In an editorial in the Journal of Marketing, Steenkamp et al. (2026) make a valuable and timely intervention by urging marketing scholars to move beyond dichotomous significance testing and to report effect sizes that speak to substantive significance. Their editorial is especially strong in its insistence on exact p-values, richer statistical reporting, and closer alignment between rigor and relevance. Yet, their framework omits the local form of Cohen's f^2, that is f(B)^2 as an effect-size measure for the contribution of an individual predictor or predictor block B within a multivariable model. That omission matters because much of marketing research relies on regression-type models in which the central theoretical question is not merely whether a model fits globally, but whether a focal construct adds meaningful explanatory power beyond competing predictors and controls. This commentary argues that the R-squared foundation of local Cohen's f(B)^2 is a strength, especially in large-sample settings. Moreover, f-squared-type local effect sizes can be extended beyond ordinary least squares to multilevel models and, more tentatively, to neural networks and other machine-learning models.

翻译：在《营销期刊》的社论中，Steenkamp等人（2026）提出宝贵且及时的倡议，敦促营销学者超越二分法的显著性检验，转而报告能反映实质性意义的效应量。该社论特别强调精确p值、更丰富的统计报告以及严谨性与相关性之间的紧密契合，其论述尤为有力。然而，他们的框架遗漏了科恩f²的局部形式，即f(B)²作为多变量模型中单个预测因子或预测因子块B贡献的效应量指标。这一遗漏至关重要，因为大多数营销研究依赖回归型模型，其核心理论问题不仅是模型整体拟合与否，更在于焦点构念能否在竞争性预测因子和协变量之外增加具有实质解释力的增量效应。本文认为，局部科恩f(B)²基于R平方的统计基础实为优势，尤其适用于大样本情境。此外，f平方型局部效应量可超越普通最小二乘法，扩展至多层次模型，并初步延伸至神经网络及其他机器学习模型。