Regulators are concerned about the quality of group audits because of poor inspection results, recent enforcement actions against component auditors, and the significance of these audits to the global economy. Yet research in this area is nascent. In response, we survey and interview US‐based global group audit leads to better understand the group audit process and their perceptions of challenges that arise on such engagements. Our findings indicate that group auditors organize their audits consistent with the client's structure, which drives component auditor selection, scoping, and fees. Group auditors routinely find fault with component auditors, perceiving that work performed and/or documentation provided is not sufficient, not appropriate and/or not communicated timely, to comply with US standards and reporting deadlines. In some cases, they perceive that the component auditor is unable and/or unwilling to comply and in others, that the component auditor misunderstands group instructions due to language proficiency or differing interpretation of the standards. Notably this perspective is ethnocentric, as group auditors almost exclusively attribute issues to component auditors. While ethnocentric tendencies appear myopic, they provide insight into unrecognized or overlooked aspects of the global firm model of cooperative arrangements. This study highlights the significance of the global firm's network structure to global group audits. Thus, we encourage intercommunity research, among (quantitative and qualitative) scholars and standard setters, to consider group audits in the broader context of firm networks.

As firms increasingly use social media to provide disclosures to investors, it is important to understand whether the characteristics that are associated with these disclosures lead to different reactions from investors than disclosures provided via more traditional channels. In this paper, we use an experiment to examine whether linguistic formality in positive news disclosures, and engagement of social media users surrounding the disclosures (e.g., “likes” and “retweets”), affect investors' judgments about a firm and its management. Results suggest that, as predicted, investors are more sensitive to signals of audience engagement when disclosures use informal rather than formal language. Specifically, when associated with signals of high audience engagement, the use of informal language leads to greater willingness to invest than the use of formal language in a disclosure. However, also as predicted, the use of informal language hurts willingness to invest when associated with signals of low audience engagement. In two follow‐up experiments, we investigate how news valence and linguistic formality are expected to affect the level of audience engagement in the first place, and we investigate whether managers strategically vary their use of linguistic formality based on characteristics of the setting. Overall, our results provide evidence on how firms might use social media disclosures to better connect with investors. This study contributes to the growing literature on linguistic attributes of disclosures, and the emerging literature investigating the consequences of issuing financial disclosures through social media.

We examine the effect of humanizing (naming) robo‐advisors on investor judgments, which has taken on increased importance as robo‐advisors have become increasingly common and there is currently little SEC regulation governing key aspects of their use. In our first experiment, we predict and find that investors are more likely to rely on the investment recommendation of an unnamed robo‐advisor, whereas they are more likely to rely on the investment recommendation of a named human advisor. Theory suggests one reason that naming a robo‐advisor may have drawbacks pertains to the complexity of the task the robo‐advisor performs. We explore the importance of task complexity in our second experiment. We predict and find that investors are less likely to rely on a named robo‐advisor when the advisor is perceived to be performing a relatively complex task, consistent with our first experiment, and more likely to rely on a named robo‐advisor when the advisor is perceived to be performing a relatively simple task, consistent with prior research on human‐computer interactions. Our findings contribute to the literature examining how technology influences the acquisition and use of financial information and the general literature on human‐computer interactions. Our study also addresses a call by the SEC to learn more about robo‐advisors. Lastly, our study has practical implications for wealth management firms by demonstrating the potentially negative effects of making robo‐advisors more humanlike in an attempt to engage and attract users.

This study examines whether a real production activity measure, firm‐level electricity consumption growth, is useful in detecting firm financial misreporting. Identifying proxies for a firm's underlying financial performance that are not a function of the firm's accounting system is essential for detection of misreporting. We propose that the difference between revenue growth and electricity consumption growth (i.e., growth wedge (GW)) is a useful signal of financial misreporting. Using electricity consumption data for Korean firms from 2006 to 2014, we find that the GW is positively associated with discretionary revenues and accruals and the likelihood of financial misreporting as proxied by accounting restatements, qualified audit opinions, and regulatory enforcement actions. The GW provides incremental information over firm characteristics and earnings management signals examined by prior research. Our findings are robust to a battery of additional tests, including within‐firm and industry comparisons that do not require access to cross‐sectional firm‐level electricity data. Overall, our study documents new evidence on the role of a real production activity measure from an independent reporting entity in detecting financial misreporting. Our evidence speaks to the potential usefulness of real activity metrics in forensic economics.

Cash flow hedge derivatives are an example of an economic transaction that is not fully portrayed in the financial statements in two key ways. First, while changes in the fair value of the derivative are recorded at each reporting date, changes in the value of the underlying purchase or sale commitment are not recorded or disclosed until that transaction occurs. Therefore, until the purchase or sale occurs, the financial statements only portray half of the economic transaction. Second, the gains/losses associated with these derivatives provide an inverse signal about the persistence of firm profitability. We document a method by which financial statement users can partially adjust for these distortions and find evidence that debt investors incorporate information conveyed by cash flow hedge gains/losses into their pricing of new debt issuances. We also find evidence that credit analysts incorporate these adjustments into their firm‐level credit ratings but are unable to find consistent evidence of similar adjustments to credit ratings on new debt issuances. Overall, our results suggest that a subset of sophisticated investors (i.e., those in public debt markets) appear to incorporate information from cash flow hedge accounting into their assessments of firm risk, and that users may benefit from enhanced disclosure about the amount and timing of a firm's future transactions that are exposed to foreign currency, interest rate, or commodity price risk as well as the amount and timing of derivatives that protect the firm from those risks.

Data analytics is transforming our global markets and significantly impacting the financial reporting environment. We investigate how auditors, company managers, and regulation interact with data analytics and one another to affect the diffusion (i.e., development and spread) of data analytics throughout the financial reporting environment. We interview company managers and their audit partners, as well as additional stakeholders, including regulators. We interpret findings from our interviews using theory that highlights the importance of dynamic interactions between people and their environments, which include the prevailing rules (e.g., regulatory guidance). Our findings contribute to the accounting literature and practice by revealing three areas of conflict emerging from stakeholders' disparate preferences for data analytics. First, we uncover growing tensions between managers and audit partners regarding audit fees. Second, we find that managers and auditors believe the lack of accounting regulation specific to data analytics causes confusion and frustration. Finally, auditors report that they strategically leverage data analytics to provide clients with business‐related insights. However, regulators voice concerns that this practice might impair auditor independence and reduce audit quality. These areas of conflict suggest a need to revisit key tensions surrounding the audit function in a contemporary context characterized with significant technological shift.