Text analysis, particularly custom dictionaries and topic modeling, has helped advance management and organization theory. Custom dictionaries involve creating word lists to quantify patterns and infer constructs, while topic modeling extracts themes from textual documents to help understand a theoretical domain. Building on these two approaches, we propose another text analysis approach called word-text-topic extraction (WTT), which enhances the efficiency and relevance of text analysis for the sake of theoretical advancement. Specifically, we first identify relevant words for a researcher's theoretical area of interest using word-embedding algorithms. That step is followed by extracting text segments from the textual corpus using a collocation process. Finally, topic modeling is applied to capture themes relevant to the specific theoretical area of interest. To illustrate the WTT approach, we explored one research area needing further theory development—innovation. Using 841 CEOs’ letters to shareholders, we found that our WTT approach provides nuanced features of innovation that differ across industry contexts. We guide researchers on decisions and considerations related to the WTT approach in order to facilitate its use in future studies.

中文翻译：

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从文本数据到理论见解：词-文本-主题提取方法的介绍和应用

文本分析，特别是自定义词典和主题建模，有助于推进管理和组织理论。自定义词典涉及创建单词列表来量化模式和推断结构，而主题建模则从文本文档中提取主题以帮助理解理论领域。基于这两种方法，我们提出了另一种文本分析方法，称为单词-文本-主题提取（WTT），它提高了文本分析的效率和相关性，以促进理论进步。具体来说，我们首先使用词嵌入算法识别研究人员感兴趣的理论领域的相关词。该步骤之后是使用搭配过程从文本语料库中提取文本片段。最后，应用主题建模来捕获与感兴趣的特定理论领域相关的主题。为了说明 WTT 方法，我们探索了一个需要进一步理论发展的研究领域——创新。通过 841 位 CEO 致股东的信函，我们发现我们的 WTT 方法提供了因行业环境而异的细微创新特征。我们指导研究人员做出与 WTT 方法相关的决策和考虑，以促进其在未来研究中的使用。