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Using the business fraud triangle to predict academic dishonesty among business students.

ABSTRACT

Crib notes, paper mills, cell phones, copying and pasting from the Internet, hand signals during exams, copying homework-the ways in which students engage in academically dishonest behaviors are numerous, and research suggests that most students cheat at some point in their college careers. In addition, some studies indicate that business students are more likely to cheat than students in other disciplines.

Much research has been conducted to determine the prevalence of academic dishonesty and to discover characteristics of those who engage in academic dishonesty. Less research attempts to develop a general model for understanding underlying motives or predispositions for engaging in this behavior. Such a model may assist faculty with efforts to reduce or eliminate academic dishonesty.

Our study develops and tests a model of student cheating that is derived from a model of dishonest behavior in business: the fraud triangle. Participants in this study were 476 business students. The study showed that each of the elements of the fraud triangle-incentive, rationalization and opportunity-is a significant determinant of student cheating. We also analyzed the results for impacts related to student GPA, student gender, hours spent working per week, student age, and frequency of partying behavior. Results show that age and frequency of partying were also significant determinants of student cheating. Implications for faculty are discussed.

INTRODUCTION

Crib notes, paper mills, cell phones, copying and pasting from the Internet, hand signals during an exam, copying homework-the ways in which students engage in academic misconduct are numerous. For the purpose of this study, academic misconduct, cheating, or dishonesty refers to any instance in which a student claims credit for the work or efforts of another without authorization or citation. Examples commonly listed on college and university Web sites include using unauthorized material or fabricated data in academic exercises, forging or falsifying academic documents or records, intentionally impeding or damaging the academic work of others, engaging in conduct aimed at making false representation of a student's academic performance, or assisting other students in any of these acts.

Research suggests that most students cheat at some point in their college careers, some as frequently as once or twice a semester (Hollinger & Lanza-Kaduce, 1999). McCabe and Trevino (1996) found that 66% of students at several prestigious colleges and universities reported cheating, and at state colleges and universities 70% reported cheating on tests and 84% reported cheating on homework assignments. Thus, the prevalence of academic dishonesty is well documented.

Previous research has been conducted to discover characteristics of students who engage in academic dishonesty; they are thought to tend toward some common characteristics. Few studies have attempted to develop general models for understanding underlying motives or predispositions for this behavior. Such models may assist faculty with efforts to detect or prevent academic dishonesty.

This study examines the academic dishonesty of business students. A model of student dishonesty based on the business model of the fraud triangle is developed and tested. We hypothesize a relationship between academic dishonesty (a type of fraud) and the incentive to cheat (e.g., to get a better grade), the opportunity to cheat (e.g., faculty do not deter cheating) and the ability to rationalize cheating (e.g., penalties are not severe so faculty don't care about cheating). The purpose of our study is to determine whether these dimensions (incentive, opportunity, and rationalization) help explain business students' attitudes toward and participation in academic dishonesty.

Faculty who know specific factors that lead to cheating will be better able to prevent and detect academic dishonesty. Therefore, we discuss implications of our results for faculty.

LITERATURE REVIEW

One reason to be concerned about business student academic dishonesty is that business students are consistently near the top of rankings of students most likely to cheat, perhaps because they've already adopted a 'bottom line' mentality (Riley, 2004). The implication is that the ends (better grades) justify the means (cheating). In addition, business majors seem to have more tolerant attitudes toward cheating (Roig & Ballew, 1994). These are disturbing findings, in part because dishonest behaviors in school may have serious ramifications for students' future behavior. Students who report they engage in academic dishonesty in the classroom are more likely to report they engage in many types of dishonesty in the work place (Sims, 1993; Nonis & Swift, 2001).

Interestingly, many students believe they are more ethical than business people (Tyson, 1990). One study found that 84% of students said they were disturbed by recent scandals in corporate America, and 77% thought CEOs should be held accountable for unethical behavior. However, these same students claimed they had cheated on exams and papers and stated they would not report fellow students for cheating (Merritt, 2002). Students (at least those who cheat) are clearly not as ethical as they may believe.

Much research has attempted to isolate characteristics of students who cheat. Students with low academic ability or low academic achievement, students who are members of a fraternity or sorority, students who are influenced by peer approval and peer cheating, and students from large state institutions may cheat more often (Bolin, 2004).

Results are somewhat mixed in determining whether gender plays a role in student cheating. Some studies have found men are more likely to cheat (Bolin, 2004; Hendershott, Drinan, & Cross, 1999). Others have found students of both genders cheat when the circumstances are right. For example, Tibbets (1999) found that women are more likely to cheat when morals and grades are their motivators, while men are more likely to cheat if they have a history of cheating behavior and if they find cheating fun or enjoyable. Many studies report that gender is not a strong predictor of cheating behavior (c.f. Franklyn-Stokes & Newstead, 1995; Nowell & Laufer, 1997; Vowell & Chen, 2004).

In addition to research on the characteristics of students who cheat, some research has attempted to model underlying motives and predispositions for engaging in academic dishonesty. For example, Vowell and Chen (2004) found that academic dishonesty is contingent upon the attitudes and behaviors of students with whom they associate. Bolin (2004) found that academically dishonest behavior is affected by both students' ability to rationalize academic dishonesty (what he terms 'attitude') and the perceived opportunity to engage in academically dishonest behavior.

Our current study builds on the work of Bolin (2004). If the dimension of incentive were added to Bolin's model, his model would parallel the business model known as the fraud triangle. The fraud triangle (Ramos, 2003) models fraudulent behavior as a function of incentive, opportunity and rationalization (see Figure 1). Fraud is defined as "the intentional deception or misrepresentation that could result in some unauthorized benefit to oneself or other person, something that is not what it pretends to be (Oxford American Dictionary). Academic dishonesty, therefore, may be characterized as academic fraud.

[FIGURE 1 OMITTED]

Given the propensity of business students to cheat and given the relationship between academic dishonesty and the continuation of dishonest behavior in the workplace, a business model such as the fraud triangle seems an apt framework for understanding the motivation for business students' academic dishonesty. When all three elements of the fraud triangle are present, fraud does not necessarily exist, but it is more likely (Ramos, 2003). Similarly, when all three elements of the triangle for cheating are present, students do not necessarily cheat but cheating is more likely. Consequently, whether in school or in business, the willingness to cheat is context-sensitive. Cheating is a practical solution to a problem, given the right circumstances.

Both students and business people report the need to weigh the "practicality" versus the "ethicality" of decisions and report trying to balance these two needs (Lawson, 2004). Sometimes, practical needs outweigh ethical considerations, and other times ethics are most important. Teachers know that students do not cheat all the time, and do not always cheat on the same tasks. Students sometimes write their own papers and other times purchase papers online. They sometimes take unauthorized notes into an exam and other times answer questions on their own. The same phenomenon exists in business. Some earnings reports may be managed, others may be accurate; some cash receipts stolen, others left in tact; some expense reports padded and others prepared accurately.

The issue of context is important because it may expose an individual to the three elements of fraud (incentive, opportunity, and rationalization). The first element is incentive/pressure: the motivation to cheat may come from within the student or from another person. Students report many different incentives to cheat. Some students may find incentive in the pressure from their parents, peers, colleges, or employers to maintain a high GPA. Others cheat because they want to be viewed as more successful, respectable, or influential (Kock & Davison, 2003).

The second element is opportunity, which may also come from different sources. Some students see their academic communities as providing opportunity to cheat when professors overlook obvious cheating during exams or make no comments about plagiarism on term papers (McCabe & Trevino, 1996). Some students see opportunities to cheat when offered answers to an exam from a student in an earlier class who took the same test. Some students see opportunities to cheat when they see others cheating.

The third element is rationalization/attitude, which represents the ability of students to see cheating as consistent with their personal codes of ethics. Students may rationalize cheating if they perceive unfair competition (McCabe & Trevino, 1996), or if they believe their actions are within the bounds of acceptable behavior (Kock & Davison, 2003). For example, students might claim not to know what level of idea borrowing is acceptable when writing a paper. In addition, the lack of enforcement of penalties for academic misconduct may contribute to a student's ability to rationalize cheating. If the university does not care enough to enforce rules, students may infer that following the rules is not terribly important.

While these reasons to cheat have been identified in prior research, prior research does not address these three elements together. Thus, the hypothesis of this study is that students are more likely to cheat when they perceive the presence of incentive, opportunity, and rationalization for cheating. Figure 1 illustrates the research model.

METHOD

Two surveys were conducted as part of this study. The preliminary survey identified behaviors business students consider academically dishonest. The final survey measured student participation in academically dishonest activities using the behaviors identified in the preliminary survey. The final survey also measured the elements of the fraud triangle, incentive, opportunity and rationalization.

Preliminary Survey

The purpose of the preliminary survey was to verify that students believed certain common academic behaviors to be forms of cheating. To test the fraud model, we needed to measure cheating and use that measure as a dependent variable. We wished to identify the items students found least acceptable to ensure a good test of our model. For example, if students found a certain behavior only somewhat unacceptable, they might be more likely to engage in that behavior. Their reasons for engaging in that behavior might not be the same as their reasons for engaging in a behavior they would rate as highly unacceptable. There are many different academic dishonesty scales, and it was not obvious which items business students would believe to be least acceptable.

The preliminary survey included 14 academically dishonest behaviors identified in prior research. Participants in this survey were 598 students in lower-division business courses at a regional Midwestern university from all business majors. Participation in the survey was voluntary and anonymous; the surveys were completed during class time. Students were asked to rate the acceptability of each behavior using a 5-point Likert scale ranging from (1) Always unacceptable to (5) Always acceptable.

Survey items were adapted from the Academic Dishonesty Scale (McCabe & Trevino, 1993; Bolin, 2004), the Attitude Toward Cheating Scale (Gardner & Melvin, 1988), and from dishonest behaviors identified by Brown and Chang (2003). Items related to homework assignments (e.g., "Copy material and turn it in as your own work"; "Collaborate on an assignment when the instructor asks for individual work"), group projects ("Take credit for full participation in a group project without doing a full share of the work"), and exams (e.g., "Copy from another student during a test"; "Give information about the content of an exam to someone who has not yet taken the exam").

The students' ratings were analyzed, and the five measures that had the lowest average ratings (see Table 2) were used as the cheating behavior measures in the final survey.

Final Survey

The purpose of the final survey was to measure student opportunity, incentive, and rationalization of academic dishonesty, to measure student participation in academically dishonest behaviors, and to obtain information used for model sensitivity analysis (see Appendix 1 for a copy of the survey).

For the questions related to opportunity, incentive, and rationalization, students used a 4-point rating scale ranging from (1) Strongly Agree to (4) Strongly Disagree. The scale purposely did not have a midpoint to prevent respondents from taking a neutral stance. The questions related to incentive, opportunity and rationalization were mixed to reduce the ability of participants to recognize the purpose of the questions. Further, some questions were reverse-worded to reduce effects from students just answering every question with the same rating without reading each item.

For the questions related to academic dishonesty, students reported the number of times they had participated in each behavior, ranging from zero times to more than five times. A similar method of measuring cheating was used in Bolin (2004). Behaviors measured included how often students had "Copied material and turned it in as your own work", "Used unfair methods to learn what was on a test before it was given", "Copied a few sentences of material from a published source without giving the author proper credit", "Helped someone else cheat on a test" and "Cheated on a test in any way."

Incentive measures were adapted from Gardner and Melvin (1983). Examples of items that might provide an incentive to cheat were hypothesized to include: the class is too difficult or too much work, students feel they can't get the grades they want without cheating, the exams are too difficult, and students do not have enough time due to outside commitments.

Opportunity measures were adapted from McCabe and Trevino (1997) and Gardner and Melvin (1983). Examples of items that might provide the opportunity to cheat were hypothesized to include: the instructor does not check for plagiarism, the instructor does not change homework assignments or exams between terms, other students are observed cheating, and the instructor does not adequately deter cheating.

Rationalization measures were adapted from Gardner and Melvin (1983) and Kock and Davison (2003). Examples of items that might provide rationalization for cheating were hypothesized to include: the instructor's grading policies or workload requirements are unfair, the instructor did not adequately explain what constitutes cheating or the penalties for being caught cheating, and faculty do not usually detect cheating.

RESULTS OF FACTOR ANALYSIS

The results of the final survey were used to test the hypothesized model of student cheating behavior. Participant statistics are shown in Table 1. Students from all business majors participated, including 199 women and 277 men. Two different orderings of the survey were used; no differences between these versions were noted in the results. Participants in the preliminary survey did not complete the final survey.

Cheating behavior was measured by the five questions shown in Table 2. To ensure these cheating behavior questions all measure a similar type of cheating, principle components factor analysis was used to confirm that these questions all loaded into the same factor. Cronbach Alpha for this factor is 0.7675, indicating high factor reliability. For the analysis of student cheating, each student's responses to these five questions were summed to create one cheating score for each student. This is consistent with prior research (e.g., Becker & Haugen, 2004). This score was the dependent variable in the analysis.

Principle components factor analysis also determined that three questions related to the incentive for students to cheat loaded into one factor (see Table 3). Cronbach alpha for this factor is 0.6762, indicating adequate factor reliability. The factor captured these questions: "In some classes, I can't get the grade I want without cheating," "I don't have enough time to complete some assignments without cheating," and "I have a difficult time keeping up with my classes." Responses to each question were summed to create a score for incentive for each participant (ratings on each question ranged from 1 to 4, yielding scores for this factor from 3 to 12). Scores for female and male students were statistically similar. Panel A of Table 3 shows the overall average score for this factor was 6.00 and the standard deviation for the factor was 1.75.

In addition, principle components factor analysis determined that three questions related to the opportunity for students to cheat loaded into one factor. Cronbach alpha for this factor is .5685, indicating adequate factor reliability. The factor captured these questions: "Many students in my classes have copied answers to a test," "Plagiarism and cheating on tests occur frequently at our school," and "Faculty do not take substantial actions to deter academic dishonesty." Responses to each question were summed to create a score for incentive for each participant (ratings on each question ranged from 1 to 4, yielding scores for this factor from 3 to 12). Scores for female and male students were statistically similar. Panel B of Table 3 shows the overall average score for this factor was 6.52 and the standard deviation for the factor was 1.46.

Four questions related to the rationalizations students give for cheating were identified through principle components factor analysis. Cronbach alpha for this factor is .7044, indicating high factor reliability. The factor captured these questions: "If a professor does not explain what he/she considers cheating, the professor can't say I cheated," "If someone leaves a test where I can read the answers, then it's his/her fault if I copy," "The faculty usually detect academic dishonesty," and "The penalties for academic dishonesty at our school are not severe." Responses to each question were summed to create a score for incentive for each participant (ratings on each question ranged from 1 to 4, yielding scores for this factor from 4 to 16). Scores for female and male students were statistically similar. Panel C of Table 3 shows the overall average score for this factor was 10.58 and the standard deviation for the factor was 2.32.

RESULTS OF MODEL TESTING

The model shown in Figure 1 was tested for all students as one group. The results are shown in Table 4. The model explains 20.42% of the total variation in student cheating behaviors and is significant (p<.01). In addition, each of the hypothesized factors had a significant impact on student cheating and all effects are in the hypothesized direction. Cheating behavior rises as a student's incentive to cheat rises (coefficient is 0.84), cheating behavior rises as the level of perceived opportunity to cheat rises (coefficient is 1.23), and cheating behavior rises as student's rationalization of cheating rises (coefficient is 0.27).

SENSITIVITY ANALYSIS

The sensitivity of these results to other variables was examined. We tested for effects of student GPA, gender, hours spent working per week, hours spent studying per week, student age, and frequency of partying during the school year.

Student GPA could potentially impact student cheating. For example, it is possible that students who earn better grades have less incentive to cheat or have less need to rationalize cheating. We tested for GPA effects by adding GPA to the model. There was no significant impact of GPA (p=0.54). Student GPA did not affect the extent of cheating or the factors for cheating.

Student gender may also affect cheating (e.g., Buckley, Wiese, & Harvey 1998); some studies have shown that women are less likely to cheat (e.g., Hendershott, Drinan, & Cross, 1999). We tested for gender effects by adding student gender to the model. There was no significant impact of gender (p=.84). Female and male students cheated to the same extent and their cheating behaviors were driven by the same factors.

Hours spent working each week may be another important determinant of student cheating. The more time a student spends working the less time is available for school work, which may provide an incentive to cheat or a reason to rationalize cheating. We tested for effects of hours spent working by adding hours worked per week to the model. There was no significant impact of hours spent working (p=.22). Students who worked more were not more inclined to cheat and did not have different reasons for cheating.

Hours spent studying may also impact cheating behavior. Students who study more should have less incentive to cheat. We tested for effects from hours spent studying by adding this variable to the model. There was no significant impact from hours spent studying each week (p=.11). The extent of student time spent studying did not affect the extent of cheating or the factors for cheating.

Student age also may impact cheating behavior. Older students may experience less panic in juggling work, school, and life and resort to cheating less often than younger students. They may have less incentive to cheat. In addition, they may be less able to rationalize cheating because they have a broader world view than younger students. We tested for an age effect by adding student age to the model. As shown in Table 5, age is a significant driver of student cheating (p=.04) and is negatively related to cheating behavior. Older students report less cheating than younger students.

The potential impact of student partying during the school year was also considered. The more frequently a student parties, the less time spent on academics. We asked students to report the frequency of their partying during the school year, and included this variable in the model. As shown in Table 5, this is an important determinant of cheating behavior (p<.01), and is positively related to the level of cheating. Students reporting they party more often also report higher levels of cheating behavior.

DISCUSSION AND IMPLICATIONS FOR FACULTY

The prevalence of academic dishonesty on college campuses in the United States has been documented through a large number of research studies. The current study attempts to model student cheating in a manner that is parallel to the fraud triangle model used in business. The hypothesis of this study is that students are more likely to cheat when they perceive the presence of incentive, opportunity, and rationalization for cheating

The results suggest that each of these fraud triangle elements is a predictive factor in student cheating behavior. Further, the results show that age and students' reported level of partying may have a significant impact of level of cheating. Student GPA, gender, hours worked each week, and hours spent studying each week were not major determinants of cheating. These results suggest that modifying the presence of elements in the fraud triangle will alter cheating behaviors.

The incentive to cheat may be difficult to reduce if it arises from outside pressures on students. Instructors may have little control over pressure students feel from parents, peers, interviewers, and scholarship committees. However, the classroom environment does influence other incentives for academic dishonesty, particularly in the area of course difficulty and workload. If students believe a course is too rigorous (they cannot keep up with the work or earn their desired grade), academic dishonesty is more likely to occur (McCabe & Trevino, 1996).

Obviously, easing course rigor or requirements-with resulting grade inflation-is not the answer. A more effective solution may be to provide incentives for students to master the course material and retain the knowledge. For instance, students in accounting and information systems classes need to know that the knowledge and skills they gain from the course are crucial for upper-level courses, internships, and business careers. Likewise, students in business communication courses should understand that employers are looking for a demonstration of excellent communication skills; they are not simply looking for a high grade on a transcript. Providing students with incentives other than grades could take away one variable in the fraud triangle and lessen the propensity to cheat.

Reducing the opportunities for cheating may be more easily addressed. University administrators and faculty who do not take substantial actions to deter academic dishonesty create an opportunity and an atmosphere that invites dishonest behaviors (McCabe, Trevino, & Butterfield, 1996). Beyond deterrence, class sizes may be limited to better allow instructors to observe all students during exams, exams and homework assignments may be changed from term to term, and test security may be improved.

Technology continues to provide challenges as it permits students to use new methods of academic dishonesty. PDAs, graphing calculators, text messaging on cell phones, and use of other electronic devices have increased students' opportunity for cheating (Riley, 2004). Instructors have a responsibility to be aware of and minimize electronic cheating. However, new technological advances will inevitably create new opportunities, particularly when traditional age students are more technologically savvy than some faculty members.

Faculty may also take steps to address rationalization. Using a course syllabus and classroom discussion to define academic dishonesty (such as plagiarism) and provide information about penalties eliminates the "you didn't tell me" rationalization. Increasing faculty use of cheating detection methods such as plagiarism prevention web sites (e.g., turnitin.com) may also have an impact. When students see that faculty are going to great lengths to detect cheating, students may get the message that faculty believe cheating is important.

In addition, school honor codes have been shown to reduce cheating, and they may reduce a student's ability to rationalize cheating as "something everyone is doing." McCabe, Trevino, and Butterfield (1996) investigated students' attitudes toward academic honesty among students from schools that had ethics codes and from schools that did not. They also examined student behaviors in the context of their employment with companies that had ethics codes and those that did not. They found that fewer dishonest behaviors were reported among participants who both attended a college with an ethics code and worked for a company that had a strongly enforced and vigorously implemented code of ethics.

Finally, the amount of time spent partying may cause students to rationalize academic dishonesty (i.e., time spent partying leads to time constraints and greater rationalization of cheating). Instructors have no obvious influence on the frequency of students' partying; however, instructors may inadvertently encourage the behavior by light-hearted comments in the classroom and assumptions that every student spends a lot of time socializing. Encouraging students to use their weekends wisely and set aside specific times for academic work may reduce partying behavior and the related rationalization for cheating.

STUDY LIMITATIONS AND FUTURE RESEARCH

The current study has several limitations. Survey participation was limited to a Midwestern college of business. This creates the likelihood of a non-representative sample and results that may not apply to other geographic regions or other sub-groups of students. In addition, variables related to student cheating including student income, social class, and other demographic variables could also influence academic dishonesty. Prior research has suggested that these variables are sometimes associated with cheating.

In addition, although the survey was anonymous and voluntary, students completed the survey in the classroom. Students may have self-reported less academic dishonesty due to the physical proximity of other students and the sensitive nature of the topic. We also may have had effects from a social desirability bias in the cheating results. The social desirability bias says that survey respondents sometimes respond to surveys in a manner that makes themselves look more socially desirable. Additional research may measure the extent of a social desirability bias in the reports of student cheating. If this bias exists in the current study, it works against our ability to find results by understating the extent of student cheating behavior. The bias should have no impact on the testing of our model.

The indication that age is a predictor of cheating behaviors also requires further exploration. A relevant question is whether the lower rate of academic dishonesty in older students shows a relationship with incentive (e.g., higher incomes), opportunity (e.g., technology ineptitude), or rationalization (e.g., cultural norms), or a unique combination of the three elements.

The level of partying also requires further study. Although the factor is significant, we did not define the term party; therefore, we can not be sure what we measured. What a student believes is "partying" and what we intended may not match. This variable may be related to personality type, alcohol use, time spent studying, or other factors.

Gaining a better understanding of how business students find incentive, opportunity, and rationalization for engaging in academic dishonesty has implications for using the business fraud triangle to understand and control academic dishonesty. The study provides instructors with a business framework for engaging students in discussions of ethical behavior not only in the classroom but also of the relationship between ethical conduct in the classroom and ethical conduct in the workplace.

REFERENCES

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Bolin, A.U. (2004). Self-control, perceived opportunity, and attitudes as predictors of academic dishonesty. The Journal of Psychology, 138(2), 101-114.

Brown, B.S. & P. Chang (2003). A comparison of academic dishonesty among business students in a public and private catholic university. Journal of Research on Christian Education, 12(1), 27-48.

Buckley, M.R., D.S. Wiese & M.G. Harvey (1998). An investigation into the dimensions of unethical behavior. Journal of Education for Business, 73(5), 284-291.

Epstein, J.A. & J.M. Harackiewicz (1992). Winning is not enough: The effects of competition and achievement orientation on intrinsic interest. Personality and Social Psychology Bulletin, 18(2), 128-138.

Franklyn-Stokes, A. & S.E. Newstead (1995). Undergraduate cheating: Who does what and why? Studies in Higher Education, 20(2), 159-172.

Gardner, W.M. & K.B. Melvin (1988). A scale for measuring attitude toward cheating. Bulletin of the Psychonomic Society, 26, 429-432.

Hendershott, A., P.F. Drinan & M. Cross (1999). Gender and academic integrity. Journal of College Student Development, 40(4), 345-354.

Hollinger, R.C. & L. Lanza-Kaduce (1996). Academic dishonesty and the perceived effectiveness of countermeasures: An empirical survey of cheating at a major public university. NASPA Journal, 33, 292-306.

Kock, N. & R. Davison (2003). Dealing with plagiarism in the information systems research community: A look at factors that drive plagiarism and ways to address them. MIS Quarterly, 27(4), 511-532.

Lawson, R.A. (2004). Is classroom cheating related to business students' propensity to cheat in the 'real world'? Journal of Business Ethics, 49(2), 189-199.

McCabe D.L. & L.K. Trevino (1996). What we know about cheating in college: Longitudinal trends and recent developments. Change, 28(1), 28-33.

McCabe, D.L., L.K. Trevino & K.D. Butterfield (1996). The influence of collegiate and corporate codes of conduct on ethics-related behavior in the workplace. Business Ethics Quarterly, 6, 461-76.

Merritt, J. (2002, December 9). You mean cheating is wrong? Business Week, 8.

Nonis, S.A. & C.O. Swift (2001). An examination of the relationship between academic dishonesty and workplace dishonesty: A multicampus investigation. Journal of Education for Business, 77(2), 69-77.

Nowell, C. & D. Laufer (1997). Undergraduate cheating in fields of business and economics. Journal of Economic Education, 28(1), 3-12.

Ramos, M. (2003). Auditor's responsibility for fraud detection. Journal of Accountancy, January, 37.

Riley, S. (2004, April 15). Teachers combat high-tech teaching. Investor's Business Daily: Internet & Technology.

Roig, M. & C. Ballew (1994). Attitudes toward cheating of self and others by college students and professors. Psychological Record, 44(1), 3-12.

Sims, R.L. (1993). The relationship between academic dishonesty and unethical business practices. Journal of Education for Business, 68(4), 107-211.

Tibbets, S.G. (1999). Differences between women and men regarding decisions to commit test cheating. Research in Higher Education, 40(3), 323-342.

Tyson, T. (1990). Believing that everyone else is less ethical: Implications for work behavior and ethics. Journal of Business Ethics, 9(9), 715-721.

Vowell, P.R. & J. Chen (2004). Predicting academic misconduct: A comparative test of four sociological explanations. Sociological Inquiry, 74(2), 226-249.

D'Arcy Becker, University of Wisconsin-Eau Claire

Janice Connolly, University of Wisconsin-Eau Claire

Paula Lentz, University of Wisconsin-Eau Claire

Joline Morrison, University of Wisconsin-Eau Claire
Table 1: Participant Descriptive Statistics

 All Students

 Number of Participants 476
Students by Major
 Accounting 112
 Finance 22
 Mgt. Info. Systems 86
 Management 72
 Marketing 130
 Business Admin. 34
 Other Business 14
 Non-Business 5
Age 22.35 years
Hours worked per week 15.75 hours
Hours studied per week 14.27 hours
GPA 3.14 / 4.0 scale

 Female Students

 Number of Participants 199
Students by Major
 Accounting 52
 Finance 5
 Mgt. Info. Systems 19
 Management 29
 Marketing 69
 Business Admin. 15
 Other Business 8
 Non-Business 2
Age 22.30 years
Hours worked per week 15.55 hours
Hours studied per week 16.00 hours
GPA 3.23 / 4.0 scale

 Male Students

 Number of Participants 277
Students by Major
 Accounting 60
 Finance 17
 Mgt. Info. Systems 67
 Management 43
 Marketing 61
 Business Admin. 19
 Other Business 6
 Non-Business 3
Age 22.37 years
Hours worked per week 15.89 hours
Hours studied per week 13.03
GPA 3.07 / 4.0 scale

Table 2: Cheating Behavior Measurement Questions from Factor Analysis
Results

Cheating Behavior Questions

(Cronbach Alpha = 0.7675)

Indicate how often you have engaged in each
 behavior since
beginning your college career Mean n=476 (Std Dev)
Copied material and turned it in as your 1.79 (1.92)
 own work
Used unfair methods to learn what was on a 1.01 (1.52)
 test before it was given
Copied a few sentences of material from a 1.40 (1.84)
 published source without giving the
 author credit
Helped someone else cheat on a test 0.67 (1.39)
Cheated on a test in any way 0.93 (1.51)
Rating scale of zero times to six
 (more than five times)

Table 3: Incentive, Rationalization and Opportunity Factors from Factor
Analysis Results Panel A: Incentive Questions *
(Cronbach Alpha = 0.6762)

Question Mean n=476 (Std Dev)

In some classes, I can't get the grade I 1.70 (0.77)
 want without cheating
I don't have enough time to complete some 1.98 (0.80)
 assignments without cheating
I have a difficult time keeping up with my 2.32 (0.77)
 classes
* Rating scale for all questions in this
 table is one (strongly disagree) to 4
 (strongly agree).
Overall Factor 6.00 (1.75)

Panel B: Opportunity Questions (Cronbach Alpha = 0.5685)

Question Mean n=476 (Std Dev)

Many students in my classes have copied 1.99 (0.71)
 answers to a test
Plagiarism and cheating on tests occur 2.28 (0.68)
 frequently at our school
The faculty do not take substantial actions 2.25 (0.65)
 to deter academic dishonesty
Overall Factor 6.52 (1.46)

Panel C: Rationalization Questions (Cronbach Alpha = 0.7044)

Question Mean n=476 (Std Dev)

If a professor does not explain what he/she
 considers cheating, the professor can't 2.52 (0.94)
 say I cheated
If someone leaves a test where I can read 2.23 (0.83)
 the answers, then it's his/her fault if
 I copy
The faculty usually detect academic 2.91 (0.76)
 dishonesty
The penalties for academic dishonesty at our 2.88 (0.91)
 school are not severe
Overall Factor 10.58 (2.32)

Table 4: Effects of Incentive, Opportunity, and Rationalization on
Student Cheating Behaviors

Regression Results: All Students Model [R.sup.2]=.2042

 Sum of P-Value
 Squares df Mean Square F (2 tailed)

Regression 3313.3599 3 1104.4532 40.369 0.00
Residual 12913.2764 472 27.3586
Total 16266.6363 475

Regression Equation: CHEAT = -10.10 + 0.84 INC +1.23 OPP + 0.27
RAT + Error

 P-Value
 Source of Variation Coefficient Std Error T (2 tailed)

Incentive (INC) 0.8350 0.1412 5.91 0.00
Opportunity (OPP) 1.2280 0.1685 7.29 0.00
Rationalization 0.2729 0.1031 2.65 0.01
(RAT)

Note that cheating is more likely when incentive, rationalization, and
opportunity are higher. Cheat Mean 5.80 (of maximum value 30) (5.84)

Table 5: Model of Student Cheating Behaviors

 Model [R.sup.2] = .2321

 Sum of Mean
Element Squares df Square F P-Value

Regression 3764.0439 5 752.8088 28.295 0.00
Residual within groups 12451.4854 468 26.5189
Total 16215.5293 473

Regression Equation: CHEAT = -7.75 + 0.81 INC + 1.19 OPP + 0.28
RAT + 0.61 PARTY - .16 AGE + Error
 P-Value
 Source of Variation Coefficient Std Error T (2 tailed)
Incentive (INC) 0.8060 0.1397 5.77 0.00
Opportunity (OPP) 1.1910 0.1682 7.08 0.00
Rationalization (RAT) 0.2800 0.1018 2.75 0.01
Party (PARTY) * 0.6140 0.1995 3.08 0.00
Age (AGE) -0.1570 0.0756 -2.08 0.04

* Party was reported as how often the student goes out to party during
the school year. Choices were never (0), once / twice a year (1), once
every 2-3 months (2), once a month (3), once a week (4) and every day
(5). Three students failed to answer this question.

APPENDIX--FINAL SURVEY
Survey of Student Attitudes (A) Fall 2004

Your class number and time:

We are conducting research on student attitudes toward various types of
academic conduct. We would like your opinions on the items listed
below. Please complete each item. Your answers will not be used on an
individual basis but will be combined with those of other students at
UWEC. Complete confidentiality of your answers is assured. Do not place
your name on these materials. Your participation is voluntary and is
appreciated!

 Strongly
 Agree Agree

1 A typical student at UWEC would strongly 1 2
 disapprove if he/she found out I cheated
 in a course

2 My closest friend would strongly 1 2
 disapprove if he/she found out I cheated
 in a course

3 If a professor does not explain what 1 2
 he/she considers cheating, the professor
 can't say I cheated

4 Many students in my classes have copied 1 2
 someone else's homework

5 Many students in my classes have copied 1 2
 the answers to a test

6 My professors grade fairly 1 2

7 My professors really care about their 1 2
 students

8 If someone leaves a test where I can read 1 2
 the answers, then it's his/her fault
 if I copy

9 If a professor leaves the room during a 1 2
 test, the professor is in effect okaying
 cheating

10 In some classes, I can't get the grade I 1 2
 want without cheating

11 I don't have enough time to complete some 1 2
 assignments without cheating

12 I have a difficult time keeping up with 1 2
 my classes

13 I feel pressure to get good grades any 1 2
 way I can

14 Maintaining my GPA is important to me 1 2

15 Plagiarism and cheating on tests occur 1 2
 frequently at UWEC

16 I have personally observed (or heard 1 2
 about) another student cheating on a test
 many times at UWEC

17 The faculty take substantial actions to 1 2
 deter academic dishonesty

18 The faculty rarely detect academic 1 2
 dishonesty

19 A typical student at UWEC would report 1 2
 another student's academic dishonesty

20 The penalties for academic dishonesty at 1 2
 UWEC are severe

 Strongly
 Disagree Disagree

1 A typical student at UWEC would strongly 3 4
 disapprove if he/she found out I cheated
 in a course

2 My closest friend would strongly 3 4
 disapprove if he/she found out I cheated
 in a course

3 If a professor does not explain what 3 4
 he/she considers cheating, the professor
 can't say I cheated

4 Many students in my classes have copied 3 4
 someone else's homework

5 Many students in my classes have copied 3 4
 the answers to a test

6 My professors grade fairly 3 4

7 My professors really care about their 3 4
 students

8 If someone leaves a test where I can read 3 4
 the answers, then it's his/her fault
 if I copy

9 If a professor leaves the room during a 3 4
 test, the professor is in effect okaying
 cheating

10 In some classes, I can't get the grade I 3 4
 want without cheating

11 I don't have enough time to complete some 3 4
 assignments without cheating

12 I have a difficult time keeping up with 3 4
 my classes

13 I feel pressure to get good grades any 3 4
 way I can

14 Maintaining my GPA is important to me 3 4

15 Plagiarism and cheating on tests occur 3 4
 frequently at UWEC

16 I have personally observed (or heard 3 4
 about) another student cheating on a test
 many times at UWEC

17 The faculty take substantial actions to 3 4
 deter academic dishonesty

18 The faculty rarely detect academic 3 4
 dishonesty

19 A typical student at UWEC would report 3 4
 another student's academic dishonesty

20 The penalties for academic dishonesty at 3 4
 UWEC are severe

For each of the following actions, indicate how often you have engaged
in each behavior since beginning your college career.

Number of times 0 1 2 3 4 5 >5

21 Copied material and turned it in as 0 1 2 3 4 5 >5
 your own work

22 Used unfair methods to learn what 0 1 2 3 4 5 >5
 was on a test before it was given

23 Copied a few sentences of material 0 1 2 3 4 5 >5
 from a published source without
 giving the author credit

24 Helped someone else cheat on a test 0 1 2 3 4 5 >5
25 Cheated on a test in any way 0 1 2 3 4 5 >5

Your age:

Your major(s):

Number of hours you work each week:

Number of hours you study each week:

Your gender: Male Female
Your current GPA:

About how often do you go out to "party" during the school year?
(Mark one)

Never Once/Twice a year Once every 2-3 months
Once a month Once a week Every day
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Author:Becker, D'Arcy; Connolly, Janice; Lentz, Paula; Morrison, Joline
Publication:Academy of Educational Leadership Journal
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2006
Words:7084
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