systems.…”

Section: Realigning the Hr Architecture To Account For Olpsmentioning

confidence: 99%

“…How does this influence what HRM practices are deployed to manage gig workers? In their recent wide‐ranging analysis of Lepak and Snell's (1999) model, Luo et al (2021) call for a closer examination of OLPs who raise significant but unexplored challenges for the assumptions underpinning the model. Considering these challenges is likely to “greatly extend the HR architecture model” (Luo et al, 2021, p. 267).…”

Section: Introductionmentioning

confidence: 99%

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Dynamism and realignment in the HR architecture: Online labor platform ecosystems and the key role of contractors

Keegan

Meijerink

2022

Human Resource Management

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Given the widespread contribution of independent contractors to organizational innovation and competitive advantage, it is timely to reassess assumptions about the HRM practices appropriate to their management and the rationale for organizations to work with them. In the original and highly influential HR architecture model of Lepak and Snell (1999), contractor status is viewed as an outcome of the low value and/or low uniqueness of human capital resulting in the proposition to externalize and manage them using either none or minimal compliance-based HRM practices.Developments in digital technologies and algorithmic management epitomized by online labor platforms prompt us to reconsider these assumptions and to challenge the proposed links between value/uniqueness of human capital, employment mode and HRM practices that are assumed by the HR architecture model. Using insights from online labor platforms, we argue that the significant benefits to firms of working with contractors, coupled with the possibilities offered by algorithmic management to efficiently monitor and regulate their behavior, provide a compelling reason for organizations to choose external employment modes even when workers are key to value creation. We challenge the alignment and stability of the relationships proposed by the HR architecture model, and offer propositions to extend the model by reconsidering the rationale for, and nature of, HRM practices associated with contractors. This reassessment is both timely and relevant given the growing prominence of business models where externalizing workers is central alongside the development of new forms of algorithmic human resource management to control them.

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The human resource architecture model: A twenty-year review and future research directions

Cited by 3 publications

References 0 publications

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Human Resource Management Model of Large Companies Based on Mathematical Statistics Equations

Dynamism and realignment in the HR architecture: Online labor platform ecosystems and the key role of contractors

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