Assessing turnover intention with explicit approaches (self‐report scales) contains several measurement limitations, including social desirability, impression management, and self‐defense, potentially resulting in reduced accuracy. To improve the accuracy of assessment, the current research conducted a two‐wave study to examine whether implicit variables provide incremental effect in predicting turnover intention, after controlling for explicit variables. A computerized text analysis program, Linguistic Inquiry and Word Count, was used to identify language dimensions in participants' writing samples, and these exported scores serve as implicit language variables. Results demonstrate that language variables provide significant incremental effect (9% of explained variance) in predicting turnover intention, and this effect lasted at a one‐month follow‐up. The language dimensions signal topics of concern associated with turnover intention.

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您的言语表达了您的想法：两波评估语言规模以预测员工离职意向的研究

使用明确的方法（自我报告的量表）评估离职意图包含一些测量限制，包括社交需求，印象管理和自卫，可能会导致准确性降低。为了提高评估的准确性，当前的研究进行了两波研究，以检查隐性变量在控制了显性变量之后是否在预测离职意图方面提供了增量作用。使用计算机化的文本分析程序“语言查询和字数统计”来识别参与者的写作样本中的语言维度，这些导出的分数用作隐式语言变量。结果表明，语言变量在预测离职意向方面具有显着的增量作用（解释差异的9％），这种效果持续了一个月的随访。语言尺寸表示与离职意图相关的关注主题。