Purpose

Given the workplace’s reinvention to accommodate the global pandemic’s novel conditions, knowledge hiding (KH) behaviour in knowledge-intensive organisations must be examined from a fresh perspective. In this context, the relationship between workplace ostracism (WO) as KH’s antecedent and quiet quitting (QQ) as its consequence is undertaken via the mediating role of KH behaviour among knowledge workers (KWs).

Design/methodology/approach

Through stratified sampling, data from 649 KWs is obtained to test the model. Partial least squares structural equation modelling (PLS-SEM) using SMART-PLS 4.0. software establishes a significant influence of WO on KH and QQ. KH significantly mediates the relationship between WO and QQ, highlighting its critical intermediary role PLSPredict evaluates the model’s predictiveness. WO and KH’s effects on QQ are examined using necessity logic by collectively applying PLS-SEM and necessary condition analysis (NCA).

Findings

The model wherein WO plays a significant role in increasing KH and QQ, with KH as a partial mediator in the relationship, has high predictive relevance. Moreover, NCA confirms WO as the key predictor variable that provides variance in QQ, followed by KH. The Importance-performance map analysis technique supports the study’s managerial implications.

Originality/value

This study enriches QQ’s emerging literature by empirically identifying its antecedents-WO and KH. Methodologically, this paper gives a model for using PLS-SEM and NCA together in relation to QQ by identifying WO as its necessary condition. Evidence of selected constructs’ interrelationships may help organisations draft leadership programmes to curtail KH and QQ behaviour.

中文翻译：

---

知识背后的喧嚣：知识密集型组织中工作场所排斥和知识隐藏对悄悄退出的作用

 目的

鉴于工作场所的重塑以适应全球大流行的新情况，必须从新的角度审视知识密集型组织中的知识隐藏（KH）行为。在这种背景下，作为 KH 前因的工作场所排斥（WO）与作为其后果的安静退出（QQ）之间的关系是通过 KH 行为在知识工作者（KW）中的中介作用来承担的。

设计/方法论/途径

通过分层抽样，获取649千瓦的数据对模型进行检验。使用 SMART-PLS 4.0 进行偏最小二乘结构方程建模 (PLS-SEM)。软件建立了WO对KH和QQ的显着影响。 KH 显着介导了 WO 和 QQ 之间的关系，突出了其关键的中介作用。 PLSPredict 评估了模型的预测能力。通过共同应用 PLS-SEM 和必要条件分析 (NCA)，使用必要性逻辑检查 WO 和 KH 对 QQ 的影响。

 发现

其中 WO 在 KH 和 QQ 的增加中发挥重要作用，并且 KH 作为关系中的部分中介，该模型具有很高的预测相关性。此外，NCA 确认 WO 是提供 QQ 方差的关键预测变量，其次是 KH。重要性-绩效图分析技术支持了该研究的管理意义。

 原创性/价值

本研究通过实证识别 QQ 的前身 WO 和 KH，丰富了 QQ 的新兴文献。在方法论上，本文通过将 WO 确定为其必要条件，给出了将 PLS-SEM 和 NCA 结合使用的与 QQ 相关的模型。所选结构相互关系的证据可能有助于组织起草领导力计划，以限制 KH 和 QQ 行为。