Scientific research demands robust findings, yet variability in results persists due to researchers' decisions in data analysis. Despite strict adherence to state‐of the‐art methodological norms, research results can vary when analyzing the same data. This article aims to explore this variability by examining the impact of researchers' analytical decisions when using different approaches to structural equation modeling (SEM), a widely used method in innovation management to estimate cause–effect relationships between constructs and their indicator variables. For this purpose, we invited SEM experts to estimate a model on absorptive capacity's impact on organizational innovation and performance using different SEM estimators. The results show considerable variability in effect sizes and significance levels, depending on the researchers' analytical choices. Our research underscores the necessity of transparent analytical decisions, urging researchers to acknowledge their results' uncertainty, to implement robustness checks, and to document the results from different analytical workflows. Based on our findings, we provide recommendations and guidelines on how to address results variability. Our findings, conclusions, and recommendations aim to enhance research validity and reproducibility in innovation management, providing actionable and valuable insights for improved future research practices that lead to solid practical recommendations.

中文翻译：

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相同的模型，相同的数据，但不同的结果：评估结构方程建模中方法选择的影响

科学研究需要可靠的发现，但由于研究人员在数据分析中的决定，结果仍然存在差异。尽管严格遵守最先进的方法规范，但在分析相同的数据时，研究结果可能会有所不同。本文旨在通过检查研究人员在使用不同方法进行结构方程建模（SEM）时分析决策的影响来探索这种可变性，结构方程建模是创新管理中广泛使用的一种方法，用于估计结构与其指示变量之间的因果关系。为此，我们邀请 SEM 专家使用不同的 SEM 估计器来估计吸收能力对组织创新和绩效影响的模型。结果显示，效应大小和显着性水平存在相当大的差异，具体取决于研究人员的分析选择。我们的研究强调了透明分析决策的必要性，敦促研究人员承认其结果的不确定性，实施稳健性检查，并记录不同分析工作流程的结果。根据我们的发现，我们提供有关如何解决结果变异性的建议和指南。我们的发现、结论和建议旨在提高创新管理中研究的有效性和可重复性，为改进未来的研究实践提供可行且有价值的见解，从而产生切实可行的建议。