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The authors aim at investigating different forms of classification shifting (CS). CS is a novel form of earnings management under which managers misclassify income statement line items and cash flow statement line items with an intent to report favorable operating performance of firms. In particular, the authors check the existence of revenue misclassification, expense misclassification and cash flows misclassification among Indian firms by taking the uniform sample of firms over a single period.

Operating revenue model (Malikov et al., 2018), core earnings expectation model (McVay, 2006) and operating cash flows model (Roychowdhury, 2006) are employed for measuring revenue misclassification, expense misclassification and cash flows misclassification, respectively. The panel data regression models are used to analyze the data for this study.

Based on the sample of 12,870 Bombay Stock Exchange (BSE) listed firm-years observations between 2010 and 2018, we find that, on average, Indian firms are engaged in revenue misclassification rather than expense misclassification to report inflated core earnings. Firms are found to be engaged in cash flows misclassification too. Besides, we find that magnitude of shifting is greater among larger firms. Results also establish that adoption of Ind AS increases the scope of shifting practices. These results are based on several robustness checks.

The results suggest that investors conduct a comprehensive review of the items of financial statements before using them in their portfolio valuation. It suggests auditors check the basis of revenue classification and standard-setting authorities, like ICAI in India, to make more mandatory disclosure requirements for classification of revenues and cash flows. It suggests lenders not to make lending decisions by looking at the operating performance metrics, as CS is the most preferred tool to positively influence the perception of lenders toward operating performance.

It is the first study that investigates different forms of classification shifting jointly for a sample of firms. Most of the earlier studies have examined one kind of classification shifting at a time. This study adds to the existing literature on earnings management by documenting that some firm-specific factors pressurize firms to prefer one form of shifting over another to report inflated core earnings.

This study aims to investigate the impact of business strategy on the classification shifting practices of Indian firms.

The study considered cost leadership and differentiation strategy. Two forms of classification shifting, namely, expense misclassification and revenue misclassification have been examined in this study. Panel data regression models are used to analyze the data for this study.

The results show that managers of cost leadership strategy firms are more likely to be engaged in expense misclassification, whereas firms following differentiation strategy are likely to be engaged in revenue misclassification. Subsequent tests of this study suggest that firms following a hybrid strategy (mix of cost leadership and differentiation) prefer revenue misclassification over expense misclassification for reporting inflated operating performance. These results imply that firms prefer the shifting tool based on the ease and need of each shifting strategy. These results are consistent with several robustness measures.

The results suggest that investors should understand business strategy before developing insights about the accounting quality of firms. Investors should conduct a comprehensive review of income statement items before using items for portfolio evaluation.

To the best of the authors’ knowledge, this is the first study to examine the association between business strategy and classification shifting.

This study aims to explore peer performance as the motivation behind gross profit manipulation through two different channels, namely, cost of goods sold (COGS) misclassification and revenue misclassification.

Gross profit expectation model (Poonawala and Nagar, 2019) and operating revenue expectation model (Malikov et al., 2018) are used to measure COGS and revenue misclassification, respectively. The panel data regression models are used to analyze the data for this study.

The study results show that firms engage in gross profit manipulation to meet the industry’s average gross margin, implying that peer performance is an important benchmark that firms strive to achieve through misclassification strategies. Further results exhibit that firms prefer COGS misclassification over revenue misclassification for manipulating gross profit, implying that firms choose the shifting strategy based on the relative advantage of each shifting tool.

The findings suggest that firms that just meet or slightly beat industry-average profitability levels are highly likely to engage in classification shifting (CS). Thus, investors and analysts should be careful when evaluating such firms by comparing them with other firms in the same industry.

First, this study is among earlier attempts to investigate CS motivated by peer performance. Second, this study investigates both tools of gross profit manipulation by taking a uniform sample of firms over the same period and provides compelling evidence that firms prefer one shifting tool over another depending on the relative advantage of each shifting tool.

Extant research shows that consumers regularly misclassify country-of-origin (COO) associated with brands. The purpose of this paper is to examine changes in behavioral intentions (i.e. purchase intentions for self and others and brand judgments) when consumers are made aware that they have misclassified the COO and then are informed of the brand’s correct origin. Drawing on cognitive dissonance theory, the authors also explore the moderating roles of consumer affinity, animosity, and product knowledge.

Two experiments test the direct and moderating effects of COO misclassification awareness on behavioral intentions.

The findings show detrimental effects of misclassification on behavioral intentions when consumers have high affinity with misclassified COO. Moreover, the experiments demonstrate a significantly greater decrease in behavioral intentions among experts than novices in the low-affinity condition and the reverse effect in the high-affinity condition.

The negative effects of COO misclassification on consumer behavioral intentions highlight the need for managers to proactively avoid misclassification. The findings should also aid managers in developing responsive marketing campaigns that consider consumer affinity, animosity, and level of product knowledge.

This research is the first to compare consumer behavioral responses before and after COO misclassification awareness. The study demonstrates that cognitive dissonance underpins the process of misclassification. It also contributes to COO literature by examining the interaction of consumer affinity and animosity with product knowledge and their influence on consumer behavior in the case of COO misclassification.

This study applies a new taxonomy of racial/ethnic misclassification that considers shifts in racial/ethnic status to investigate physical and emotional responses to racial treatment among different misclassification types. It finds that the odds of reporting physical and emotional symptoms increase 3.3 and 2.9 times, respectively, among individuals who experience racial/ethnic status loss (i.e., are misclassified into a racial/ethnic category with lower status compared to their self-reported category) compared to their correctly classified counterparts. In contrast, individuals who experience racial/ethnic status gain (i.e., are misclassified into a racial/ethnic category with higher status compared to their self-reported category) are no more likely to suffer from symptoms compared to correctly classified individuals. The results suggest that being misclassified per se does not necessarily harm well-being, but the loss of social status inherent in some types of misclassification does.

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data augmentation, Gibbs sampling, and Metropolis–Hastings to obtain estimates of the misclassification probabilities and the treatment effect. Simulations demonstrate that the proposed Bayesian estimator accurately estimates the treatment effect in light of misclassification and endogeneity.

The study aims to examine the impact of the firm life cycle on the misclassification practices of Indian firms. The study also examines the impact of International Financial Reporting Standards (IFRS) on the misclassification practices of Indian firms.

The study uses Dickinson (2011) cash flow patterns to classify firm-years under life cycle stages. Two forms of misclassification, namely revenue misclassification and expense misclassification have been examined in this study.

Based on a sample of 19,268 Bombay Stock Exchange (BSE) firm-years spanning over ten years from March 2010 to March 2019, results show that firms operating at high (low) life cycle stage are more likely to be engaged in revenue (expense) misclassification, implying that firms substitute between the classification shifting tools depending upon ease and needs of each tool. Further, our results demonstrate that the magnitude of expense shifting has been significantly increased among test firms (firms reporting under IFRS) relative to benchmark firms (firms reporting under domestic GAAP) in the post-IFRS adoption period, implying that adoption of IFRS negatively affects the accounting quality of Indian firms.

The study considers only two main forms of misclassification, namely revenue and expense misclassification. However, future research may explore the cash flow misclassification.

The findings suggest that standard-setting authorities make more mandatory disclosure requirements under IFRS to curb the corporate misfeasance of classification shifting.

First, the study is among the earlier attempts to examine the impact of the firm life cycle on misclassification practices. Second, the study explores the unique Indian institutional settings concerning the phased-manner implementation of IFRS and examines its impact on the classification shifting practices of firms.

Medical bankruptcies occur when an individual experiences an acute or chronic health event, and the costs of care exceed the individual’s ability to pay. In such cases, the individual typically files for bankruptcy. There is an extensive literature that estimates the prevalence of medical bankruptcy, but studies either select a population whose medical care is extremely expensive or chooses ad hoc thresholds for medical bankruptcy categorizations. In both cases, the prevalence of medical bankruptcy is biased. The purpose of this paper is to estimate the actual prevalence of medical bankruptcies in a manner that avoids these limitations.

Data are randomly drawn from a single US Bankruptcy Court district. Following the literature, an ad hoc threshold of medical debts which places the bankruptcy filer “at risk” for a medical bankruptcy is postulated. Misclassification analyses are used to estimate the likelihood of a medical bankruptcy filing while adjusting for the use of ad hoc thresholds.

The naive prevalence of medical bankruptcy is 23.1 percent, but exceeds 50 percent when accounting for misclassification. Many individuals are “ostensibly” medically bankrupt. They are already seriously indebted, and any outside financial shock, including but not limited to medical bills, can push these debtors into insolvency.

Bankruptcy is an important social safety net. An improved understanding of the types and magnitudes of medical debts which precipitate a bankruptcy filing can lead to policies that improve outcomes for bankruptcy filers and reduce the social costs of bankruptcy.

Industrial relations, organizational behavior, and human resource management scholars have studied numerous aspects of internal workplace conflict resolution, ranging from the design of conflict resolution systems to the processes used for resolving conflicts to the outcomes of the systems. Scholars from these specialties, however, have paid considerably less attention to external workplace conflict resolution through litigation. This chapter analyzes certain areas of such litigation, focusing specifically on workplace conflicts involving issues of managerial and employee misclassification, independent contractor versus employee status, no-poaching agreements, and executive compensation.

Leading recent cases involving these issues are examined, with particular attention given to the question of whether the conflicts reflected therein could have been resolved internally or through alternative dispute resolution (ADR) methods rather than through litigation.

Implications of this analysis are drawn for workplace conflict resolution theory and practice. In doing so, I conclude that misclassification disputes could likely be resolved internally or through ADR rather than through litigation, but that no-poaching and executive compensation disputes could very likely not be resolved internally or through ADR.

The chapter draws on and offers an integrated analysis of particular types of workplace conflict that are typically treated separately by scholars and practitioners. These include misclassification conflicts, no poaching and labor market competition conflicts, and executive compensation conflicts. The originality and value of this chapter are to show that despite their different contexts and particular issues, the attempted resolution through litigation of these types of workplace conflicts has certain common, systematic characteristics.

To report inflated operating performance indicators, such as operating revenue and operating profit, managers vertically reposition revenue and expense items inside the income statement. This study aims to investigate the relationship between credit market incentives and these practices.

This study examined a sample of 1,592 Bombay Stock Exchange-listed companies from 2009 to 2021 and tested them using panel data regression models. The propensity score matching method and different measurements of classification shifting practices are used to validate the results.

The conclusions drawn from the empirical data show that firms prefer revenue shifting over expense shifting to prevent debt covenant violations. It shows that the firm’s classification-shifting practices are driven by credit market incentives. This finding is consistent with the notion of positive accounting theory that firms engage in classification shifting (earnings management) to avoid violation of debt covenants. Further, the firm’s preference for revenue shifting is in line with the ease-need-advantage-based shifting framework where firms choose the shifting tool based on costs and constraints associated with each tool.

The finding suggests that if managers heavily rely on revenue shifting to avoid debt covenant violations, the firm may end up breaking these covenants based on its actual operating performance. Managers may use aggressive accounting techniques to prevent covenant violations, which can be a warning indicator of financial difficulties or operational problems. It highlights the necessity for creditors and investors to carefully evaluate a company’s financial stability outside of the financial statements that are publicly disclosed. Authorities should create separate forensic accounting standards for auditors to check revenue items and stop the corporate misfeasance of revenue shifting.

The study is among the earlier attempts to provide empirical evidence on credit market incentives behind classification shifting practices. It is the first study that documents the substitution relationship between classification shifting forms for avoiding violation of debt covenants.