Trustworthiness in Computational Theory Construction: Dimensionalization and Category Surfacing1
针对计算理论建构中的维度化与类别浮现方法，提出一个框架帮助研究者通过设计选择（如理论词汇优先还是实践词汇优先）来提升研究的可信度，对使用主题建模、词嵌入等工具进行理论建构的学者有直接指导作用。
In this methods article, we unpack how researchers can foster trustworthiness in dimensionalization and category surfacing (DCS), a key method family within the genre of computational theory construction (CTC). Information systems (IS), management, and organizational scholars are increasingly leveraging DCS tools such as topic modeling, word embeddings, and clustering to surface latent categories and dimensions from textual data for theory construction. Yet they struggle because evaluations of such research often default to transparency, operationalized as replicability and accountability, which obscures the analytical choices that actually make DCS research rigorous. In this study, we recast transparency as a means toward trustworthiness. We treat researchers’ analytical moves as the primary unit of methodological reasoning in how they design, conduct, and disclose their choices across research phases. We develop a framework that authors, reviewers, and editors can use to construct and evaluate DCS research. The framework specifies how trustworthiness arises from the interplay of two research design choices: primacy to theoretical versus practice lexicons, and whether the content of texts or the structure of the corpus carries the theoretical load. We articulate expectations for conduct and disclosure across these design choices, clarifying how proportionate reasoning anchors trustworthiness. We conclude with implications for advancing trustworthiness within the broader CTC community and across other computational approaches to research.