Prioritizing and Establishing Cause and Effect Relationships Among Financial Reporting Quality Metrics

Abstract

Despite the widespread use of financial reporting quality (FRQ) metrics, the literature provides little evidence regarding the prominence and causal relationships among them. The article provides empirical evidence on prioritizing FRQ metrics and examining the causal relationships among them. Through extensive literature review and expert inputs, 12 FRQ metrics were finalized and analysed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. India, a developing country, was selected for empirical study. Empirical results indicate that opinion in auditors’ report is the most prominent metric to examine FRQ, followed by value relevance in the context of fair value accounting. A formal scientific categorization of the metrics into cause and effect groups has also been attempted. The causal relationships among FRQ metrics and their relative prominence would help in interpreting empirical studies, which employ these metrics. The study suggests that the stakeholders can focus on prominent FRQ metrics to examine FRQ of the reporting entity before taking their decisions.

Keywords

Decision-Making Trial and Evaluation Laboratory Financial Reporting Quality Auditors’ Report Fair Value Accounting Notes to Accounts Value Relevance

Executive Summary

India has recently adopted fair value-based Indian Accounting Standards (Ind-AS), which are in convergence with globally accepted International Financial Reporting Standards (IFRS). The paradigm shift from historical cost to fair value accounting (FVA) complicates the evaluation of financial reporting quality. Financial reporting quality (FRQ) has been the topic of numerous empirical studies, which use different metrics to examine FRQ. Most commonly used metrics are accruals quality, earnings smoothness, earnings benchmarks, earnings persistence, earnings predictability, timely recognition of losses and value relevance. Even if these metrics are widely used, the studies provide little insights regarding these metrics being substitutes or serving as complementary to each other and the relative prominence of these metrics in the context of FVA. The present study attempts to fill this gap. The research questions addressed by the study are as follows:

How to prioritize the extant metrics used to examine FRQ in the context of FVA?

How to investigate the cause and effect relationships among the extant FRQ metrics used to examine FRQ?

For the study, the interviews with 14 experts having extensive knowledge and experience in accounting were conducted. Additional metrics (such as information in notes to accounts, deviations from standard accounting practices, policies adopted in ascertaining fair values, opinion in auditors’ report and financial ratios) to examine FRQ were also suggested by the experts.

Based on the experts’ views, opinion in auditors’ report is considered as the most prominent metric that can be used to examine FRQ, followed by value relevance and information in notes to accounts. A satisfactory audit report creates intense credibility and instils confidence about the accounts in the perception of the user, while a qualified report eats into the very roots of reliability of such accounts. Besides, since FVA has entrenched itself and the fundamental paradigm of accounting, the auditors’ role has become all the more crucial and challenging. From a mere attester of tangible evidence, they are now required to make judgemental assessments of the veracity of fair value estimates that are provided by the management. These fair values must provide value relevant information to different stakeholders.

The study suggests that the researchers can employ the prominent metrics out of different metrics to examine FRQ in the context of FVA. If multiple metrics are employed to examine the effect of the factors (e.g., accounting standards, corporate governance mechanisms) on FRQ, the causal relationships among them would indicate that these metrics are not solely influenced by the factors but they are also influenced by other metrics.

The results of the study would provide fruitful insights to the stakeholders and the auditors. The stakeholders can focus on prominent metrics to examine FRQ of the reporting entity. They can give credence to the opinion in auditors’ report while evaluating decision usefulness of the accounting information. Adverse opinion can adversely affect the utility of the underlying statements for decision-making. The study recommends that the managers should convey the information about their discretion to the stakeholders in applying different accounting practices, which can be relevant for decision-making. Regulatory bodies should revise existing standards on auditing and develop new ones in the fair value context.

Before providing an opinion, the auditors should examine the adequacy of the information provided in notes to accounts and policies and methods adopted in the ascertainment of fair values to ensure the reliability of accounting information. Auditors can also assess whether the accounting practices adopted by the reporting entity deviate from standard accounting practices and analyse the reasons for those deviations.

Introduction

A clearly perceptible paradigm shift from historical cost to fair value-based reporting is envisaged in the newly introduced International Financial Reporting Standards (IFRS) across the globe. The shift to fair value accounting (FVA) complicates the evaluation of financial reporting quality (FRQ). Although it enhances the relevance of reported information, the reliability is compromised. In active markets, market prices act as the best estimates of fair values and these estimates carry acceptable levels of verifiability. However, in inactive markets, financial reports based on managers’ fair value estimates of assets and liabilities might not be reliable due to the subjectivity involved in ascertaining fair values. Developing countries lack sufficient expertise that is required for successful implementation of fair value model (Kumarasiri & Fisher, 2011). Although the researchers use various metrics to examine FRQ, there is a need to investigate their prominence in the context of FVA. In addition to commonly used FRQ metrics in the literature, the following metrics, namely, opinion in auditors’ report, information in notes to accounts, policies adopted in ascertaining fair values and deviations from standard accounting practices can also be used to examine the quality of reported accounts. After the adoption of FVA, the role of the auditor has shifted from the certification of values based on tangible evidence to the authentication of fair values based on managerial estimates (Singh & Doliya, 2015). Hence, the auditors’ report assumes special and extra significance. Opinions in auditors’ report can be valuable inputs in examining FRQ of the reporting entity. To examine the reliability of fair value-based reported accounts, the users of financial information can focus on the prominent FRQ metrics in the context of FVA.

Financial reporting quality is a measure of the extent to which the reported financials reflect the true economic performance of the firm. As in the context of other abstract management concepts, FRQ is ambiguous to the extent it relates to the multiple uses of accounting information (Ball, Robin, & Wu, 2003). As such, an all-pervasive definition and characterization of FRQ are impracticable; yet, the appropriateness of financial information not only to shareholders, but also to other stakeholders such as lenders, suppliers, regulatory bodies and others, may be considered as the cardinal attribute of FRQ. Earnings are considered to be a proxy for examining FRQ because changes in income statement alter the balance sheet figures (Ball & Shivakumar, 2005). FRQ, more specifically, earnings quality is considered as high if the reported figures ‘provide more information about the features of a firm’s financial performance that are relevant to a specific decision made by a specific decision-maker’ (Dechow, Ge, & Schrand, 2010, p. 344). High FRQ is attained by adhering to the qualitative attributes of financial statement information, namely, reliability, relevance, timeliness and comparability (International Accounting Standards Board [IASB], 2008).

There is a plethora of empirical studies on FRQ, which examine the effect of changes in accounting standards, corporate governance and enforcement mechanisms on FRQ, and evaluate FRQ trends across countries and over different time periods. The studies employ single or multiple metrics to examine FRQ. Dechow et al. (2010) provided an extensive review of earnings quality (or FRQ) metrics, their determinants and consequences. Even if these metrics are widely used, the studies provide little insights regarding these metrics being substitutes or serving as complementary to each other and their relative prominence in the context of FVA. These issues are of cardinal importance in explaining the empirical studies, which examine FRQ. The present study attempts to fill this gap. The objective of the study is to use cognitive mapping process for prioritizing FRQ metrics and examining their causal relationships through Multiple-criteria decision-making (MCDM) approach.

Specifically, the article addresses the following research questions:

How to prioritize the extant FRQ metrics used to examine FRQ, in the context of FVA?

How to investigate the cause and effect relationships among the extant FRQ metrics used to examine FRQ?

MCDM models such as Decision-Making Trial and Evaluation Laboratory (DEMATEL) and interpretative structural modelling (ISM) have been proven efficacious in resolving complex and intertwined problems to assist decision-making (Bacudio et al., 2016). DEMATEL, as compared to ISM, helps in ranking of elements and detecting the intensity of causal relationships among elements of a system with the help of a causal map (Gandhi, Mangla, Kumar, & Kumar, 2015; Kumar & Dixit, 2018a). The strength of DEMATEL approach is that it assists decision-makers to analyse the causal relations among different criteria. It also aids in categorizing elements into cause and effect groups and in identifying the primary causal elements that greatly affect other elements. This study thus employs DEMATEL as a tool to address the research questions. The researchers use DEMATEL approach in many situations, including, IFRS adoption, financial reporting supply chain, portfolio and investment analysis, waste management and corporate sustainability (Altuntas & Dereli, 2015; Kumar & Dixit, 2018b; Lan & Zhong, 2016; Liu & Lee, 2014; Tseng, 2017).

The study contributes to the literature by prioritizing FRQ metrics in the context of FVA and by identifying the cause and effect relationships among FRQ metrics. To examine FRQ in the context of FVA, the studies can employ the prominent FRQ metrics out of different FRQ metrics. If multiple FRQ metrics are employed to examine the effect on FRQ of the factors (e.g., accounting standards, corporate governance mechanisms), the causal relationships among them would indicate that these metrics are not solely influenced by the factors but they are also influenced by other metrics.

The rest of the article is structured as follows: the second section provides academic evidence on FRQ metrics used to examine FRQ. The third section provides an overview of DEMATEL approach. The fourth section shows the empirical study and the proposed research framework. The fifth section discusses the empirical results and inferences, followed by the implications of the study. The seventh section enlists limitations of the study, future scope and offers concluding remarks.

Literature Review

Empirical research employs different FRQ metrics to measure FRQ. Most commonly used FRQ metrics are accruals quality, earnings smoothness, earnings benchmarks, earnings persistence, earnings predictability, timely recognition of losses and value relevance. This section briefly reviews the academic evidence on the metrics employed to measure FRQ and explains FRQ metrics recommended by the experts.

Quality of Accruals

Accruals are the estimates of realizations of future cash flows. Accruals are of two types—normal (non-discretionary) accruals, which are necessary to make earnings more communicative about entity’s fundamental performance, and abnormal (discretionary) accruals, which generally show accounting distortions emanating from the adoption of earnings management (EM). Researchers commonly use Jones (1991) or adapted models to measure such accounting distortions resulting from EM (e.g., Ecker, Francis, Olsson, & Schipper, 2013; Ferentinou & Anagnostopoulou, 2016; Keung & Shih, 2014; Rusmin, 2010; Ugrin, Mason, & Emley, 2017). Entities reporting unusually high accruals need to be investigated for discretionary accruals (DA). Obviously, a high level of DA generally represents dressing and as such lower FRQ. In addition to Jones (1991) model, some other models for deciphering accruals in reported accounts include ‘Jones model (1991), modified Jones model (Dechow, Sloan, & Sweeney, 1995), Dechow and Dichev (2002) approach, performance matched model (Kothari, Leone, & Wasley, 2005), and discretionary estimation errors approach (Francis, LaFond, Olsson, & Schipper, 2005)’ (Dechow et al., 2010, p. 359).

Accruals quality is the dominant metric among other FRQ metrics that has a significant cost of equity effects (Francis, LaFond, Olsson, & Schipper, 2004). It is determined on the basis of mapping of accruals into operating cash flows realizations (Dechow & Dichev, 2002). Poor mapping indicates low accruals quality. Low accruals quality is related to the high costs of equity and debt (Francis et al., 2005).

Accruals quality is measured by the standard deviation of residuals of the regression of accruals on cash flows. The standard deviation of the residuals represents unexpected working capital accruals. Accruals quality would be high if there is a low level of unexpected working capital accruals (Gassen & Sellhorn, 2006). High accruals quality implies high FRQ (Barth, Landsman, Lang, & Williams, 2012; Chen, Tang, Jiang, & Lin, 2010). Accrual quality indicates FRQ effectively because entities with poor FRQ exhibit high information asymmetry and this association is stronger for entities with low accruals quality (Cerqueira & Pereira, 2017).

Earnings Smoothness

Earnings smoothness metric is based on the volatility of change in net income, ratio of volatility of change in net income to volatility of change in operating cash flows and the correlation between cash flows and accruals (Ahmed, Neel, & Wang, 2013; Barth, Landsman, & Lang, 2008; Capkun, Collins, & Jeanjean, 2016; Lang, Raedy, & Wilson, 2006; Lang, Raedy, & Yetman, 2003).

Accrual-based accounting should, in the normal course, smoothen out the earnings volatility relative to the cash flow-based accounting. It is also acknowledged that accrual-based accounting better captures the underlying firm performance than cash flow accounting (SFAC no. 1) and, therefore, results in superior FRQ. Smoother earnings are assumed to be more value relevant for decision-making. On the flip side, however, earnings smoothing can mask underlying sharp changes in firm performance. Managers may be opportunistically motivated to smoothen the variations in earnings to conceal such impulsive economic effects. Earnings smoothing may reduce the investors’ perception of underlying earnings variability. This would reduce the cost of capital and boost the market value of the entities (Trueman & Titman, 1988).

To obtain a clear picture of the underlying firm economics, it is absolutely imperative to extricate inherent earnings smoothing from opportunistically motivated earnings smoothing. The abnormal earnings smoothness can, then, also be employed as a measure of EM (Barth et al., 2008; Chen et al., 2010). Artificially smooth earnings may distort decision usefulness. Such earnings are less informative about true fundamentals and depress FRQ (Bhattacharya, Daouk, & Welker, 2003; Leuz, Nanda, & Wysocki, 2003).

Earnings Benchmarks

To examine FRQ, researchers use two types of earnings benchmarks, namely, small profits threshold and analysts’ forecasts of earnings. Managers may have incentives to manage earnings towards these earnings benchmarks. It is seen that earnings distribution across firms is generally skewed with a majority of firms showing small profits but not reporting small losses (Burgstahler & Dichev, 1997; Jeanjean & Stolowy, 2008; Leuz et al., 2003). It is likely that firms having small losses deliberately indulge in EM to report small profits. Frequency of small profits indicates upward EM (Ahmed et al., 2013; Barth et al., 2008; Capkun et al., 2016). Entities may also indulge in EM to meet or beat analysts’ forecasts of earnings, thereby distorting the allocation of funds. In fact, meeting or beating analysts’ forecasts provides lucrative motivation for EM (Dechow et al., 2010; Degeorge, Patel, & Zeckhauser, 1999; Matsumoto, 2002). Such EM practices distort FRQ.

Earnings Persistence and Earnings Predictability

Earnings persistence and earnings predictability are time series measures of FRQ (Schipper & Vincent, 2003). Earnings persistence refers to the extent to which earnings in the current period sustain in future periods (Lipe, 1990). It is a maintained assumption in the studies on earnings persistence that persistence is a desirable property of earnings because it enhances the usefulness of earnings in equity valuation decisions. Persistent earnings are viewed by investors as sustainable and more stable and, hence, relatively compatible to use in discounting models (Dechow et al., 2010; Schipper & Vincent, 2003). Therefore, high earnings persistence represents high FRQ (Gaio, 2010; Gassen & Sellhorn, 2006; Liu & Sun, 2015; Penman & Zhang, 2002).

Akin to earnings persistence, earnings predictability is also a desirable characteristic of earnings. Earnings predictability is defined as ‘the ability of past earnings to predict future earnings’ (Lipe, 1990, p. 50). Also, if current earnings are better predictors of future earnings, this signals that they are of high quality (Barker & Imam, 2008; Gaio, 2010; Parte-Esteban & García, 2014). Earnings predictability also enhances the precision of forecasts of earnings (Lys & Soo, 1995).

However, there is limited evidence whether earnings persistence and/or predictability serves as suitable metrics for FRQ. It needs to be emphasized that persistence and predictability of earnings are, themselves, functions of the business environment in which entity operates, for example, entity’s size, competition, the volatility of operations, capital intensity, reporting alternatives and others.

Timely Recognition of Losses

The maxims of accounting conservatism and prudence dictate that unrealized losses be recognized forthwith while gains are accounted on realization. This asymmetric recognition results in a systemic phase difference in the timeliness of earnings. Reverse regression of earnings on stock returns, taking stock returns as a proxy for news, indicates that earnings reflect bad news/large losses in returns in a more timely manner relative to good news/gains through accruals and further that ‘earnings is more timely than cash flow primarily in reflecting bad news’ (Basu, 1997, p. 7). The prudence concept of accounting also requires the same. The less timely the recognition of large losses is the more noise is incorporated in the financial statements, thereby reducing the overall FRQ (Ball et al., 2003). It is believed that timely recognition of large losses enhances the decision relevance of financial statements. Hence, timely loss recognition, that is, forthwith reporting of large losses rather than deference to future periods, adds to FRQ.

According to Ball and Shivakumar (2005), timely recognition of losses is believed to (a) reduce the chances of predicted negative Net Present Value (NPV) investment decisions and continuing present investments with negative cash flows; (b) provide accurate information for pricing of debt; and (c) facilitate transfer of decision rights from managers of loss-making entities to lenders, by enforcing debt agreement rights, if debt covenants are violated. Furthermore, equity investors also perceive timely loss recognition to result in improving FRQ (Ball & Shivakumar, 2005; Ball, Robin, & Sadka, 2008). Therefore, more timely recognition of losses is an indicator of high FRQ (Barth et al., 2008; Chen et al., 2010; García, Alejandro, Sáenz, & Sánchez, 2017; Lang et al., 2006).

Value Relevance

‘An accounting amount is defined as value relevant if it has a predicted association with equity market values’ (Barth, Beaver, & Landsman, 2001, p. 79). Thus, value relevance is construed in the context of decision relevance information to investors in firm valuation or a reliable manifestation in the share price. The measurement of value relevance metric is based on return-based ERCs (Earnings Response Coefficients) and explanatory power (R²) from a regression of stock price on book value of equity and net income. Substantive evidence subsists on value relevance as a metric of FRQ (Cormier & Magnan, 2016; Lang et al., 2006; Liu & Thomas, 2000; Paananen & Lin, 2009).

In the context of FRQ, standard setters predominantly focus on the equity investors’ perspective as they have an ownership claim in the company. It is, thus, appropriate that high-quality accounting information should be reflected in share prices (Cahan, Emanuel, & Sun, 2009). The prevalence of this nexus would imply the worthiness and value relevance of reported accounting information to make rational and informed investment decisions. The metric directly relates earnings to decision usefulness, specifically of equity valuation decisions, which is quality personified. Since FRQ is a broad-spectrum attribute relating decision usefulness for all stakeholders, the results from this metric need to be interpreted in context.

Further Financial Reporting Quality Metrics (Expert Inputs)

There are additional five FRQ metrics recommended by the experts to examine FRQ. Experts believe that the accounting revolution towards FVA may enhance the scope of accounting manipulations by increasing subjectivity in the ascertainment of fair values of assets and liabilities. The reliability of financial reports might be compromised on this account. The users of financial information need to deeply examine other avenues to assess the reliability of fair value-based information. Opinion in auditors’ report, information in notes to accounts and policies adopted in ascertaining fair values can be valuable inputs to examine FRQ. The experts also perceive that deviations from standard accounting practices and financial ratios may be a useful metric to examine FRQ.

Opinion in Auditors’ Report

Experts believe that the role of the entity’s auditors undergoes a complete metamorphosis after the adoption of FVA. It is radically escalated to one of exercising an extremely judgemental task in a comprehensive evaluation of (occasionally unsupported) validation of estimates endorsed as fair by the management of the entity. The adoption of FVA seems to compromise on the reliability of inputs to the accounting reports insofar as the availability of transactional evidence may be unavailable in many instances. The auditors’ report assumes special and extra significance in such a scenario, since auditors would, in general, be expected to adhere to stringent norms of reliability to compensate for this intrinsic lack of tangible verifiability of fair value-based information. More often than not, fair value inputs take the form of estimates provided by the management based on market data and subjective perceptions. The end-users of financial statements being far removed from the entity’s management have limited access to the grass-root information on which fair value estimates are premised. As such, such users rely heavily on the auditors’ assessment of the reliability and quality of the statements. The auditors provide opinions on the state of affairs of the entity and whether the accounts reflect a true and fair view. Comments (specifically adverse) reported in auditors’ report could, prima facie, indicate dilution of reporting quality. Adverse opinion in auditors’ report, thus, carries a massive perception-based influence insofar as the users of financial statements are concerned.

Information in Notes to Accounts

The notes to accounts are an integral part of financial statements. They provide additional information pertaining to the operations of the entity and also contain additional disclosures on various issues dealt with the reported accounts. They provide how an entity arrives at particular amounts reported in financial statements. They contain detailed information about significant accounting policies, various disclosures, methods applied for different accounting treatments, explanation of inconsistencies and justification of assumptions and estimates. An entity needs to provide sufficient information in the notes to accounts in accordance with accounting standards and guidelines. According to the experts, if the entity does not want to disclose sufficient information that could materially affect the decisions of the users of financial statements, it can be a useful metric to examine FRQ.

Deviations from Standard Accounting Practices

Experts perceive that accounting practices adopted by an entity, which are deviant from standard accounting practices adopted by similar companies, could be construed to dilute FRQ. This is notwithstanding that the practices adopted by the reporting entity are, otherwise, consistent with the extant regulatory provisions. Here, similar companies refer to companies of similar dimensions and same industry group. The experts argue that the comparability issue may arise if an entity follows accounting practices, which deviate from accounting practices of similar companies, even though such practices are permissible under accounting standards. Comparability is also one of the qualitative attributes of financial statement information (IASB, 2008). Hence, the deviations from standard accounting practices could undermine FRQ.

Policies Adopted in Ascertaining Fair Values

It is believed that the policies adopted in ascertaining fair values also gain significance after the adoption of FVA. These policies may be helpful in examining FRQ. ‘IFRS 13, Fair Value Measurement’ provides guidelines to measure fair values. In active markets, market prices act as the best estimates of fair values and these estimates carry acceptable levels of verifiability. However, in inactive markets, financial reports based upon managers’ estimates of fair values of assets and liabilities might not be reliable due to the subjectivity involved in ascertaining fair values. In such situations, the users of financial information should deeply assess the policies adopted in ascertaining fair values. Inconsistencies in these policies or improper justification for these policies could impair FRQ.

Financial Ratios

Experts recommend that financial ratios may also be used to examine FRQ. On the one hand, these ratios may motivate managers to manipulate (particularly in the case of fuzzy accounts) so as to present meaningful values of various ratios. On the other hand, these ratios, if circumspectly analysed, could assist the investor in extracting valuable information about the financial health of the entity.

Research Methodology

DEMATEL is a structural modelling approach that is applied to detect and analyse the causal relationships among different criteria in a complex system (Gabus & Fontela, 1972), with the help of mathematical techniques and matrix operations. The approach helps in discovering feasible solutions through hierarchical structures (Tzeng, Chiang, & Li, 2007). The Geneva Research Centre of the Battelle Memorial Institute first proposed this approach. The approach is used to resolve multi-dimensional issues by pinpointing the interdependencies among criteria. It can also be used in ranking the criteria for long-term decision-making (Miao, Xu, Zhang, & Jiang, 2014; Seleem, Attia, & El-Assal, 2016). The output of DEMATEL takes the form of a digraph and a causal diagram, which highlight the effect of one factor on another (Lin, 2013). The causal diagram portrays the contextual relationships among different criteria.

The DEMATEL method is summarized in the following steps:

Step 1: Establish the average direct-relation matrix A: in this step, each expert was asked to rate FRQ metrics and to form a pairwise comparison matrix on the basis of the comparison scale. The scale designed has five levels: ‘0 (no influence), 1 (very low influence), 2 (low influence), 3 (high influence), 4 (very high influence)’. The initial data can be obtained as the pairwise matrix, that is, (n × n) non-negative matrix can be established as X^k = $[x_{i j}^{k}]$ To incorporate all the responses from H respondents, the average direct relation matrix A = $[a_{i j}]$ is developed by using the following equation:

a_{i j} = \frac{1}{H} \sum_{K = 1}^{H} x_{i j}^{k}

(1)

where $x_{i j}^{k}$ = degree of influence of metric i to metric j with respect to kth respond, n = number of metrics and H = total experts.

Step 2: Construct the normalized direct relation matrix, N, using the following equation:

\begin{matrix} N = A \times λ \\ λ = \min [\frac{1}{\max \sum_{j = 1}^{n} a_{i j}}, \frac{1}{\max \sum_{i = 1}^{n} a_{i j}}] \end{matrix}

(2)

Step 3: Develop the total relation matrix, T, using the following equation:

T = N {(I - N)}^{- 1}

(3)

where I represents identity matrix.

Step 4: Construct a causal map: the sum of rows and columns in T matrix is denoted by D_i and R_j, respectively, where D_i represents whether metric i directly or indirectly affects other metrics and R_j represents whether metric j is affected by other metrics. To construct a causal map, D_i + R_j and D_i – R_j are computed, which represent ‘prominence’ and ‘relation’, respectively.

Empirical Study: Indian Accounting Environment

India has recently adopted fair value-based Indian Accounting Standards (Ind-AS), which are in convergence with globally accepted IFRS. The adoption of Ind-AS has been made mandatory with effect from 1 April 2016 for certain companies based on their net worth and listing status. The perceptions of stakeholders towards the adoption of Ind-AS have been studied (Deb & Das, 2018; Sharma, Joshi, & Kansal, 2017). However, limited literature seems to exist on how Ind-AS affect FRQ of Indian companies (e.g., Khursid & Swain, 2018; Rudra & Bhattacharjee, 2012; Vardia, Kalra, & Soral, 2016). Besides, the relative prominence of the various FRQ metrics in the context of India’s accounting environment appears yet to be investigated. By prioritizing FRQ metrics and identifying the interaction among them, the study will contribute significantly to the detailed assessment of the impact of the transition to the new Ind-AS from the perspective of different stakeholders. The steps involved in the proposed framework for analysing FRQ metrics are shown in Figure 1.

Figure 1.

Source: The authors.

The empirical study involves two phases. In the first phase, the FRQ metrics for examining FRQ were identified based on extensive literature review and expert inputs. A summary of the identified FRQ metrics is presented in Table 1. The benefit of using MCDM techniques like DEMATEL is that it is based on the opinion of the experts and thus does not require a larger sample. For the empirical study, 14 experts with extensive knowledge and experience in accounting were selected. The experts have more than 10 years of experience in the accounting domain. The sample size for the study is consistent with the prior studies (Bacudio et al., 2016; Gupta & Barua, 2018; Hsu, Kuo, Chen, & Hu, 2013; Kaur, Sidhu, Awasthi, Chauhan, & Goyal, 2018; Li, Hu, Zhang, Deng, & Mahadevan, 2014; Raghuvanshi, Agrawal, & Ghosh, 2017; Wu & Tsai, 2011; Wu, Chen, & Shieh, 2010; Zhang, Sun, & Xue, 2019; Zhou, Shi, Deng, & Deng, 2017), which also considered small sample size for applying DEMATEL. In these studies, the sample size varies from 3 to 13. The responses from the experts were collected through personal interviews. Interviews with the experts were conducted to identify the relationship among FRQ metrics and to prioritize FRQ metrics in measuring FRQ in the FVA environment. Each expert was requested to provide the responses in a pair-wise comparison matrix for DEMATEL analysis. Experts were also given an opportunity to suggest other FRQ metrics to examine FRQ. After taking expert inputs, additional five metrics to examine FRQ were incorporated, namely, information in notes to accounts, deviations from standard accounting practices, policies adopted in ascertaining fair values, opinion in auditors’ report and financial ratios. Thus, 12 FRQ metrics were finalized. In the second phase, DEMATEL methodology is applied to analyse the data collected from the experts and accordingly, the results are interpreted.

Table 1.

Summary of Financial Reporting Quality Metrics to Examine FRQ

Metrics to assess FRQ	Code	References
Value relevance	M1	Ohlson (1995), Francis and Schipper (1999), Barth et al. (2001), Paananen and Lin (2009), Cormier and Magnan (2016)
Earnings smoothness	M2	Trueman and Titman (1988), Leuz et al. (2003), Myers et al. (2007), Barth et al., (2008), Dayanandan et al. (2016)
Quality of accruals	M3	Jones model (1991), Dechow and Dichev (2002), Francis et al. (2004), Kothari et al. (2005), Rusmin (2010), Barth et al. (2012), Ecker et al. (2013), Cerqueira and Pereira (2017)
Earnings benchmarks	M4	Burgstahler and Dichev (1997), Degeorge et al. (1999), Jeanjean and Stolowy (2008), Ahmed et al. (2013)
Timely recognition of losses	M5	Basu (1997), Ball et al. (2000), Ball et al. (2003), Ball and Shivakumar (2005), Ball et al. (2008), García et al. (2017)
Earnings persistence	M6	Lev (1983), Penman and Zhang (2002), Schipper and Vincent (2003), Dechow et al. (2010), Liu and Sun (2015)
Earnings predictability	M7	Lipe (1990), Schipper and Vincent (2003), Barker and Imam (2008), Parte-Esteban and García (2014)
Information in notes to accounts	M8	Expert input
Deviations from standard accounting practices	M9	Expert input
Policies adopted in ascertaining fair values	M10	Expert input
Opinion in auditors’ report	M11	Expert input
Financial ratios	M12	Expert input

Source: The authors.

Results and Discussion

Table 2 shows the average direct relation matrix (A) containing average pairwise comparisons among FRQ metrics. Table 3 reflects the normalized direct relation matrix (N) of FRQ metrics based on Equation (2). With the help of Equation (3), the total relation matrix (T) is constructed and is tabulated in Table 4. The threshold value (α = 0.302) is computed by taking the average plus one standard deviation of the values in the total relation matrix. The values of prominence (D + R) and relation (D − R) are depicted in Table 5.

Table 2.

Average Direct Relation Matrix (A)

Metrics	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12
M1	0.000	1.625	1.625	2.313	1.125	2.563	2.500	1.438	2.688	1.750	3.500	1.125
M2	3.250	0.000	1.563	1.625	3.313	3.375	3.188	1.688	1.500	1.875	3.000	1.125
M3	3.063	3.313	0.000	1.563	3.750	1.313	1.375	1.563	1.625	1.750	1.438	2.000
M4	1.813	1.375	1.375	0.000	1.063	1.438	1.000	1.313	1.125	1.250	1.750	1.000
M5	3.563	1.063	2.000	1.063	0.000	3.938	1.000	1.000	1.000	1.000	1.313	1.000
M6	1.000	2.000	1.063	1.000	1.000	0.000	3.250	1.375	1.500	1.313	2.000	1.000
M7	1.938	1.938	2.000	1.000	1.063	3.250	0.000	1.438	1.000	1.563	2.188	1.000
M8	3.563	3.063	3.625	1.000	2.188	1.063	1.000	0.000	2.063	3.500	3.750	1.000
M9	2.500	1.625	1.625	1.000	1.063	1.000	1.750	1.500	0.000	1.875	3.375	1.000
M10	3.500	3.500	1.938	1.375	2.250	1.750	1.063	1.625	1.000	0.000	3.750	2.188
M11	2.563	2.063	1.875	1.375	1.938	2.375	2.313	2.875	2.625	2.563	0.000	1.438
M12	1.188	1.375	1.000	1.375	1.000	1.000	1.313	1.000	1.000	1.000	1.625	0.000

Source: The authors.

Table 3.

Normalized Direct Relation Matrix (N)

Metrics	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12
M1	0.000	0.058	0.058	0.083	0.040	0.092	0.089	0.051	0.096	0.063	0.125	0.040
M2	0.116	0.000	0.056	0.058	0.119	0.121	0.114	0.060	0.054	0.067	0.107	0.040
M3	0.110	0.119	0.000	0.056	0.134	0.047	0.049	0.056	0.058	0.063	0.051	0.072
M4	0.065	0.049	0.049	0.000	0.038	0.051	0.036	0.047	0.040	0.045	0.063	0.036
M5	0.128	0.038	0.072	0.038	0.000	0.141	0.036	0.036	0.036	0.036	0.047	0.036
M6	0.036	0.072	0.038	0.036	0.036	0.000	0.116	0.049	0.054	0.047	0.072	0.036
M7	0.069	0.069	0.072	0.036	0.038	0.116	0.000	0.051	0.036	0.056	0.078	0.036
M8	0.128	0.110	0.130	0.036	0.078	0.038	0.036	0.000	0.074	0.125	0.134	0.036
M9	0.089	0.058	0.058	0.036	0.038	0.036	0.063	0.054	0.000	0.067	0.121	0.036
M10	0.125	0.125	0.069	0.049	0.081	0.063	0.038	0.058	0.036	0.000	0.134	0.078
M11	0.092	0.074	0.067	0.049	0.069	0.085	0.083	0.103	0.094	0.092	0.000	0.051
M12	0.043	0.049	0.036	0.049	0.036	0.036	0.047	0.036	0.036	0.036	0.058	0.000

Source: The authors.

Table 4.

Total Relation Matrix (T )

Metrics	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12
M1	0.248	0.263	0.234	0.215	0.219	0.300	0.275	0.211	0.255	0.241	0.365	0.168
M2	0.389	0.235	0.257	0.213	0.312	0.363	0.323	0.239	0.239	0.267	0.381	0.185
M3	0.360	0.318	0.184	0.197	0.311	0.274	0.242	0.215	0.223	0.241	0.305	0.199
M4	0.230	0.188	0.168	0.095	0.160	0.195	0.164	0.153	0.151	0.165	0.229	0.122
M5	0.314	0.205	0.209	0.151	0.145	0.304	0.194	0.163	0.170	0.179	0.247	0.139
M6	0.226	0.226	0.175	0.140	0.173	0.168	0.253	0.170	0.176	0.183	0.258	0.133
M7	0.275	0.243	0.218	0.153	0.192	0.291	0.165	0.185	0.176	0.206	0.285	0.145
M8	0.426	0.357	0.337	0.206	0.301	0.302	0.266	0.196	0.272	0.335	0.428	0.195
M9	0.300	0.236	0.211	0.156	0.195	0.223	0.224	0.192	0.145	0.221	0.329	0.147
M10	0.390	0.341	0.261	0.202	0.278	0.301	0.250	0.232	0.219	0.199	0.397	0.215
M11	0.360	0.298	0.261	0.199	0.264	0.315	0.284	0.269	0.267	0.284	0.278	0.190
M12	0.190	0.170	0.141	0.130	0.143	0.164	0.158	0.130	0.133	0.142	0.204	0.077

Source: The authors.

D + R values are used to show the degree of prominence and ranking of FRQ metrics. Figure 2 depicts the D + R (prominence) values of each FRQ metric, arranged in descending order. The ranks of different FRQ metrics are provided in Table 5 (opinion in auditors’ report (M11) > value relevance (M1) > earnings smoothness (M2) > information in notes to accounts (M8) > policies adopted in ascertaining fair values (M10) > quality of accruals (M3) > earnings persistence (M6) > earnings predictability (M7) > timely recognition of losses (M5) > deviations from standard accounting practices (M9) > earnings benchmarks (M4) > financial ratios (M12).

Table 5.

Prominence and Relation

Metrics	D	R	Prominence (D + R)	Relation (D − R)	Ranking
M1	2.995	3.707	6.702	−0.712	2
M2	3.402	3.079	6.482	0.323	3
M3	3.070	2.657	5.727	0.412	6
M4	2.021	2.056	4.078	−0.035	11
M5	2.419	2.694	5.113	−0.275	9
M6	2.281	3.200	5.481	−0.919	7
M7	2.533	2.797	5.330	−0.264	8
M8	3.621	2.357	5.978	1.264	4
M9	2.578	2.427	5.005	0.152	10
M10	3.286	2.663	5.949	0.623	5
M11	3.270	3.705	6.974	−0.435	1
M12	1.782	1.915	3.697	−0.133	12

Source: The authors.

Figure 2.

Source: The authors.

Based on the experts’ views, opinion in auditors’ report (M11) is considered as the most prominent metric that can be used to examine FRQ. It holds the top position among all FRQ metrics as it has maximum (D + R) value, that is, 6.974. Experts recognize that the role of the entity’s auditors is cardinal to the reporting accounts being of satisfactory quality. This needs to be necessarily so. An auditor has been held to be a ‘watchdog’ and is supposed to exercise surveillance over the trustworthiness of the reported accounting statements. A satisfactory audit report creates intense credibility and instils confidence about the accounts in the perception of the user, while a qualified report eats into the very roots of reliability of such accounts. Ensuring accurate reporting in adherence with the extant statutory requirements is essentially the duty and responsibility of the auditor.

Besides, ever since FVA has entrenched itself and the fundamental paradigm of accounting, the auditors’ role has become all the crucial and challenging. From a mere attester of tangible evidence, they are now required to make judgemental assessments of the veracity of fair value estimates that are provided by the management. The auditors scrutinize fair value estimates by assessing the reasonableness and adequacy of related disclosures of these estimates which comes under International Standard on Auditing [ISA] 540 (International Auditing and Assurance Standards Board, 2008). The auditors face difficulty in auditing the fair value estimates due to estimation uncertainty and lack of reliable information. They rely on managerial estimates for determining fair values (Abdullatif, 2016; Doliya & Singh, 2016). Thus, in the context of FVA, the auditors’ report assumes special and extra significance, since auditors would, in general, not be expected to compromise on reliability of the information. Specifically, an adverse opinion in auditors’ report could, prima facie, indicate dilution of reporting quality. Hence, opinion in auditors’ report could be valuable inputs in examining FRQ of the reporting entity.

In addition, experts perceive that value relevance (M1), earnings smoothness (M2), information in notes to accounts (M8), and policies adopted in ascertaining fair values (M10) should also be given importance to examine FRQ, specifically in the context of FVA. Experts believe that FRQ also depends on the manner in which fair value estimates are arrived at and the policies followed in respect thereof. Policies adopted in ascertaining fair values play a crucial role in examining FRQ. If there is any inconsistency in using these policies, this represents a flawed FRQ. Information in the notes regarding fair values could be very useful in assessing the reasonableness of fair value estimates. Value relevance is also a prominent FRQ metric because the fair values are value relevant, if they are measured reliably, and provide relevant information to investors. High value relevance is an indicator of high FRQ (Barth et al., 2008; Paananen & Lin, 2009). Conversely, low value relevance indicates low FRQ.

The investors are sceptical of level 2 and level 3 fair value estimates because of unobservable market data. The value relevance of level 2 and level 3 fair value estimates is lower than that of level 1 fair value estimates (Siekkinen, 2016).

As mentioned earlier, although smoother earnings are assumed to be more value relevant for decision-making, they can also mask underlying sharp changes in firm performance. Managers may be opportunistically motivated to smoothen the variations in earnings to conceal such impulsive economic effects. Experts argue that the volatility caused by fair values that can affect earnings smoothness should also be considered while examining FRQ.

On the other hand, FRQ metric, that is, financial ratios (M12), obtains the bottom position in the prominence list as it has scored minimum (D + R) value, that is, 3.697. The financial ratios generally tell about the entity’s performance. FRQ cannot be solely assessed by analysing financial ratios. Thus, financial ratios are not considered as a prominent FRQ metric in examining FRQ as compared to other metrics.

Figure 3 depicts D − R (net cause/effect) values of each FRQ metric. The D − R values help in dividing FRQ metrics into two groups, that is, cause group and effect group. An FRQ metric with positive D − R is categorized in the cause group, while an FRQ metric with negative D − R is categorized in the effect group.

Figure 3.

Source: The authors.

Figure 4 shows the overall cause–effect relationship map that reflects the interdependencies among all FRQ metrics simultaneously. Only values greater than the threshold value in T matrix are plotted. The upper half of the map shows the FRQ metrics that comes under the cause group and lower half reflects the FRQ metrics that comes under the effect group.

Figure 4.

Source: The authors.

Based on Figures 3 and 4, information in notes to accounts (M8), policies adopted in ascertaining fair values (M10), quality of accruals (M3), earnings smoothness (M2) and deviations from standard accounting practices (M9) are classified under the cause group. It is noted that information in notes to accounts (M8) has the highest positive D − R value (1.264), implying that M8 is the primary causal FRQ metric. It has the highest effect on both M11 and M1 with respective values of 0.428 and 0.426 (see Table 4). Notes to accounts provide useful information to the auditors and investors in examining the quality of accounts. The auditors consider the information in notes to accounts while providing their opinion. The equity investors also consider the information in notes while taking their investment decisions.

Other seven FRQ metrics are placed in the effect group: earnings benchmarks (M4), financial ratios (M12), earnings predictability (M7), timely recognition of losses (M5), value relevance (M1), opinion in auditors’ report (M11) and earnings persistence (M6).

Implications of the Study

The study contributes to the existing literature by investigating the relative prominence of FRQ metrics in the context of FVA and the cause and effect relationships among the identified FRQ metrics. The results of the study can be helpful to the researchers in understanding the classification of FRQ metrics into cause and effect groups and their relative prominence. In the Indian context, the results can be beneficial in examining the effect of new accounting standards, that is, Ind-AS on FRQ of Indian companies using FRQ metrics in future empirical studies, because Ind-AS have been mandatory applicable since 2016. To examine FRQ in the context of FVA, the studies can employ the prominent FRQ metrics out of different FRQ metrics. If multiple FRQ metrics are employed to examine the effect on FRQ of the factors (e.g., accounting standards, corporate governance mechanisms), the causal relationships among them would indicate that these metrics are not solely influenced by the factors but they are also influenced by other metrics.

The results of the study would also provide fruitful insights to the stakeholders. The stakeholders can focus on prominent FRQ metrics to examine FRQ of the reporting entity. Stakeholders such as investors, lenders and suppliers can give credence to the opinion in auditors’ report while evaluating decision usefulness of the accounting information. Adverse opinion can adversely affect the utility of the underlying statements for decision-making.

The findings of the study suggest that the managers of the entities should provide proper disclosures of accounting information to all stakeholders according to their demands. Managers use their discretion in applying different accounting practices. They should convey the information about their discretion to the stakeholders, which may be relevant for decision-making. Auditors can examine the adequacy of the information provided in notes to accounts and policies adopted in the ascertainment of fair values to ensure the reliability of accounting information. Auditors can also assess whether the accounting practices adopted by an entity deviate from standard accounting practices and analyse the reasons for those deviations. The results of the study reveal that opinion in auditors’ report is the most prominent FRQ metric among all FRQ metrics. Hence, the study recommends that the regulatory bodies should revise existing standards on auditing and develop new ones in the fair value context. They should also provide proper and detailed guidelines to the auditors in scrutinizing the fair value estimates.

Concluding Remarks, Limitation and Future Research

Financial reporting quality has been the topic of numerous empirical studies, which use different FRQ metrics to examine FRQ. However, there is a paucity of research on studying the interaction among the extant FRQ metrics and the relative prominence of the FRQ metrics in examining FRQ, specifically in the context of FVA. The present study attempts to fill this gap. In this study, 12 FRQ metrics for examining FRQ are identified based on extensive literature review and expert inputs. The study applies the DEMATEL methodology to analyse the cause and effect relationships among FRQ metrics and their relative prominence. Empirical results are obtained on the basis of experts’ views. The results show that opinion in auditors’ report is considered as the most prominent metric that can be used to examine FRQ, followed by value relevance, earnings smoothness, information in notes to accounts and policies adopted in ascertaining fair values. Specifically, an adverse opinion in auditors’ report, prima facie, indicates dilution of reporting quality. Hence, the auditors’ opinion can provide valuable inputs in examining FRQ of the reporting entity. Value relevance and policies adopted in ascertaining fair values can also be very useful in examining FRQ of the entity, in the context of FVA.

The output of DEMATEL in the form of cause–effect relationship map classifies FRQ metrics into cause and effect groups. Information in notes to accounts is the primary causal FRQ metric. It significantly affects other FRQ metrics (M1, M2, M3, M10 and M11). Notes to accounts contain disclosures of the information regarding accounting amounts that can influence the assessment of FRQ. Opinion in auditors’ report and value relevance are highly effected FRQ metrics. This implies that other FRQ metrics affect the perceptions of the auditors and investors regarding FRQ. Financial ratios and earnings benchmarks are given the least priority among all FRQ metrics by the experts.

The study has its own limitation. The empirical results are based on the personal views of the 14 experts from India. Biasness in their views cannot be ruled out. Statistical validation of the results is required to enhance the reliability of the results. Future work can employ structural equation modeling (SEM) technique with a large sample of experts from India and other countries to validate the results statistically. A fuzzy or grey DEMATEL method can be used to resolve the issues such as biasness and ambiguous judgement of the experts in an uncertain environment. The content analysis of company reports can also be used to examine FRQ.

Footnotes

Acknowledgements

The authors are grateful to the editor and anonymous referees of the journal for their valuable suggestions to improve the quality of the article. The authors reiterate their overwhelming thanks & gratitude to the respondents viz. accounting standard setters, auditors, academicians, & accountants for providing their valuable responses.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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