Data-Driven Decisions in Employee Compensation utilizing a Neuro-Fuzzy Inference System

Escolar-Jimenez, Caryl Charlene; Matsuzaki, Kichie; Okada, Koji; Gustilo, Reggie C.

doi:10.30534/ijeter/2019/10782019

Cited by 16 publications

(12 citation statements)

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“…The sophistication of AI-controlled frameworks has lately expanded to such a degree that no human intercession is required for their structure and deployment (Arrieta et al , 2020). The impact of the sophistication can be seen from the employment of AI techniques in the hiring and recruitment process (such as shortlisting of CVs from career sites, direct and break down video interviews) (Gupta et al , 2018; Nawaz, 2019; Rab-Kettler and Lehnervp, 2019; Wan Chik and Arokiasamy, 2019), predicting performance (Chen and Chien, 2011; Lopes et al , 2018; Nazri et al , 2019), and automation of tasks (Escolar-Jimenez et al , 2019; Gupta et al , 2018). Researchers posit that hybrid AI techniques (such as fuzzy artificial neural network (FANN), adaptive-network-based fuzzy inference systems (ANFIS), fuzzy transaction data-mining algorithm (MFTDA)), when employed to solve various HR problems, can produce more effective results (Jantan et al , 2009; Masum et al , 2018).…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…The emergence of AI has fundamentally transformed many organizations (Bankins and Formosa, 2020;Barro and Davenport, 2019;Kaplan and Haenlein, 2019;Jia et al, 2018). The crucial role of AI techniques in the workplace is to support complex HR managerial When technology meets people: AI and HRM decision-making by enhancing the quality and pace of the decision-making process (Escolar-Jimenez et al, 2019;Reddy et al, 2019;Ranjan et al, 2008;Saidi Mehrabad and Fathian Brojeny, 2007;Sturman et al, 1996). The sophistication of AI-controlled frameworks has lately expanded to such a degree that no human intercession is required for their structure and deployment (Arrieta et al, 2020).…”

Section: Closing Remarks On the State Of Ai -Hrm Domainmentioning

confidence: 99%

“…Compensation focuses on the employees’ remuneration discretionally complemented by equity sharing, profit, or both (Devanna et al , 1982). As a subfunction that could facilitate extensive numeric data-driven decision-making to design an efficient AI-based compensation system, it is surprising that only one article addressed this subfunction of HRM (Escolar-Jimenez et al , 2019). As the subfunction deals with an enormous amount of numeric data, AI could potentially design a robust compensation system.…”

Section: Analysis and Findingsmentioning

confidence: 99%

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When technology meets people: the interplay of artificial intelligence and human resource management

Qamar

Agrawal

Samad

et al. 2021

JEIM

100

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PurposeAn original systematic review of the academic literature on applications of artificial intelligence (AI) in the human resource management (HRM) domain is carried out to capture the current state-of-the-art and prepare an original research agenda for future studies.Design/methodology/approachFifty-nine journal articles are selected based on a holistic search and quality evaluation criteria. By using content analysis and structural concept analysis, this study elucidates the extent and impact of AI application in HRM functions, which is followed by synthesizing a concept map that illustrates how the usage of various AI techniques aids HRM decision-making.FindingsA comprehensive review of the AI-HRM domain’s existing literature is presented. A concept map is synthesized to present a taxonomical overview of the AI applications in HRM.Research implications/limitationsAn original research agenda comprising relevant research questions is put forward to assist further developments in the AI-HRM domain. An indicative preliminary framework to help transition toward ethical AI is also presented.Originality/valueThis study contributes to the literature through a holistic discussion on the current state of the domain, the extent of AI application in HRM, and its current and perceived future impact on HRM functions. A preliminary ethical framework and an extensive future research agenda are developed to open new research avenues.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Section: Closing Remarks On the State Of Ai -Hrm Domainmentioning

confidence: 99%

Section: Analysis and Findingsmentioning

confidence: 99%

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When technology meets people: the interplay of artificial intelligence and human resource management

Qamar

Agrawal

Samad

et al. 2021

JEIM

100

View full text Add to dashboard Cite

show abstract

“…The complexity of intelligent tools and systems is determined by the feature of the objects of study. For example, medical computer systems are quite complex, as they include elements of hybrid, dynamic and, in some cases, distributed expert systems [1]- [5]. Problems with the implementation of such systems are associated with uncertainty, inaccuracy of knowledge, and the large dimension of the subject area.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Structure of Intellectual Means of Decision-Making Using a System-Oriented NFO Approach

Moheb¹

2019

IJETER

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This article developed the structure of an intelligent decision-making system based on the constructed NFO-models, which demonstrated an increase in the effectiveness of decisions made in the medical diagnosis of some cardiovascular diseases. The presented architecture leads to the adoption of reliable decisions through the use of the verbal method of presenting data. This system uses existing approaches to formalization and diagnostics, conducts data mining, and provides the user with complete information for making a decision. Intelligent decision-making system can be used both in clinical practice by novice specialists, and in training students and doctors in continuing education institutions.

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“…Any phenomenon or process can be represented as some data set [1], [2]. The analysis of such data helps to obtain new data, new knowledge.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Coherence as a Tool for Visualizing the Relationship between Glomerular Filtration Rate and Renal Artery Blood Flow Velocity

Ali¹

2019

IJETER

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The study of glomerular filtration rate using renal scintigraphy with enhancement and blood flow velocity in renal artery has been carried. We estimated the relationship between glomerular filtration rate and renal artery blood flow rate for each kidney and their mutual influence. To explain the results of the relationship and visualization of the research process, we used wavelet coherence. The expediency of using the wavelet methodology for conducting intellectual diagnostics for the detection of kidney diseases is shown.

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Data-Driven Decisions in Employee Compensation utilizing a Neuro-Fuzzy Inference System

Cited by 16 publications

References 0 publications

When technology meets people: the interplay of artificial intelligence and human resource management

When technology meets people: the interplay of artificial intelligence and human resource management

Modeling the Structure of Intellectual Means of Decision-Making Using a System-Oriented NFO Approach

Wavelet Coherence as a Tool for Visualizing the Relationship between Glomerular Filtration Rate and Renal Artery Blood Flow Velocity

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