The importance of positive employee experience and its development through using predictive analytics

IF 1.8 Q3 MANAGEMENT
Donát Vereb, Zoltán Krajcsák, Anita Kozák
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引用次数: 0

Abstract

Purpose

The study aims to explore the organizational benefits of positive employee experience and to provide a framework for measuring it. The positive employee experience has a profound impact on employees’ attitudes; thus, it is particularly important to what extent an organization can create the conditions supporting this.

Design/methodology/approach

The study is based on literature review and the framework needs to be empirically tested to draw final conclusions.

Findings

Organizational performance and success are influenced by employees’ well-being, commitment, job satisfaction and the high level of individual performance. However, this grouping of variables is not exhaustive, but in practice, it is often not necessary to fully understand the complex and complicated relationships among the organizational variables. However, a positive employee experience has an impact on all of these variables. According to our understanding and experience, the task of management is not to strengthen the variables describing employee attitudes individually, based on the knowledge of specific relations presented in the management literature and selected for the sake of a single research, but to create an acceptable level of the positive employee experience, which is able to strengthen these variables in a way that is useful for the organization.

Originality/value

In this study, the authors introduce the concept of the positive employee experience and the ways and steps to measure it. The authors review the methodology of predictive analytics, the main principles of data collection and the types of data with their possible applications. Finally, the limitations of the framework and the risks of enhancing the positive employee experience are also discussed.

积极的员工体验的重要性以及通过使用预测分析来发展员工体验
目的本研究旨在探讨积极员工体验对组织的益处,并提供一个衡量积极员工体验的框架。积极的员工体验对员工的态度有着深远的影响;因此,组织能在多大程度上创造支持员工体验的条件尤为重要。研究结果组织的绩效和成功受员工的幸福感、承诺、工作满意度和高水平的个人绩效的影响。然而,这种变量分组并非详尽无遗,但在实践中,往往没有必要充分理解组织变量之间错综复杂的关系。然而,积极的员工体验会对所有这些变量产生影响。根据我们的理解和经验,管理的任务不是根据管理文献中介绍的特定关系知识,为了单一的研究而选择单独加强描述员工态度的变量,而是创造一个可接受的员工积极体验水平,它能够以一种对组织有用的方式加强这些变量。 原创性/价值在本研究中,作者介绍了员工积极体验的概念以及衡量员工积极体验的方法和步骤。作者回顾了预测分析的方法、数据收集的主要原则和数据类型及其可能的应用。最后,作者还讨论了该框架的局限性以及增强积极员工体验的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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