Sustainable training practices: predicting job satisfaction and employee behavior using machine learning techniques

Gupta, Akriti; Chadha, Aman; Tiwari, Vijayshri; Varma, Arup; Pereira, Vijay

doi:10.1057/s41291-023-00234-5

Cited by 13 publications

(5 citation statements)

References 96 publications

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“…For example, (50) has mentioned that academics and practitioners have paid specific attention to psychological contracts as emulated in many publications in The Social Sciences Citation Index (SSCI). The impact of psychological contracts on job happiness raises employees' chance of being motivated, and job satisfaction fosters organisational citizenship behaviour (51). The last few decades, (52) have studied ineffective workplace job satisfaction patterns and their relationship to psychological contracts.…”

Section: Discussionmentioning

confidence: 99%

The Role of Job Satisfaction on Psychosocial and Mental Health in Oil & Gas Employees: A Systematic Review

Mohd Rani,

Kamarunzaman

2023

MJMHS

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Introduction: The job criteria for the Oil and Gas (O&G) industry greatly value operational precision, production targets, and cost efficiency. In the meantime, employment fulfilment predicts happiness and job performance at work. Workers’ mental health has recently been one of the most contentious problems, and the oil and gas industry is no exception. Methods: Through a comprehensive examination of major academic databases such as Scopus, Web of Science (WoS), and Dimensions.ai, this paper proposes to investigate the mediation influence of employment fulfilment across psychosocial aspects and mental health among O&G employers. The current study combined different research designs, and the assessment was conducted in accordance with the ROSES (Reporting Standards for Systematic Evidence Syntheses) publication requirement. ROSES provides four essential phases in article selection: identification, screening, eligibility, and quality assessment. Results: According to the findings, 11 articles met the ROSES criteria. Thematic analysis was performed to identify emerging themes related to the study issue of this work. Based on the investigation, there were two prominent themes: 1) motivation and 2) performance. Conclusion: This study is unique due to the fact it helped to bridge the gap and improve knowledge regarding the function of job satisfaction in the oil and gas industry. Future research should investigate the impact of additional factors influencing employees’ mental health in Malaysia.

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Section: Discussionmentioning

confidence: 99%

The Role of Job Satisfaction on Psychosocial and Mental Health in Oil & Gas Employees: A Systematic Review

Mohd Rani,

Kamarunzaman

2023

MJMHS

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“…As per Gupta et al (2023), to counter these challenges and enhance the accuracy of performance forecasting, there is an escalating interest in optimizing organizational machine learning and analytics techniques. These methodologies enable companies to leverage the power of data and advanced algorithms to pinpoint patterns, retrieve meaningful insights, and make more accurate forecasting of employee performance.…”

Section: Introductionmentioning

confidence: 99%

Employee Performance Prediction: An Integrated Approach of Business Analytics and Machine Learning

Hasan,

Ray,

Chowdhury

2024

JBMS

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Workforce performance prediction plays an instrumental role in human resource management since it facilitates pinpointing and nurturing high-performing staff, fortifying employee planning, and boosting overall productivity. This study presents a consolidated approach that integrates business analytics and machine learning methodology to forecast personnel performance. The proposed model leverages data-driven info from distinct sources, entailing performance metrics, staff data, and contextual factors, to tailor accurate predictive models. The study examined different aspects of data analytics such as feature engineering, data preprocessing, model selection, and evaluation metrics. The findings of this report demonstrate the efficiency of the consolidated approach in forecasting workforce performance, therefore presenting valuable insights for companies to make informed decisions associated with talent management and resource allocation.

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“…In the realm of modern human resource management (HRM), precise human action recognition (HAR) has emerged as a fundamental element across several domains. These domains encompass human resource recruitment (HRR) [1], employee performance assessment (EPA) [2], and employee training (ET) [3]. In the context of HRM, it is important to explore effective and efficient HAR methods for recruitment selection, employee performance assessment, and providing personalized training.…”

Section: Introductionmentioning

confidence: 99%

“…At present, mainstream approaches [10][11][12][13] to human action recognition tend to focus on capturing either spatial or temporal information in isolation, while ignoring the intricate interactions between these two dimensions. Symmetrically capturing space and time information and understanding the interactions between them are crucial for HAR tasks in complex HRM environments [1][2][3]. The temporal and spatial dimensions of movement are intertwined and together shape the characteristics and patterns of movement.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Information Fusion and Filtration for Human Action Recognition

Zhang,

Li,

2023

Symmetry

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Human action recognition (HAR) as the most representative human-centred computer vision task is critical in human resource management (HRM), especially in human resource recruitment, performance appraisal, and employee training. Currently, prevailing approaches to human action recognition primarily emphasize either temporal or spatial features while overlooking the intricate interplay between these two dimensions. This oversight leads to less precise and robust action classification within complex human resource recruitment environments. In this paper, we propose a novel human action recognition methodology for human resource recruitment environments, which aims at symmetrically harnessing temporal and spatial information to enhance the performance of human action recognition. Specifically, we compute Depth Motion Maps (DMM) and Depth Temporal Maps (DTM) from depth video sequences as space and time descriptors, respectively. Subsequently, a novel feature fusion technique named Center Boundary Collaborative Canonical Correlation Analysis (CBCCCA) is designed to enhance the fusion of space and time features by collaboratively learning the center and boundary information of feature class space. We then introduce a spatio-temporal information filtration module to remove redundant information introduced by spatio-temporal fusion and retain discriminative details. Finally, a Support Vector Machine (SVM) is employed for human action recognition. Extensive experiments demonstrate that the proposed method has the ability to significantly improve human action recognition performance.

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Sustainable training practices: predicting job satisfaction and employee behavior using machine learning techniques

Cited by 13 publications

References 96 publications

The Role of Job Satisfaction on Psychosocial and Mental Health in Oil & Gas Employees: A Systematic Review

The Role of Job Satisfaction on Psychosocial and Mental Health in Oil & Gas Employees: A Systematic Review

Employee Performance Prediction: An Integrated Approach of Business Analytics and Machine Learning

Spatio-Temporal Information Fusion and Filtration for Human Action Recognition

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