Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM

Meijerink, Jeroen Gerard; Boons, Mark; Keegan, Anne; Marler, Janet H.

doi:10.1080/09585192.2021.1925326

Cited by 52 publications

(32 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Big data and AI technologies have brought new growth points to the recruitment of talents in the digital information age, constantly updating and iterating, optimizing the organizational structure, and rationally allocating talents [ 4 , 5 ]. If the HRM of the enterprise cannot be kept up with the pace of enterprise development in time to meet the new strategic goals of the enterprise, it will be difficult to obtain development benefits and technical convenience [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Jiaping

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Jiaping

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…A subject that has received much attention from mass media and conceptual scholars is the impact of AI on Human Resource Management (HRM) and career development (Meijerink et al, 2021). HR is familiar with digital tools facilitating daily work (Allal-Ch erif et al, 2021) but recently it is AI that transforms multiple activities of HRM (Vrontis et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…A subject that has received much attention from mass media and conceptual scholars is the impact of AI on Human Resource Management (HRM) and career development (Meijerink et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we refer to this gap in our research. Building on the recent debate on algorithmic HRM (Evans and Kitchin, 2018; Meijerink et al. , 2021), we simulate a biased algorithm giving too strict recommendations regarding disciplinary actions for employees violating the work rules.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we refer to this gap in our research. Building on the recent debate on algorithmic HRM (Evans and Kitchin, 2018;Meijerink et al, 2021), we simulate a biased algorithm giving too strict recommendations regarding disciplinary actions for employees violating the work rules. We intend to answer the research question: will humans oppose biased algorithm recommendations regarding disciplinary actions in an organisation?…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fired by an algorithm? Exploration of conformism with biased intelligent decision support systems in the context of workplace discipline

Bartosiak

Modliński

2022

CDI

View full text Add to dashboard Cite

PurposeThe importance of artificial intelligence in human resource management has grown substantially. Previous literature discusses the advantages of AI implementation at a workplace and its various consequences, often hostile, for employees. However, there is little empirical research on the topic. The authors address this gap by studying if individuals oppose biased algorithm recommendations regarding disciplinary actions in an organisation.Design/methodology/approachThe authors conducted an exploratory experiment in which the authors evaluated 76 subjects over a set of 5 scenarios in which a biased algorithm gave strict recommendations regarding disciplinary actions at a workplace.FindingsThe authors’ results suggest that biased suggestions from intelligent agents can influence individuals who make disciplinary decisions.Social implicationsThe authors’ results contribute to the ongoing debate on applying AI solutions to HR problems. The authors demonstrate that biased algorithms may substantially change how employees are treated and show that human conformity towards intelligent decision support systems is broader than expected.Originality/valueThe authors’ paper is among the first to show that people may accept recommendations that provoke moral dilemmas, bring adverse outcomes, or harm employees. The authors introduce the problem of “algorithmic conformism” and discuss its consequences for HRM.

show abstract

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

Keegan

Meijerink

2022

Human Resource Management

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM

Cited by 52 publications

References 47 publications

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Fired by an algorithm? Exploration of conformism with biased intelligent decision support systems in the context of workplace discipline

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

Contact Info

Product

Resources

About