The Relationship between Internal Audit Assurance Frequency and Earnings Management Intent and Behavior: A Theory of Planned Behavior Approach
Dereck Barr-Pulliam Assistant Professor University of Wisconsin–Madison 975 University Avenue Madison, WI 53706 (608) 509-6097
[email protected]
December 2017
This paper has benefitted from helpful comments on prior versions from Jennifer Altamuro, James Bierstaker, Lucy Chen, Emily Griffith, and Erica Harris; participants at the 2016 Audit Midyear Meeting, the 2016 International Symposium on Auditing Research; workshop participants at the University of Mississippi and Villanova University; and participants in both phases of the study.
The Relationship between Internal Audit Assurance Frequency and Earnings Manipulation Intent and Behavior: A Theory of Planned Behavior Approach
ABSTRACT: This study uses Ajzen’s (1991) theory of planned behavior (TPB) to examine whether the internal audit function’s use of continuous auditing affects managers intent and actual behavior related to earnings manipulation. I conduct an experiment where 265 corporate managers assume the role of a divisional vice president incentivized by an annual bonus based on divisional performance. At midyear, participants signal their intent and actual earnings manipulation behavior in a setting where the division is on target to miss the annualized earnings target if the manager does nothing. Consistent with theory, univariate analyses suggest that managers who indicate higher perceptions on each of the three TPB elements were more likely to manipulate earnings. Multivariate analyses support a positive relationship between each measure and earnings manipulation intent, however, the attitudes measure is statistically insignificant. Related to actual behavior, I predict and find that managers who intend to manipulate earnings actually do so. Further, I find that participants who intend to manipulate earnings prefer to use accounting estimates relative to accounting expenditures as their earnings manipulation mechanism in this setting. In additional analyses, I find managers with current or prior audit committee experience were less likely to manipulate earnings. Lastly, I find that managers are indeed less likely to manipulate earnings when the internal audit function uses continuous auditing. This study contributes to knowledge by disentangling earnings manipulation intent from actual behavior. Results of the study should be of interest to auditors, academics, and financial statement preparers. KEYWORDS: internal audit; continuous auditing; Theory of Planned Behavior; earnings manipulation
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I.
INTRODUCTION
The purpose of this study is to examine the effect of internal audit function (IAF) quality on managerial discretion in a financial reporting setting. Specifically, I model the IAF’s use of continuous auditing using Ajzen’s (1991) theory of planned behavior (TPB) to examine 1) whether continuous auditing affects managers’ intentions to manipulate earnings and 2) whether mangers who intend to manipulate earnings prefer accruals-based (AEM) relative to real earnings management (REM) (Roychowdhury 2006; Healy and Whalen 1999). This study connects and extends two research streams that examine factors that affect managerial opportunism. The first stream identifies factors that increase internal audit quality such as continuous auditing (e.g., Barr-Pulliam 2017; Brown-Liburd, Issa and Lombardi 2015) and related literature suggests that higher quality mitigates actual opportunistic manipulation of earnings to achieve a benchmark (e.g., Abbott, Daugherty, Parker and Peters 2016; Ege 2015; Prawitt, Smith and Wood 2009). Continuous auditing allows for more frequent monitoring of management’s choices, which could reduce opportunism and increase ethicality because it also requires management to provide more timely explanations to senior management and the board regarding financial reporting decisions (e.g., Merchant and Rockness 1994). However, the benefits of continuous auditing may be context-dependent (Gonzalez and Hoffman 2017). While incentive compensation activated when managers achieve these benchmarks is a primary driver of this behavior (Graham, Harvey and Rajgopal 2005), little prior research separately examines earnings manipulation intentions from actual behavior. Accordingly, I contribute to this second line of research by disentangling intent from action to examine how measure how managers utilize the menu of options available to achieve an earnings target. 2
The second steam of research examines the relationship between the use of AEM and REM to achieve a benchmark. On the one hand, these studies suggest a tradeoff such that REM is more frequent for companies employing a high quality external auditor and (1) that issue a seasoned equity offering (Cohen and Zarowin 2010), (2) have strong incentives to manage earnings upward (Chi, Lisic and Pevzner 2011), or (3) that make open-market stock repurchases (Burnett, Cripe, Martin and McAllister 2012). On the other hand, this research suggests that firms use AEM and REM as complements throughout the year based on the relative costs (Zang 2011). Much attention in prior research examining the relationship between IAF quality and earnings manipulation focuses on AEM and meeting or beating analysts’ forecasts, I extend this line of research by examining whether REM and or AEM is likelier when the IAF uses continuous auditing. I use Ajzen’s (1991) theory of planned behavior (TPB) to examine the causal factors that separate managers who intend to manipulate earnings from those who do not. TPB consists of three primary components: perceived behavioral control, perceived subjective norms, and attitudes toward the specified behavior. Perceived behavioral control refers to an individual’s beliefs about the degree of difficulty required to perform a task such as earnings manipulation considering the institutional controls in place like the frequency of monitoring. Perceived subjective norms refer to beliefs about the social acceptability of the behavior, while attitudes refer to whether individuals perceive the behavior as favorable or unfavorable. According to the TPB, each factor should positively influence behavioral intentions, operationalized as manipulating earnings in this study. I contribute to prior research in accounting that examines the TPB (e.g., Brown, Hays and Stuebs 2016; Bagley, Dalton and Ortegren 2012; Bobek, Hatfield and Wentzel 2007) by integrating opinions about IAF assurance frequency to assess how they affect behavioral control. 3
I conduct an experiment in two phases. In the first phase, I distributed a belief elicitation survey to 22 corporate managers solicited through three university alumni networks not included in phase two of the study to identify the salient beliefs practitioners hold about earnings manipulation and the perceived benefits and or limitations of continuous auditing by the IAF. Based on content and frequency analyses, I developed the final TPB survey, which includes 12 questions representing the three elements of the TPB. In the second phase of the experiment, participants were 265 corporate managers solicited from the alumni networks. For context and framing, I ask participants to assume the role of a divisional vice president of a hypothetical manufacturing company. Participants learn about the background of the company and learn that after the first half of the fiscal year, their division is on target to miss its annualized earnings target. This setting provides an incentive to manipulate earnings such that missing the target makes the divisional vice president ineligible for an annual bonus. Regarding the IAF, participants learn that the company’s IAF uses technology that enables it to provide assurance to senior management and the audit committee that is significantly timelier than the traditional mode of reporting. I emphasize the continuous nature of this form of assurance. Any adjustments to the underlying accounting information appear as variances from the budgeted and prior year amounts in auditors’ analyses require follow-up with an internal audit manager immediately when identified, and any unsubstantiated adjustments appear in the final audit report. These adjustments also reduce the division’s profit and, if necessary, the company immediately reverses any bonuses previously paid to the divisional vice president. Hence, the frequency of IAF assurance improves the timeliness of and response to identified opportunism.
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To measure behavioral intent, I ask participants to signal their intention to manipulate earnings on a 10-point Likert-type scale. I measure actual earnings manipulation behavior similarly but separately ask participants who indicated they would manipulate earnings to select either an estimate (the allowance for doubtful accounts) or an expenditure (advertising expense) as the mechanism. These options measure earnings manipulations with implications for accruals or cash flows, respectively (Zang 2011). For both intent and actual behavior, the dependent measures ask the same question from the fist- and third-person perspectives. I use the first-person measure as the primary dependent variable and include the difference between the 1st and 3rd person measures as a covariate to control for social desirability bias related to the willingness to manipulate earnings (Fisher and Tellis 1998). Participants also completed the TPB questionnaire. I use structural equation modeling (SEM) to analyze whether the tenets of the TPB predict intention to manipulate earnings and linear regression to assess whether these intentions predict actual manipulation of earnings. I predict and find that perceived behavioral control and subjective norms positively influence intent to manipulate earnings. While positive, I predict but do not find that attitudes toward earnings manipulation behavior statistically significantly influences intent. I find similar results in the SEM and regression analyses. Related to actual behavior, I predict and find that managers who intend to manipulate earnings actually do so. In this analysis, I also find that participants who intend to manipulate earnings prefer to use estimates relative to expenditures as their earnings manipulation mechanism in this setting. This study contributes to knowledge by disentangling earnings manipulation intent from actual behavior and by helping to examine factors that explain and link IAF quality and managerial discretion in financial reporting. The study should be of interest to auditors, academics, and 5
financial statement preparers. First, this study contributes to knowledge by disentangling earnings manipulation intent from actual behavior. In addition, the results contribute to the stream of research examining efforts by the IAF to increase audit quality and the associated effect on managers’ willingness to manipulate earnings and the mechanism chosen. Second, related to audit practice, the results suggest that increasing the frequency and thereby the quality of assurance provided by the IAF may indeed influence management’s financial reporting competence (DeFond and Zhang 2014) which could help management demand for higher external audit quality. This in turn could also improve external audit quality by increasing reliance on the IAF, which prior research suggests could affect factors such as external audit reporting timeliness and fees (e.g., Pizzini, Lin and Ziegenfuss 2015). Lastly, related to financial statement preparers, this study illuminates the cost-benefit relationship associated with increased monitoring of management to address information asymmetry. Technology permits assurance that is more frequent and less costly as the IAF could automate many routine tasks. However, the need for human interpretation and investigation of findings could lessen the advantages of using technology (Barr-Pulliam 2017). I organize the remainder of this paper as follows. Section II discusses the background develops the hypotheses. Section III describes the research methodology while Section IV discusses the results and additional analyses. Lastly, Section V concludes.
II.
BACKGROUND AND HYPOTHESIS DEVELOPMENT
The Relationship Between Accruals-Based and Real Earning Management Managers exercise discretion in financial reporting and have a menu of options available to help them achieve earnings benchmarks. These options fall into two broad categories: accruals6
based (AEM) and real earnings management (REM). The options differ in that AEM is a manipulation of accounting estimates (accruals) such as the allowance for doubtful accounts that impacts bad debt expense and REM is the result of departures from normal operating practices such as a reduction in quality control or advertising expenditures. REM is perceived as costlier in the long-run because it has cash flow implications, whereas accruals do not. Further, accruals reverse in the future (Healy and Whalen, 1999; Roychowdhury 2006). While managers often prefer REM over AEM (Graham, Harvey and Rajgopal 2005), extant research suggests managers tradeoff between the two forms of earnings management, especially when firms employ a high quality external auditor (Ewert and Wagenhofer 2005; Cohen, Dey, and Lys 2008; Cohen and Zarowin 2010; Chi et al. 2011; Burnett et al. 2012) or use the two as substitutes throughout the year Zang (2011). Related to IAF quality, the literature on IAF quality generally follows the emphasis on AEM found in the external audit literature and mostly uses archival data to test its research questions. These studies find that a high-quality IAF helps to mitigate AEM (e.g., Prawitt et al., 2009, Ege, 2015; Abbott et al., 2016). However, none examine the influence of IAF quality on managers’ opportunistic use of REM alone or its relationship with AEM. Internal Audit Quality and Managerial Discretion in Financial Reporting The mechanism to improve IAF quality that I examine is continuous auditing. Prior research in auditing (Barr-Pulliam, Nkansa and Walker 2017; Brown-Liburd et al. 2015; Jans, Alles, and Vasarhelyi 2014) and practitioner sentiment (AICPA 2012; PwC 2006) suggest that continuous auditing helps to improve audit quality by enhancing effectiveness and efficiency of assurance activities. The IAF achieves these enhancements by shortening audit cycle times and
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thereby increasing its interactions with management and the audit committee. 1 On the one hand, if the salience of these initiatives extends to management, then continuous auditing should inhibit opportunistic earnings manipulation due to management’s need to more frequently justify unsubstantiated adjustments to accounting numbers to senior management and/or the audit committee (Barr-Pulliam 2017). On the other hand, increased monitoring could incite negative affect, manifest distrust and or cause greater risk aversion and risk taking, and thus more opportunism in financial reporting (e.g., Baiman 1990; Wiseman and Gomez-Mejia 1998). This latter point assumes that managers as agents in the principal-agent framework are indeed risk- and effort-averse and that they act opportunistically to achieve these incentives (Baiman, 1990). This study contributes to knowledge by examining whether continuous auditing affects managers’ earnings manipulation intentions using Ajzen’s (1991) theory of planned behavior (TPB). The Theory of Planned Behavior (TPB) In this study, I apply the TPB which has been used in various accounting contexts such as fraud (e.g., Brown et al. 2016; Carpenter and Reimers 2005), the decision between two accounting job offers (Bagley et al. 2012), and taxation (Bobek et al. 2007). I further examine whether earnings manipulation intent is predictive of actual behavior, which I operationalize as the mechanism managers select to manipulate earnings (either AEM or REM). The TPB links one’s beliefs and behavior. While similar to the theory of reasoned action (Madden, Ellen and Ajzen 1992), it differs in that TPB is used to predict deliberate and planned behavior such as earnings manipulation. Ajzen (1991) suggests that attitudes alone insufficiently predict deliberate behavior. As a result, 1
I define continuous auditing as use of technology to perform near real-time assurance activities such as identifying anomalies, analyzing transaction patterns, and testing the operating effectiveness of internal controls. Importantly, I distinguish continuous auditing (an assurance function) from continuous monitoring (a management function).
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the TPB improves upon the theory of reasoned action by including perceived behavioral control with other attitudinal measures. Accordingly, the TPB describes behavioral intention as a function of perceived behavioral control, perceived subjective norms, and attitudes toward the behavior (see Figure 1).Given its usefulness in a number of settings, I extend the TPB in two ways. First, I test its application in an earnings management setting whereby I examine factors that help to explain corporate managers’ intentions to manipulation earnings to achieve a specific earnings target. I define intent as a manager’s subjective probability assigned to the likelihood of actually engaging in earnings manipulation. Perceived behavioral control describes an individual’s identification and assessment of the factors that increase the difficulty of performing a specific behavior such as earnings manipulation. In the second extension of the TPB, I integrate perceptions of higher internal audit assurance frequency as an additional factor that could increase the salience of behavioral control in this setting. I describe this integration in Section III. Perceived subjective norms represent an individual’s normative beliefs about the acceptability of manipulating earnings, while attitudes toward (earnings manipulation) behavior symbolize a person’s judgment of the costs associated with performing the behavior. As discussed in detail in the next sections, the TPB suggests that each factor influences behavioral intent. Perceived Behavioral Control Ajzen (1991) defines perceived behavioral control as an individual’s beliefs about the difficulty associated with performing the behavior of interest. Managers in the elicitation phase of this study, discussed below, suggest that prior experience with manipulating earnings and specific control mechanisms in place within their companies significantly impact intent and ability to manipulate earnings. Managers note that more frequent observations of their work and more 9
frequent reporting of any findings by the IAF to their supervisory managers and/or the board increases the difficulty to manipulate earnings. The extent to which managers perceive a lack of (increase in) behavior control, for example due to increased IAF assurance, the TPB suggests lower (higher) intentions to manipulate earnings. Consistent with the TPB, I propose the following: H1:
Managers who perceive higher (lower) behavioral control will signal higher (lower) earnings manipulation intentions.
Perceived Subjective Norms Perceptions of subjective norms are an individual’s beliefs about what characterizes normal behavior and the associated absence or presence of pressure from others to perform a behavior such as earnings manipulation (Ajzen 1991). In the belief elicitation phase of this study, managers identified both internal and external influences on their intentions from a number of referent groups including peer managers, auditors, and regulators. The TPB posits that when an individual believes that these referent others support or focus less on their behavior, he or she is more likely to engage in that behavior. Accordingly, I expect managers’ earnings manipulation intent will be influenced by how they normalize such behavior and whether they perceive social pressure from these important others. Therefore, I propose the following hypothesis: H2:
Managers who perceive higher (lower) subjective norms will signal higher (lower) earnings manipulation intentions.
Attitudes Toward Behavior Attitudes, within the TPB framework, signal an individual’s view of a particular behavior (Ajzen 1991). Both positive and negative evaluations of the target behavior drive beliefs about the likelihood of achieving a specified outcome and drive an evaluation of the outcome itself. In the context of earnings manipulation, belief likelihoods might manifest in the form of a manager 10
evaluating whether adjusting accounting numbers will achieve an earnings target. In the outcome evaluation phase, the manager develops his or her positive and negative assessments of the earnings manipulation choice. In the elicitation phase described in the next section, managers’ belief likelihoods centered around their own experience manipulating earnings, but more specifically whether this experience served them such that adjusting accounting earnings is the preferred method of achieving a targeted outcome relative to other approaches such as restructuring a corporate division or investing in a new enterprise risk management system. Outcome evaluations included beliefs about the legality, ethical nature, commonality and frequency of earnings manipulation. I expect that managers’ attitudinal beliefs will influence their earnings manipulation intentions, as suggested in the following hypothesis: H3:
Managers’ attitudes will affect their earnings manipulation intentions.
The Association between Intent and Action In this study, I contribute to prior research by separately measuring managers’ earnings manipulation intentions and actual behavior. The preceding hypotheses predict that overall attitudes, subjective norms, and perceived behavioral control will each influence managers’ earnings manipulation intentions. Informed by the TPB and prior research on continuous auditing, these factors provide theoretical support for and reasons why managers decide to manipulate earnings. However, absent testing a measure of intent and a measure of action, this study could only infer that the link between the two applies in this setting. As a result, I measure both and expect the intuition of the TPB to hold in this setting. I state this expectation as follows: H4:
Managers’ earnings manipulation intentions will positively affect their actual earnings manipulation behavior. 11
III.
EXPERIMENTAL DESIGN
Phase One: Elicitation Survey Following the intuition of Fishbein and Ajzen (2010); the illustrative example provided by Sutton, French, Hennings, Mitchell, Wareham and Griffin (2003); and prior research in accounting using the TPB (e.g., Brown et al. 2016; Bertrand, Schram and Vaassen 2013), I first conducted an elicitation survey. In the Summer of 2014, I identified a stratified random sample of 25 corporate managers with either operations, accounting, or finance experience across alumni databases of business school alumni from two large public and one private university. I restricted invitations to participants who 1) graduated no later than 2008 and 2) had self-reported middle- and uppermanagement titles such as chief financial officer, chief executive officer, vice president, and divisional manager. Each alumnus received an initial electronic request to participate in Qualtrics and a follow-up two weeks later. Of the 25 selected, 24 (96.00%) alumni started the survey, and 22 of the 24 had complete responses. The usable response rate is 88.00%. Survey Design The elicitation survey solicited beliefs about manipulating earnings to achieve a specific earnings benchmark. The 11 survey items were informed by prior research on behavioral implications of continuous auditing (e.g., Barr-Pulliam 2017; Brown-Liburd et al. 2015) and field studies that examine earnings manipulation preferences (e.g, Graham et al. 2005; Nelson, Elliott and Tarpely 2002). Participants ranked and commented on each response to support the ranking. The cover page of the survey described a case-based scenario adapted from Barr-Pulliam (2017) that orients participants to the purpose of the elicitation exercise. Participants assume the role of a divisional vice president of a global manufacturing company. The case facts first describe 12
the company and the participant’s role in the company, the structure and implications of the IAF and its use of technology on assurance methodology and timeliness. I hold the IAF size, Chief Audit Executive reporting lines, and IAF relationship with the external auditor constant. Next, for a specific division under the vice president’s control, participants learned that after the first half of the fiscal year, the division would not meet its annualized projected divisional profit. If the divisional vice president does nothing, the division could miss its budgeted divisional income for the year. This is important because divisional vice presidents receive a fixed salary and an incentive-driven annual bonus based on how they control budget variances for the division, irrespective of overall company performance. I aggregated responses from the completed surveys by question, coding them first using content analysis, and second using frequency analysis to determine which factors best represent perceived behavioral control, subjective norms, and attitudes forming the basis of intentions. Two PhD students without knowledge of the research questions initially coded and categorized the open-ended questions. Inter-rater agreement was 89.56 percent. The two coders mutually resolved differences with the author. Following is a description of each TPB element. Measure of Perceived Behavioral Control (PerceivedControl) To develop the initial survey items related to perceived behavioral control, I included three statements identified from research. These statements assesses managers’ perceptions of continuous auditing, which increases the frequency of IAF assurance beyond the traditional periodic audit (e.g., every three years) on their autonomy and risk-taking behavior, as well as the level of scrutiny they feel (Barr-Pulliam 2017). The remaining open-ended questions sought to measure other factors that might affect managers’ ability to manipulate earnings and focused on 13
both control beliefs and the power of control (as in Brown et al. 2016). Related to both, I asked participants to note, in order of importance, the top five factors they believe would constrain their ability to adjust accounting numbers and why they believed these factors were important. The most frequently mentioned concepts from the elicitation phase centered on prior experience with manipulating earnings and how frequently they did so. In addition, they separately noted that internal control mechanisms such as a respected IAF who frequently observes and reports on their work and the speed at which management and/or the board might learn of their reporting decisions (e.g., the quality of the company’s accounting system). Based on the content and frequency analysis previously described, five factors emerged (see Table 2). I reverse coded four questions to ensure that higher (lower) ratings indicate greater (less) perceived behavioral control. Participants assessed the three assurance frequency-related questions on a 7-point Likert-type scale anchored by 1 (definitely false) and 7 (definitely true). The remaining questions used a 10-point Likert-type scale anchored by 1 (definitely false) and 10 (definitely true). Following prior research, I averaged each participant’s score across the measures to derive PerceivedControl and to test H1. Measure of Perceived Subjective Norms (PerceivedNorms) The elicitation survey questions related to perceived subjective norms focused on factors that signify normalizing behavior and the related social pressure associated with manipulating earnings (Ajzen 1991). I asked participants to identify others who are internal or external to their company who might influence whether they intended to manipulate earnings. Respondents referenced groups that included peer managers within the firm and the industry; stakeholders and their related expectations of management; the extent of testing operations and/or accounting 14
numbers that appear in the financial statements by the IAF; the extent of focus by external auditors on adjustments to accounting numbers reflective of operational decisions; and regulators. Based on the content and frequency analysis previously described, the most frequent groups noted were other managers and both internal and external auditors. Like Brown et al. (2016), I categorize focus on peer managers as a descriptive norm that signals whether managers’ internal motivation reflects patterns of expected behavior. I categorize focus on of the work of the two auditor groups as an injunctive norm reflective of managers’ perception as to whether auditors might anticipate and or test whether they manipulated earnings. I reverse coded the two auditorrelated questions to ensure that higher (lower) ratings indicate greater (less) perceptions of subjective norms. Participants assessed all questions using a 10-point Likert-type scale anchored by 1 (definitely false) and 10 (definitely true). I averaged each participant’s score across the measures to arrive at the PerceivedNorms variable used in testing H2. Measure of Attitudes Toward (Earnings Manipulation) Behavior (Attitudes) To identify attitudes toward earnings manipulation behavior, the elicitation survey asked respondents to identify factors in two categories. The first includes beliefs that reflect the likelihood that the intended behavior (earnings manipulation) would result in the expected outcome (reaching an earnings target). The second category includes positive and negative beliefs one might have after manipulating earnings. Based on the content and frequency analysis previously described, one factor emerged related to belief likelihoods—whether managers perceive earnings management as the preferred method of manipulating earnings. Three factors emerged that represent positive or negative beliefs—whether managers perceived earnings management as legal, ethical, or common. 15
Participants assessed all questions using a 10-point Likert-type scale anchored by 1 (definitely false) and 10 (definitely true). I averaged each participant’s score across the measures to arrive at the Attitudes variable used in testing H3. Pilot Study Refinements Prior to phase two, I pilot tested the final TPB questionnaire with 25 second-year MBA students with an average of 5 years of prior corporate mid- to upper-level management experience attending a large public university. The purpose was to assess the clarity, understandability, and length of time to complete the survey. Participants were enrolled in a financial statement analysis course and provided feedback that resulted in improvements to the final instrument’s exposition. Phase Two: Participants and Dependent Measures In the fall of 2014, I elicited participation in Phase 2 of the study from a stratified random sample of 1,500 alumni included in the previously described databases. I sent an initial request to participate electronically using Qualtrics and a follow-up two weeks later. Three hundred eleven (20.73%) alumni started the survey, and 293 of the 311 had complete responses. To arrive at the number of usable responses, I removed 15 participants who either reported a current title that did not reflect management (e.g., supervisor, accountant, or engineer) or that retired more than 3 years prior to the date of the experiment and 18 participants who spent less than 5 minutes on the experiment. 2 The 265 usable responses reflect a response rate of 17.67%. I find no systematic differences based on early versus late respondents or by affiliated university.
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Average completion time from the pilot study was 11 minutes while the average completion time of the actual sample was 12.50 minutes. I find that the participants who spent less than 5 minutes had responses that were significantly different from those who spent at least 5 minutes on the experiment.
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As indicated in Table 1, participants have on average 6 to 10 years of management experience and were 52.45% female. Of the 265 participants, 51.70% held at least one certification such as CPA (23.39%) and CIA (4.53%). Prior experience includes external audit (15.09%), internal audit (19.25%) and 29.43% of participants have current or prior experience on an audit committee. Industries with significant representation include financial services (15.09%), manufacturing (14.72%), retail (14.34%), and technology (13.58%). The majority of participants held middle management positions (72.08%) defined as non-C-Suite but at least at a divisional level. The most common divisions of the company where participants work includes finance/accounting (29.06%), operations (25.66%), general management (20.38%), and marketing/sales (12.45%). Importantly, 64.15% (46.04%) of participants report that their company leverages technology to enable continuous monitoring by management (continuous auditing by the IAF). These demographics suggest participants were appropriate and represent a diverse perspective across industries and functional roles. I use variables significantly correlated with the dependent measures as explanatory variables as explained in Section IV. [INSERT TABLE 1 HERE] Dependent Measures Participants reported both behavioral intent and actual behavior related to earnings manipulation. Specifically related to intent, I asked participants to signal their intention to manipulate earnings on a 10-point Likert-type scale anchored by 1 (definitely do nothing) and 10 (definitely do something). Participants respond from the first-person (In a similar situation, what would you do?) and third-person (What do you believe the VP would do?) perspectives to control for potential social desirability related to earnings manipulation (e.g., Fisher and Tellis 1998). In 17
the analyses, I use the first-person perspective as the primary measure of intent (EM_Intent) and include the difference between the first- and third-person perspectives as a covariate (SDB) (e.g., as in Kaplan, Pope, & Samuels 2011). Related to actual behavior (EM_Behave), I took a similar approach by assessing the extent to which participants would actually manipulate earnings. However, participants indicating behavioral likelihood greater than one on the previously described scale selected whether they would 1) adjust an accounting estimate (operationalized as the allowance for doubtful accounts) or 2) adjust an accounting expenditure (operationalized as advertising expense). These options measure earnings manipulation with implications for accruals and real activities. I ensure that participants understand that either selecting option 1 or 2 would equally help the division to achieve the budgeted income benchmark by the same margin. This approach allows me to isolate differences in preference while holding all other factors constant. As suggested by prior research, I include an option to “do nothing” in both the intent and actual behavior measurements because in practice some managers may not follow the logic provided by the TPB suggesting that intentions signal subsequent behavior. Prior research also suggests that participants may “express uncertainty by moving to the midpoint of qualitative scales” (Nelson and Skinner 2013, 38) absent such an option. I estimate Equation 1 below to examine the relationship between intent and behavior: EM_Behave = B0 + B1 EM_Intent + B2 CPA + B3 CIA + B4 AC_Experience + B5 SDB + e where EM_Behave, EM_Intent, and SDB are measured as previously described. I include control variables correlated with behavior from untabulated univariate analyses. These control variables include CPA which is equal to 1 if the participant is a certified public accountant and zero 18
otherwise; CIA which is equal to 1 if the participant is a certified internal auditor; and AC_Experience which is equal to 1 if the participant has audit committee experience. Participants also completed the TPB questionnaire measuring perceived behavioral control (5 questions), perceived subjective norms (3 questions), and attitudes toward behavior (4 questions) noted in Table 2. A potential concern about participants’ responses to the actual behavior dependent measure and the TPB questionnaire is that the order of presentation could influence how participants respond. To mitigate this concern, I counterbalance the order of presentation such that half of the participants complete the TPB questionnaire versus the actual behavior dependent measure first. Within each of the measures, I also randomize the order of presentation of the statements. Untabulated results suggest that randomization was successful. The experiment concluded with a post-experimental questionnaire that collected demographic and other classifying information based on theory and prior research.
IV.
RESULTS AND ADDITIONAL ANALYSES
I use structural equation modeling (SEM) to analyze whether the tenets of the TPB predict intent to manipulate earnings and linear regression to assess whether intent predicts actual manipulation of earnings. I next describe three categories of analyses designed to test the relevance and reliability of my TPB and earnings management intent measures. First, following Brown et al. (2016), I divided participants into two groups based on their average intent to manipulate earnings (EM_Intent) where scores above (below) the median represent higher (lower) EM_Intent. For each factor, I examine the difference in means for each measure between participants signaling low versus high intent. Panel B of Table 2 suggests that managers expressing higher intent to 19
manipulate earnings perceived greater behavioral control (t = -2.68, p < 0.01), perceived earnings manipulation to be more common (t = -2.22, p = 0.01), and have more positive attitudes about earnings manipulation (t = -1.54, p = 0.06). I also examine if there are differences across the three continuous auditing-related factors included in the measurement of PerceivedControl. As indicated in Panel C of Table 2 managers are less likely to manipulate earnings because they perceive that continuous auditing indeed inhibits autonomy (PBC1, t = 2.60, p < 0.01), discourages risk taking (PBC2, t = 3.44, p < 0.01) and provides too much scrutiny (PBC3, t = 1.92, p = 0.03). [INSERT TABLE 2 HERE] Measurement Model and Construct Validity Analyses In the second category of analyses, I employ a Structural Equation Model (SEM) in AMOS to estimate a measurement model that permits examination of the construct validity of my TPB measures. I separately conduct factor analysis to examine the validity of the TPB construct. Results of these analyses appear in Table 2. 3 Each of the three TPB factors has sufficient construct convergent validity as composite reliability scores (based on Crohnbach’s alpha) are within the 0.70 threshold advocated by Hair, Black, Babin, Anderson, and Tatham (2006) and which suggests convergent validity of the construct (Fornell and Larcker 1981). The Crohnbach’s alpha scores range from 0.71 to 0.85. Each measure explains at least 63% of the variance and has factor loadings that range from 0.49 to many in excess of 0.80. I calculate the average response to each of the measures of perceived behavioral
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In untabulated results, I find that the measurement model has sufficient fit as the root mean square error of approximation (RMSEA = 0.08) and comparative fit index (CFI = 0.92) each fall within acceptable levels (Hu and Bentler 1999; Browne and Cudeck 1993). The Chi-square to degrees of freedom ratio of 2.7 also is within the 3-to-1 threshold suggested by Carmines and McIver (1981) and the Tucker Lewis Index (TLI = 0.90) is within acceptable levels (Tucker and Lewis 1973).
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control (PerceivedControl), perceived subjective norms (PerceivedNorms), and attitudes toward earnings management behavior (Attitudes). Using confirmatory factor analysis, I use these average scores to determine whether the three measures sufficiently represent the TPB construct. I identify only one factor that explains 71% of the variance and that has a composite reliability of 0.72. Factor loadings for each of three TPB components are each in excess of 0.75. In the final category of analyses, I assess the discriminant validity of the TPB factors (untabulated) using tests suggested by Fornell and Larker (1981) and Churchill (1979). I create a correlation matrix that places the Chronbach’s alpha for the TPB measures along the diagonal. In each column, I place the bivariate Pearson correlation between each of the measures. While each of the TPB measures are distinct constructs, it is likely they are highly correlated. I find evidence to support this assertion and examine it further in the additional analyses discussed latter. Variance inflation factors in linear regression analyses suggest neither exceeds the conventional 2.0 threshold. These finding suggest that the constructs are reliable and that the TPB model is valid. Structural Model Analyses To examine further H1, H2, and H3, I estimate the structural model reported in Figure 2. The figure includes the standardized path coefficients and standard errors for each element of the TPB. Additionally, the figure includes the covariance between the elements. I find that this model has satisfactory fit statistics as the RMSEA (0.08), CFI (0.95), TLI (0.91) and Chi-square to degrees of freedom (3.43) indices all fall within acceptable thresholds (Hu and Bentler 1999; Browne and Cudeck 1993; Carmines and McIver 1981; and Tucker and Lewis 1973). Recall that H1 predicts that high (low) perceived behavioral control (PerceivedControl) will affect managers’ intent to manipulate earnings. Consistent with this prediction, I find that the 21
relationship between PerceivedControl and EM_Intent is positive and significant (standardized coefficient = 0.17, p < 0.01). This finding suggests that when managers believe that the frequency of IAF assurance is less prohibitive and when they have frequently manipulated earnings, their intent to manipulate earnings increases. Relatedly, H2 predicts that higher (lower) perceived subjective norms (PerceivedNorms) will affect managers’ intent to manipulate earnings. Consistent with this prediction, I find that the relationship between PerceivedNorms and EM_Intent is positive and significant (standardized coefficient = 0.22, p < 0.01). This finding suggests that normalizing earnings manipulation increases managers’ intentions to do so. Lastly, H3 predicts that attitudes toward earnings manipulation behavior (Attitudes) will affect managers’ intent to manipulate earnings. Consistent with this prediction, I find that the relationship between Attitudes and EM_Intent is positive but the relationship is insignificant (standardized coefficient = 0.08, p = 0.19). Unlike the findings for H1 and H2, this result is inconsistent with the univariate results comparing the bivariate correlations and tests in Panel B of Table 2. The results collectively suggest that perceptions of behavioral control and subjective norms play a more significant role in estimating managers’ intentions to manipulate earnings in this experimental setting. In the following regression, I examine the relationship between EM_Intent and the three TPB elements: EM_Intent = B0 + B1 PerceivedControl + B2 PerceivedNorms + B3 Attitudes + B4 SDB + e where EM_Intent, PerceivedControl, PerceivedNorms, Attitudes, and SDB are constructed as previously described. Results in Panel A of Table 3 are consistent with the SEM estimation presented in Figure 2. I find that PerceivedControl (t = 2.81, p < 0.01) and PerceivedNorms (t = 3.79, p < 0.01) are significant and positive predictors of intentions to manipulate earnings. These
22
results provide further support for H1 and H2, respectively. Consistent with the SEM, Attitudes is positively related to intent but is insignificant (t = 1.27, p = 0.20). [INSERT TABLE 3 HERE] The Relationship between Intentions and Actual Behavior Rather than infer from theory that earnings manipulation intentions predict actual behavior, H4 examines it directly. As indicated in Table 4 Panel A, I find that managers who intend to manipulate earnings actually do so (t = 7.98, p < 0.01). I also find that managers with either current or prior audit committee appointments (t = -2.73, p < 0.01) are less likely to manipulate earnings. I find no significant relationship between EM_Behave and whether participants have either a CPA or a CIA license. These results provide support for H4. Recall that in addition to EM_Behave, manager participants also indicated either their preference for an accruals-based or a real earnings manipulation option. To determine which method of earnings manipulation managers preferred, I add an indicator variable to Equation 1 that represents the earnings manipulation method each participant chose (EM_Method): EM_Behave = B0 + B1 EM_Intent + B2 EM_Method + B3 CPA + B4 CIA + B5 AC_Experience + B6 SDB + e
(Eq. 1a)
where EM_Method is an indicator variable coded as 0 if managers chose to adjust accounting expenditures (REM) and 1 if participants chose to adjust accounting estimates (AEM). All other variables are as defined for Equation 1. Note that this comparison excludes managers who chose to do nothing (n = 40) in the EM_Behave measure. To prevent a mismatch between measures, display logic programmed into the Qualtrics only presented the EM_Method options if participants entered a value of at least 2 for the EM_Behave measure. 23
As indicated in Table 4 Panel B, EM_Intent remains positive and significant (t = 3.28, p < 0.01). I find that neither certification as a CPA (t = 1.06, p = 0.29) or a CIA (t = -1.00, p = 0.32); nor audit committee experience (t = -0.63, p = 0.53) are statistically significant. I do, however, find that EM_Method is positive and significant (t = 9.08, p < 0.01) suggesting that managers who intend to manipulate earnings not only do so, but also prefer to adjust accounting estimates. This latter finding differs from prior archival auditing research, which suggests that measures of IAF quality are associated with less accruals-based earnings manipulation (e.g., Ege 2015; Prawitt et al. 2009). However, this result follows experimental research that suggests that management may prefer accruals-based measures in the presence of efforts to increase the quality of the IAF because of internal auditors’ greater familiarity with the operations of the company. This familiarity could help internal auditors to generate independent assumptions about the reasonableness of operational decisions, which could increase the likelihood that they report real relative to accruals-based earnings manipulation (Barr-Pulliam 2017). Collectively, these findings further support H4. [INSERT TABLE 4 HERE] Additional Analyses Additional Predictors of Intentions In the test of H3, untabulated univariate results suggest a positive and significant correlation between Attitudes and EM_Intent. In addition, Figure 2 suggests a significant covariance between PerceivedNorms and Attitudes (p = 0.02). Variance inflation factors in estimating Equation 2 were at acceptable levels and allayed concern for multicollinearity. The first of two categories of additional analyses examines whether other combinations of two of the three TPB variables support the univariate results that suggest Attitudes is a significant predictor of 24
intent to manipulate earnings. In untabulated results estimating Equation 2 by excluding Attitudes, I find that both PerceivedControl (t = 3.03, p < 0.01) and PerceivedNorms (t = 3.97, p < 0.01) remain positive and significant. Not surprisingly, estimating Equation 2 by excluding PerceivedNorms, I find that PerceivedControl (t = 3.21, p < 0.01) remains positive and significant and that Attitudes (t = 1.73, p = 0.08) becomes marginally significant. Lastly, excluding only PerceivedControl from Equation 2, I find that PerceivedNorms (t = 4.02, p < 0.01) remains positive and significant while Attitudes (t = 1.51, p = 0.13) is positive but insignificant. Additional Predictor of Intentions As previously indicated, the post-experimental questionnaire collects optional demographic data on each participant. Using this data, I conduct the second of two categories of additional analyses. I specifically examine whether demographic or professional factors are predictive of earnings manipulation intent. Bivariate correlations (untabulated) between the primary measure of intent (EM_Intent) and each of the demographic variables collected suggest that the participant’s category of management (MgmtLevel) (p = 0.01) is positive and significantly correlated. These univariate analyses suggest that managers closer to the C-Suite intend to manipulate earnings more. I estimate Equation 2a to test this relationship: EM_Intent = B0 + B1 PerceivedControl + B2 PerceivedNorms + B3 Attitudes + B4 MgmtLevel + B5 SDB + e (Eq. 2a) where EM_Intent, PerceivedControl, PerceivedNorms, Attitudes, and SDB are constructed as previously described; and MgmtLevel is each participant’s self-reported management level coded as 1 (middle), 2 (upper), or 3 (other). Panel B of Table 3 shows that results for PerceivedControl (t = 3.28 p < 0.01), PerceivedNorms (t = 3.60, p < 0.01), and Attitudes (t = 1.28, p = 0.20) hold 25
and that MgmtLevel is positive and significant (t = 2.16, p = 0.03). This finding support the univariate results and provide additional support for H1 and H2.
V.
CONCLUSION
In this study, I use a modified version of Ajzen’s (1991) theory of planned behavior (TPB) to examine factors that affect management’s intent to manipulate earnings. The TPB permits an examination of whether managers’ perceptions of behavior control, subjective norms, and or attitudes toward the target behavior influence intent and thus behavior. In the measurement of perceived behavioral control, I include factors that specifically indicate how managers perceive the IAF’s use of technology to improve assurance frequency and quality through greater efficiency and effectiveness. These factors include perceptions about the impact on risk taking, autonomy, and the level of scrutiny higher frequency assurance provides. While much of the prior research using the TPB only examines intent, I also examine whether intent predicts actual behavior. In the experiment, 265 corporate managers signal both their behavioral intent to manipulate earnings and actual earnings manipulation behavior. I use structural equation modeling (SEM) and linear regression to examine the relationship between each of the tenets of the TPB and intent. Consistent with theory, I find that perceived behavioral control, subjective norms, and attitudes positively influence intent to manipulate earnings. However, the link between attitudes and intent is not statistically significant. Also consistent with theory, I find that managers who intend to manipulate earnings actually do so and that they prefer accounting estimates relative to expenditures as their earnings manipulation mechanism in this setting.
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While this study is subject to limitations common in experiments, I believe this study contributes to knowledge and has implications for auditors, academic research, and financial statement preparers. This study contributes to knowledge examining factors that separate managers who intend to manipulate earnings from those who do not. In this way, the study is one of the first to disentangle earnings manipulation intent from actual behavior. The findings also provide insights about additional factors that affect the relationship between the quality of the IAF and opportunism in a setting where management has significant discretion. For auditors, the findings suggest that continuous auditing is another mechanism whereby the IAF can increase not only assurance frequency but also quality by mitigating opportunism in financial reporting. However, like any effort to increase audit quality, it is subject to limitations. For academic research, this study extends the TPB to a new setting within accounting. In addition, the study empirically tests a dynamic setting where the IAF can demonstrate its value to an organization by helping to improve not only the operating effectiveness of controls but also financial reporting quality. Lastly, related to financial statement preparers and users, this study illuminates cost-benefit argument for an investment in technology that serves in a monitoring capacity and enables better enterprise risk management. Further, this investment affords the IAF the opportunity more efficiently and effectively to provide assurance on whether controls are operating effectively and whether management sufficiently manages risk.
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Variable Definitions: Perceived Behavioral Control [PerceivedControl] = average of participants’ beliefs as measured by PBC1 – PBC5 and defined in Table 2 Perceived Subjective Norms [PerceivedNorms] = average of participants’ beliefs as measured by PSN1 – PSN3 and defined in Table 2 Attitude Toward (Earnings Management) Behavior [Attitudes] = average of participants’ beliefs as measured by ATB1 – ATB4 and defined in Table 2 Earnings Management Intentions [EM_Intent] = average of participants’ Phase 1 responses to DV1 which measures intentions as defined in Table 2 Earnings Management Behavior [EM_Behave] = average of participants’ Phase 2 responses to DV2 which measures actual behavior as defined in Table 2
TABLE 1 Participant Demographics Number and Percentage of Participants in Each Category (n = 265) Variable Mean Years Management Experience Professional Certification Certified Public Accountant (CPA) Certified Internal Auditor (CIA) Other Certification Multiple Certifications None Professional Experience Prior External Auditor Prior Internal Auditor Current Audit Committee Prior Audit Committee Gender Female Male Management Level Middle Manager (non-C-Suite but at least at the divisional level) Upper Management (e.g., C-Suite) Other (unclassified) Division of the Company Finance/Accounting Operations General Management Marketing/Sales Human Resources Research & Development Other Industry Financial Services Manufacturing Retail Technology Other Services Industry Health Care Government Construction Transportation Utilities Mining/Oil/Gas Other At your company, does… Management use continuous (controls) monitoring? the IAF employ continuous auditing (assurance)?
Number [Percentage] 6 - 10 years [35.09] 62 [23.39] 12 [4.53] 63 [23.77] 23 [8.68] 128 [48.30] 40 [15.09] 51 [19.25] 19 [7.17] 59 [22.26] 136 [52.45] 126 [47.55] 191 [72.08] 68 [25.66] 6 [2.26] 77 [29.06] 68 [25.66] 54 [20.38] 33 [12.45] 11 [4.15] 10 [3.77] 12 [4.53] 40 [15.09] 39 [14.72] 38 [14.34] 36 [13.58] 21 [7.92] 20 [7.55] 15 [5.66] 10 [3.77] 6 [2.26] 4 [1.51] 3 [1.13] 33 [12.45] 170 [64.15] 122 [46.04]
TABLE 2 Panel A: Descriptive Statistics and Factor Analysis Item Description Earnings Manipulation Intentions (DV1)* In a similar situation, what would you do? What do you believe the VP would do? Earnings Manipulation Behavior (DV2)* In a similar situation, what would you do? What do you believe the VP would do? Perceived Behavioral Control (composite reliability = .75; extracted variance = .63) The frequency of IAF assurance inhibits managerial autonomy.** a The frequency of IAF assurance discourages risk taking.** a The frequency of IAF assurance provides too much scrutiny.** a I frequently adjust accounting numbers to achieve a specific earnings target.*** The frequency of assurance by the IAF affects my decision to adjust earnings.*** a Perceived Subjective Norms (composite reliability = .71; extracted variance = .63) How often others adjust accounting numbers affects my earnings management approach.*** I believe that earnings management is the focus of Internal Auditors.*** a I believe that earnings management is the focus of External Auditors.*** a Attitudes Toward Behavior (composite reliability = .85; extracted variance = .70) I believe that earnings management is LEGAL.*** I believe that earnings management is ETHICAL.*** I believe that earnings management is PREFERABLE.*** I believe that earnings management is COMMON.*** Theory of Planned Behavior (composite reliability = .72; extracted variance = .71) aI
Variable
Mean
Std. Dev.
Factor Loading
EM_Intent DV1_VP
5.60 6.04
3.10 2.80
N/A N/A
EM_Behave DV2_VP
6.00 6.74
2.76 1.16
N/A N/A
PBC1 PBC2 PBC3 PBC4 PBC5
5.12 4.41 5.06 4.18 5.01
3.00 3.01 2.74 1.21 1.66
0.49 0.82 0.87 0.68 0.94
PSN1 PSN2 PSN3
6.05 7.28 7.57
1.35 1.73 2.03
0.62 0.94 0.94
ATB1 ATB2 ATB3 ATB4
8.40 7.36 6.29 7.95
1.53 1.75 1.65 1.30
0.81 0.92 0.84 0.76
TPB
N/A
reverse score these measures to reflect that higher (lower) ratings reflect greater (less) control or subjective norms. *Primary intent (actual behavior) dependent measure assessed on a 10-point Likert-type scale anchored by 1 = likely (definitely) do nothing and 10 = likely (definitely) do something. I ask the question in 1st [what would you do] and 3rd person [what do you think the VP would do] and use the difference to control for social desirability bias. **Items measured on a 7-point Likert-type scale anchored by 1 = definitely false and 7 = definitely true. ***Items measured on a 10-point Likert-type scale anchored by 1 = definitely false and 10 = definitely true. I report composite reliability (Cronbach’s alpha) and the extracted variance (percentage explained by the observed variables).
TABLE 2 (Continued) Panel B: TPB Factor Means by Intent to Manipulate Earnings Less Likely to More Likely to Manipulatea Manipulate Variable (n = 111) (n = 154) Diff.b t-stat 3.80 5.45 -1.65 -2.68 PerceivedControl 6.46 7.34 -0.88 -2.22 PerceivedNorms 6.96 7.89 -0.92 -1.54 Attitudes Panel C: Continuous Audit-Related Factors within Perceived Behavioral Control Less Likely to More Likely to Manipulate Manipulate Variabled (n = 111) (n = 154) Diff.b t-stat PBC1 5.68 4.72 0.96 2.60 PBC2 4.94 3.68 1.27 3.44 PBC3 5.45 4.79 0.65 1.92 a
p-valuec
