Younis Sundus, Ahsan Ali, Chatteur Fiona M
University of Engineering and Technology (UET), Taxila, Pakistan.
Chifley Business School, Torrens University, Adelaide, SA Australia.
Soc Netw Anal Min. 2023;13(1):28. doi: 10.1007/s13278-023-01031-w. Epub 2023 Feb 2.
Contemporary research of employee social network analysis has grown far beyond the conventional wisdom of network and turnover theory; however, what is missing is a comprehensive review highlighting new perspectives and network constructs from a retention viewpoint. Since turnover is a concurrent component of retention, the analysis of the factors of quit propensity can result in a pre-emptive strategy for retention. This paper aims to capture the current state of the field and proposes a conceptual model for retention by exploring and the We identified 30 papers exploring voluntary turnover in social network constructs. Findings show that central network position is not always associated with negative turnover. Eigenvector, structural holes, and K-shell also prove to be a strong predictor of turnover. The snowball turnover of employees in similar network positions is pronounced in scenarios where employee sentiment is negative with poor group efficacy, entrepreneurship, and group values. This paper focuses on several themes to coalesce different determinants of an organizational network to demonstrate how social network theory has evolved to predict employee turnover. The resulting conceptual model suggests how to identify star performers and propose retention strategies.
当代员工社交网络分析研究已经远远超越了网络和离职理论的传统认知;然而,缺失的是一篇从留任意角度突出新观点和网络结构的全面综述。由于离职是留任的一个并发组成部分,对离职倾向因素的分析可以产生一种留任的先发制人策略。本文旨在了解该领域的当前状况,并通过探索和提出一个留任概念模型。我们确定了30篇探讨社交网络结构中自愿离职的论文。研究结果表明,网络中心位置并不总是与负面离职相关。特征向量、结构洞和K壳层也被证明是离职的有力预测指标。在员工情绪消极、团队效能、创业精神和团队价值观较差的情况下,处于相似网络位置的员工的滚雪球式离职现象很明显。本文关注几个主题,以整合组织网络的不同决定因素,展示社交网络理论如何演变以预测员工离职。由此产生的概念模型提出了如何识别明星员工并提出留任策略。