Archival Accounting Research Research Articles

Healthcare Accounting Research: An Analysis, Review, and Suggestions for Future Work

ABSTRACT This paper analyzes and reviews healthcare-related accounting literature, with a focus on research published between 1990 and 2020. We systematically collect a global sample of 413 papers and categorize each paper along several dimensions, including accounting sub-discipline, research methodology, journal quality, and geographic origin. We find that high-quality accounting journals publish healthcare-related research throughout the period, but publication in the most elite (i.e., “top 3”) journals is declining. Further, we find that archival accounting research in healthcare is primarily a North American undertaking, while field studies and case studies are more popular internationally. We review the sampled research along with additional (pre-1990 and post-2020) literature in an inclusive summary of extant healthcare-related accounting research, organized by topic and focused on identifying avenues for additional research. We then discuss several healthcare-related public data sources.

The Use of New Data Sources in Archival Accounting Research: Implications for Research in Accounting Information Systems and Emerging Technologies

ABSTRACTWe explore the connections from phenomena to new data and from data to theories. While starting with philosophical perspectives from positive economic theory to positive accounting theory, we emphasize the relevance and importance of new data sources to archival research in accounting and the implications to research in accounting information systems and emerging technologies. We present a number of studies in accounting that exemplify the critical role of data in the discovery of theories from phenomena. We argue that data in conventional empirical studies limit methodology to require assumptions and complex econometric treatments for general business settings. Therefore, recent developments in “Big Data” and data analytics, particularly the improved data availability from a variety of new sources, have made strong research designs possible. We provide evidence that 88 percent of the JIS and 100 percent of the JETA empirical archival publications in the recent two years used new data sources.

Non-random sampling and association tests on realized returns and risk proxies

This paper investigates how data requirements often encountered in archival accounting research can produce a data-restricted sample that is a non-random selection of observations from the reference sample to which the researcher wishes to generalize results. We illustrate the effects of non-random sampling on results of association tests in a setting with data on one variable of interest for all observations and frequently-missing data on another variable of interest. We develop and validate a resampling approach that uses only observations from the data-restricted sample to construct distribution-matched samples that approximate randomly-drawn samples from the reference sample. Our simulation tests provide evidence that distribution-matched samples yield generalizable results. We demonstrate the effects of non-random sampling in tests of the association between realized returns and five implied cost of equity metrics. In this setting, the reference sample has full information on realized returns, while on average only 16% of reference sample observations have data on cost of equity metrics. Consistent with prior research (e.g., Easton and Monahan The Accounting Review 80, 501–538, 2005), analysis using the unadjusted (non-random) cost of equity sample reveals weak or negative associations between realized returns and cost of equity metrics. In contrast, using distribution-matched samples, we find reliable evidence of the theoretically-predicted positive association. We also conceptually and empirically compare distribution-matching with multiple imputation and selection models, two other approaches to dealing with non-random samples.

Outliers and Robust Inference in Archival Accounting Research

This study examines the nature of outliers in archival accounting research, and evaluates the merits and limitations of robust estimators in identifying and downweighting their influence. Using simulated and actual data, we demonstrate how outliers arise non-randomly from the data generating process, research design choices such as scaling, and model misspecification. We find that robust estimators lead to more precise inferences than OLS in common archival data, but these estimators can also bias inferences due their downweighting of substantial and non-random proportions of the data. In addition, we demonstrate that a failure to account for nonlinear relations can induce biases in robust estimators that are more severe than with OLS. We advise researchers to carefully evaluate the source and nonrandom nature of outliers in their samples, to be cautious in implementing and interpreting robust estimators, and to evaluate and report the sensitivity of these methods to critical design choices. We also highlight the usefulness of combining data visualizations with robust estimators to assess both the nature of outliers and their impact on inference.

Non-Random Sampling and Association Tests on Realized Returns and Risk Proxies

This paper investigates how data requirements often encountered in archival accounting research can produce a data-restricted sample that is a non-random selection of observations from the reference sample to which the researcher wishes to generalize results. We illustrate the effects of non-random sampling on results of association tests in a setting with data on one variable of interest for all observations and frequently-missing data on another variable of interest. We develop and validate a resampling approach that uses only observations from the data-restricted sample to construct distribution-matched samples that approximate randomly-drawn samples from the reference sample. Our simulation tests provide evidence that distribution-matched samples yield generalizable results. We demonstrate the effects of non-random sampling in tests of the association between realized returns and five implied cost of equity metrics. In this setting, the reference sample has full information on realized returns, while on average only 16% of reference sample observations have data on cost of equity metrics. Consistent with prior research (e.g., Easton and Monahan 2005), analysis using the unadjusted (non-random) cost of equity sample reveals weak or negative associations between realized returns and cost of equity metrics. In contrast, using distribution-matched samples, we find reliable evidence of the theoretically-predicted positive association. We also conceptually and empirically compare distribution-matching with multiple imputation and selection models, two other approaches to dealing with non-random samples.

Using partial least squares in archival accounting research: an application to earnings quality measuring

ABSTRACTDespite the advantages of Structural Equation Modelling (SEM) over regression models that have contributed to its popularisation in several fields of research in social sciences, it has not been broadly applied in archival accounting research. In this paper, we present a guidance for the application of SEM – and, particularly, the Partial Least Squares (PLS) method – to the (arguably) most recurrent topic on empirical archival accounting research: earnings quality. We highlight several problems that arise in earnings quality measuring, indicating how PLS can help to overcome them. We also run a simulation process whose results show that PLS method outperforms the other approaches even in situations of limited information.

Measuring Earnings Quality: A Proposal of a Theoretical Framework and an Empirical Method

Despite the advantages of Structural Equation Modelling (SEM) over regression models that have contributed to its popularization in several fields of research in social sciences, it has not been broadly applied in archival accounting research. In this paper, we present a guidance for the application of SEM –and, particularly, the Partial Least Squares (PLS) method– to the (arguably) most recurrent topic on empirical archival accounting research: earnings quality. We highlight several problems that arise in earnings quality measuring, indicating how PLS can help to overcome them. We also run a simulation process whose results show that PLS method outperforms the other approaches even in situations of limited information.

Text Data Sources in Archival Accounting Research: Insights and Strategies for Accounting Systems' Scholars

ABSTRACT This paper reviews the emerging computer-aided text analysis (CATA) accounting literature through proposing a model of the corpus linguistic research production process, followed by analysis of the main text archival data sources in published papers in the Top Six accounting journals from 2010 to 2016. Reviewed papers appear in a 5 × 5 matrix that includes five categories of text data (i.e., SEC filings, conference call transcripts, earnings press releases, financial analyst reports, and other sources) and five categories of text measures (i.e., tone, readability, similarity, firm characteristics and environment, and other measures). A brief review of the CATA literature published in two AIS journals is followed by a summary of the tools and KS (knowledge and skills) observed in the reviewed research. Finally, we offer implications by discussing four issues related to CATA accounting research. We conclude that the emerging CATA accounting research offers unique opportunities for knowledgeable AIS scholars.

Should Corporate Governance and Corporate Social Responsibility Work in Tandem?

In addition to financial returns, investors and creditors are increasingly focused on corporate governance and corporate social responsibility (CSR) practices of their portfolio firms. Economic theories, such as agency theory and enlightened stakeholder theory explain the effects of corporate governance or CSR on corporate outcomes (Jensen and Meckling, 1976; Jensen, 2010). However, these economic theories do not adequately address the effects on corporate outcomes of interrelationships between corporate governance and CSR in a sophisticated manner. Koonce and Mercer (2005) note that psychology theories enable researchers to examine the behaviors of managers, auditors, investors and analysts in a concrete way. They further note that archival accounting researchers neglect psychology theories — despite their usefulness — for their research. Building on sensemaking theory from psychology (Starbuck and Milliken, 1988; Weick, 1995) and analyzing US firms from 1993 to 2006, I show that incongruent practices between corporate governance and corporate social responsibility causes investors and credit agencies to have equivocality about firms, leading to their negative identification of the firms which, in turn, decreases firm value and credit ratings. I show that having a balance between corporate governance and CSR is important for firm value and credit ratings. This study makes contributions in terms of firm policy implications, in that management and board members of US firms often pay attention to shareholder value maximization, but disregard stakeholder concerns. This study also helps correct misconceptions of some investors who focus too much on short-termism — believing that firms’ engaging in CSR destroys firm value — by providing evidence that encouraging firms to pursue balanced corporate governance and CSR benefits investors and creditors, as well as society.

The Associations Among Earnings Quality, Voluntary Disclosure, Information Asymmetry, and Cost of Equity Capital: A Structural Equation Modeling Approach

Structural equation modeling (SEM), a methodology currently underutilized by archival accounting researchers, enables examination of paths linking constructs in complex models. In this paper, we first discuss SEM, including basics, underlying assumptions, advantages and disadvantages, properties of the estimates, and best practices. We then apply SEM to an accounting topic by using common factor analysis to create latent variables (constructs or factors) for earnings quality, voluntary disclosure, information asymmetry, and cost of equity capital and then examining the paths among those constructs. We find that higher earnings and voluntary disclosure quality are negatively associated with cost of capital both directly and indirectly through information asymmetry. Further, the paths from voluntary disclosure quality to cost of capital are significant even after controlling for the paths from earnings quality to cost of capital. SEM allows us to perform our analysis, which would not be possible with other techniques, and potentially offers fruitful avenues for future research in accounting.

Linear valuation without OLS: the Theil-Sen estimation approach

OLS-based archival accounting research encounters two well-known problems. First, outliers tend to influence results excessively. Second, heteroscedastic error terms raise the specter of inefficient estimation and the need to scale variables. This paper applies a robust estimation approach due to Theil (Nederlandse Akademie Wetenchappen Ser A 53:386–392, 1950) and Sen (J Am Stat Assoc 63(324):1379–1389, 1968) (TS henceforth). The TS method is easily understood, and it circumvents the two problems in an elegant, direct way. Because TS and OLS are roughly equally efficient under OLS-ideal conditions (Wilcox, Fundamentals of modern statistical methods: substantially improving power and accuracy, 2nd edn. Springer, New York 2010), one naturally hypothesizes that TS should be more efficient than OLS under non-ideal conditions. This research compares the relative efficiency of OLS versus TS in cross-sectional valuation settings. There are two dependent variables, market value and subsequent year earnings; basic accounting variables appear on the equations’ right-hand side. Two criteria are used to compare the estimation methods’ performance: (i) the inter-temporal stability of estimated coefficients and (ii) the goodness-of-fit as measured by the fitted values’ ability to explain actual values. TS dominates OLS on both criteria, and often materially so. Differences in inter-temporal stability of estimated coefficients are particularly apparent, partially due to OLS estimates occasionally resulting in “incorrect” signs. Conclusions remain even if winsorization and the scaling of variables modify OLS.

Linear Valuation Without OLS: The Theil-Sen Estimation Approach

OLS-based archival accounting research encounters two well-known problems. First, outliers tend to influence results excessively. Second, heteroscedastic error terms raise the spectre of inefficient estimation and the need to scale variables. This paper applies a robust estimation approach due to Theil (1950) and Sen (1968) (TS henceforth). The TS method is easily understood and it circumvents the two problems in an elegant, direct way. Because TS and OLS are roughly equally efficient under OLS-ideal conditions (Wilcox 2010), one naturally hypothesizes that TS should be more efficient than OLS under non-ideal conditions. This research compares the relative efficiency of OLS vs. TS in cross-sectional valuation settings. There are two dependent variables, market value and subsequent year earnings; basic accounting variables appear on the equations’ right-hand side. Two criteria are used to compare the estimation methods’ performance: (i) the inter-temporal stability of estimated coefficients and (ii) the goodness-of-fit as measured by the fitted values’ ability to explain actual values. TS dominates OLS on both criteria, and often materially so. Differences in inter-temporal stability of estimated coefficients are particularly apparent, partially due to OLS estimates occasionally resulting in “incorrect” signs. Conclusions remain even if winsorization and the scaling of variables modify OLS.

Using Psychology Theories in Archival Financial Accounting Research

Psychologists have studied human behavior for over a century and, as a result, have developed a robust set of theories regarding how people behave. Most financial accounting issues deal with matters of human behavior, such as the judgments and decisions of managers, investors, analysts, and auditors. Consequently, psychology offers a rich pool of theories from which financial accounting researchers can draw to motivate hypotheses and interpret results. Despite this, archival accounting researchers traditionally have relied almost solely on theories based in financial economics. We argue that two major obstacles to the use of psychology theories by archival researchers has been a lack of awareness about the theories that are available and when their use would be most productive. Our paper attempts to bridge this gap. Specifically, we describe a number of psychology theories that are applicable to financial accounting issues, lay out the circumstances where they may be especially useful to archival researchers, and provide a number of specific examples of how psychology theories provide new insights about financial accounting issues.

Fair Values, Comprehensive Income Reporting, and Bank Analysts' Risk and Valuation Judgments

We investigate whether the measurement and reporting of comprehensive income in financial statements systematically affects commercial bank equity analysts' investment risk assessments and valuation judgments. In an experiment in which 80 buy side analysts specializing in banking and financial institutions participated, we vary whether a bank is exposed to or hedged against interest rate risk across three different comprehensive income accounting regimes - piecemeal fair value accounting with comprehensive income reported in the statement of changes in equity, piecemeal fair value accounting with comprehensive income reported in a separate statement of performance, and full fair value accounting with comprehensive income reported in a separate statement of performance. When fair value changes are measured and reported on a piecemeal basis, we find no investment risk or valuation judgment differences across CI reporting regimes. Although we find that the analysts' investment risk judgments are influenced by the banks' risk management strategies, their valuation judgments are not. Only when fair value changes are measured completely and reported transparently (i.e., full fair value accounting) do analysts' valuation judgments distinguish between banks with different levels of risk. The study contributes to three avenues of literature concerned with financial-reporting transparency, risk, and fair value accounting. First, our ex ante evidence informs accounting standard setters as they evaluate whether to move to full fair value accounting for all financial instruments, and assess the degree to which differences in comprehensive income measurement and reporting affect the information analysts obtain from financial statements. Second, the study adds to the experimental accounting literature that examines comprehensive income reporting format effects and valuation judgments. Third, we add to archival accounting research on the information in comprehensive income by testing market participants' judgments under different comprehensive income reporting regimes, which cannot be directly investigated with archival data.