Purpose

A wealth of studies have identified numerous antecedents to online self-disclosure. However, the number of competing theoretical perspectives and inconsistent findings have hampered efforts to obtain a clear understanding of what truly influences online self-disclosure. To address this gap, this study draws on the antecedent-privacy concern-outcome (APCO) framework in a one-stage meta-analytical structural equation modeling (one-stage MASEM) study to test a nomological online self-disclosure model that assesses the factors affecting online self-disclosure.

Design/methodology/approach

Using the one-stage MASEM technique, this study conducts a meta-analysis of online self-disclosure literature that comprises 130 independent samples extracted from 110 articles reported by 53,024 individuals.

Findings

The results reveal that trust, privacy concern, privacy risk and privacy benefit are the important antecedents of online self-disclosure. Privacy concern can be influenced by general privacy concern, privacy experience and privacy control. Furthermore, moderator analysis indicates that technology type has moderating effects on the links between online self-disclosure and some of its drivers.

Originality/value

First, with the guidance of the APCO framework, this study provides a comprehensive framework that connects the most relevant antecedents underlying online self-disclosure using one-stage MASEM. Second, this study identifies the contextual factors that influence the effectiveness of the antecedents of online self-disclosure.

中文翻译：

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见森林、见树木：网上自我表露前因的元分析

 目的

大量研究已经确定了网上自我表露的众多前因。然而，大量相互竞争的理论观点和不一致的研究结果阻碍了人们清楚地了解真正影响在线自我表露的因素。为了解决这一差距，本研究利用一阶段元分析结构方程模型（一阶段 MASEM）研究中的前提-隐私关注-结果 (APCO) 框架来测试法理在线自我披露模型，该模型评估影响网上自我披露的因素

设计/方法论/途径

本研究使用一阶段 MASEM 技术对在线自我披露文献进行荟萃分析，其中包括从 53,024 人报告的 110 篇文章中提取的 130 个独立样本。

 发现

研究结果表明，信任、隐私关注、隐私风险和隐私收益是网络自我表露的重要前因。隐私关注可能受到一般隐私关注、隐私体验和隐私控制的影响。此外，调节分析表明，技术类型对在线自我表露与其某些驱动因素之间的联系具有调节作用。

 原创性/价值

首先，在 APCO 框架的指导下，本研究提供了一个综合框架，使用单阶段 MASEM 连接在线自我披露的最相关的前因。其次，本研究确定了影响在线自我表露前因有效性的背景因素。