Journal of Business Ethics Forthcoming
WHY DO COLLEGE STUDENTS CHEAT?
Mark G. Simkin
Accounting & Information Systems
University of Nevada, Reno
Accounting & Information Systems
University of Nevada, Reno
ABSTRACT. More is known about the pervasiveness of college cheating than reasons why
students cheat. This paper reports the results of a study that applied the theory of reasoned action
and partial least squares methodology to analyze the responses of 144 students to a survey on
cheating behavior. Approximately 60% of the business students and 64% of the non-business
students admitted to such behavior. Among cheaters, a “desire to get ahead” was the most
important motivating factor—a surprising result given the comprehensive set of factors tested in
the study. Among non-cheaters, the presence of a “moral anchor” such as an ethical professor
was most important. The paper also includes a set of important caveats that might limit this
work and suggests some avenues for further study.
Key Words: cheating, ethical behavior, student dishonesty, student misconduct
On April 27, 2007, the Dean of the Fuqua College of Business at Duke University announced
that 24 students—nearly 10 percent of the graduating class of 2008—had been caught cheating
on a final exam (Conlin, 2007). A year later, the school was still dealing with the fallout from
the incident, which included expelling the guilty students, readmitting and counseling the
suspended ones, and dealing with the national attention garnered by the event (Damast, 2008).
A large body of research suggests that the student cheating uncovered at Duke is not an
isolated event, but rather a microcosm of a pervasive and growing part of worldwide university
activity. However, while a large number of individuals and organizations express concern for
such trends, less is known about what to do about it or, more importantly, how to reverse it.
The purpose of our research was to study this problem in greater depth. In particular, we
wanted to test the hypothesis that the theory of reasoned action can explain cheating behavior,
detect its most important causal influences, and identify what factors motivate students to cheat.
We also wanted to know what factors are most likely to deter students from cheating—a very
real and important objective to teaching faculty.
The next section of this paper discusses student cheating in greater depth, identifies the major
stakeholders in the problem, and explains why cheating is important to them. In turn, the third
section of the paper discusses the theory of reasoned action, presents our hypotheses, and
describes the partial-least-squares methodology we used to test them. The fourth section
presents our results, the fifth section presents some caveats and directions for further research,
and the last section summarizes our discussions and presents our conclusions.
Why is cheating important? A variety of interested parties and stakeholders agree that cheating at
the college level has become problematic. Who are these interested parties and why their
The Importance of College Cheating
Perhaps of greatest import is the fact that cheating in college classes is now best described as
“rampant.” A meta study by Whitley (1998), for example, found that across 46 studies, an
average of 70.4% of the college students have cheated in college. In newer studies (Klien,
Levenburg, McKendall, & Mothersell, 2007; McCabe, Butterfield, & Trevino, 2006; Rokovski
& Levy, 2007), the means were 70%, 86%, and 60%, respectively. Viewed in an historical
perspective, there is also considerable evidence that college cheating is growing (Rokovski &
Levy, 2007). A study by Bowers (1964), for example, found that only 26% of students admitted
to some form of copying in college, compared to 52% in a similar study conducted in 1994
(McCabe & Bowers, 1994). Similarly, Ogilby (1995) found that self-reported student cheating
in colleges increased from 23% to 84% in the years from 1940 to 1982.
Recent experiences with such financial disasters as Enron, Worldcom, and Tyco
Corporations have led the general public to ask “how can such things happen?” (Gulli, Kohler, &
Patriquin, 2007). Thus, a third reason why college cheating may be important is because of the
suspected link between such behavior in academia and subsequent unethical behavior in the
workplace (Thompson, 2000). A number of studies have found a strong relationship between
“cheating” at college and “unethical behavior” at work. Sims (1993), for example, found a high
correlation between these two factors, leading him to conclude that dishonesty was less a matter
of “an immediate opportunity to cheat” and more dependent upon “a general attitude about
honesty in the workplace.” Similarly, Nonis and Swift (2001) found that the tendency to cheat at
work was highly correlated with the frequency of cheating in college—a finding echoed by
Davis and Ludvigson (1995), Swift, Denton and Nonis (1998), and Crown and Spiller (1998).
Finally, Lawson (2004) found a similar relationship between “unethical workplace behavior” and
Those who develop and administer certification examinations are particularly concerned
stakeholders in the matter of cheating. Examples include the American Institute of Certified
Public Accountants (which develops the CPA examination), the ISC2 (which administers the
Certified Information Systems Security Professional examination), and software vendors such as
Microsoft (who conduct a variety of information technology certification examinations). A
study conducted by the Association of Test Publishers in 2007, for example, revealed that 75%
of them found evidence of cheating on their certification examinations, and most developers also
reported that copies of past, and sometimes current, examinations were available for sale on the
Internet (Lavelle, 2008; Thibodeau, 2007). In a chilling recreation of a common form of college
cheating, surrogates are also available for hire to take certification examinations in return for fees
up to several thousand dollars (Thibodeau, 2007).
More recently, a number of authors have noted that technology has given students greater
access to learning resources on the Internet, but has also increased the number of ways that
students can cheat (Etter, Cramer, & Finn, 2006). The Internet provides a channel for purchasing
term papers, course test banks, and solution manuals to class textbooks from Internet vendors.
Emailing friends the answers to examination or homework questions to be given or covered in
later sections of classes is a new twist on information sharing. A real time example would be the
use of text messaging to send test answers during examinations, or even using cell phones to take
pictures and email test materials to others.
Finally, a number of writers have begun to question the concept of what constitutes
“academic dishonesty” and therefore what are punishable offenses. If success in the corporate
world requires teamwork, they argue, then “shared information” and “group success” should be
the tools by which to measure academic performance, not individual efforts (Conlin, 2007). For
example, Robert I. Sutton, the dean of the Stanford University School of Design, recently stated
“If you found somebody to help you write an exam, in our view that’s a sign of an inventive
person who gets stuff done” (Conlin, 2007). Few academicians known to these authors share
Dean Sutton’s view. Most of our colleagues feel that widespread cheating at a university
tarnishes the reputation of the institution, demeans the value of the degrees granted at them, and
disappoints those employers who find that the student graduates cannot adequately perform the
work suggested by their majors (Knowledge, 2004).
Cheating and Colleges of Business
Business schools would appear to have a particularly strong interest in cheating activity. We
have already identified one reason for this—the apparent link between “cheating in college” and
“cheating in the workplace.” Studies consistently find that the propensity to cheat in college
carries over to the workplace—a concern of particular interest for professional schools preparing
students for business careers. The hope is that ethical behavior, if understood and internalized at
the college level, will carry over to their employment.
A related matter is the growing public expectation that business programs include
components that teach ethical behavior. In the field of insurance, for example, Eastman,
Eastman, and Iyer (2008) note that ethical behavior impacts property-liability and life insurance
business as well as the reputations, business success, and professional relationships of those
working in the field. This is one reason why the Association for the Advancement of Collegiate
Schools of Business (AACSB) accreditation requirements includes the mandate to include
business ethics as a formal and required component of an applicant school’s undergraduate
degree programs (AACSB, 2009).
A third reason why colleges of business should be concerned with student cheating is the
growing body of empirical evidence that, despite the widespread inclusion of course segments
about ethical behavior, business students continue to cheat more than non-business students. For
example, a study by Harris (1989) found that business majors have lower ethics than other
majors. Similarly, Eastman (1996) found that insurance students have significantly lower levels
of ethics than insurance professionals, and Caruana et al.(2000) found that business students had
the highest cheating rate between business, engineering, science, and humanities students.
A final reason why colleges of business are concerned with student cheating is the belief that
such behavior tarnishes the reputation and perceived quality of those educational institutions that
experience blatant episodes of cheating, or that appear to tolerate it (Gulli et al., 2007). This
concern is especially important to private institutions, which must necessarily compete with
public schools for both student enrollments and alumni donations.
Explaining College Cheating with the Theory Of Reasoned Action
The widespread practice of college cheating is perhaps better understood than the reasons why
college students cheat. After all, “cheating” would appear to be an overt act and one that
requires some effort on the part of the participants. Why do college students cheat?
One possible explanatory factor may simply be “opportunity.” Although such happenstance
might not apply in proctored-examination environments, this explanation seems more
appropriate in situations where students have access to online resources. In a study of
plagiarism, for example, Abdolmohammadi and Baker (2008) found that the papers from over
one-third of their undergraduate students and over 20 percent of their graduate students were
copied from web sources.
A second possible explanation is the “desire to succeed.” If “winning is everything,” then
cheating simply becomes a tool to use in pursuit of this higher goal. Such an attitude is
surprising to the authors, because it seems to conflict with the goals of “group success” that now
pervades much of K-12 education. Limited time constraints—e.g., because of athletic
activities—or the perception that cheating is a natural part of a student’s culture—may reinforce
A third possible explanation why college students cheat is the small or non-existent penalties
that some instructors impose for infractions. A growing number of universities known to these
authors, for example, now insist that faculty at most assign a grade of “zero” for the assignment
or test on which students cheated—and this only if an instructor both catches, and is able to
prove, that a student cheated.
Yet a fourth possible explanation for college cheating is the reluctance many professors now
harbor to prosecute student cheaters—a trend that again enhances the environment for such
behavior. At the authors’ school, for example, instructors must document student misconduct,
and, if challenged by the accused student(s), prove their claim in open hearings. The belief that
the penalized and resentful students who remain in classes after such incidents “poison” the class
environment and negatively affect subsequent student evaluations of the class and the professor
adds to this reluctance—thereby leading to a more forgiving, and perhaps permissive,
environment for such behavior.
A fifth explanation for college cheating is a growing trend to redefine what constitutes
“cheating.” Donald McCabe (2006), founder and president of Duke University’s Center for
Academic Integrity, states that “stealing a glance on a test, a bit of plagiarism [is] just not on
people’s radar screen anymore.”
A final factor that might explain cheating behavior—or more accurately, explain why some
students do not cheat—is “moral code.” In their study, for example, Abdolmohammadi and
Baker (2008) found that “moral reasoning” was a significant variable in a linear regression of
such explanatory factors, and therefore seemed to explain why students with high moral codes
engaged in less cheating than those without them.
The Theory of Reasoned Action Framework
Although modeling something as variable as human behavior is fraught with the potential for
limited success, several researchers have attempted to create abstract representations of student
integrity. Relevant studies include those involving economic students (Bisping, Patron, &
Roskelly, 2008), engineering students (Harding, Mayhew, Finelli, & Carpenter, 2007; Yeo,
2007), marketing majors (Chapman, Davis, Toy, & Wright, 2004), marketing and management
students (Kisamore, Stone, & Jawahar, 2007), business majors (Wilson, 2008) and criminal
justice and legal studies students (Lanier, 2006).
The fundamental question the authors wanted to address is “why do college students cheat?”
We began with a fundamental tenant, widely cited in the literature, that cheating is not a random,
accidental, or impulsive act, but rather a premeditated, intentional, deliberate one that requires
forethought and planning (Deci & Ryan, 2000). Given this premise, the theory of reasoned
action (TRA) developed by Azjen and Fishbein (1980) would appear to be an excellent tool for
evaluating the intention to cheat.
At its core, TRA asserts that an individual’s beliefs, value system, and referential figures
(e.g., parents, teachers, or peers) explain subsequent planned behavior. TRA is widely
recognized today as a practical framework for explaining rational human behavior, and has
proven a valuable aid in explaining a wide variety of diverse behavioral phenomena (Sheppard,
Hartwick, & Warshaw, 1988), including criminal recidivism (Kiriakidis, 2008), Internet
purchasing activities (Barkhi, 2008), and athlete training patterns (Anderson & Lavallee, 2008).
We therefore considered it to be a useful tool for the exploratory task we wanted to accomplish
Figure 1 provides the specific TRA construct we used for our study. Thus, our model
includes what the literature identifies as major determinants of cheating, including “availability,”
“gaming,” “getting ahead,” “time demands,” “culture,” “morals,” and “risk,” as reflective
indicators. Items related to the influence of “family,” “friends,” and “professors” were relatively
independent, causing, forming or changing the student’s subjective norm and were therefore
categorized them as “formative variables” in our model. Because both attitude and subjective
norm have been shown to affect intentions in numerous previous studies, we also included the
effect of referents to the individual student’s subjective norm.
Figure 1 - Theory of Reasoned Action Framework
To measure the effects of the factors and referents discussed above upon student cheating
behavior, the authors developed a survey which they administered at a major public university in
the western United States. The survey respondents were the students taking a required MIS class
in this school’s college of business. Although participation in the study was voluntary, the
promise of extra homework credit resulted in the majority of the students in all six sections of the
course anonymously completing the online web survey.
This work had three major objectives. First, we wanted to test the theory of reasoned action
as a useful model of cheating behavior. Second, if our model was viable, we wanted to measure
the relative strength of the factors identified above as causal predictors of cheating activity.
Finally, we were interested in examining the differences between self reported cheaters and non-
cheaters. In other words, we wanted to know whether the causal factors motivating these two
groups were the same
We note that the answers to these questions extend beyond the normative ability to model a
particular type of human behavior. Our ultimate goal was to determine how best to deter student
cheating and encourage ethical conduct—an objective that requires a deeper understanding of
cheating and non-cheating behavior. If, for example, students cheat simply because they feel
that others are cheating, the corrective for this is much different than if students cheat because
they have little fear of detection.
A total of 158 students completed our survey. Best practices using PLS analysis discussed below
require researchers to deal with missing data in respondent surveys. Possible treatments are (1)
replace missing values with mean values, (2) replace missing values with a regressed value, or
(3) eliminate the associated survey response from further consideration. We chose to remove
observations with missing values. Our final sample, therefore, contained 144 usable responses.
In our final sample, 66 respondents were female and 78 were male. The mean age of a
participant was 22.5 years with a standard deviation of 4.02 years. Probably because the
participants were taking a “300-level class,” the average student had a “junior” class standing.
The self-reported mean number of class hours was 13.9 with a standard deviation of 3.4. Thirty-
nine students reported working while attending classes. The average student worked 24.4 hours
per week with a standard deviation of 10.69. Of the 144 respondents, 87 (60%) reported that
they had cheated an average of 6.1 times. A total of 57 students stated they had never cheated.
Partial Least Squares Analysis
We used partial least squares (PLS) to analyze the data following structural equation modeling
techniques (Chin, Marcolin, & Newsted, 2003; Gefen & Straub, 2005). There were several
reasons for this choice. PLS makes fewer demands on the underlying data distribution and
sample size, and it is also capable of analyzing both reflective and formative indicators (Chin,
1998b). Because of these advantages, PLS analysis is now commonly used in conducting
information systems research and provides a robust way of analyzing survey data (Chin, 1998a;
Chin et al., 2003; Gefen & Straub, 2005; Gefen, Straub, & Boudreau, 2000).
This study used SmartPLS (Ringle, Wende, & Will, 2005) to model our reflective indicators
model behavioral beliefs and our formative indicators represent independent referent items. To
analyze the psychometric properties of the reflective measures, we calculated the Average
Variance Extracted (AVE), Composite Reliability (ρc ), Cronbach’s Alpha (CA), Latent Variable
Correlations and Cross Loadings.
Table 1 - AVE, ρc, and Cronbach’s Alpha
Formative Indicators AVE ρc CA
Attitude Toward Cheating 0.80 0.92 0.87
Availability 0.79 0.92 0.86
Culture 0.72 0.88 0.80
Getting Ahead 0.80 0.92 0.87
Intention to Cheat 0.88 0.96 0.93
Morals 0.79 0.88 0.70
Risk 0.71 0.83 0.69
Time Demands 0.75 0.90 0.84
Table 1 reports the AVE, ρc, and CA for the latent variables. Although there is no standard
method for calculating statistically acceptable composites, the generally accepted rule is for
composite reliability to be greater than 0.7 (Yi & Davis, 2003). In this study, the lowest
composite reliability was for Risk at 0.83, thereby demonstrating sufficient reliability for all
The latent variable correlations and factor loadings were derived following Gefen and Straub
(2005) using SmartPLS and are provided in Appendix A. Reliabilities of individual items were
examined by verifying loadings greater than 0.7. One loading (C4) was marginal at 0.67.
However, all cross loadings for this variable were much less than this loading. Eleven of the 22
indicators loaded greater than 0.9, 10 indicators loaded greater than .8 and only the one
mentioned here, C4, loaded at less than 0.7. Overall, therefore we felt that these results
demonstrated good discriminant and convergent validity.
Analysis and Results
We formulated our structural path model to test the Theory of Reasoned Action framework. We
calculated the partial least squares path values and followed with a bootstrap re-sampling
method, generating 500 samples to evaluate our model. We then calculated the statistical
significance for each path using t-tests. Figure 2 shows the β coefficients and p values extracted
via PLS. The model accounted for a significant portion of variance in individual intention to
cheat (R2 = 0.58). Student attitude toward cheating accounted for a considerable amount of this
variance (R2 = 0.62).
(0.24) 0.01 ***
(-0.07) 0.04 **
GA3 Getting Ahead
T3 Time Demands
ATC1 ATC2 ATC3
Intention to Cheat
ITC1 ITC2 ITC3
**= p<.0 5
Figure 2 – All Respondents β, p val, R2
Global Fit Measure of TRA Model
Tenenhause et al. (2005) suggest a global goodness-of-fit measure for Partial Least Squares
modeling—GoF (0 < GoF < 1). This fit measure is determined by taking the square root of the
product of the geometric mean of the average communality and the average R2 of endogenous
constructs - GoF = sqrt(average(AVE)*average(Rsq)). Wetzels et al. (2009) propose a cut-off
value for communality of 0.5 as suggested by Fornell and Larcker (1981). The purpose of this
modification to GoF was to establish R2 effect size based on Cohen (1988). By substituting 0.50
for the minimum average AVE, GoF criteria for small, medium and large effect sizes were set at
the following values: GoFsmall = 0.1, GoFmedium = 0.25, and GoFlarge = 0.36. These values serve as
baseline values for validating Partial Least Square models globally. Calculating this value for
our model produced a GoF = 0.55 which exceeds the GoFlarge = 0.36 suggesting that our TRA
model performs well when compared to these baseline values (Wetzels et al., 2009).
All Student Findings
Table 2 reports our statistical findings for all students involved in the survey. We found that
both “student attitude toward cheating” and “subjective norm” were significant determinants of
cheating within the Theory of Reasoned Action model. In other words, both objective influences
and subjective norms appear to affect a student’s decision to cheat. The coefficient for “attitude
toward cheating” was β = 0.59 with p < 0.01, and the coefficient for “subjective norm” was β =
0.24 with p < 0.01.
Table 2 – All Respondent Results – Mean, Standard Deviation, t-test and p-value
Attitude Toward Cheating→Intention to Cheat 0.59 0.56 0.10 7.27 0.00***
Availability→Attitude Toward Cheating 0.00 0.00 0.06 0.02 0.99
Culture→Attitude Toward Cheating 0.14 0.14 0.10 1.46 0.15
Getting Ahead→Attitude Toward Cheating 0.51 0.52 0.11 5.03 0.00***
Morals→Attitude Toward Cheating -0.16 -0.16 0.06 2.63 0.01***
Risk→Attitude Toward Cheating -0.07 -0.08 0.06 1.19 0.24
Subjective Norm→to Cheat 0.24 0.28 0.09 3.57 0.00***
Time Demands→Attitude Toward Cheating 0.08 0.07 0.12 0.64 0.53
***= p<.01 **= p<.05 *=p<.10
[Alex: The values in this table appear to be in alphabetical order. I suggest we reorder the rows
so that they are in the same order that the variables appear in the model of Figure 2, ok?]
It is only logical that not all those factors that might affect student cheating do so equally. In
this study, we found only one statistically-significant motivator: a student’s “desire to get ahead”
(β = 0.45 and p < 0.01). Neither “opportunity to cheat” (“availability”) nor “time demands”
seemed to strongly influence student cheating behavior. To us, this is consistent with the theory
of reasoned action, which suggests that cheating is better explained by underlying motivational
forces (in this case, the “desire for advancement”) than by opportunistic or environmental ones.
In short, these results suggest that “cheating” is a reasoned, deliberate action rather than an
accidental or spontaneous one.
It is also interesting to ask “what deters a student from cheating?” In this study, we identified
only one statistically-significant deterrent: “moral beliefs.” These were inversely related to
cheating with β = -0.07 and p < 0.05. In addition, we also found one referent marginally
influenced this group’s subjective norm—“family,” with β = 0.34 and p < 0.10.
Of equal interest to us were the two deterrents that did not appear to affect cheating behavior:
“culture” and “risk.” This suggests that neither culture (i.e., the “acceptability” of cheating as a
cultural norm) nor the risks involved (and attendant fear of penalties) dissuades students from
cheating. The absence of “risk” is particularly interesting to us because it implies that our
students do not worry much about getting caught cheating. This finding makes us wonder
whether (1) the risks of detection at our school are abnormally low (e.g., because of large classes
or lax vigilance) or (2) the penalties for getting caught cheating in most of our classes are too
mild. [Alex: Did I go too far here?]
Because the referent variables were modeled reflectively, they were not reported in the
partial least squares analysis. Table 3 details the path value, t-test and p value for the referent
variables. Interestingly for all students, “friends” (i.e., the influence of peers) and “professors”
(the influence of professors either as moral anchors or as enforcers) did not appear to impact a
student’s intention to cheat.
Table 3 - Referent β, t-test, p val
β t-test p value
Family 0.34 1.69
Friends 0.20 1.15 0.25
Professors 0.23 1.28 0.20
***= p<.01 **= p<.05 *=p<.10
Cheaters versus Non-Cheater Findings
It is possible that the motivations and deterrents for cheaters are different than those for
non-cheaters. Treating our sample as a homogeneous group has the potential to hide such
differences. For this reason, we split our dataset into two groups—cheaters (n = 87) and non-
cheaters (n = 57) — and analyzed each set independently. We used the same structural model for
both groups and (following Wetzels et al., 2009) believe that the number of respondents for each
group is adequate for independent analysis.
Table 4 – Cheaters vs. Non-Cheaters β, t-test, p val
Cheaters n=87 Non-Cheaters n=57
β t-test p value β t-test p value
Attitude Toward Cheating→Intention to Cheat 0.56 6.22 0.00*** 0.42 3.06
Availability→Attitude Toward Cheating 0.01 0.02 0.98 0.09 0.84 0.40
Culture→Attitude Toward Cheating 0.14 1.54 0.13 0.17 0.70 0.49
Getting Ahead→Toward Cheating 0.63 5.44 0.00*** 0.24 1.00 0.32
Morals→Attitude Toward Cheating -0.19 2.82 0.01*** -0.17 1.23 0.22
Risk→Attitude Toward Cheating -0.15 2.12 0.04** -0.01 0.02 0.98
Subjective Norm→to Cheat 0.30 2.45 0.02** 0.48 3.18
Time Demands→Attitude Toward Cheating -0.05 0.28 0.78 0.23 0.63 0.53
***= p<.01 **= p<.05 *=p<.10
Table 4 reports our results and reveals some interesting differences. Similar to the general
group, the theory of reasoned action constructs for cheaters vs. non-cheaters were highly
significant. In particular, “attitude toward cheating” was statistically significant, as was
“subjective norm,” suggesting that both cheater and non-cheaters responses support the theory of
reasoned action. Interestingly, there was a marked difference between the groups concerning
“getting ahead.” For cheaters, “getting ahead” was a significant determinant of attitude toward
cheating, while for non-cheaters it was not.
Two other constructs were important for cheaters. “Morals” and “risk” had a significant
inverse relationship with “attitude toward cheating,” but neither “morals” nor “risk” was
significant for non-cheaters. “Time demands” did not significantly influence attitude toward
cheating for either group, suggesting that “an insufficient amount of time” for class work or
studying—the type of problem that might be voiced by student athletes or students who were
working in outside jobs—was not a strong influence on cheating behavior in this sample. This
was an interesting finding to us because over 80 percent of our students work full or part time.
Table 5 reports p values for both cheater’s and non-cheater’s subjective norm formative
indicators, and shows that the referent variables for “cheaters” also varied from the “non-
cheater” sample. For example, while the opinions of “family” remained a significant influence
on cheaters’ subjective norms, neither “friends” nor “professors” appeared to have a strong
impact on “non-cheaters.” What we think this means is that parents, siblings, and other family
members are likely to influence a cheater’s intention to cheat, in contrast to non-cheater choices,
where “family” influences were not strong. We wonder whether students are learning that
cheating is acceptable at home.
For non-cheaters, the influence of “professors” was significant, suggesting that the opinions
of “family” and “friends” are less likely to be an important factor in a student’s decision to cheat.
In a sense, this is good news. It suggests to us that professors may sometimes act as “moral
anchors” and positively influence students not to cheat.
Table 5 – Cheaters vs. Non-Cheaters, referent β, t-test, p val
Β t-test p value β t-test p value
Family 0.15 1.98 0.05** 0.05 0.87 0.39
Friends 0.36 1.19 0.24 0.25 0.93 0.36
Professors 0.17 0.65 0.52 0.34 2.30 0.02**
***= p<.01 **= p<.05 *=p<.10
Caveats and Directions for Further Research
The subject of “cheating” is a delicate matter, as are the results of the studies that attempt to
investigate the determinants of such behavior. One obvious concern to us was the use of
voluntary and self-reported data concerning behavioral intention (Sheppard et al., 1988). This is
particularly problematic for the task at hand, inasmuch as we asked our respondents to self report
infractions of their own university’s student code of conduct. The facts that (1) students
answered anonymously, and (2) our final percentage of 60 percent is consistent with other
studies mitigate, but probably do not completely overcome, these concerns.
We also note that we used a consistent and invariant construct to model human behavior,
which is often neither. In our defense, we note that several researchers have attempted to model
academic integrity including engineering students (Harding et al., 2007; Yeo, 2007), marketing
majors (Chapman et al., 2004), marketing and management students (Kisamore et al., 2007),
business majors (Wilson, 2008) and criminal justice and legal studies students (Lanier, 2006).
But we also note that the statistical reliability that we found for our model may be an anomaly,
and we believe that further testing is appropriate.
A third caveat pertains to the venue within which we conducted our study, which was limited
to the students in various sections of one class at one university. Although our results are
consistent with earlier findings on both the widespread prevalence of cheating, these findings
must be confined to the context within which they were made—a single, multi-section MIS class
required of all business majors at a major, western university. One obvious direction for further
work is to perform similar analyses at alternate schools such as at private institutions and/or in
classes required, say, of all students (such as English, Mathematics, or Western Civilizations
A fourth caveat is the fact that, both in the interests of brevity and expediency, we did not
examine every conceivable reason that might motivate a student to cheat, or constrain a student
from cheating, on a test or assignment. Instead, we focused on what we identified as the major
determinants of cheating, but recognize that we might have missed the one important factor that
causes a particular student to cheat on a particular examination or plagiarize on a particular term
paper. Similarly, our model did not allow us to examine the cross-product effects of our
determinants—for example, in order to identify what factors deter a student with the desire to get
ahead from cheating. [Alex: does this last sentence make sense?] Again, these seem like
important avenues for further work.
A fifth concern for us is the possibility that the factors that motivate cheaters or restrain non-
cheaters may differ by the type of cheating involved. We recognize the possibility that those
factors that lead to cheating on a take-home assignment or test may not be the same ones that
lead to cheating on an in-class examination. A larger sample, with more questions that
distinguish between these different types of activities, is required to address this issue.
Finally, we note that our survey was taken during particularly challenging economic times.
The extent to which such circumstances encourage cheating behavior is unknown to us, but we
recognize again that such factors as “the need to get ahead at all cost” might be stronger during
such periods than in alternate, more prosperous circumstances.
The results of our model suggest several additional directions for future research. One
particularly interesting one to us is to investigate further how “professors” influence student
cheating. For example, does the “role of moral anchor” mean that professors must exhibit
exemplary behavior in the classroom or should they simply enforce rules that deter cheating?
One promising way to answer such questions may be to use the theory of planned behavior
(TPB) instead of the theory of reasoned action as a model of student cheating. This TPB
framework may be a superior choice because it includes a measure of perceived behavioral
control and, as Miller (2005) points out, "…involves the addition of one major predictor,
perceived behavioral control, to the model. In particular, this addition accounts for times when
people have the intention of carrying out a behavior, but are thwarted because they lack
confidence or complete control over such behavior (Miller, p. 127). Attitudinal differences
between cheaters and non-cheaters and how the moral anchor role may be implemented would be
important to both academia and practitioners. TPB may be able to isolate these differing
Student cheating in college appears to be both a pervasive and growing phenomenon. This trend
is of particular concern to colleges of business, who not only commonly teach business ethics but
often find that cheating is even more common among business students than among non-business
The fundamental question of interest to the authors was not “do college students cheat”
(which, unfortunately, appears to be well-answered in the affirmative), but “why do students
cheat?” To answer this question, we used the theory of reasoned action and a partial least squares
statistical package to analyze 158 voluntary student surveys that asked questions on cheating
behavior. Our major findings were as follows: (1) Approximately 60 percent of business
students admitted to having cheated at least once while attending college. (2) The most
important reason why the students in our sample cheated was the “desire to get ahead.” (3) A
surprising result was that this factor appears to be more important than such alternate, but
seemingly equally-relevant, variables as “attitude towards cheating,” “opportunity to cheat,
“cultural or moral acceptance of cheating as an established norm,” “low risk of detection,” or
“heavy time demands.”
By separating the cheaters from the non-cheaters, we also found one important reason why
students refrain from cheating: the presence of a moral anchor in a faculty member whose
opinion mattered. This finding adds to the literature on cheating and offers hope to academic
faculty that their efforts to restrain students from cheating are both needed and valuable.
We realize that our results our tentative, and should be treated with care. Among our caveats
are: (1) the imprecision of modeling any type of human behavior, (2) the limitations of our
survey including the setting (a single course), the type of student (those taking a particular
college of business class), and our choice of causal variables and referents, (3) the confounding
that might have resulted from treating all types of cheating behavior the same, and (4) the
deteriorating economic environment within which we conducted our survey.
Finally, our results also suggest some potential prescriptive action for college faculty and
administrators. For example, because cheater’s perceptions of “getting ahead” appear to
significantly affect their attitude toward cheating, studying cases involving individuals who cheat
to get ahead but who subsequently suffer negative consequences might be useful. Another
avenue could be reinforcement of an intolerant collegiate culture about cheating—i.e., build a
moral culture that encourages students to “do what is right” rather than “do what is personally
best.” Again, cases that emphasize this particular point might be effective as well as have the
potential to reinforce the thinking of non-cheaters. Finally, because “low risk” seems to affect
cheating behavior, professors might want to take class time to clearly define cheating and
unequivocally identify the potential negative outcomes of cheating behavior.
Latent Variable Correlations
Intention to Cheat
Attitude Toward Cheating 1.00
Availability 0.26 1.00
Culture 0.59 0.19 1.00
Getting Ahead 0.75 0.37 0.62 1.00
Intention to Cheat 0.73 0.30 0.57 0.59 1.00
Morals -0.50 -0.08 -0.39 -0.45 -0.49 1.00
Risk -0.30 -0.09 -0.28 -0.22 -0.28 0.31 1.00
Time Demands 0.68 0.32 0.66 0.81 0.58 -0.44 -0.35 1.00
Latent Variable Loadings
Cheating Availability Culture
to Cheat Morals Risk
ATC1 0.85 0.25 0.47 0.63 0.62 -0.46 -0.26 0.60
ATC2 0.93 0.21 0.58 0.69 0.66 -0.42 -0.28 0.65
ATC3 0.90 0.24 0.54 0.69 0.68 -0.47 -0.28 0.57
A1 0.24 0.89 0.13 0.30 0.24 -0.03 -0.12 0.27
A3 0.22 0.91 0.21 0.35 0.30 -0.11 -0.04 0.30
A4 0.23 0.86 0.17 0.33 0.26 -0.08 -0.07 0.29
C1 0.58 0.18 0.91 0.64 0.55 -0.41 -0.30 0.72
C2 0.57 0.24 0.94 0.57 0.53 -0.33 -0.24 0.58
C4 0.31 0.00 0.67 0.31 0.35 -0.23 -0.13 0.27
GA1 0.76 0.36 0.64 0.91 0.61 -0.46 -0.21 0.71
GA2 0.66 0.33 0.53 0.92 0.54 -0.42 -0.13 0.71
GA4 0.56 0.29 0.49 0.84 0.42 -0.31 -0.27 0.78
ITC1 0.75 0.27 0.54 0.59 0.95 -0.43 -0.29 0.59
ITC2 0.63 0.26 0.54 0.53 0.94 -0.51 -0.27 0.51
ITC3 0.67 0.30 0.53 0.56 0.93 -0.43 -0.24 0.51
M1 -0.42 -0.03 -0.32 -0.41 -0.37 0.88 0.21 -0.38
M3 -0.47 -0.11 -0.37 -0.40 -0.49 0.90 0.33 -0.40
R3 -0.27 -0.15 -0.26 -0.21 -0.24 0.29 0.87 -0.33
R4 -0.24 0.02 -0.20 -0.17 -0.23 0.23 0.82 -0.26
T2 0.70 0.32 0.55 0.80 0.62 -0.41 -0.28 0.86
T3 0.48 0.27 0.56 0.58 0.44 -0.41 -0.32 0.85
T4 0.53 0.23 0.59 0.68 0.39 -0.31 -0.32 0.89
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