Copyright 2008 ProQuest Information and LearningAll Rights ReservedCopyright 2008 American Risk and Insurance Association, Inc. Risk Management and Insurance Review
Spring 2008
EDUCATIONAL INSIGHTS; Pg. 209 Vol. 11 No. 1 ISSN: 1098-1616
52759
6141 words
ACADEMIC DISHONESTY: AN EXPLORATORY STUDY EXAMINING WHETHER INSURANCE STUDENTS ARE DIFFERENT FROM OTHER COLLEGE STUDENTS
Eastman, Jacqueline K; Iyer, Rajesh; Eastman, Kevin L.
Kevin L. Eastman is a Professor of Risk Management and Insurance, Department of Finance and Quantitative Analysis, College of Business Administration, Georgia Southern University, P.O. Box 8151, Statesboro, GA 30460-8151; phone: 912-681-0193 (Work); 912-764-3946 (Home); e-mail: keastman@georgiasouthern.edu . Jacqueline K. Eastman is an Associate Professor of Marketing, Department of Management, Marketing & Logistics, College of Business Administration, Georgia Southern University, P.O. Box 8154, Statesboro, GA 30460-8154; phone: 912-871-1870 (Work); 912-764-3946 (Home); e-mail: jeastman@georgiasouthem.edu . Rajesh Iyer is an Assistant Professor of Marketing, Department of Marketing, Foster College of Business, Bradley University, Peoria, IL 61625; phone: (309) 677-2266; e-mail: riyer@bradley.edu This article was subject to double-blind peer review.
ABSTRACT
This exploratory study compares academic dishonesty scores for insurance students in one insurance program to those for other college students using survey data from business and nonbusiness students at two universities. Academic dishonesty was measured using a modified version of a scale developed by McCabe and Trevino, with a higher score indicating greater academic dishonesty. The average score on total academic dishonesty was significantly higher for insurance students than for other business students and lower, but not significantly so, than the scores for nonbusiness students. Regression analysis indicates that a significant predictor of academic dishonesty for both insurance students and other business students is the perceived relevance of the work to the student's major coursework. There were some differences, however, in the other significant predictors for insurance students versus other business students.
Specifically, year in school was significant only for insurance majors, while membership in a Greek social organization and a belief that there was a low risk of getting caught were significant only for other business majors. Furthermore, the significant predictors of academic dishonesty were different for insurance students and nonbusiness students. Overall, the results indicate that insurance students are more likely to engage in academically dishonest behavior than other business students, and the motivation for academic dishonesty differs for insurance students and other students (both nonbusiness and other business). This suggests a need for insurance educators to address academic dishonesty using an approach that is somewhat different than that used for other students.
Many in the educational system are concerned with the problem of academic dishonesty and the rate at which it is increasing (McCabe and Trevino, 1997; Park, 2003). The estimate of how many students cheat ranges dramatically. McCabe and Trevino (1997, p. 379) offer a range from 13 percent to as high as 95 percent, and Park (2003) states it as at least 50 percent of students. In the business literature, Kidwell et al. (2003) and Chapman et al. (2004) found that 75 percent of students reported cheating. Their findings are similar to the 63 percent found by Nonis and Swift (1998). Finally, there is concern that academic dishonesty is increasing due to changes in technology that make it easier for students to cheat (Born, 2003; Park, 2003).
This issue is critical for business schools as it seems to mirror the growing concerns of ethical problems in the business community (Chapman et al., 2004; Kidwell et al., 2003; Stancavage, 2007). There is concern that students who perceive that people in the business community act unethically may also feel the need to act unethically in order to advance in their business careers (Lawson, 2004). Research also indicates that those who cheat in college are more likely to cheat on the job (Swift and Nonis, 1998). "Ethical behavior lapses in business may be presaged by ethical lapses of students (cheating) who plan to make business their career" (Alimon et al., 2000, p. 411). Thus, there is a relationship between students' ethical behavior in the classroom and their attitude toward ethical behavior in the business world (Lawson, 2004). This suggests an increased need for business schools to address academic dishonesty because what students learn as acceptable behavior in the classroom impacts their expectation of what is acceptable professionally.
In the insurance field Specifically, there have been discussions of both ethical concerns and approaches to addressing them (see, e.g., Cooper, 1998; Cooper and Frank, 2002; Cooper and Frank, 2005). These ethical challenges impact both the property-liability and life insurance businesses (Cooper and Frank, 2002), are attracting political and legal attention (Ruquet, 2007), and impact insurance professionals' reputations, businesses, and professional relationships (Hamburger, 2007). In terms of insurance students, Eastman et al. (1997) found that insurance students demonstrated significantly lower levels of both personal and professional ethics than did insurance agents and a significantly greater likelihood of actually behaving unethically. While the insurance literature does not directly address the issue of academic dishonesty on the part of insurance majors, there has been considerable discussion about the need for ethics education (Driskill, 1991; Mullane, 1994; Levy, 2006). Thus, the issue of academic dishonesty, including the extent of the problem for students majoring in insurance, has not been addressed in the existing insurance literature. This is the subject of this study.
Given the ethical challenges that exist in the insurance industry and findings that the ethical attitudes and behaviors that students exhibit in the classroom can later impact their choices as professionals, we feel it is vital that insurance professors address these issues. The purpose of this research is to measure the level of academic dishonesty of both business and nonbusiness college students and to identify the reasons for academic dishonesty on the part of these students. We then test whether insurance students differ from other business students as well as nonbusiness students in terms of academic dishonesty and unethical behavior. The article begins with a review of the literature dealing with the definition of academic dishonesty, the influences on academic dishonesty, the comparative honesty of students, and our hypotheses. We then discuss the methods used to measure academic dishonesty and unethical behavior and the sampling approach used in this study. Finally, we give the results of our hypothesis tests and present the implications for insurance educators based on those results.
LITERATURE REVIEW
Academic dishonesty has been defined by Lambert et al. (2003, p. 98) as behavior that breaches "the submission of work for assessment that has been produced legitimately by the student who will be awarded the grade, and which demonstrates the student's knowledge and understanding of the context or processes being assessed." The most common forms of academic dishonesty are copying a few sentences without proper citation, working on individual assignments with others, having someone check over a paper before turning it in, and getting questions and/or answers on a test from someone who has already taken it (Brown, 1996; Kidwell et al., 2003). Swift and Nonis (1998) found a significant relationship among students who cheated on exams and students who cheated on projects, which suggests that students who cheat engage in more than one cheating practice.
Influences on Academic Dishonesty
In looking at what influences academic dishonesty, McCabe and Trevino (1997) found that academic dishonesty was impacted by both individual characteristics (such as age, gender, and grade point average (GPA)) and contextual factors (including peers, Greek membership, and perceived penalties for academic dishonesty). The literature suggests that younger, immature students cheat more than older, mature students; that is, upperclassmen cheat less than lowerclassmen (McCabe and Trevino, 1997; Allmon et al., 2000; Park, 2003; Straw, 2002). Similarly, Lambert et al. (2003) found that older students were more likely than younger students to view scenarios of academic dishonesty as serious offenses, while Kuther (2004) found that upperclassmen saw a bigger ethical problem with professors who ignored cheating than did freshmen. However, Brown (1995) found the ethics of graduate business students similar to undergraduates, despite graduate students perceiving themselves as more ethical. In terms of gender, McCabe and Trevino (1997) found who men reported a higher level of academic dishonesty than women, and Buckley et al. (1998) similarly reported that males had a higher probability of engaging in unethical behavior. Lambert et al. (2003) found that females were more likely to view scenarios of academic dishonesty as serious cheating. Leming (1980) found that under a low-risk condition, women cheated more than men, but that a higher risk of punishment reduced the risk of cheating only for women. With respect to GPA, students with a lower GPA are more likely to cheat as they have more to gain and less to lose than students with a higher GPA (Straw, 2002). Finally, Allmon et al. (2000) found that religious orientation may impact perceptions of ethical classroom behaviors for business students.
Williams and Hosek (2003) stress that even dishonest students are rational, and the decision to cheat is a conscious decision based on their evaluation that the benefits of cheating outweigh the risks. Per Pullen et al. (2000, p. 616), "causal factors run the gamut from large classes, impersonal relationships with professors, competition for jobs, gaining higher GPAs in order to enter graduate school, to a culture that appears to accept cheating readily as a normal part of life." The literature offers the following additional reasons for student cheating: a lack of understanding of what is plagiarism (Park, 2003), efficiency gain (Payne and Nantz, 1994; Park, 2003), time management problems (Payne and Nantz, 1994; Lambert et al., 2003; Park, 2003), personal values (Payne and Nantz, 1994; Park, 2003), defiance/lack of respect for authority (Park, 2003), negative attitudes toward teacher/class (Payne and Nantz, 1994; Park, 2003), temptation/opportunity (Park, 2003), a lack of deterrence (Payne and Nantz, 1994; Park, 2003), a personal crisis (Lambert et al., 2003), peer pressure (Payne and Nantz, 1994), and cheating seen as having a minimal effect on others (Payne and Nantz, 1994).
While the web is a resource for both students and faculty, some are concerned that this generation of students may have a different idea of what is considered "fair use" (Scanlon, 2004) and hence the web could be increasing the problem of plagiarism. One-quarter of college students surveyed have plagiarized from the Internet, but students perceive that a significantly larger percentage of students are doing so (Scanlon, 2004, pp. 161-162). Per Scanlon (2004), the concern is that if students perceive that Internet cheating is commonplace, they will be more likely to engage in it.
Finally, students and universities tend to view academic dishonesty in very different ways. Roig and Marks (2006) found that students have significantly more tolerant attitudes toward cheating than professors, even when there is an honor code in place. For students, cheating is evaluated primarily in terms of its effect on their peers, with a strong consensus that the least acceptable forms of behavior are those that hurt other students (Ashworth and Bannister, 1997, p. 187). Academic dishonesty is less likely when students perceive that their peers disapprove of such misconduct (McCabe and Trevino, 1997). However, if students see their peers successfully get away with cheating, they are more likely to cheat (McCabe, 1999; McCabe and Trevino, 1993,1997). Even perceived peer behavior has a strong effect on cheating (McCabe and Trevino, 1997; McCabe et al., 2006). There is a robust false consensus effect in that students significantly overestimate the degree to which others cheat (Chapman et al., 2004). As a result, they perceive cheating as a normative behavior and believe their own behavior is more honest than that of their peers. Finally, students involved in extracurricular activities such as Greek organizations were more likely to cheat (McCabe and Trevino, 1997; Straw, 2002; Park, 2003).
Comparative Honesty of Students
In comparing the academic dishonesty of students by major the results have been mixed. Some have found that business students ranked highest for self-reported levels of cheating, followed by engineering and humanities (Meade, 1992; Park, 2003). In a study of discarded cheat sheets, Pullen et al. (2000) found significantly more business cheat sheets compared to other disciplines. Rettinger and Jordan (2005) offered that business students have less critical attitudes toward cheating and greater grade orientation than liberal arts students. McCabe et al. (2006) found that graduate business students cheated more than nonbusiness graduate students. Brown (1996), however, found few differences by major for academic dishonesty. Furthermore, Iyer and Eastman (2006) found that nonbusiness students were more likely to cheat than business students. Thus, while the evidence is mixed, there is some research suggesting that business majors engage in unethical behavior more frequently than nonbusiness majors.
This study compares the levels and predictors of academic dishonesty for insurance students to those of other business students and to nonbusiness students. Our first hypothesis relates to the relative levels of academic dishonesty, and the second hypothesis deals with the predictors of academic dishonesty for these groups of students. There is nothing in the literature to suggest a significant difference in the levels of academic dishonesty for insurance students versus other business students. Given the conflicting evidence, our null hypothesis is that there is no significant difference in the relative levels of academic dishonesty of these groups:
H1: The level of academic dishonesty for insurance students will be similar to that of other business students and nonbusiness students.
With respect to the predictors of academic dishonesty, the literature has not suggested a difference between insurance students and other majors. Thus, our null hypothesis is that the predictors of academic dishonesty for these groups are not significantly different:
H2: The significant predictors of academic dishonesty for insurance students will be similar to those of other business students and to nonbusiness students.
METHODOLOGY
Sample and Procedure
Similar to McCabe and Trevino (1997), we looked at students at multiple universities. Our study focuses on two state universities in the southern region of the United States using convenience samples of different classes for different majors. A letter requesting faculty cooperation was sent out before the start of the semester in which we planned to collect the data so that faculty had sufficient time to plan how to incorporate our request into their syllabi. The lead time also gave us the opportunity to try to attain adequate student representation from the different majors offered on the two state university campuses.
An instruction sheet was given to each faculty member who agreed to participate in the study. All instructors read the same introductory script to their students that included: (1) the purpose of the study, (2) the amount of time it would take for the students to complete the survey instrument, and (3) reassurance as to the confidentiality and anonymity of their responses. Paulhus (1984) noted that there is less likely to be a social desirabiilty bias in the findings when more anonymity is assured in the surveying process.
A total of 459 students completed the survey. All students present in the classes when the survey was handed out completed the survey. The only students who did not complete the survey were those who were absent that day for class. Thus, the researchers did not perceive an issue with nonresponse bias. Additionally, there was a good balance of insurance (105 students), other business (132 students), and nonbusiness (197 students) majors surveyed. There were an additional 25 students who did not list a major; while these students were included in the sample, they were not included in the hypotheses tests looking at the impact of major. The 197 nonbusiness majors included a variety of fields: Education (33 students), Biology/Chemistry/Science (23 students), Computer Science/Technical Studies (19 students), Nursing (16 students), Communications/Mass Media/Public Relations (15 students), EngUsh (11 students), Art/Interior Design (11 students), Psychology/Sociology/Anthropology (10 students), Criminal Justice (10 students), Math (nine students), History (eight students),General Studies (seven students), Political Science (six students), Public Administration (six students), Pre-Med/Pharmacy (five students), Exercise Science (five students), and Speech Pathology (three students). Thus, the sample was a good representation of majors at the universities surveyed.
In comparing the insurance students sample to the sample of noninsurance business students and nonbusiness students, there were some noteworthy differences. As shown in Table 1, the insurance subsample had a greater percentage of men, a greater percentage of upperclassmen, a higher GPA, a lower percentage employed and/or who worked fewer hours, and a greater percentage who were members of Greek social organizations. Some of these differences may be attributable to the fact that the insurance students surveyed were exclusively at one of the two universities. Additionally, there was more homogeneity among the insurance student sample; for example, they were almost aU juniors and seniors.
Survey Instrument
A pretest of the questionnaire was conducted with 45 students, in which they were asked to provide explicit feedback as to the ease of understanding the questions, their perception of the purpose of the study, and the time needed to complete it. The resulting survey measured both the tendency of the respondents to engage in academically dishonest behaviors and the reasons for doing so.
McCabe and Trevino (1993,1997) developed a measure of academic dishonesty that asked students about 12 types of self-reported academic dishonesty on a one (never) to five (many times) scale. This scale involves a list of academically dishonest behaviors and asks the respondents to indicate whether they have ever engaged in those activities. Others have used similar measures based on either a subset of these original items (McCabe et al., 2001; Chapman et al., 2004) or with additional items included (Bolin, 2004; Brown, 1995,1996,2000; Kidwell et al., 2003). The Perceived Cheating Index used by Allen et al. (1998) included 12 specific forms of cheating. Many of the items were similar in concept to those utilized by McCabe and Trevino (1993,1997) and Brown (1995,1996, 2000).
We measured academic dishonesty based on McCabe and Trevino's (1993) academic dishonesty scale, along with additional items to address changes in technology since that scale was initially created. A summary of the scale items used in this study is provided in Table 2. Fourteen items were similar to those also used by Brown (1996, 2000) and Kidwell, Woznik, and Laurel (2003). Three items were added to address changes in technology: using a cell phone to text message for help during an exam, using a cell phone or another device to photograph an exam, and purchasing or finding a paper on the Internet to submit as one's own work. The resulting academic dishonesty scale included 17 items evaluated on a five-point scale (never, once, few times, several times, and many times). McCabe and Trevino (1997) found their scale to be reliable (Cronbach's alpha of 0.83) but did not report any exploratory or confirmatory factor analysis to address the dimensionality of the scale. Neither Brown (1996, 2000) nor Kidwell et al. (2003) reported any reliability or factor structure on the items. We evaluated the internal consistency of the scale by computing the coefficient alpha and found the Cronbach's alpha for the overall scale to be 0.878 suggesting the scale is reliable. To be consistent with how the scale has been used in the literature, we treated academic dishonesty as a single construct.
The survey also measured the reasons for engaging in academically dishonest behavior as shown in Table 3. To measure the reasons for unethical behavior, we used the eleven items from Brown's (1995, 1996, 2000) "Reasons for Unethical Behavior" scaled on a five point scale: not at all likely, somewhat likely, neutral, likely, and very likely. This scale asks the respondents to indicate the reasons why they would engage in unethical behavior. Brown (1996,2000) did not report any reliability measure and treated the items as a single construct. We found the scale to be reliable with a coefficient alpha for the scale of 0.933.
RESULTS
The hypothesis regarding the levels of academic dishonesty for insurance students versus other business students and nonbusiness students was tested using analysis of variance (ANOVA) to examine the difference in the mean responses on the academic dishonesty scale for these groups. The null hypothesis tested was that the mean score on the academic dishonesty scale was the same whether one was a nonbusiness major (mean = 1.901, standard deviation = 0.537), other business major (mean = 1.688, standard deviation = 0.374), or insurance major (mean = 1.870, standard deviation = 0.402). This hypothesis was not supported as illustrated in Table 4. The mean score for overall academic dishonesty was highest for the nonbusiness students, followed by the insurance students and then the business students.
In comparing the means more specifically between insurance majors and nonbusiness majors, and between insurance majors and other business majors, Tukey tests were utilized. The tests indicated that there was no significant difference between the mean reported levels of academic dishonesty for insurance majors versus nonbusiness majors, but there was a significant difference in the means scores between insurance majors and other business students (p = 0.009). These results suggest that the insurance students in our sample were significantly more Ukely to engage in academically dishonest behaviors than other business majors, but not as compared to nonbusiness students.
Regression analysis was used to test the second hypotheses addressing the predictors of academic dishonesty. Three separate regression analyses were run: one for insurance students, one for other business students, and one for nonbusiness students. For each regression, the dependent variable is the sum total of the academic dishonesty scale. The independent variables are: gender (male = 1, female = 2), employment (not employed = 1, employed less than 10 hours weekly = 2, working between 10 and 20 hours = 3, working between 20 and 40 hours = 4, and working more than 40 hours a week = 5), Greek membership (yes = 1, no = 2), GPA (0.00 to 4.00), year in college (freshman = 1, sophomore = 2, junior = 3, senior = 4, graduate student = 5), and the responses to the 11 questions related to likely reasons for unethical behavior. The results of these regressions are provided in Table 5 through Table 7.
The regression results for the sub-sample of insurance students only are shown in Table 5. For this group, the significant predictors of academic dishonesty were year in college (t = -2.724, p = .008) and one item from the "reasons for unethical behavior" scale (the work is irrelevant to the student's coursework, t = 3.619, p = 0.001). For other business students (Table 6), the significant predictors of academic dishonesty were Greek membership (t = -1.998, p = 0.049) and two items from the "reason for unethical behavior" scale (low risk of getting caught, t = 2.228, p = 0.028; and the work is irrelevant to the student's major coursework, t = 1.757, p = 0.082). Finally, for the nonbusiness students (Table 7), four of the reasons for unethical behavior were significant predictors of academic dishonesty: want to get a high grade (t = 2.243, p = 0.027), have the time but do not want to study (t = -1.999, p = 0.048), do not have the time to study (t = 1.712, p = 0.089), and the material is difficult to understand (t = 2.961, p = 0.004).
The hypothesis that the significant predictors of academic dishonesty will be similar for insurance students, other business students, and nonbusiness students was only partially supported. For both insurance majors and other business majors, those with higher scores on the academic dishonesty scale (i.e., those reporting performing more academically dishonest behaviors) were more likely to cite the irrelevance of the material to their major coursework as the reason for their academically dishonest behavior (though it was only somewhat significant for the other business students). This suggests that business faculty must illustrate to all students the relevance of the material to their major, their other coursework, and their business careers. However, there were some differences between insurance students and other business students. Membership in Greek organizations was significant for other business students but not for insurance students. There were also differences in the significance of some of the other "reasons for unethical behavior." For the other business students, a low risk of getting caught was significant, which suggests a need for faculty to maintain strict controls over exams and projects to raise the risk. For the insurance majors, the year in school was significant. This result suggests that students may be less likely to cheat as they progress in their insurance program, a finding that is supported by others in the literature (McCabe and Trevino, 1997; AUmon et al., 2000; Park, 2003; Straw, 2002). Thus, the results of the regression analysis suggest areas that business faculty members can address in preventing academic dishonesty. They also suggest that preconceived notions of who is likely to commit academic dishonesty, such as students belonging to Greek organizations, may not always hold.
Finally, in comparing the significant predictors of academic dishonesty for insurance students and nonbusiness students, there were no simUarities. These results suggest that the reasons for academic dishonesty vary significantly between insurance students and nonbusiness students and that faculty need to address academic dishonesty somewhat differently based on major.
DISCUSSION
Our analysis suggests that overaU there is a statisticaUy significant relationship between the level of academic dishonesty and a student's major field of study (insurance, other business, or nonbusiness). Nonbusiness students were the most likely to engage in academically dishonest behaviors, followed by insurance students, and then other business students. In addition, the reasons for acting dishonestly varied somewhat based on major.
For insurance as well as other business majors, the perceived irrelevance of the work to their major had a significant impact on academic dishonesty. However, this factor was not significant for nonbusiness majors, and none of the significant predictors of academic dishonesty for insurance students matched those for nonbusiness students. These results indicate that the reasons for engaging in academically dishonest behavior can vary by major and that insurance (and other business) faculty must (1) stress how the class material relates to their other major coursework and to a career in insurance, and (2) structure their courses to illustrate the current relevance of the materials and assignments to the real world. Furthermore, based on our findings, we suggest that the feedback insurance educators provide students demonstrate the relevance of learning for insurance students' success in school and in their career.
In terms of differences between insurance and other business majors, year in school had a significant impact on insurance majors but not other business majors. In addition, neither Greek membership nor the risk of getting caught had a significant effect on the behavior of the insurance students, but both impacted other business majors. The results indicated that upperclassmen in insurance are less likely to engage in academicaUy dishonest behaviors than those just starting to take major courses in insurance. Thus, for new majors in particular, it is important that insurance professors understand and be consistent with overall university poUcies related to academic dishonesty and communicate these policies to students in the course syllabus and through a discussion of expectations both at the start of the term (Burnett, 2002) and throughout the semester (McLafferty and Foust, 2004). Per Spangenberg and ObermUler (1996), most professors assume that students know what cheating is and what the consequences are, and therefore few make this expUcit in their syllabus and instructions. However, younger students may be less aware of these concepts than upperclassmen and hence are more Ukely to need specific guidance from the instructor. This suggests that professors must increase awareness among students about what is considered academicaUy dishonest behavior and why it will not be tolerated (Allen et al., 1998; Born, 2003; McLafferty and Foust, 2004; Swift and Nonis, 1998), and follow through consistently with their policies (Chapman et al., 2004).
Additional Suggestions for Preventing Academic Dishonesty
The idea that academic dishonesty can be prevented through the proper structuring of courses and assignments has been discussed previously in the Uterature. "When students are instructed appropriately and given certain types of assignments, plagiarism is minimized or rendered virtually impossible" (McLafferty and Foust, 2004, p. 186). With respect to papers and projects, Born (2003) stresses the need for faculty to be proactive by treating a paper or project as a process rather than a product. This may involve, for example, breaking a project up into several steps with drafts submitted along the way (Scribner, 2003; Williams and Hosek, 2003; McLafferty and Foust, 2004). Scribner (2003, p. 32) Usts practices that are most Ukely to enable students to plagiarize, including: (1) assignments that do not keep up with advances in the field, (2) utilizing the same assignment repeatedly, (3) making unrealistic assignments, (4) failing to teach the skiUs needed to successfully complete the assignment, (5) not taking the time to check students' sources, and (6) accepting papers with incomplete citations. FinaUy, Pfeffer (2003) recommends that faculty stress to students that their work should serve as a reflection of their skills and character, and not just be performed in order to get a grade.
With respect to examinations, Born (2003) recommends designing questions that require discussion rather than memorization, assigning different questions to different students, giving more frequent tests/quizzes, not aUowing make-up tests, and updating the test materials used by faculty. Chapman et al. (2004) express the concern that web-based tests may be encouraging students to cheat and suggest that this be addressed by imposing strict time limits, posting answers only after everyone has completed the test, and using a large database of questions in which each student gets a unique test.
If prevention tactics are not enough, there are things professors can do to detect academic dishonesty once it has occurred. Technology makes it easier for faculty to determine if their students have plagiarized (Park, 2003). McLafferty and Foust (2004, p. 187) note that there are three tools that can be used to investigate Internet copying: (1) search engines (by inserting a unique phrase from the paper); (2) plagiarism web sites such as Plagiserve and Turnitin com, which compare submitted papers with online sources and papers in their database; and (3) software that checks for identical wording between specific sources (e.g., to determine if students are recycling papers within a course from semester to semester).
Finally, professors can also address academic dishonesty through their treatment of students (Brown, 2000). Faculty need to build a trusting student-faculty relationship (Born, 2003) in which faculty work to develop a better rapport with their students (McLafferty and Foust, 2004). Students suggest that they would be less likely to cheat in a class in which they felt the professor was truly interested in their learning and treated the students with respect (Chapman et al., 2004). Scribner (2003, p. 34) stresses that students need to be taught how to synthesize ideas and facts and faculty need to model ethical behavior for their students. Thus, the literature offers a variety of general suggestions for addressing academic dishonesty. While it may be difficult for an individual faculty member to measure if any of these ideas decrease cheating or the desire to cheat, the uterature needs to continue to offer best practices in teaching to enhance the classroom experience and discourage academic dishonesty.
CONCLUSION
The findings of this study suggest that academic dishonesty should be a concern to insurance educators. Previous research suggests that academic dishonesty is often predictive of unethical professional behavior (Swift and Nonis, 1998; AUmon et al., 2000; Lawson, 2004). Lawson (2004) stresses the need to educate students to be able to make ethical decisions in their business careers, and Stancavage (2007) describes the impact of efforts by both industry and academia to discuss how they are affected by ethical chaUenges and to improve business ethics. Unexpected ethical situations can arise in the insurance industry, and insurance students must be taught how to evaluate and address these situations effectively (Amrhein, 2007).
This article illustrates that insurance students are somewhat different than other students both in terms of the amount of self-reported academic dishonesty and in the factors that predict or motivate their academically dishonest behavior. Insurance educators need to address the issue of academic dishonesty and are a key component in preventing and dealing with the issue (Allen et al., 1998; Burnett, 2002). It is the faculty who set the tone and expectations in the classroom, who create the exams and assignments, who monitor the work product of their students, and who are responsible for mentoring their students. Future research though is needed to better understand what impacts academic dishonesty for insurance majors and how insurance educators can best address it.
There are several limitations to this study. First, while multiple universities were utilized in the study, only one of the universities had insurance majors. Per the American Risk and Insurance Association website, there are 47 coUege and university programs in Risk Management and Insurance. Gardner and Schmit (1995, p. 625) note that "more than 26,000 students took RMI courses during 1992 through 1993 at over 200 responding schools." Thus, future research at additional universities is needed to determine if this relationship is generalizable to insurance majors at other institutions. Thus, we stress that these results are exploratory. Second, while the overall sample is large, the relatively small size of the insurance major limited the sample of insurance majors to only 105 students. Furthermore, the sample of insurance students was very homogenous in that they were mostly male, upperclassmen, who worked less than 20 hours a week. Thus, additional research with more insurance majors from a variety of universities and insurance programs is needed.
There are also several potential areas for future research. First, class size may be a significant variable impacting the level of academic dishonesty and the factors that affect it. Due to the fact that aU the classes that were included in our convenience sample were smaller sections, we were not able to measure if class size had an impact. Further research is needed to compare levels of academic dishonesty of students taught in smaller sections versus students taught in larger sections. second, there is a need to determine whether faculty actions can impact academic dishonesty and, ii so, the type of actions that work the best. There are questions as to whether faculty can significantly impact the motivation of students to cheat and the types of faculty activities that would be most effective in doing so. Many of the activities suggested in the literature as means for addressing academic dishonesty may not be practical in larger class sections. Thus, future research needs to offer clear guidance on what actions are most effective. A third idea for future research would be to compare the insurance students' levels of academic dishonesty to their levels of ethics and to their behavioral intentions as future insurance professionals when faced with ethical issues in the insurance field (similar to the ethical scenario approach used by Eastman et al., 1996). Thus, while this article hopes to identify some of the differences between insurance students and other students, more research is needed to better understand and deal with academic dishonesty for insurance majors.
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IMAGE TABLE, TABLE 1, Demographics of Sample (By Percentage of Respondents)IMAGE TABLE, TABLE 2, Academic Dishonesty ItemsIMAGE TABLE, TABLE 3, Unethical Behavior ItemsIMAGE TABLE, TABLE 4, ANOVA Analysis and Post Hoc Tukey AnalysisIMAGE TABLE, TABLE 5, Regression Analysis for the Insurance Students Sample (The Dependent Variable Was the Sum Total of the Academic Dishonesty Scale)IMAGE TABLE, TABLE 6, Regression Analysis for the Business Student Sample (The Dependent Variable Was the Sum Total of the Academic Dishonesty Scale)IMAGE TABLE, TABLE 7, Regression Analysis for the Nonbusiness Student Sample (The Dependent Variable Was the Sum Total of the Academic Dishonesty Scale)
April 14, 2008
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