Controlling the Control Condition: A Critical Methodological Review of Control Conditions in Experimental Management Research
回顾了2021至2023年顶级管理期刊的958个实验，发现20%的研究因果主张缺乏设计支持，控制条件选择与设计缺乏透明度，为此为研究者、评审人和编辑提供了改进指南。
Control conditions are essential to establishing causal relationships in experimental management research, yet they receive little attention compared to treatments. This study thus examines the current state of control-condition selection and design in top-tier management journals, reviewing 958 experiments from 421 study papers published from 2021 to 2023. Our review shows that researchers use true and pseudo-control conditions. True control conditions—such as no-treatment, all-but-treatment, and treatment-as-usual controls—provide a baseline for interpreting the effect of the treatment condition. In contrast, pseudo-control conditions (e.g., opposite-treatment-level or alternative-treatment designs) allow relative comparisons across conditions without providing a baseline. Notably, 20% of the studies we examined presented causal claims that were not supported by their designs, opening the risk of their results being misinterpreted and their effect sizes being exaggerated. These issues were further exacerbated by a lack of method transparency and construct validity. In response, we offer guidelines not only for primary study researchers to support the selection and design of control conditions, thereby enhancing transparency and yielding valid interpretations of causal claims, but also for research synthesists, reviewers, and editors to evaluate the same.