1 E- Cheating Among College Business Students: A Survey Brenda Sheets Paula Waddill This study investigated the extent that business students admitted to electronic cheating along with variables that might mediate cheating. Data from 177 students indicated that nearly half of the sample reported cheating. Students who cheated were more likely to be male, younger, and have lower grade point averages than those who reported having never cheated. Students cheated on out- of- class assignments using mainly e- mail or fax, and the programmable calculator was the tool participants most often used to cheat on in- class tests. Likelihood of cheating was unrelated to knowledge of the university s or professors policies. Age and grade point average (GPA) were predictors of cheating. Recommendations for strengthening academic integrity follow the discussion. Introduction Following the days of scandals involving high- profile CEOs from corporations including Enron, WorldCom, Tyco, and Shell, attention focused not only on the business schools from which these leaders had received their business degrees, but also on business schools across the nation. Stakeholders questioned how well business school leaders and faculty were fostering the academic integrity of students, as well as what else business schools could do to decrease the possibility of future corporate malfeasance in the business world. Understanding that academic integrity among business students is an academic imperative and a critical issue (Krehmeyer, 2007), many business school leaders and faculty have revised their academic policies and/or promoted honor code systems (McCabe, Butterfield, & Treviño, 2006; McCabe, Treviño, & Butterfield, 1999, 2001, 2002). Pfeffer (2007) reminded business academicians, however, that there s still room for improvement (p. 42). He advised that they use one or more assessment tools to identify problems associated with academic dishonesty in business schools and establish among themselves, alumni, and students an open dialogue regarding strategies that will not only help deter dishonest students, but will also reassure honest students that [instructors] have their welfare at heart (Whitley & Keith- Spiegel, 2002, p. 82). One substantive means of assessment Pfeffer recommended was a student survey. A concern that has long threatened students academic integrity is paper- based cheating, (Bowers, 1964; Hawley, 1984; McCabe, 2001; Schab, 1991) which includes copying test answers, using crib notes, plagiarizing text, or falsifying bibliographies. Since much previous research already has been done on paper- based cheating, this study provides an extended understanding of a similar problem known as electronic cheating or e- cheating. Non- academic sources, such as national and university newspapers, trade magazines, television news segments, and education Web sites have acquired and disseminated most information Brenda Hayden Sheets is Associate Professor, Department of Management, Marketing, and Business Administration, College of Business and Public Affairs, Murray State University, Murray, Kentucky. Paula J. Waddill is Professor, Department of Psychology, College of Humanities and Fine Arts, Murray State University, Murray, Kentucky. Information Technology, Learning, and Performance Journal, Vol. 25, No. 2 4
2 E- Cheating Among College Business Students: A Survey 5 about e- cheating. Donald McCabe (2002), a prominent researcher in the area of cheating, responded to the paucity of scholarly research on e- cheating, writing, readers would [like] to learn more about forms of cheating enabled by new technologies which receive relatively little attention (p. 297). This study on e- cheating is one of the first steps in filling a gap in the scholarly research on academic dishonesty. Purpose and Research Questions The purpose of this study was to use a student survey assessment tool in a business school setting to investigate (a) the extent of students e- cheating behavior, (b) student characteristics associated with e- cheating, (c) student preference of e- cheating methods, (d) student appraisal of academic policy regarding cheating, and (e) predictors of cheating. This study addressed the following research questions: 1. Approximately what percentage of students engage in e- cheating? 2. What are some of the characteristics, in terms of gender, age, and grade point average (GPA), of students who report using e- cheating methods to cheat on tests and out- of- class assignments? 3. Is there an association between students engaging in e- cheating on tests and those engaging in e- cheating on out- of- class assignments? 4. What are the primary methods used by students to engage in e- cheating on tests and on out- of- class assignments? 5. Is students knowledge of official academic policy on e- cheating related to the the extent of their reported practice of e- cheating? 6. What variables predict cheating behavior? Related Literature Until the emergence of the public Internet and sophisticated electronic devices, students primarily engaged in paper- based cheating (Cizek, 2003; McCabe, 2001). Today, in addition to paper- based cheating, they may choose to participate in e- cheating the illicit use of electronic devices such as pagers, calculators, fax machines, hand- held and/or desktop computers, Palm Pilots/pocket PCs, and/or cell phones to complete a test or an out- of- class assignment. E- cheating, a mutant outgrowth of paper- based cheating, has become an area of concern in education (Bushweller, 1999; Kleiner & Lord, 1999). Extent of Cheating To know the gravity of the problem of e- cheating in a business school, officials must assess the extent of its practice. The percentage of students who acknowledged having engaged in e- cheating at some time during their college career varies extensively across an array of disciplines. Estimates of students who engaged in electronic plagiarism ranged from a low of 10% (McCabe, 2001) to a moderately high 50% (Underwood & Szabo, 2003) and 62.5% (O Neil, 2003). Most studies on cheating among business students, however, have not cited whether the extent of cheating was in the form of paper- based cheating, e- cheating, or a combination. For example, the specific form of cheating was not indicated by Meade (1992) who found that 87% of business majors reported having engaged in cheating acts, nor was it reported by McCabe (1997) who indicated that 84% of business majors admitted to cheating. In studies conducted among business graduate students, Brown (1995) found as many as 80% participated in dishonest practices more than once while in graduate school, and McCabe, Butterfield, and Treviño (2006) reported 56% acknowledged having cheated one or more times in an academic year. Basic Characteristics of Students Who Cheat Gender, age, and GPA are among the personal characteristics of students important for
3 6 Sheets & Waddill business school leaders and faculty to assess in order to become aware of the descriptive profiles of students who would likely admit to e- cheating. Similar to studies on the extent of cheating, most studies that associate student characteristics with cheating do not specify whether the form of cheating is paper- based cheating, e- cheating, or a combination. Many studies, including those by Buckley, Wiese, and Harvey (1998), Genereux and McLeod (1995), McCabe and Treviño (1996), Roig (1997), and Underwood and Szabo (2003), reported no major difference between the genders of students who admitted to cheating. McCabe and Treviño explained that as a greater number of women attended college, they became more motivated to compete academically with men and modeled their behavior on the men s, even when the men s behavior was dishonest. Findings, however, are inconsistent. Studies by Davis, Grover, Becker, and McGregor (1992), Newstead, Franklyn- Stokes, and Armstead (1996), and Tang and Zuo (1997) noted that men cheated more than women. Tang and Zuo indicated that society seemed to expect men to display higher academic ability than women; thus, men resorted to cheating when they were unable to actually meet that expectation. There are also inconsistent findings pertaining to the association between age and cheating. While Tang and Zou (1997) reported that older students cheated more than younger students, many researchers indicated that younger students had a greater tendency to cheat (Genereux and McLeod, 1995; McCabe & Treviño, 1997; Newstead, Franklyn- Stokes, & Armstead, 1996). Underwood and Szabo (2003), whose study focused on e- cheating, found that according to college seniors, underclassmen were more likely to engage in e- cheating than were seniors. The researchers explained that perhaps older students believed that they had more to lose in terms of their college careers if they cheated than younger students. Today, however, because students of all ages use computers, the Internet, and electronic devices as part of their courses, as well as in their daily lives, age may no longer be a factor in predicting the use of electronic devices to cheat. Although little is known regarding an association between GPA and e- cheating, the literature has revealed a significant relationship between GPA and paper- based cheating. Hawley (1984) reported students with lower GPAs plagiarized term papers more than students with higher GPAs. Scheers and Dayton (1987) found that 86% of students with the lowest GPAs admitted to copying examination answers, while only 21% of those with the highest GPAs admitted to the same behavior. Roig (1997) found that undergraduates with lower GPAs were less able to distinguish between plagiarized text and correctly paraphrased text than were those with higher GPAs. He noted this inability could possibly explain why those with lower GPAs engaged more frequently in the dishonest behavior of plagiarism than those with higher GPAs. Although previous research has established relationships between the use of paper- based cheating methods and certain demographic characteristics, the same patterns may not exist with e- cheating. The power of technology provides a variety cheating methods that are not possible with paper. Some of these methods include cell phones, programmable calculators, pagers, Internet applications such as chat rooms, and e- mail. Methods of E- cheating Business school officials should assess common electronic methods of cheating and use this knowledge to devise ways to counter students illicit use of them. The variety of methods has become known as the new cheat sheets (Lathrop & Foss, 2000, p. 2). With approximately two thirds of young people using cell phones (The Yankee Group, personal communication, September 15, 2005), it is not surprising that students choose this device as a popular tool for e- cheating (Kleiner & Lord, 1999). With camera cell phones, students can zoom in and take
4 E- Cheating Among College Business Students: A Survey 7 relatively clear pictures of test questions and post them on Web sites where other students who plan to take the same test later may access the questions online and look up corresponding answers (Bushweller, 1999). Another popular electronic cheat sheet is the programmable calculator. Students can program their calculators outside of class to store information pertinent to potential test questions. In testing sessions, students are able to retrieve the information inconspicuously. Should an instructor wish to check the students calculators to see that no information has illicitly been stored, tech- savvy students can program their calculators to look as though the memory and storage of data have been cleared, when, in fact, nothing has been erased (Bushweller, 1999). Similarly, students can use pagers and beepers for e- cheating. Bushweller explained, Before an exam, [a student] calls the pager and leaves as much test information in a message as possible. Then [he or she] sets the pager to vibrate rather than beep. When test time arrives, [the student] turns on the pager and it instantly becomes an electronic cheat sheet (1999, 29). Online listservs, forums, chat rooms, , and, to some extent, fax machines are other means by which students can illicitly collaborate with friends to complete homework or provide answers to take- home essay exams (Bushweller, 1999; Kleiner & Lord, 1999). With the use of the Internet and a personal computer, students can plagiarize text from other students computers, library databases, and numerous commercial sources (Campbell, Swift, & Denton, 2002). One of the popular commercial sources includes Internet research paper mills, which are online Web sites that offer prewritten reports, term papers, and custom research on an extensive number of topics. Some of these online paper mills provide downloadable papers for free, while others require a fee. A few current online research paper mills are A1- termpaper, CheatHouse, Cyber Essays, Papers Inn, and School Sucks. The Kimbel Library at Coastal Carolina University has compiled a more comprehensive list (Bates & Fain, 2006). Knowledge of Institutional Policy on Cheating Several studies advocate the need for universities to implement, promote, and reinforce official academic policies that stipulate proper rules of behavior and consequences of unacceptable student practices for the purpose of discouraging cheating (Davis, Grover, Becker, & McGregor, 1992; McCabe & Treviño, 1993, 1997; Nuss, 1984; Pincus & Schmelkin, 2003; Saunders, 1993). It is important for business school leaders and faculty to be cognizant of how well students understand and adhere to their university s and faculty s policies on cheating. According to Ashworth, Bannister, and Thorne (1997), only a few students took time to understand and follow official academic policy because they considered the policy vague and its enforcement often inconsistent. However, McCabe and Pavela (2000), McCabe and Treviño (1993), McCabe, Butterfield, and Treviño (2006), and McCabe, Treviño, and Butterfield (2002), reported that honor code schools (schools where students may take unproctored tests, make written pledges not to cheat, and delegate student bodies with the responsibility to handle violations of academic integrity) tend to have fewer students who cheat than do non- honor code schools. Method The study took place in the 2005 spring semester at a Midwestern public regional institution of higher education having five colleges and a graduate school with an enrollment of approximately 10,000 students. The College of Business is accredited by the Association to Advance Collegiate Schools of Business (AACSB). The university s Institutional Review Board approved the study, and only students who were willing volunteers participated.
5 8 Sheets & Waddill Participants In an attempt to avoid demand characteristics and reduce volunteer response bias, the researchers asked students if they would like to take part in a survey about college student behaviors. Participants were unaware of the study s focus on cheating. Student volunteers were enrolled in randomly selected course sections in each of the following departments of the College of Business: Accounting (2 sections), Management/ Marketing/Business Administration (3 sections), Computer Science and Information Systems (3 sections), Organizational Communication (4 sections), Economics and Finance (2 sections), and Journalism and Mass Communication (2 sections). Of the 475 students enrolled in the 16 sections, 281 (59.16%) volunteered to take part. Because the focus of this study was on electronic cheating among business students, the analysis included data from business majors only. Among the business majors were ten international students. According to Hofstede (1984), individuals from other cultures may hold a different set of cultural norms than Americans regarding acceptable behavior in certain academic situations. In light of that possibility, and because 10 was too small a sample to allow them to constitute a separate comparison group, data from the international students was not included in the analysis. The final sample consisted of 177 business students who represented a variety of majors in the university s College of Business. Table 1 provides a breakdown of the general demographic characteristics of the sample. Materials Participants in the study completed a questionnaire booklet containing a demographics survey and two self- report measures. The primary instrument was a 67- item questionnaire developed by the first author (copy available upon request from the first author) used to evaluate the frequency of e- cheating on tests and out- of- class assignments via a variety of electronic devices and methods. The questionnaire items queried students use of each of the following electronic methods of cheating: cell phone without camera, cell phone equipped with camera, pager, programmable calculator, hand- held device (e.g., PDA, pocket PC), text messaging, online chats, listservs, forums, e- mail, and fax. Research reviews by Lathrop and Foss (2000) and Cizek (2003), along with research by Campbell, Swift, and Denton (2002), formed the basis for the inclusion of these electronic methods in the questionnaire. While it is quite possible that a professor might authorize the appropriate use of devices in certain circumstances, for example, a calculator to determine numerical values on Table 1. Demographic Characteristics of Business Students Variable Gender Male Female Classification Freshman Sophomore Junior Senior Graduate/Other Major Accounting Advertising Business Admin/International Business Computer Science/Information Systems Finance Journalism/Mass Communication Management Marketing Organizational Communication Public Relations Age a GPA b (on 4.0 scale) NOTE. N = 177. a n = 176. b n = 172. n (%) 72 (41%) 105 (59%) 39 (22%) 42 (24%) 56 (32%) 33 (19%) 6 (3%) 26 (15%) 10 (6%) 45 (25%) 12 (7%) 4 (2%) 15 (8%) 5 (3%) 19 (11%) 32 (18%) 9 (5%) M= SD= 4.57 Median= 20 M= 3.08 SD=.49 Median= 3.07
6 E- Cheating Among College Business Students: A Survey 9 a test or an electronic database search for an out- of- class assignment, the focus of the study was on students reported frequency of using the devices in unauthorized ways, i.e., cheating. Therefore, the questionnaire items specifically asked them to report the frequency with which they had used a particular kind of device to cheat on a particular kind of activity, either an in- class test or an out- of- class assignment. To help participants better estimate the frequency of their behaviors and to encourage more accurate and honest responses, survey questions asked them to respond with respect to their behavior during the previous academic year. For each type of electronic device, students responded to a question asking if they had owned such a device during the previous academic year. If they answered no, they were instructed to go on to the next section of the survey; if they responded yes, then they were instructed to answer the questions related to their use of the device to cheat. These questions were organized in a multiple- choice format. For example: How often did you use your cell phone to cheat on a test inside the classroom? All the time Frequently (More than half the time) About half the time Occasionally Never How often did you use your cell phone to cheat on an out- of- class assignment? All the time Frequently (More than half the time) About half the time Occasionally Never Questionnaire items about students gender, class rank, age, GPA, and major/area measured potential demographic variables related to the likelihood and frequency of cheating. To allow for analysis of the relationship of cheating to knowledge of formal ethical policies, two survey questions in a multiple- choice format addressed students level of knowledge of the university s general policies on cheating and plagiarism and its specific policies on the misuse of electronic devices for cheating. Two additional questions related to their level of knowledge of their business professors general policies on cheating and plagiarism and their professors specific policies on the misuse of electronic devices for cheating, for example: Do you know what the university policy says about academic misuse of cell phones, pagers, and other high- tech forms of cheating? Yes Some of it No, none of it Cronbach s coefficient α helped evaluate the reliability of the survey to determine the degree to which participants tended to answer similar items about cheating in similar ways. The survey showed excellent inter- item reliability (coefficient α =.95). In order to assess the degree to which answers on the survey were influenced by participants tendency to give the right answers rather than their honest responses, respondents first completed Crowne and Marlowe s (1960) Social Desirability Scale (SDS). This self- report scale is composed of 33 true and false items designed to measure the extent to which participants tend to respond in a socially or culturally desirable manner rather than indicating their true opinions or beliefs. Crowne and Marlowe have reported very good reliability for the SDS, as demonstrated by values on two standard measures of reliability (cf. Nunnally, 1978), an internal item- consistency coefficient (KR- 20) of.88 and a test- retest correlation of.89. The SDS scores evaluated the validity of the responses on the electronic cheating survey.
7 10 Sheets & Waddill Procedure In order to protect students privacy and increase their sense of the confidentiality of their responses, in each class in which the questionnaire was distributed the instructor ended class, left the room, and did not return for the administration of the survey. Neither of the authors was the instructor of record for any of the classes participating in this study. The investigator, the first author, then told the students that if they wished to volunteer for the research project, they should stay, but if they did not want to participate they were free to leave. The investigator also stated that any of the students who had already completed the survey from a previous class should refrain from taking it again. At this time, the investigator exited the classroom and waited out of sight for three minutes, allowing students who did not wish to participate to depart without being seen by the investigator. When the investigator returned, she distributed a one- page letter of consent and an explanation of the questionnaire along with the questionnaire booklet itself. The booklet consisted of the demographics page, followed by the SDS, followed by the e- cheating questionnaire. The questionnaires were anonymous; students did not provide names or other identifying information that would allow responses to be identified with any particular student. To further protect anonymity, upon completion of the survey, students dropped their questionnaire booklet through the slit of a large box that was outside the classroom door. Participants completed their booklets within 15 minutes. Results The level of significance for all analyses was set at.05. Table 2 summarizes these analyses. Social Desirability In general, students tendency to report that they had or had not cheated was not associated with a bias toward giving socially desirable responses. For test behavior, social desirability scores did not differ significantly between those who reported never cheating (M = 15.65, SD = 5.71) and those who reported cheating at least occasionally (M = 14.40, SD = 5.49), t(175) = 1.23, p = For out- of- class assignments, those who said they had never cheated had a slightly higher desirability score (M = 15.96, SD = 5.42) than those who said they had cheated (M = 14.19, SD = 6.03), t(175) = , p = Social desirability bias did not differ significantly between males (M = 15.12, SD = 5.82) and females (M = 15.53, SD = 5.59), t(175) = , p = Thus, it appeared that students who said they had never cheated were generally no more likely to be giving the right response than those who said they had cheated. Extent of Cheating In reference to the first research question regarding the extent of cheating, of the 40% of students who indicated they used various electronic methods of cheating, 99% or more reported using a particular method no more than occasionally or half the time. Therefore, for analysis purposes, frequency of use was converted to a dichotomous variable (never use, use at least occasionally). In order to further evaluate the circumstances under which students use electronic methods of cheating, the venue in which the cheating took place (tests, out- of- class assignments) also constituted a variable in several of the following analyses. Demographic Variables In reference to the second research question, Table 3 presents means and percentages of the demographic variables of interest for students who reported cheating or not cheating on tests and out- of- class assignments separately as well as the overall reported frequency regardless of venue.
8 E- Cheating Among College Business Students: A Survey 11 Table 2. Summary of Analysis on E- Cheating Gender Cheating differed as a function of gender. Overall, more men than women reported cheating at least occasionally, χ 2 = 4.94, p = In terms of venue, men were slightly more likely than women to use at least one electronic device to cheat on tests, but the difference was not statistically significant, χ 2 = 0.40, p = For out- of- class assignments, however, men were much more likely to cheat than women, χ 2 = 6.74, p = Age Research Question 1. Approximately what percentage of students engage in e- cheating? 2. What are some of the characteristics, in terms of gender, age, and GPA of students who report using e- cheating methods to cheat on tests and out- of- class assignments? 3. Is there an association between students engaging in e- cheating on tests and those engaging in e- cheating on out- of- class assignments? 4. What are the primary methods used by students to engage in e- cheating on tests and on out- of- class assignments? 5. Is students knowledge of official academic policy on e- cheating related to the the extent of their reported practice of e- cheating? 6. What variables predict cheating behavior? Analysis 40% of students reported having engaged in high- tech cheating. Gender: 50% of men reported cheating compared to 33% of women ((χ 2 = 4.94, p =.0263). Men cheat more on out- of- class assignments. Men and women cheat equally on tests Age: Those who reported cheating on tests were significantly younger than those who did not report cheating (t = , p =.0010). The same pattern was true for those who reported cheating on out- of- class assignments (t = , p =.0010) GPA: Those who cheated on tests had a lower overall GPA than those who did not cheat (t = , p =.0441). Those who cheated on out- of- class assignments also had a lower GPA than those who did not cheat (t = , p =.0016). The likelihood of cheating on tests was significantly related to the likelihood of cheating on out- of- class assignments (χ 2 = 31.27, p <.0001). Students reported using programmable calculators most often on tests and e- mail/fax most often on out- of- class assignments, followed by the programmable calculator. Students cheating behavior was not related to their knowledge of the university s or of the business professors general policy on cheating and plagiarism (including their policy on the misuse of high- tech devices to cheat). The variables of age and GPA predict cheating behavior. variance of age between those who reported having cheated and those who reported never having cheated, F = 14.61, p <.0001, the ages of the two groups were compared using the approximate t- statistic for unequal variances. The results indicated that those who cheated on tests at least occasionally were significantly younger than those who did not, t = , p =.0010, as were those who cheated on out- of- class assignments, t = , p = In analysis of overall behavior, the same pattern emerged. Those who reported cheating at least occasionally on tests and/or assignments were significantly younger than those who did not report cheating, t = , p = One female participant did not report her age. Because of a significant difference in the
9 12 Sheets & Waddill Table 3. Characteristics of Business Students Who Did and Did Not Report Using High- Tech Devices to Cheat on Tests and Out- of- Class Assignments Tests Assignments Overall Did Not Did Not Did Not Cheated Cheated Cheat Cheat Cheat Cheated Mean Age (SD) (5.05) (1.56) (5.43) (1.42) (5.67) (1.48) Mean GPA (SD) 3.12 (.48) 2.94 (.40) 3.16 (.46) 2.92 (.50) 3.17 (.47) 2.95 (.49) Sex Male 75% 25% 56% 44% 50% 50% Female 79% 21% 74% 26% 67% 33% Knows Honesty Policy University General 85% 80% 86% 78% 88% 78% University High- Tech 39% 40% 40% 39% 41% 38% Business Professors 80% 73% 81% 71% 82% 72% General Business Professors High- Tech 58% 60% 58% 59% 58% 58% GPA Five students did not report their GPA. Students who cheated at least occasionally on tests had a lower overall GPA than those who did not cheat, t = , p = Those who cheated on out- of- class assignments also had a lower GPA than those who did not cheat, t = , p = The same pattern occurred for the overall analysis, with students who reported cheating at least occasionally on tests and/or assignments having a significantly lower GPA than those who did not cheat, t = , p = Type of Cheating In reference to the third research question regarding an association between e- cheating on tests and on out- of- class assignments, the likelihood of cheating on tests was significantly related to the likelihood of cheating on out- of- class assignments, χ 2 = 31.27, p < Forty percent of the sample reported cheating in at least one venue. Of that number, 34% reported cheating at least occasionally on both tests and out- of- class assignments, 44% reported cheating on out- of- class assignments but not on tests, and 17% reported cheating on tests but not on out- of- class assignments. Electronic Methods of Cheating In reference to the fourth research question, the primary methods of cheating used by students are summarized in Table 4. Among those cheating on tests, 78% reported using only one kind of method to cheat, 10% reported using two methods, 8% reported using three methods, and 3% reported using four methods. No one reported using more than four methods. Of the methods used to cheat on tests, programmable calculators were used most often, followed by text messaging and online chats. A student s specific business major was neither related to the type of method he or she used to cheat in general, χ 2 = 20.25, p =.2616, nor to programmable calculator use in particular, χ 2 = 24.80, p = In fact, of the 30 accounting and finance majors, only one reported using a programmable calculator to cheat. Among those cheating on out- of- class assignments, 63% reported using only one method, 15% reported using two methods, 15% reported using three methods, and 7% reported using four methods. No one reported using more than four methods. Of the methods students used to cheat on out- of-
10 E- Cheating Among College Business Students: A Survey 13 class assignments, e- mail/fax was used most often, followed by chats and programmable calculators. Those who used more methods to cheat in one venue, tests or out- of- class, used more methods in the other venue, r =.53, p < In addition, those who used more Table 4. Percentage of Business Students Who Reported Using Various High- Tech Cheating Methods on Tests and on Out- of- Class Assignments Method Ownershi p Tests Assignment s Cell Phone (no camera) 80% 3% 2% Cell Phone (with 7% 2% <1% camera) Pager 2% 1% 2% Programmable 80% 12% 16% Calculator Hand- Held Device 13% 2% 2% Text- Messaging NA 5% 6% Chats/Listservs/Forum NA 13% 4% s E- mail/fax NA NA 16% NOTE. N = 177. Percentages represent students reporting using the method at least occasionally; e- mail/fax was provided as a survey option only for out- of- class assignments. a Percentage of students who said they owned the device; ownership is not applicable to electronic activities (text- messaging, chats, etc.) methods to cheat outside of class generally had lower GPAs, r = -.19, p = The same trend was apparent for cheating on tests, but the correlation did not reach statistical significance, r = -.11, p = Younger students tended to use more methods to cheat, but the correlations with age were not significant either for tests, r = -.12, p =.1158, or for out- of- class assignments, r = -.13, p = Knowledge of Academic Honesty Policies In reference to the fifth research question, the survey also queried students perceived level of knowledge of the university s general policy on cheating and plagiarism, the university s policy on electronic cheating and plagiarism, their own business professors general policy on cheating and plagiarism, and their business professors policies on the misuse of high- tech devices to cheat. Table 5 presents the percentage of students who indicated that they had at least some knowledge of each of those four policies. Compared to students who did not report cheating, students who reported cheating on tests did not have less knowledge of the university s general or specific policy or their business professors general or specific policies, all χ 2 < 1. The likelihood of cheating on out- of- class assignments was also unrelated to knowledge of any of the academic honesty policies, largest χ 2 = 2.36, p = Logistic Regression Finally, in reference to the sixth research question, a logistic regression analysis incorporated the demographic and knowledge variables, with cheating on tests and/or assignments as the outcome variable and age, GPA, sex, and academic honesty policy knowledge as predictors. For the purpose of this analysis, an overall variable summing the number of policies (university general policy, university high- tech policy, business professors general policy, business professors high- tech policy) about which a student had at least some knowledge measured the knowledge of honesty policy. Thus, the score on this variable could range from 0 to 4 (M = 2.59, SD = 1.35). As shown in Table 6, a test of the full model with all four predictors was statistically significant, indicating that the predictors as a set distinguished between those who said they had cheated at least occasionally and those who said they had never cheated. According to the Wald criterion, only age and GPA reliably predicted cheating behavior. Overall, the variance in cheating behavior accounted for by the full model was.16 using Nagelkerke s R 2, which is an analog to R 2 in multiple linear regression (Tabachnick & Fidell, 2001).
11 14 Sheets & Waddill Table 5. Honesty Policy: Characteristics of Business Students Who Did and Did Not Report Using High- Tech Devices to Cheat on Tests and Out- of- Class Assignments Knows Honesty Policy Did Not Cheat Tests Assignments Overall Cheated Did Not Cheat Cheated Did Not Cheat Cheated University General 85% 80% 86% 78% 88% 78% University High Tech 39% 40% 40% 39% 41% 38% Business Professors General Business Professors High- Tech 80% 73% 81% 71% 82% 72% 58% 60% 58% 59% 58% 58% Discussion It is important for business school leaders and faculty to stay abreast of forces that may negatively impact the academic integrity of their schools, curricula, and students. By means of a survey, this study examined the threat of electronic cheating (e- cheating), its practice, commonly used methods, characteristics of electronic cheaters, the association between knowledge of policy and electronic cheating, and predictors of e- cheating. The study used two self- report measures. Table 6. Logistic Regression Analysis of High- Tech Cheating as a Function of Demographic and Knowledge Variables Variable B SE B Wald P Test (z- ratio) Age GPA Sex Overall Policy Knowledge Intercept χ 2 = 21.64, p =.0002 R 2 =.16 NOTE. N= 171; one student did not report age and five students did not report GPA. Sex was coded as 0= male, 1= female; overall policy knowledge= number of academic honesty policies about which at least some information is known (range= 0 to 4). R The primary instrument 2 = Nagelkerke s R was a survey 2 value. developed by one of the authors to determine the extent of students use of electronic methods of cheating. The second was the Social Desirability Scale (SDS) designed by Crowne and Marlowe (1960) to assess the degree to which answers on the survey were influenced by students tendency to give the right answers rather than their honest responses. Overall, those who said they had never cheated on tests and/or assignments had a slightly higher social desirability than those who said they had cheated at least occasionally; social desirability bias did not differ significantly between males and females. Thus, it appeared that students who said they had never cheated were slightly more likely to give the right response than those who said they had cheated, which indicates that the reported frequencies of electronic cheating in the survey may underestimate the actual frequencies of such cheating among the students in the sample. With regard to the extent of e- cheating, the frequency with which our sample of business students reported using electronic cheating methods (40%) was substantially lower than the frequencies of over 80% for paper- based cheating reported by business students in other studies (McCabe, 1997; Meade, 1992). Our results may well underestimate the actual frequency of electronic cheating behavior among business students, because the business students who took part in this study were volunteers. Thus, it is possible that students who cheated may have been less likely to participate than those who did not cheat. In addition, survey responses were somewhat correlated with social desirability, indicating that those who said they did not cheat were more likely to be concerned about giving the right response
12 E- Cheating Among College Business Students: A Survey 15 (i.e., did not cheat) than those who admitted to cheating, and so may have underreported their actual cheating frequency. The lower percentages may also be indicative of fewer students owning certain types of electronic devices or having less experience with using them to cheat, rather than of an actual decrease in cheating behaviors among college business students. Another area of assessment was the characteristics of students who admitted to e- cheating. In reference to gender, we found that electronic cheating was more common among males. However, this gender difference was related to the venue in which the cheating took place. Males used electronic cheating methods much more frequently than females on out- of- class assignments, but both genders were equally likely to use electronic methods to cheat on tests. Regarding age, this study found that the business students who admitted to e- cheating were younger than those who did not cheat. Perhaps younger students are more prone to use electronic devices to cheat simply because they have grown up using them and are more familiar with possible uses (and misuses) of such devices. On the other hand, perhaps younger students are less mature or take their education less seriously (Haines, Diekhoff, LaBeff, & Clark, 1986) and, consequently, engage in risk- taking acts such as using electronic equipment to cheat for the sake of improving their grades. Findings from the Pew Internet and American Life Project (2005, 2006) indicate that younger people are significantly more likely than older people to use cell phones to send text messages or take pictures, although younger and older users are about equally likely to use e- mail, a communication technology that has been available for a number of years. Thus, it may be that age differences in electronic cheaters reflect a combination of more knowledge about some forms of technology coupled with less maturity or less clarity about the ethical boundaries that define when the use of such technology is and is not appropriate for academic work. In reference to GPA, our study revealed electronic cheaters have a lower cumulative GPA than those who reported never having cheated. Perhaps students having lower averages feel a sense of pressure (Haines, et al., 1986; Davis et al., 1992) to raise poor grades to satisfactory levels, and, in order to do so, will cheat to meet the challenge. It may also be that students with a lower GPA do not allocate as much time to study as do those having higher averages and therefore resort to cheating more often. As electronic devices continue to become more prolific in our society and we depend on them to make our jobs easier, it would be interesting to survey business students at five or six year- intervals to see if there would be any measurable differences in the extent which students use electronic devices for cheating based upon their gender, age, and GPA. Another area of focus revealed that among students who cheated in only one venue, cheating on out- of- class assignments was more likely than on in- class tests. Perhaps those students felt they were less likely to be caught if they used unauthorized methods outside the classroom. An even more interesting finding was that over one third of the students who engaged in e- cheating reported cheating on both tests and out- of- class assignments. Perhaps the inclination to cheat in both environments suggests that some students place no limitations on their cheating practices; instead, they appear willing to cheat at any location or in any situation. Is it possible that students who have the tendency to cheat in college also have a similar disposition to cheat in their present and/or future jobs? As noted by Saunders (1993), An unethical student is likely to be an unethical practitioner ( 39). It would be interesting for researchers to survey former business graduates in the workplace to learn who admits to cheating in their jobs, and of those, who may recall having cheated when they were students. The ease and convenience offered by electronic equipment may explain students e- cheating (Bushweller, 1999;
13 16 Sheets & Waddill Cizek, 2003) in both venues. On the other hand, it is possible that students cheating in both venues reflects an amoral attitude to cheating in general, with little or no concern for the rightness or wrongness of their actions (Forsyth & Berger, 1982; Haines et al., 1986; Eisenberg, 2004). Research in e- cheating needs to address the following question: Do business students who engage in e- cheating on tests or on out- of- class assignments have a similar inclination to cheat on tests and out- of- class assignments in other disciplines, or is the inclination unique to the discipline of business? Another area of investigation in this study was the methods of e- cheating used by business students. The programmable calculator was the most popular device for cheating on tests, followed by text messaging and online chats. At first, it may appear that students who engaged in e- cheating by using calculators were those enrolled only in business courses where calculations were routinely performed, such as in accounting or finance. However, no association was found between students unique business major and any particular type of method used to cheat. In short, no association was made between students business major and their use of programmable calculators. Because programmable calculators can store basically any general form of information regardless of the subject matter, students who were enrolled in any business course could illicitly retrieve information pertinent to the course from their calculators during a test or for an out- of- class assignment. Initially, it might seem curious that students also would also favor cheating on tests by using text messaging and online chats. However, for classroom- based courses that included an online test component delivered via a Web platform like Blackboard or Web- CT, at least some tests would have been taken outside of the classroom setting, and accessing online help during exams would have been possible. Further, it is not surprising that students preferred using e- mail/fax for cheating on out- of- class assignments, with the chats, listservs, forums and programmable calculators closely following. Students would be more likely to have access to these methods outside of class than they would in the classroom. Also, students completing class work outside a classroom setting, unlike those taking a test in a classroom, can afford the delayed feedback often associated with chats, listservs, and forums. Our study found there was no significant difference in the number of electronic methods students used to cheat on tests or on out- of- class assignments. Most students chose to use no more than one method in either venue. For testing, it is understandable that more students chose to use only one method, since one electronic device is easier to manipulate and less detectable than two or more. It appears that for out- of- class cheating, students found just one method of cheating to be sufficient for their needs. Another area of study examined the association between students knowledge of the university s and their business professors academic policies on cheating and the students practice of cheating. Similar to the findings in the study by Ashworth et al., (1997), students in our study were indifferent to policies on cheating. It may be that students have established their own rules or norms regarding cheating, and they might ignore those constructed by school administrators and business school officials. Perhaps, students chose to cheat in spite of their knowledge of policy because they believed that they would not be caught, or, if they were detected, the consequences would be minimal. It is also possible that some students perceived academic policy less as a guide to deter cheating and more as a legal document to be used to prosecute them when caught in a blatant cheating incident. Although some studies have recommended the need for a written policy (McCabe & Treviño, 1997; Pincus & Schmelkin, 2003; Saunders, 1993), it is unlikely in view of our findings that this would make any difference in reducing the extent of e- cheating. Finally, this study examined variables that might be predictors of e- cheating. According
14 E- Cheating Among College Business Students: A Survey 17 to the Wald criterion, only age and GPA reliably predicted cheating behavior. Our study found the identical variables of age and GPA were predictors. Since our study found that knowledge of policy had no significant influence on students cheating behavior, knowledge would not be an expected predictor. Although it is somewhat surprising that gender did not turn out to be a predictor, since findings indicated males tended to engage in e- cheating more than females, much of the literature has reported inconsistent findings relating to gender and cheating (Tang & Zuo, 1997; Underwood & Szabo, 2003). Recommendations Below are some suggestions that may positively impact the quality of a business school curriculum and the academic integrity of students, both of which may serve to influence students to refrain from cheating in school and to act with professional integrity in their present and/or future workplace. Administer periodic assessments of e- cheating among students in business schools to stay abreast of the extent of e- cheating. Identify characteristics of students who have a tendency to cheat and be aware of common electronic cheating methods. Routinely examine the business curriculum and self- evaluate one s own teaching methodologies to determine how effectively students values of academic integrity are being promoted. Design and implement a plan to respond to weaknesses found in the assessments. Assure that the business curriculum, including its courses in ethics, meet the robust and excellent standards set forth for a business curriculum and faculty by the AACSB for the process of accreditation and re- accreditation. Become an active member of the Center for Academic Integrity, an organization founded by Donald McCabe to promote the values of academic integrity among students. Nurture a climate of academic integrity by adopting a set of honor codes. Form student discussion groups that focus on issues of academic honesty. Implement procedures to process alleged improprieties, and acquire administrative support for these measures (McCabe, 2001; McCabe & Treviño, 2002; McCabe et al., 2006). The implementation of these strategies can help discourage cheating and promote academic integrity among students in business schools. References Ashworth, P., Bannister, P., & Thorne, P. (1997). Guilty in whose eyes? University students perceptions of cheating and plagiarism in academic work and assessment. Studies in Higher Education, 22(2), Bates, P., & Fain, M. (2006, October). Cheating 101: Internet paper mills. Coastal Carolina University, Kimbel Library. Retrieved November 15, 2008, from presentations/papermil.html Bowers, W. J. (1964). Student dishonesty and its control in college. New York: Bureau of Applied Social Research, Columbia University. Brown, B. S. (1995). The academic ethics of graduate business students: A survey. Journal of Education for Business, 70(3), Buckley, M. R., Wiese, D. S., & Harvey, M. G. (1998). An investigation into the dimensions of unethical behavior. Journal of Education for Business, 73(5), Bushweller, K. (1999, March). Digital deception- The Internet makes cheating easier than ever. Electronic School. Retrieved July 9, 2005, from school.com/199903/0399f2.html Campbell, C. R., Swift, C. O., & Denton, L. T. (2002). Cheating goes hi- tech: Online term paper mills. Journal of Management of Education, 24(6), Cizek, G. J. (2003). Detecting and preventing classroom cheating- promoting integrity in assessment. Thousand Oaks, CA: Corwin Press.
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16 E- Cheating Among College Business Students: A Survey 19 Pew Internet and American Life Project (2006). Cell phone use. Retrieved September 17, 2010 from Reports/2006/PIP_Cell_phone_study.pdf Pfeffer, J. (2007). What s right and still wrong with business schools. BizEd (Publication: Association to Advance Collegiate Schools of Business). Retrieved April 15, 2007, from Archives/JanFeb07/p42-49.pdf Pincus, H. S., & Schmelkin, L. P. (2003). Faculty perceptions of academic dishonesty: A multidimensional scaling analysis. The Journal of Higher Education, 74(2), Roig, M. (1997). Can undergraduate students determine whether text has been plagiarized? The Psychological Record, 47(1), Saunders, E. J. (1993). Confronting academic dishonesty. Journal of Social Work Education, 29, Schab, F. (1991, Winter). Schooling without learning: Thirty years of cheating in high school. Adolescence, 26(104), Scheers, N. J., & Dayton, C. M. (1987). Improved estimation of academic cheating behavior using the randomized response technique. Research in Higher Education, 26(1), Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4 th ed.). Boston: Allyn and Bacon. Tang, S., & Zuo, J. (1997). Profile of college examination cheaters. College Student Journal, 31(3), Underwood, J., & Szabo, A. (2003). Academic offences and e- learning: Individual propensities in cheating. British Journal of Educational Technology, 34(4), Whitley, B. E., Jr., & Keith- Spiegel, P. (2002). Academic dishonesty: An educator s guide. Mahwah, NJ: Lawrence Erlbaum Associates.
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