Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction

Srivastava, Praveen Ranjan; Eachempati, Prajwal

doi:10.4018/jgim.20211101.oa23

Cited by 45 publications

(24 citation statements)

References 31 publications

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“…Machine-learning techniques have been used to create models that predict attrition (Saradhi and Palshikar, 2011). Rombaut and Guerry (2018) used a logistic regression model and a decision tree technique to analyze turnover, while Srivastava and Eachempati (2021) used deep learning techniques to forecast employee attrition. The identification and quantification of factors causing attrition can help identify processes to reduce turnover by alleviating their impact.…”

Section: Hr Functionsmentioning

confidence: 99%

Trends in the thematic landscape of HR analytics research: a structural topic modeling approach

Thakral

Srivastava

Dash

et al. 2023

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PurposeThe growth of the global labor force and business analytics has significantly impacted human resource management (HRM). Human resource (HR) analytics is an emerging field that creates value for employees and organizations. By examining the existing studies on HR analytics, the paper systematically reviews the literature to identify active research areas and establish a roadmap for future studies in HR analytics.Design/methodology/approachA portfolio of 503 articles collected from the Scopus database was reviewed. The study has adopted a Latent Dirichlet allocation (LDA) topic modeling approach to identify significant themes in the literature.FindingsThe HR analytics research domain is classified into four categories: HR functions, statistical techniques, organizational outcomes and employee characteristics. The study has also developed a framework for organizations adopting HR analytics. Linking HR with blockchain technology, explainable artificial intelligence and Metaverse are the areas identified for future researchers.Practical implicationsThe framework will assist practitioners in identifying statistical techniques for optimizing various HR functions. The paper discovers that by implementing HR analytics, HR managers and business partners can run reports, make dashboards and visualizations and make evidence-based decision-making.Originality/valueThe previous studies have not applied any machine learning techniques to identify the topics in the extant literature. The paper has applied machine learning tools, making the review more robust and providing an exhaustive understanding of the domain.

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Section: Hr Functionsmentioning

confidence: 99%

Trends in the thematic landscape of HR analytics research: a structural topic modeling approach

Thakral

Srivastava

Dash

et al. 2023

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“…Additionally, future research can include validation of our proposed framework using other types of Lean Six Sigma projects such as process design, process redesign, and infrastructure implementation (Brassard et al, 2017). Our study focuses on integrating the Quality 4.0 and COVID-19 characteristics and other relevant frameworks, such as Industry 4.0 (Peng et al, 2021;Hughes et al, 2020), Logistics 4.0 (Kucukaltan et al, 2020), Work 4.0 (Saputra et al, 2021), quality analytics systems (Lee et al, 2021), and AI-based employee management systems (Srivastava and Eachempati, 2021), may also enrich the existing considerations to better manage the WFA workforce and engage stakeholders. Future studies should consider the factors from these relevant frameworks to further strengthen the relationships with the stakeholders.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Work from anywhere: remote stakeholder management and engagement

Tran

Carden²,

Zhang³

2022

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PurposeThe 2019 coronavirus disease (COVID-19) pandemic has significantly disrupted the traditional ways for organizations to function, mandating their prompt adaptation and reaction to issues related to employees, operations, and communication. Therefore, the purpose of this research is to report on a total quality management (TQM) implementation as a sample motivational case to support the need for a new work from anywhere (WFA) stakeholder framework that can be used by other companies as they build, maintain, and continuously improve a WFA culture and environment for successful Quality 4.0 and COVID-19 initiatives.Design/methodology/approachQuality 4.0 that specifies an adaptive quality environment can help organizations quickly identify and respond to problems during the COVID-19 pandemic. Analyzing a Lean Six Sigma implementation through the lens of Quality 4.0 in a case study, the authors propose a new stakeholder engagement framework for companies to build, maintain, and continuously improve the culture and environment for facilitating social interactions.FindingsThe authors’ proposed framework consists of three major phases: (1) identify and plan stakeholder management, (2) manage stakeholder engagement, and (3) sustain stakeholder engagement. The first phase entails organizations supporting a culture of trust, engaging, and empowering stakeholders, identifying stakeholders, planning for stakeholder management, and inquiring about problems in the process. The second phase requires engaging stakeholders via participatory buy-in. The third phase focuses on monitoring stakeholder relationships, including modifying strategies and plans.Practical implicationsThis study bears some valuable implications for practitioners. First, the stakeholder management process should be established upon a culture that promotes trust, engagement, and empowerment. Second, the authors identify tools that can assist in the execution of WFA activities. Third, the organizational team needs to actively manage the WFA stakeholders and then sustain the stakeholder relationships. Finally, the implementation of the framework herein can be supported and strengthened by tools and techniques that promote participation and buy-in from stakeholders to ensure the project team hears, trusts, and values their contributions.Originality/valueThis study contributes to the existing literature and provides practical guidance for managers to effectively manage the workforce and engage stakeholders.

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“…Very few management researchers have until now paid attention to the study of HR analytics (Marler and Boudreau, 2017). Thereafter many types of research related to predicting employee attrition (Srivastava and Eachempati, 2021; Harsha et al , 2020; Setiawan et al , 2020); big data (Dahlbom et al , 2020; Yahia et al , 2021); artificial intelligence in HRM (Achchab and Temsamani, 2021; Pillai and Sivathanu, 2020), talent acquisition (Ghosh and Basu, 2020; Necula and Strîmbei, 2019); predicting employee absenteeism (Lawrance et al , 2021), etc were conducted. The evolution of HR analytics has replaced heuristic and intuition-based decisions with data-driven decisions with reduced subjectivity (Rasmussen and Ulrich, 2015).…”

Section: Background Of the Studymentioning

confidence: 99%

A critical review of HR analytics: visualization and bibliometric analysis approach

Arora

Prakash

Dixit

et al. 2022

IDD

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Purpose This study aims to analyze the existing literature in human resource analytics and highlights the future research agenda and trends in the same context. It deals with evaluating regional distribution, identifying key authors, publications, journals and keyword occurrences while examining current literature. Design/methodology/approach A total of 127 articles exported from the Scopus database were systematically analyzed using bibliometric analysis through VOSviewer, including performance analysis and science mapping of the literature studied. Findings This research postulates the inconsistency between the number of publications and citations received by an author. There was an increase in collaborative research over the years. Human Resource Management Review was regarded as the most influential journal with maximum citation. Maximum publications came from Asian countries. The study revealed that the author with maximum citation were mostly the first authors of the most cited documents. Practical implications This research may be beneficial for both researchers and human resource (HR) practitioners because it identifies the research gaps and research needs in the HR analytics domain. Besides, this study recognizes the patterns in HR analytics literature that helps researchers better understand the subject area. Originality/value This research incorporates bibliometric analysis for analyzing HR analytics literature to establish a more exhaustive and systematic understanding of the research area. This research contributes to the existing body of literature and assists fellow researchers in future studies.

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Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction

Cited by 45 publications

References 31 publications

Trends in the thematic landscape of HR analytics research: a structural topic modeling approach

Trends in the thematic landscape of HR analytics research: a structural topic modeling approach

Work from anywhere: remote stakeholder management and engagement

A critical review of HR analytics: visualization and bibliometric analysis approach

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