Operating cash flows are a critical input to valuation activities. Research and practice indicate that the most common method of presenting operating cash flows, the indirect method, is viewed as overly complex. We experimentally examine whether two theoretically motivated factors represent shortcomings of the indirect method: (1) inadequate linking of related components of net income and changes in working capital accounts, and (2) inconsistent income statement labeling between components of net income and their corresponding working capital changes. Drawing on causal reasoning theory, we predict and find that these factors hinder the decision usefulness of the operating section of the statement of cash flows. The results of our study are informative to the academic literature and standard setters, as we examine underlying reasons why the indirect method may be viewed unfavorably.

Financial accounting standard setters recently changed the accounting for estimates involving credit risk. The change shifted the accounting from an incurred‐loss model (ILM), which is based on credit‐risk conditions that exist at the time of the estimate, to a model based on expected credit losses over the life of the asset in question (ELM), which considers expectations of future conditions. Our paper is based on the theoretical premise that ELM will reduce surprise via its Day 1 forewarning, which, in turn, should lessen the potential for hindsight bias (i.e., Monday‐morning quarterbacking) and judging management as having made a poor quality lending decision if an actual credit loss later occurs. Three experiments support this premise, revealing when ELM accounting should have a differential impact relative to the prior accounting.

Classification of financial statement elements into categories is an inherent, integral part of the financial reporting system. Although prior research documents that categories influence users’ perceptions of included items, we explore the reverse effect—how classifying items in a category can impact perceptions of the category itself and its other members. Using categorization theory from psychology and accounting literature and exploiting the classification challenges inherent to the crypto asset setting, we predict and find that classifying individual items into a category can impact equity analysts’ perceptions of the category itself and perceptions of the category's other members. We also explore the boundaries of this effect and find that categories like intangibles, with fewer common prototypical features, are more susceptible to these classification effects. Our results show several ways in which balance sheet classification could lead to unintended consequences for users.

Financial reporting standard-setting bodies are expected to include academic research in their decision-making processes. However, little is known about whether, why, and how they engage with research. We interview staff from two major accounting standard-setting bodies and, using the information seeking and communication model as a theoretical lens, identify factors shaping their research engagement. These factors include perceptions of academic research, search difficulties, and challenges with assessing the credibility and utility of academic research. Our findings reveal an unstructured, idiosyncratic approach to information seeking, a reliance on academic papers over direct researcher interaction, a significant lack of two-way communication, and concerns relating to incentive differences between academics and standard setters. These structural issues limit the extent to which academic research can inform standard setting. We provide insights on improving the inclusion of academic research in the standard-setting decision-making process and contribute to broader discussions on research relevance.