Original Article

Managers’ Beliefs about False Performance Reports: How High Goals and Low Performance Levels Can Be Unethical Warning Signals

Authors: {'first_name': 'Knut Ivar', 'last_name': 'Karevold'}


This study examines managers’ beliefs about how goals and performance levels can motivate overstatement of accomplishments and compares if managers’ moral assumptions are consistent with research showing that goals can motivate low and as well as high performers to unethically overstate achievements. Seven hundred thirty-eight top- and mid-level managers were in four different studies asked to judge case vignettes where employees, teams, and firms considered what performance level to report. There were four main conditions: with/without performance goals and low/high performance levels, and managers rated the likelihood of overstating the reports beyond actual achievements. A majority believed that goals make overstatements more likely and that low performance increases this tendency. The study suggests that managers are morally aware that goals can promote unethical reporting but can ignore that high performers might cheat. Implications for management training, reporting systems, performance appraisals, and organizational roles are discussed.
Keywords: performance overstatementsethicalmoralgoals 
DOI: http://doi.org/10.16993/sjwop.49
 Accepted on 28 Nov 2020            Submitted on 31 Jan 2018


Research on ethical behavior in organizations indicates that a range of factors can influence whether individuals and firms act morally, and that firms can engage in a range of activities that can be classified as misconduct (Greve, Palmer, & Pozner, 2010; Tenbrunsel & Swith-Crowe, 2008; Trevino, Weaver, & Reynolds, 2006). It seems clear that organizational as well as individual factors play a role in stimulating managers and firms to act fraudulently (Trevino, Weaver, & Reynolds, 2006). The recent examples of organizational misconduct also suggest that fraudulent actions can take place over considerable time, but without being detected by leaders or controllers responsible for overseeing the activities and performance reports.

Given the significant negative consequences of unethical conduct for the involved firms, an important question is whether managers are aware of the conditions that lead to false performance reporting. If managers are aware, they can detect and report ethical errors; if they are unaware, they might overlook moral deviances.

This study investigates managers’ beliefs about how goals and achievement levels can motivate unethical performance reporting. Two questions are explored: 1) Do managers believe that goals can increase the likelihood of performance overstatements? and 2) Do managers expect that poor performers will be more likely to exaggerate their achievements compared to high performers?

The study has implications for detection of fraudulent behavior by comparing managers’ beliefs with actual empirical examples of unethical performance reporting in organizations. A review of the literature suggests that performance goals and high performance might increase the likelihood of unethical reporting. If managers do not believe that high performers might falsely add to their performance reports, they will see no reason to check their validity. As managers’ ethical beliefs influence how they act, it is important to understand more about this aspect of organizational behavior.

Managers’ Moral Decision Making

How do managers’ beliefs influence what they expect from others at work? During the recent years we have seen an increasing interest in studying managers’ moral beliefs and in how cognitive and psychological processes might limit ethical judgments (Bazerman & Gino, 2012).

Studies of human decision making suggest that managers’ judgments are influenced by two different mental decision systems; the fast and intuitive System 1 and the slower analytical System 2 (Kahneman, 2003). System 1 uses imprecise heuristics to quickly assess situations. In studies of moral cognitions, we find a similar distinction between analytical moral decision making and intuitive but limited moral considerations.

The concept of bounded ethicality suggests that managers might not be aware that they are misperceiving an ethical choice or performing misconduct (Bazerman & Tenbrunsel, 2011). Conditions of time, performance, or cognitive pressure might enhance managers’ tendency to make faulty intuitive judgments and not fully consider moral issues (Mead et al., 2009; Gino et al., 2011).

When making ethical decisions, managers intuitive judgments will be influenced by their beliefs in combination with how the decision task is framed in the actual decision situation. The beliefs can influence what the managers assume to be true, while the formulation of the decision task can influence how the managers judge the facts and options that are presented. The managers’ moral judgments will be influenced by their ethical beliefs as well as the situation specific characteristics of the decision task.

Managers’ Moral Beliefs

An aspect of fast-paced intuitive decision making is to rely on subjective beliefs about the domain one is judging (Johnson-Laird, 1989), suggesting that managers rely on existing beliefs to judge new situations. Managers’ belief systems are assumed to be domain specific, such as how to make financial, human, technical, or moral choices. Moral beliefs (Tenbrunsel & Smith-Crowe, 2008) refer to assumptions that influence the ethical judgments. The beliefs draw peoples’ attention to certain aspects of a situation or choice and influence how the available information is processed.

Moral beliefs are assumed to influence how managers consider the ethical aspects of a choice. Moral beliefs are widely defined as thoughts about how others are affected by a decision (Rest, 1986) and more narrowly defined as being aware that a choice has moral components (Tenbrunsel & Messick, 2004). When moral beliefs are evoked, this can influence managers to perceive the same situation differently than when other sets of beliefs are triggered by the choice situation (Trevino et al., 2009). Moral beliefs can thus elicit different responses than judgments influence by business or performance belief (March, 1994; Messick, 1999; Tenbrunsel & Smith-Crowe, 2008).

However, moral beliefs might not always influence actual ethical behaviors (Singhapakdi, Vitell, & Kraft, 1996; Valentine & Fleishmann, 2003), but activation of moral frames has been found to correlate positively with moral motivation (Singhapakdi, 1999) and can be cued by issue characteristics such as the “moral intensity” (Jones, 1991) or by message format such as moral language (Butterfield, Trevino, & Weaver, 2000).

How can managers’ moral beliefs influence their trust in performance reports? Beliefs are psychological stereotypes about how certain individuals and groups tend to behave. If a situation seems representative for a stereotype (Bazerman & Moore, 2013), this can influence the decision-makers’ confidence about potential outcomes. The stereotyped beliefs influence expectations of how others can behave.

We have not been able to identify any specific studies on managers’ stereotypes of high and low performers. However, we speculate that good performance is associated with positive characteristics such as competence, knowledge, and persistence, but low performance can be associated with negative characteristics such as incompetence and low commitment. We therefore expect that managers will believe that high performers are less likely to exaggerate their achievements compared with poor performers. The positive stereotypes about high achievers can be inconsistent with negative beliefs about cheater and liars, and this can influence the managers to not expect that those who do well also will cheat.

Framing of the Performance Deviations

Another possibility is that managers’ judgments are influenced by how the deviations are framed relative to performance goals in the decision tasks. Goals are reference points that serve to specify how positive or negative an objective performance is judged based on what level it is compared with (Kahneman & Tversky, 1979). Goals frame the same outcomes as gains or losses and make them seem positive or negative (Keren, 2011).

Studies (Heath, Larrick, & Wu, 1999) suggest that goals can influence performance so that it is more motivating to invest additional work closer to a goal than further from it. A behavioral ethics perspective on goals suggests that they can serve as aspirational reference points (Heath, Larrick, & Wu, 1999), and influence people to cheat to achieve them (Bazerman & Gino, 2012).

Closer to a goal, it can be particularly tempting to overstate accomplishments because a small performance increment is associated with a more significant gain in psychological value (Kahneman & Tversky, 1979). Further from a goal, each additional performance increment has a lower psychological value.

Also, very close to the goal, the absolute value of overstated performance is logically relatively small, perhaps making the perceived social and psychological costs of exaggerating seem less significant. Loss frames are typically associated with risk-seeking choices (Kahneman & Tversky, 1979), such as performance misrepresentations.

According to this perspective, the managers will expect that performers closer to a goal will be more likely to cheat than those who are further from achieving it. The managers will focus less on the absolute performance levels and more on how close or distant the achievements are relative to the goals. Subsequently, high and low performers can be equally tempted to cheat as long as they fall just short of reaching their goals. Those furthest from their goals would feel least tempted to falsely add anything to their performance statements because they will have psychologically less to gain from it.

Empirical Studies of False Performance Reports

What do we know about false performance reports from empirical studies? Are managers, teams, and firms close to meeting goals more prone to exaggerate their achievements – or do poor performers cheat more independent of goals and aspirations?

Previous research suggests that performance shortfalls relative to thresholds can increase the likelihood of overstating achievements (see Greve, Palmer, & Pozner, 2010; Tenbrunsel & Swith-Crowe, 2008; Trevino, Weaver, & Reynolds, 2006).

It has been suggested that goals can promote misrepresentation of performance in a number of ways. Goals can decrease moral awareness and promote focus on performance outputs (Barsky, 2008; Tenbrunsel & Messick, 1999). Goals can increase attention on the task, decrease moral awareness, and increase moral disengagement (Barsky 2008), reduce capacity for self-regulation (Welsh & Ordonez, 2014), and increase risk taking (Larrick, Heath, & Wu, 2009). Ordonez, Schweitzer, Galinsky, and Bazerman (2009) argue that goals can motivate unethical ways of goal achievement, as well as misrepresentation of performance relative to the goals. Barsky (2008) suggests that goals can reduce individuals’ ability to recognize ethical aspects of a choice, and to disengage people from internal and social moral controls by providing rationalizations for their unethical behavior.

Studies have shown that performance feedback focused on lack of goal achievement can backfire and motivate unethical performance overstatements (Ordonez, Schweitzer, Galinsky, & Bazerman, 2009; Ordonez & Welsh, 2015). Linking incentives to goal achievement can make things worse and reward cheating and over-reporting further (Schweitzer, Ordonez, & Douma, 2004). Those who assume that they might easily reach a goal can be more prone to cheating than those believe it will be difficult (Moore, Wakeman, & Gino, 2014). Schweitzer, Ordonez, and Douma (2004) observed that mere goals as well as reward goals can motivate individuals to exaggerate their performance; they found that individuals who nearly achieve their goals can be more prone to misrepresenting their achievements compared with those further away from their goals and that rewards for goal achievement enhanced this effect. Linking incentives to goal achievement can make things worse and reward cheating and over-reporting further (Schweitzer, Ordonez, & Douma, 2004).

How do firms’ absolute performance levels influence the risk of misleading performance statements? One stream of research suggests that low performers under financial strain may be particularly prone to providing wrong performance information (Agnew, Piquero, & Cullen, 2009) because of the pressures and tensions associated with poor results. Several studies find misconduct to be more frequent among firms with low or declining profits, firms in depressed industries, and firms threatened by competition (Vaughan, 1999), failing organizations (Agnew et al., 2009; Vaughan, 1999), those with resource limitations (Vaughan, 1983), and those with financial problems (Simpson, 2002).

In contrast, Mishina, Dykes, Block, and Pollock (2010) suggest that the highest performers and most prominent firms are likely to overstate their achievements because of the gap between high aspirations and actual performance. Positive performance is assumed to escalate ambitions so they at one point become difficult to achieve, and thus tempting managers and firms to overstate their financial reports. Further, goal achievement is assumed to be associated with escalating confidence and inflated beliefs about future success, which also can promote inflated reporting. Some studies support that high-performing firms are more likely than poor achievers to perform misconduct (Harris & Bromiley, 2007; Mishina et al., 2010). For both sets of firms, performance shortfalls relative to goals could partly explain why they would misrepresent their achievements.

The Purpose of the Present Study

The above review of studies suggests that goals and high performance can motivate managers, teams, and firms to falsify performance reports. These studies investigate the choices and behaviors of those responsible for the work and performance reporting.

The present study focuses on managers who oversee, evaluate, or control the performance reports. What will they believe to be true? Will they expect that those who report their achievements can cheat and thus be sensitive to potential false reports, or will they trust the reports to be true? The study investigates two questions:

  1. Do managers believe that performance goals are associated with higher likelihood of performance overstatements?
  2. Do managers believe that good performers closer to goal achievement can be more likely to overstate what they have achieved compared with poorly performers?


To study these questions, I designed a set of vignettes describing performance-reporting situations in work contexts. The vignettes were situations with/without performance goals, performances close to or distant from the goal, and performance reported as work, money, and time.

Seven hundred thirty-eight top and middle managers from different industries judged the vignettes and predicted whether the managers, teams, and firms described in the vignettes would escalate their performance beyond the actual achievements. The details of each study are presented in the results section.

The data were collected in four studies with range of absolute amounts of misrepresentation, from relatively minor ones that can be associated with an individual’s performance during a week (a few hours, a few Euros of the budget, and a couple of items extra added to the completed amount of work) to significant amounts associated with firms’ performance over the course of a full year (several thousand hours, millions of Euros, and significant value of performance).


The participants in all of the studies were top- and mid-level managers from a range of different industries. I recruited them through my role as organizational psychologist, and they were invited to take part in a broader study of mangers’ perceptions of performance deviations. The managers’ responses were collected either by paper-and-pencil surveys in workshops or through web surveys sent to the participants emails based on agreement with the CEOs of the participating firms. More detailed information about the participants is provided in the descriptions of each study in the results section.


Each set of respondents was asked to judge 2–3 vignettes, typically taking 2–3 minutes to complete. The studies were completed in the same sequence as reported in this paper. The set of vignettes described professionals, management teams, and firms in different conditions: with/without performance goals and performances that were lower, on or above their goals. The vignettes were designed specifically for this research.

There were four studies. More specific information about the vignettes, response options, and conditions is provided in the detailed descriptions of each study in the Results section. For a general overview, this section of the paper describes the overall flow of the four studies.

Study 1 had two vignettes. Vignette 1a (3 × 2 design) described a consultant about to complete the weekly report in terms of money, work, or time. In all of the vignettes the absolute achievements were the same, but one set of consultants had a 10% higher goal and the others not. Vignette 1b (2 × 2 design) described pairs of consultants with the same performance level, where one set had a goal and the other not. Who would cheat? One hundred twenty managers responded.

Study 2 had one vignette, which was a replication of vignette 1a in Study 1. One hundred eighty-four managers responded.

Study 3 had two vignettes. Vignette 3a (2 × 2 design) described firms that delivered their yearly performance statements for which the absolute achievements were the same, but where one set of firms had clear goals and the other set not, and where one set underperformed 10% and the other set met the goals. Vignette 3b compared two sets of management teams, where one nearly achieved its goals and the other was further from succeeding. Which team would exaggerate their achievements? One hundred fifty managers responded.

Study 4 had one vignette with three conditions. It was similar to vignette 3b, but the lowest performing set of teams had done even worse than in 3b. The respondents were to select if the high or low performing teams most likely would exaggerate their achievements. Two hundred eighty-four managers responded.


Study 1


Participants were 120 mid-level managers working in the Nordic energy and transportation industry. About 50% worked in energy production and distribution, and about 50% worked with planning and administration of public transportation systems. A majority (about 80%) of the respondents were men, and most of them had a higher-level education in engineering or administration.

Reponses were collected during management training programs; survey forms were distributed just before breaks and collected immediately afterwards. This gave a high response rate; in total about 95% completed the form.


The questionnaire had two sections; In section 1 there were 2 × 3 conditions and section 2 had 2 conditions. Participants were randomly allocated to one of the conditions.

In section 1 the managers (in different conditions) judged three vignettes describing consultants who had a specific weekly a) work goal, b) monetary budget, or c) time budget, or consultants without a weekly goal for the same three performance dimensions. The managers were asked to rate the likelihood of a consultant inflating his performance with 10%. This was the text:

Imagine an IT-consultant working on a project. The consultant is about to complete his weekly report. The client has no way of checking how much he has actually done. (In conditions with goal/budget: He has a goal of testing 40 components/budget of 40,000/budget of 40 hours). This week he has tested 36 components/worked for 36,000/worked 40 hours. How likely is it that he will exaggerate and report that he has tested 40 components (worked for 40,000/worked 40 hours)?

In section 2 the managers judged three vignettes that described pairs of consultants. For each pair they were to select the one that most probably would exaggerate his weekly report by 10%. Both consultants had performed equally well, but only one of them had a specific goal. Performance was described in terms of accomplished units of work, money, or time. In Condition 1, the consultant with the goal underperformed by 10%. In Condition 2, the consultant with a goal had achieved it.

This was the text of the three vignettes in condition 1 (underperformance):

Which of these consultants do you think would be most tempted to exaggerate a bit and report that he has performed somewhat more tha\n what is the case?

  • Consultant A has a budget of testing 40 components (working for NOK 40000/working 40 hours). He has tested 36 components (worked for 36000/worked 36 hours)
  • Consultant B has no specific target. He has tested 36 components (worked for NOK 36000/worked 36 hours).

This was the text of the three vignettes in Condition 2 (performance as expected):

Which of these consultants do you think would be most tempted to exaggerate a bit and report that he has achieved 44 tests (worked for NOK44000/worked 44 hours)?

  • Consultant C has a budget of testing 40 components (working for 40000/working 40 hours). He has tested 40 components (worked for 40000/worked 40 hours)
  • Consultant D has no specific target. He has tested 40 components (worked for 40000/worked 40 hours).


The managers expected the consultants with a goal to be significantly more likely to overstate how much they had achieved relative to those without a goal.

The results are displayed in Table 1. There was significant main effect of goal (2 × 3 ANOVA, F(1, 111) = 12.533, p = 0.001), and also a significant interaction between goal and performance dimension (F(2, 111) = 3.775, p = 0.026), showing that monetary and time exaggerations were more likely in the goal condition, but not work achievements.

Table 1

Mean probability ratings (1–5) of exaggerated reports for consultants with or without a goal.

Goal No goal


Performance 2.93 (1.21) 2.92 (0.95)
Money 3.41 (1.10) 2.58 (0.90)
Time 3.90 (0.81) 2.50 (1.28)
Total 3.36 (1.14) 2.67 (1.07)

The results for the second section of the questionnaire (see Table 2) show that the majority of managers (about 72%) believed that consultants who achieved their goals would be less likely to overstate their reports compared with consultants who had no performance target. The consultants who achieved less than expected were believed (by about 70%) to be more likely to cheat if they had a goal. The results are similar (and highly significant) for performance, money, and time.

Table 2

Which consultant is more likely to hand in an exaggerated report? Number of participants selecting consultant with or without goal in two conditions.

Performance as expected Underperformance χ2 p

Goal No goal Goal No goal

n % n % n % n %

Performance 24 (30.7) 54 (69.3) 27 (69.3) 12 (30.7) 15.64 .000
Money 23 (40.3) 54 (59.3) 27 (71.1) 11 (28.9) 17.56 .000
Time 19 (24.7) 58 (75.3) 27 (71.1) 11 (28.9) 22.80 .000

Study 2


The participants were 184 top- and mid-level managers working in companies within the Nordic energy industry (69%) and food industry (31%). In the energy industry 95% were males, and in the food industry 73% were males. Altogether 77% of the respondents were top managers, either CEOs or managers reporting to a CEO, and 23% were mid-level managers. A total of 417 managers were asked to respond, giving a total response rate of 44%. The respondents from the energy industry were recruited from an industry organization for companies involved in production, distribution, and trading of electricity. The respondents from the food industry came from one of the major ones in Scandinavia that also had several international subsidiaries.


In Study 2 the managers were asked to judge the same vignette as in Section 1 of Study 1 and asked to rate how likely it was that the consultant would inflate his performance with 10%, on a six-point scale, from 1 (not probable) to 6 (very probable). There were 2 × 3 versions of the vignettes where three versions described consultants who had a specific weekly a) performance goal, b) monetary budget, or c) time budget, and three versions described consultants without a weekly goal or budget for the same three dimensions. Participants were randomly allocated to one of the conditions. All responses were collected through a web-based survey tool. Participants received an invitation email from the CEO of their company followed by an email with a link to the survey.


This study replicated the effect of goals on likelihood of overstating achievements, but there were no differences between the performance dimensions. Table 3 shows that there was a significant main effect of goal (2 × 3 ANOVA, F(1, 141) = 4.223, p = 0.042), and that the managers in this study believed the consultants with goals more likely would overstate their achievements than those who did not have performance targets.

Table 3

Mean probability ratings (1–6) of exaggerated reports for consultants with or without a goal.

Goal No goal


Performance 3.38 (0.97) 2.93 (1.17)
Money 3.00 (1.38) 3.10 (1.52)
Time (hours) 3.53 (1.13) 2.64 (1.07)
Total 3.33 (1.18) 2.87 (1.24)

Study 3


Participants were 150 top managers working in the Scandinavian pharmaceutical industry (70.0%), a law firm (20.7%), and an oil and gas service firm (9.3%). The managers from the pharmaceutical industry represented 22 different firms, all of which were members of their firm’s management team, and firm sizes varied from less than 50 employees to more than 500. Participants were randomly allocated to one of the conditions and received an invitation email from the CEO of their company, followed by an email with a link to the survey. The response rate of was in total 78%, with only minor variations across the conditions.


This questionnaire had two sections.

In Section 1, there were two conditions where the managers were presented with three pairs of firms and asked to rate which of them would be most tempted to exaggerate their annual reports. For each pair they were to select the one that would exaggerate their annual report by 10%. Both firms had achieved identical results. The first firm had a goal, whereas the second had no specific goal. Performance was described as work performance (number of projects), time (number of consulting hours sold), and performance time (number of months needed to complete projects). The first condition consisted of three pairs of firms where the one with a goal underperformed by 10%. In the second condition, the firm with a goal had fully achieved it. Participants were randomly allocated to one of the conditions.

This was the text of the three vignettes in condition 1 under-performance:

Which of these firms can be tempted to exaggerate and report that they completed 10 projects during a year (sold 100,000 consulting hours?/typically complete their projects within 9 months?)

  • Firm A’s target was to complete 10 projects, but they completed 9 projects (The firm’s target was to sell 100,000 hours, but they sold 90,000 hours/The firm’s target was to spend 9 months, but they typically spent 10 months)
  • Firm B’s target was to do their best, and they completed 9 projects (Sold 90,000 hours/typically spent 10 months)

This was the text of the three vignettes in condition 2 performance as expected:

Which of these firms can be tempted to exaggerate and report that they completed 10 projects during a year (sold 100,000 consulting hours?/Typically complete their projects within 9 months?)

  • Firm A’s target was to complete 9 projects, but they completed 9 projects (The firm’s target was to sell 90,000 hours, and they sold 90,000 hours/The firm’s target was to spend 10 months, but they typically spent 10 months)
  • Firm B’s target was to do their best, and they completed 9 projects (Sold 90,000 hours/typically spent 10 months)

In Section 2 the managers were asked to compare two firms that had underperformed relative to their ambitions. There were three conditions. In all conditions Firm A had performed well and nearly achieved its ambitions (either 95%, 90%, or 85% achievement) and considered stating that they had done even better (100% in all conditions), while Firm B performed poorly and only achieved 60% (in all conditions) but considered reporting that they had achieved 10 or 15% more (70% in Conditions 1 and 2, 75% in Condition 3). There were two versions of each condition, one specified the reported performance level (i.e., 70%, 75%, or 95%, 100%), and the other highlighted the amount of overstated performance (i.e., 5%, 10%, 15%). As it did not make a difference how the exaggerations were formulated, responses to these two sub-conditions were pooled.

This was the vignette text:

Which of these management teams could be most tempted to exaggerate?

  • Management team A achieved 95% (Condition 2: 90%, Condition 3: 85%) of the revenue goal, but considered reporting that they had achieved 100%.
  • Management team A achieved 60% of the revenue goal, but considered reporting that they had achieved 70% (Condition 2: 70%, Condition 3: 75%)


Table 4 shows that a majority of managers (about 79%) expect that firms that underperform will be more tempted to overstate their performance when they have a goal than when they are performing as best they can. Most of the managers (about 83%) think that firms that meet their goals will be less tempted to exaggerate their achievements than those who are trying to do their best.

Table 4

Which firm is more likely to hand in an exaggerated report? Number (percentages) of participants selecting firm with or without specific target in two conditions.

Performance on target Performance below target χ2 p

Goal No goal Goal No goal

n % n % n % n %

Projects 13 (18.0) 59 (72.0) 49 (69.1) 22 (30.9) 37.80 .000
Consulting hours 15 (20.3) 57 (69.7) 62 (86.1) 10 (13.9) 61.66 .000
Completion time 9 (12.7) 62 (87.3) 57 (71.9) 13 (18.1) 66.92 .000

Table 5 shows that managers believe poorly performing firms (60% goal achievement) are more tempted to exaggerate better performing ones closer to the goal (85–95% goal achievement). This tendency increases with the magnitude of the exaggeration (χ2 (2, N = 144) = 9.17, p = 0.01), so that it seems significantly more likely to exaggerate by 15% from 60–75% than 15% from 85–100%.

Table 5

Which firm is more tempted to report inflated performance? Number (percentages) of participants selecting Firm A or Firm B in three conditions.

Firm A n % Firm B n % binomial p

Condition 1: 95–100 27 (58.7) 60–70 19 (41.3) n.s.
Condition 2: 90–100 23 (40.4) 60–70 34 (59.6) n.s.
Condition 3: 85–100 11 (26.8) 60–75 30 (73.2) 0.025

Study 4


Participants were 284 top- and mid-level managers working in companies within the Nordic energy industry (67%), food industry (23%), and oil and gas industry (10%). In the energy industry, 95% were males; in the food industry, 73% were males; and in the oil and gas industry, 100% were males. Altogether 74% of the respondents were top managers, either CEOs or managers reporting to a CEO, 23% were mid-level managers, while 3% were professionals without management responsibilities. A total of 484 managers were asked to respond, yielding a response rate of 59%. All responses were collected through a web-based survey tool. Participants received an invitation email from the CEO of their company followed by an email from the authors with a link to the survey.


The participants were randomly allocated to one of three conditions and asked to judge which of two firms would be more tempted to exaggerate its performance: Firm A was close to achieving its goal and considered overstated its performance with 5 or 10% (from 95% to 100%, 90%–100%, and from 85% to 95%, respectively), while Firm B had achieved 40% of its aspirations and considered overstated its performance with 10% (from 40% to 50% in all three conditions). This was the text for Conditions 1–3:

Which of these management teams can be more tempted to exaggerate?

  • Management team A achieved 95% (90%, 85%) of the revenue goal. Considered reporting that they had achieved 100% (100%, 95%)
  • Management team B achieved 40% of the revenue goal. Considered reporting that they had achieved 50%


The managers (see Table 6) believed it would be more tempting to exaggerate when a firm is performing poorly and has only achieved 40% of its ambitions, than to inflate performance when a firm has achieved between 85–95% of the goal, χ2 (1, N = 141) = 4,43, p = 0.035. When a firm nearly has achieved its goal, the managers believe that it will be more tempting to add 10% and claim full goal accomplishment than to add 10% and claim the firms has achieved 95%, χ2 (1, N = 88) = 9.014, p = 0.003.

Table 6

Which firm is more tempted to report inflated performance? Number (percentages) of participants selecting Firm A or Firm B in three conditions.

Firm A n % Firm B n % binomial p

Condition 4: 95–100 24 (45.3) 40–50 29 (54.7) n.s.
Condition 5: 90–100 25 (53.2) 40–50 22 (46.8) n.s.
Condition 6: 85–95 9 (22.0) 40–50 32 (78.0) 0.00


The purpose of the four studies was to examine managers’ judgments about how goals and performance levels can motivate false performance reports. The study –compared managers’ judgments with recent research indicating that goals can motivate both low and high performers to overstate achievements.

Goals and Performance Overstatements

The results show that managers expect that introducing performance goals and underperforming by 10% increase the likelihood of overstating achievements. In all of the studies of the individual consultants completing their weekly timesheets, the managers believed that the ones who feel short of their goals would be more likely to exaggerate what they had achieved or what compensation they were entitled to.

The managers had similar beliefs about the effects of goals on management teams’ and firms’ performance overstatements; those that that underperform by 10% relative to their goals are more likely to exaggerate their performance than firms with the same performance level, but do not have performance goals that make the performance seem like a shortfall. Compared with a general ambition to do one’s best, focusing on achieving a specific goal is assumed to increase the likelihood of delivering an overstated performance report.

The studies also show that managers believe goals can motivate over-reporting of shortfalls for several different performance dimensions. Managers clearly believe that goals can motivate over-reporting of money, time and work – such as how much money one has earned, how much work has been done in terms of number of units produced or number of projects completed, how much time has been spent doing the work, how many hours have been sold, and how long time projects have taken to be completed.

An interesting result is that the managers in one of the studies believed that it might be more tempting to over-report how much money and time has been spent, than how much work has been done. In most organizations, amounts of work or production are more observable and possible to verify, while it can be more difficult to verify the truthfulness of time and money claims. It may be that this is reflected in the managers’ beliefs, and this can be further investigated in future research. Compared with work performance, time might be perceived as a vaguer and less specific dimension. Our findings are consistent with previous studies that show that people can be more risk seeking in terms of time (Okada & Hoch, 2004), more flexible in their evaluation of time (Hsee, 1995), and use heuristics more for time (Saini & Monga, 2008). The difference between overstatements of time relative to money might be due to differences in moral judgment, where lying and cheating about money can be considered a greater error and more reprehensible than misrepresenting time. It might be psychologically easier to claim additional hours compared with additional money, as time only indirectly is converted to money. Previous studies have found that people make larger false claims about other values than money (Ariely, 2008).

Managers’ beliefs about the effects of goal setting on performance overstatements seem consistent with recent research showing that goal setting can backfire and not only motivate higher performance, but also motivate higher reporting of achievements than what really has been accomplished (Barsky, 2008).

If the results from this study are transferred to organizations and firms, we can assume that managers can be aware that individuals and groups that have performance goals might be more likely to exaggerate what they have achieved. This can stimulate managers to look further into the actual achievements and verify what really has been accomplished – and subsequently uncover any irregularities.

Performance Levels and Performance Overstatements

The managers in this study believe that those who are further away from the goal are more likely to overstate their achievements, compared to those who nearly achieve their targets. Clearly, the managers assume that poor performers are more likely to act dishonestly compared with the management teams and firms who nearly have achieved their objectives. In the present studies, the managers judged it as more likely to add 10% when achieving around 50% or 75%, compared to achieving around 90%. According to the motivational characteristics of goals and recent research on goals and misconduct, the lower performers should be less prone to performance uplifts (Barsky, 2008).

The results suggest that managers can be more worried that poor performers will deliver incorrect performance reports, and therefore spend more investigating the truthfulness of these. If managers believe that the good performers might not be tempted to inflate their achievements, they might not spend time on verifying the actual achievements – so that the lies of good performers might not be detected because managers do not expect that these are likely to deliver incorrect performance reports.

The results from this study suggest that managers might be biased in their judgments of poor versus high performers, and that they have more positive beliefs about those who do well compared to those that struggle. Perhaps they associate negative performance with other negative characteristics as well and cannot believe that the good performers could do bad things.

Theoretical Implications: Judgments by Beliefs or Decision Tasks?

The present studies investigated how managers responded to ethical decision tasks. The design of the study does not allow inferences about by what mental processes the managers judged the vignettes. The first introductory section of the paper discusses how intuitive moral judgments can be influenced by existing beliefs in combination with the situation specific decision tasks.

On the one hand, the managers were asked to use their moral beliefs and judge the deviations based on an ethical cognitive mindset. From this perspective, the results suggest that managers believe that performance goals will make unethical performance reporting more likely. Managers’ moral beliefs can be that high performers have invested substantial “honest” effort in nearly accomplishing their ambitions will not ruin this by lying about their achievements. Managers’ stereotypes of consultants can be that they lie more about hours (which cannot be controlled by the client) than work (which the client can verify).

If the results in the present study reflect managers beliefs about truthful performance reporting, they suggest that managers might question the performance reports of poor performers and ignore how high performers might cheat. If teams or firms are motivated by ambitious goals, the managers might doubt if the good reports are truthful.

In contrast, the decision tasks in the present study were explicitly designed to compare how goals as reference points influence ethical judgments. It is possible that the formulation of the decision situation influenced what outcomes the managers found most likely. This implies that the presentation format of performance reports will influence whether managers believe in or doubt the results. If the reports have information about specific high goals and low deviations, the managers might be alerted about potential exaggerations. If the reports only include information about low or high performance, the managers’ moral attention might on the poorly performing units and not on the best ones.

Thus, future studies could investigate how moral beliefs and moral tasks influence subsequent moral judgments. It could be useful to investigate how goals as reference points influence each stage of the moral decision process (Hunt & Vitell, 1986; Rest, 1986; Tenbrunsel & Smith-Crowe, 2008).

Strengths and Limitations

A strength of this study is that top- and mid-level managers responded to the case vignettes. The majority of participants were experienced managers from a broad range of different organizations and with knowledge of ethical business practices. Thus, results might be representative of how managers judge moral dilemmas in work settings.

The participants were randomly allocated to one of the experimental conditions, to prevent characteristics of the participants from systematically skewing the responses. Analyses of the responses from the different subgroups of respondents indicate no systematic differences or interactions, indicating that the respondents’ differing experiences and roles have not skewed the results in a particular direction.

Another strength of this study is that the managers’ beliefs were measured as responses to realistic situations at work. As the managers took the role of observers that predicted how someone else might act dishonestly, this probably reduced social desirability effects and limited the motivation to present a positive self-image.

The study design with short vignettes made it possible to study aspects of moral judgments that are typically difficult to observe in natural work situations. According to Baron (2007), this is one of the most common and frequently used methods in decision research.

The results do not tell us how performance information is weighted relative to other aspects such as roles, behavior, the relationships at work and organizational climate and culture. A potential weakness in the data collection approach is that only one method (surveys) was used. It would have been an advantage to use additional methods to identify how messages and performances are framed. To gain a more differentiated understanding of framing of performance levels and misconduct, experiments allowing observations of actual decision behaviors could be beneficial. Adding information about actual evaluations based on observations, interviews, or archival data could provide additional insight about how framing of performance deviations can be associated with misconduct.

Further, the vignettes describing individual misconduct might be more representative than the firm vignettes, as the individuals performed relatively small unethical acts when they added a bit to their weekly reports and the consequences could be imaged as quite limited. In contrast, firm misconduct requires cooperation and consent from a group of managers over a longer period of time and resulting in potentially greater damages to firm reputation and relations within the firm. Firms’ performance statements are typically official reports subject to third-party audits and controls that increase the risk of being exposed. The amounts of exaggerated performance are also substantially greater for firms, which might make it seem less probable to cheat. Also, recent literature on firm misconduct has suggested gaps between goals and performance to be one of several factors that enhance the probability of misconduct; goals can thus be a necessary, but not sufficient influence of unethical behavior. These conditions are difficult to capture in scant vignettes.

Implications for Managers and Organizations

This study suggests that managers might not be fully aware of the conditions that promote performance overstatements. They might expect that those with explicit performance goals could cheat, but not the ones who do well and nearly achieve their ambitions. They will be more critical of those who do poorly. How can managers’ and organizations’ capability of detecting erroneous performance reports be improved?

One approach could be to provide training to increase managers’ knowledge and awareness. Management development programs could aim at increasing moral awareness and spotting faulty statements. Such training might also be relevant for internal and external organizational control agents, such as accountants and auditors. Another approach could be to redesign reporting and accounting systems with warning signals require control of certain reports.

It could also be beneficial to redesign performance systems to reduce incentives for false reporting. Goals and performance feedback can be conceptualized as elements of organizational infrastructures (Ordonez & Welsh, 2015); they are processes and routines part of the formal structures of organizations. Conceptual models of organizational misconduct typically distinguish between the influence of individual characteristics and organizational conditions (see e.g., Trevino, Weaver, & Reynolds, 2006). Organizational “ethical infrastructures” seem to have positive effects on ethical behavior; if organizations have formal and informal systems that include ethical standards, communication, surveillance, and sanctioning, this can increase moral awareness and reduce chances of misconduct (Greve, Palmer, & Pozner, 2010; Tenbrunsel & Swith-Crowe, 2008; Trevino, Weaver, & Reynolds, 2006).

This study shows that good performers can do bad things, but that managers believe that the bad performers are more likely to hand in a wrong report. To prevent misconduct, it seems productive to be slightly doubtful of the true success of the good performers.

Competing Interests

The author has no competing interests to declare.


  1. Agnew, R., Piquero, N., & Cullen, F. T. (2009). General strain theory and white-collar crime. In Simpson, S., & Weisburd, D. (Eds.), The criminology of white-collar crime, 35–60. Springer. DOI: https://doi.org/10.1007/978-0-387-09502-8_3 

  2. Ariely, D. (2008). Predictably irrational: The hidden forces that shape our decisions. Harper-Collins. 

  3. Baron, J. (2007). Thinking and deciding. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511840265 

  4. Barsky, A. (2008). Understanding the ethical cost of organizational goal-setting: A review and theory development. Journal of Business Ethics, 81, 63–81. DOI: https://doi.org/10.1007/s10551-007-9481-6 

  5. Bazerman, M., & Gino, F. (2012). Behavioral ethics: Toward a deeper understanding of moral judgment and dishonesty. Annual Review of Law and Social Science, 8, 85–104. DOI: https://doi.org/10.1146/annurev-lawsocsci-102811-173815 

  6. Bazerman, M., & Tenbrunsel, A. E. (2011). Blind spots: Why we fail to do what’s right and what to do about it. ISBN: 9781400837991. DOI: https://doi.org/10.1515/9781400837991 

  7. Butterfield, K. D., Trevino, L. K., & Weaver, G. R. (2000). Moral awareness in business organizations: Influences of issue-related and social context factors. Human Relations, 53, 981–1018. DOI: https://doi.org/10.1177/0018726700537004 

  8. Greve, H. R., Palmer, D., & Pozner, J. E. (2010). Organizations gone wild: The causes, processes, and consequences of organizational misconduct. The Academy of Management Annals, 4, 53–107. DOI: https://doi.org/10.1080/19416521003654186 

  9. Harris, J., & Bromiley, P. (2007). Incentives to cheat: The influence of executive performance and firm performance on financial misrepresentation. Organization Science, 18, 350–367. DOI: https://doi.org/10.1287/orsc.1060.0241 

  10. Heath, C., Larrick, R. P., & Wu, G. (1999). Goals as reference points. Cognitive Psychology, 38, 79–109. DOI: https://doi.org/10.1006/cogp.1998.0708 

  11. Hsee, C. K. (1995). Elastic justification: How tempting but task-irrelevant factors influence decisions. Organizational Behavior and Human Decision Processes, 62, 330–337. DOI: https://doi.org/10.1006/obhd.1995.1054 

  12. Hunt, S. H., & Vitell, S. (1986). A general theory of marketing ethics. Journal of Macromarketing, 6, 5–16. DOI: https://doi.org/10.1177/027614678600600103 

  13. Johnson-Laird, P. N. (1989). Mental models. In: Posner, M. I. (Ed.), Foundations of cognitive science, 469–499. The MIT Press. 

  14. Jones, T. M. (1991). Ethical decision making by individuals in organizations: An issue contingent model. Academy of Management Review, 16, 366–395. DOI: https://doi.org/10.5465/amr.1991.4278958 

  15. Kahneman, D. (2003). A perspective on judgment and choice. Mapping bounded rationality. American Psychologist, 58, 697–720. DOI: https://doi.org/10.1037/0003-066X.58.9.697 

  16. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291. DOI: https://doi.org/10.2307/1914185 

  17. Keren, G. (2011). Perspectives on framing. Psychological Press. DOI: https://doi.org/10.4324/9780203854167 

  18. Larrick, R. P., Heath, C., & Wu, G. (2009). Goal-induced risk taking in negotiation and decision making. Social Cognition, 27, 342–364. DOI: https://doi.org/10.1521/soco.2009.27.3.342 

  19. March, J. G. (1994). A primer on decision making: How decisions happen. Free Press. 

  20. Mead, N., Baumeister, R. F., Gino, F., Schweitzer, M., & Ariely, D. (2009). Too tired to tell the truth: Self-control resource depletion and dishonesty. Journal of Experimental Social Psychology, 45, 594–97. DOI: https://doi.org/10.1016/j.jesp.2009.02.004 

  21. Messick, D. M. (1999). Alternative logics for decision making in social settings. Journal of Economic Behavior & Organization, 39, 11–30. DOI: https://doi.org/10.1016/S0167-2681(99)00023-2 

  22. Mishina, Y., Dykes, B. J., Block, E. S., & Pollock, T. G. (2010). Why “good” firms do bad things: The effects of high aspirations, high expectations, and prsominence on the incidence of corporate illegality. Academy of Management Journal, 53, 701–722. DOI: https://doi.org/10.5465/amj.2010.52814578 

  23. Moore, C., Wakeman, S. W., & Gino, F. (2014). Dangerous expectations: Breaking rules to resolve cognitive dissonance. HBS Working Paper. DOI: https://doi.org/10.2139/ssrn.2486576 

  24. Okada, E. M., & Hoch, S. J. (2004). Spending time versus spending money. Journal of Consumer Research, 31, 313–332. DOI: https://doi.org/10.1086/422110 

  25. Ordonez, L. D., Schweitzer, M. E., Galinsky, A. D., & Bazerman, M. H. (2009). Goals gone wild: The systematic side effects of over-prescribing goal setting. Working Paper 09-083, Harvard Business School. DOI: https://doi.org/10.2139/ssrn.1332071 

  26. Ordonez, L. D., & Welsh, D. T. (2015). Immoral goals: How goal setting may lead to unethical behaviour. Current Opinion in Psychology, 6, 93–96. DOI: https://doi.org/10.1016/j.copsyc.2015.06.001 

  27. Rest, J. R. (1986). Moral development: Advances in research and theory. Praeger Reynolds. 

  28. Saini, R., & Monga, A. (2008). How I decide depends on what I spend: Use of heuristics is greater for time than money. Journal of Consumer Research, 34, 914–922. DOI: https://doi.org/10.1086/525503 

  29. Schweitzer, M. E., Ordonez, L., & Douma, B. (2004). Goal setting as a motivator of unethical behavior. Academy of Management Journal, 47, 422–432. DOI: https://doi.org/10.2307/20159591 

  30. Simpson, S. S. (2002). Corporate crime, law, and social control. Cambridge. DOI: https://doi.org/10.1017/CBO9780511606281 

  31. Singhapakdi, A., Vitell, S. J., & Kraft, K. L. (1996). Moral intensity and ethical decision-making of marketing professionals. Journal of Business Research, 36, 245–255. DOI: https://doi.org/10.1016/0148-2963(95)00155-7 

  32. Singhapakdi, A. (1999). Perceived importance of ethics and ethical decisions in marketing. Journal of Business Research, 45, 89–99. DOI: https://doi.org/10.1016/S0148-2963(98)00069-1 

  33. Tenbrunsel, A., & Messick, D. (1999). Sanctioning systems, decision frames, and cooperation. Administrative Science Quarterly, 44, 684–707. DOI: https://doi.org/10.2307/2667052 

  34. Tenbrunsel, A., & Messick, D. (2004). Ethical fading: The role of self-deception in unethical behavior. Social Justice Research, 17, 223–236. DOI: https://doi.org/10.1023/B:SORE.0000027411.35832.53 

  35. Tenbrunsel, A. E., & Swith-Crowe, K. (2008). Ethical decision making: Where we’ve been and where we’re going. Academy of Management Annals, 2, 545–607. DOI: https://doi.org/10.1080/19416520802211677 

  36. Trevino, L. K., Weaver, G. R., & Reynolds, S. J. (2006). Behavioral ethics in organizations: A review. Journal of Management, 32, 951–990. DOI: https://doi.org/10.1177/0149206306294258 

  37. Valentine, S., & Fleishmann, G. (2003). Ethics training and businesspersons’ perceptions of organizational ethics. Journal of Business Ethics, 52, 391–400. DOI: https://doi.org/10.1007/s10551-004-5591-6 

  38. Vaughan, D. (1983). Controlling unlawful organizational behavior. University of Chicago Press. 

  39. Welsh, D. T., & Ordonez, L. D. (2014). The dark side of consecutive high performance goals: Linking goal setting, depletion, and unethical behavior. Organizational Behavior and Human Decision Processes, 123, 79–89. DOI: https://doi.org/10.1016/j.obhdp.2013.07.006