Inflection points, kinks, and jumps identify places where the relationship between dependent and independent variables switches in some important way. Although these switch points are often mentioned in management research, their presence in the data is either ignored, or postulated ad hoc by testing arbitrarily specified functional forms (e.g., U or inverted U-shaped relationships). This is problematic if we want accurate tests for our theories. To address this issue, we provide an integrative framework for the identification of nonlinearities. Our approach constitutes a precursor step that researchers will want to conduct before deciding which estimation model may be most appropriate. We also provide instructions on how our approach can be implemented, and a replicable illustration of the procedure. Our illustrative example shows how the identification of endogenous switch points may lead to significantly different conclusions compared to those obtained when switch points are ignored or their existence is conjectured arbitrarily. This supports our claim that capturing empirically the presence of nonlinearity is important and should be included in our empirical investigations.

拐点、扭结和跳跃标识了因变量和自变量之间的关系以某种重要方式切换的位置。尽管这些转换点在管理研究中经常被提及，但它们在数据中的存在要么被忽略，要么通过测试任意指定的函数形式（例如，U 或倒 U 形关系）来临时假设。如果我们想要对我们的理论进行准确的测试，这是有问题的。为了解决这个问题，我们提供了一个用于识别非线性的综合框架。我们的方法构成了研究人员在决定哪种估计模型可能最合适之前想要执行的先导步骤。我们还提供了有关如何实施我们的方法的说明，以及该过程的可复制说明。我们的说明性示例显示，与忽略开关点或任意推测其存在时获得的结论相比，内源性开关点的识别如何导致明显不同的结论。这支持我们的主张，即凭经验捕捉非线性的存在很重要，应该包括在我们的实证研究中。