Fearless path for human resource personnel’s through analytics: a study of recent tools and techniques of human resource analytics and its implication

Saxena, Mansi; Bagga, Teena; Gupta, Sangeeta

doi:10.1007/s41870-021-00677-z

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…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 ]. In practice, it remains on the problems of low screening efficiency, poor job matching ability, and technology outflow in traditional recruitment [ 7 , 8 ]. Scholars propose to use data mining to reduce the amount of data that needs to be processed.…”

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 people- and culture-driven parts of implementing change may be the sole emphasis of HR departments. Using data to its fullest potential is a crucial component of strategic planning (Saxena et al , 2021). Thanks to data analytics, many crucial business and HR decisions, including recruiting, restructuring and other choices, can be made with less ambiguity.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying the enablers of HR digitalization and HR analytics using ISM and MICMAC analysis

Verma

Rana

Meher

2023

IJOA

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Purpose This study aims to identify the enablers of human resource (HR) digitalization and HR analytics. This paper also aims to build a relationship map using interpretive structural modeling. Design/methodology/approach A systematic literature review is used to identify the key enablers of HR digitalization and HR analytics. Ten expert opinions have been taken from the key officials of IT firms located in New Delhi North Central Region. Findings This study is focused on the enablers of HR analytics. It is found that change management (CM) in the organization is the key enabler of implementing HR digitalization and analytics in an organization. However, other elements like learning culture, training and development, E-learning management and HR transformation (HRT) play a vital role in implementing HR analytics. It is also found that implementing artificial intelligence for HR practices is the ultimate goal for every organization. Research limitations/implications Management teams in IT firms should focus on the continuous learning process in the organization. The CM should be expedited for digitalization and adoption of HR analytics. Managers must go through the ramification of HRT, which possesses diligence in HR analytics and artificial intelligence. Originality/value This study explicitly talks about the enablers of HR digitalization and HR analytics. It also explores the relationship between the enablers. This study also describes the driving and dependence power of all the enablers.

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“…Moreover, the use and dissemination of data-driven decision-making (DDD) in the engineering and construction sector is limited (Ahmed et al, 2018). As HR analytics is still considered a novel concept (McCartney and Fu, 2022), its adoption is in its nascent stages, specifically in the Indian context (Willis Towers Watson, 2018) and HR professionals in India are still hesitant to adopt and implement this technology (Saxena et al, 2021). Although HR managers realize the importance of workforce analytics, they are still not confident about the HR team's skills in comprehending and interpreting analytics (Vargas et al, 2018).…”

Section: Hr Analytics In the Indian Industry 1973mentioning

confidence: 99%

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

Arora

Prakash

Mittal

et al. 2022

ECAM

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PurposeDespite 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/approachA 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.FindingsThe 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 implicationsBased 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/valueThis 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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Fearless path for human resource personnel’s through analytics: a study of recent tools and techniques of human resource analytics and its implication

Cited by 8 publications

References 34 publications

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Identifying the enablers of HR digitalization and HR analytics using ISM and MICMAC analysis

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

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