Introduction
Problem Statement:
Ethical behavior may be viewed by many to be absent in the current business environment. Unethical behavior of businesses has been very costly to the public. As a result, the government has stepped in to prevent other unethical actions. An example of this would be when The Sarbanes-Oxley Act of 2002 was established in response to the many accounting scandals that have recently occurred such as Enron, World Com, and Tyco.
Unethical behavior is also prevalent at college universities, especially among business majors. According to Kenneth J. Chapman, “The incidence of academic dishonesty has been increasing throughout the past few decades. Past research has indicated that business students cheat more than their peers in other disciplines across the university” (Chapman, 2004). This problem primarily concerns the colleges of business and impacts the future of the business environment.
Literature Review:
There are many problems concerning academic integrity among business students. One problem is how to measure cheating, or in other words, where to draw the line. In a recent study, focus groups comprised of marketing students were held to determine the groups overall beliefs on cheating. The results were surprising. Students felt that there is a distinct difference between self-interest and social-interest cheating. Self-interest cheating was considered to be unethical, but social-interest cheating was seen as acceptable because its purpose was to help others. According to Chapman, “In social-interest cheating, a student is just trying to help a friend get a better grade. Many students said they would help their friends get better grades (social interest cheating), and they did not think it was terribly unethical” (Chapman, 2004). For the purposes of this research study, ethical beliefs of the sample must be investigated as well as actual acts of academic dishonesty.
There are also many different variables related to cheating that can be examined. Variables discussed included situational and individual variables. Some demographic variables that can be examined are age, major, gender, work status, GPA, and semester standing. These variables have been used in many studies researching academic integrity. However, there have also been studies that examined members of fraternities and the incidence of them cheating.
One study tested the hypothesis that members of fraternities and sororities are more likely to cheat than non-members. According to Eric and Jason Storch, “The findings of this study suggest that students who are extremely active within their fraternal organization may be at higher risk to engage in academic dishonesty” (Storch, 2002). A possible further study of this problem could be the examination of the percentage of business majors that are associated with fraternal organizations as compared to non-business majors.
Another hypothesis that was tested was whether there were cheating differences by major. The hypothesis was that marketing majors cheat significantly more than their peers in other business disciplines (Chapman, 2004). The independent variable was major and the dependent variable was acts of cheating. This study found that marketing students had a higher frequency of cheating than any other major. This problem is a concern for all marketing faculty and students because this study questions the credibility of marketing majors. This study could be expanded further by conducting descriptive research to find out the specific characteristics of marketing majors. This could help further explain why it is assumed they cheat more often.
Research Objectives:
The research objective for this project is to see if there is any difference in cheating behavior between marketing and non-marketing majors. Another objective is to explore the relationship between gender and academic dishonesty. The last research objective of this study is to develop important predictor variables for academic dishonesty, based on the ethical and personal beliefs of the sample.
Research Design
Sampling Process:
The target audience for this study is the business student body surveyed by Dr. Batory and Dr. Fundaburk. The sample will consist primarily of students throughout Bloomsburg University and Wilkes University. The sample was taken from an available group that had access to the survey. The available group for Bloomsburg University consisted of students currently taking Marketing Research and Marketing Principles and Practices in the fall 2005 semester. The target audience was reached via the Internet and through self administered surveys. The respondents are mostly comprised of business majors; however, there are a few non-business majors in the total sample data. The age of the available group is approximately 18 to 24 years of age with the exception of a few outliers. The total sample size includes 191 participants.
Hypothesis:
The hypothesis for this study is that there is a significant difference between marketing and non-marketing majors concerning academic integrity. Marketing majors may be more likely to cheat because they have a different set of ethical and personal beliefs as compared to their peers in other majors. It is also believed that there is a correlation between ethical and personal beliefs and academic dishonesty. Another hypothesis is that there is a significant difference between the gender of the respondents and their instances of academic dishonesty.
The independent variables include major and gender. The dependent variables used to measure unethical behavior and the beliefs of the participants include ethical beliefs, personal beliefs, and actual incidences of academic dishonesty.
The journal article entitled “Academic integrity in the business school environment: I’ll get by with a little help from my friends,” explores similar relationships between cheating differences by major. One dependent variable that was used to determine the cheating differences was the number of instances of actual cheating. This study found that marketing students had a significantly higher average cheating score than other students in different business disciplines (Chapman, 2004). This study examined other possible explanations for the ethical differences between marketing and non-marketing majors. Variables used in the study included belief questions and demographic variables, such as gender, age and GPA. Even after examining and testing these variables, it was confirmed in this study that marketing students cheat more than other business majors (Chapman 2004).
Concepts and Operational Constructs:
The demographic data collected included the gender and the area of study of the participants. These two concepts are relatively concrete. They are also easy to measure, understand, and define. Three concepts that were measured for the dependent variable in this research study were academic dishonesty, ethical beliefs, and personal beliefs. These three concepts are much harder to define and measure.
The first concept, academic dishonesty, was measured based on the number of times each participant has been academically dishonest during their college education. Each participant was able to select from five categories concerning acts of academic dishonesty. These five categories included: 0, 1-3, 4-6, 7-9, and 10+ acts of academic dishonesty. An example of the question used to measure academic disnoesty is displayed in Exhibit 1.1 below.
Exhibit 1.1
Academic Dishonesty
| About how many acts of academic dishonesty (if any) have you participated in during your college education? | 0 | 1-3 | 4-6 | 7-9 | 10+ |
There are many interpretations of what constitutes cheating, but by asking a broader question concerning specific acts of academic dishonesty relaxes the confusion and uncertainty for the participants.
Ethical beliefs and personal beliefs were measured and defined based on a set of belief statements included in the questionnaire. Each participant indicated whether he or she agreed or disagreed with each ethical and personal belief statement using a non-forced 6 point Likert scale. An example of an ethical belief statement and personal belief statement are both displayed in Exhibit 1.2 and 1.3.
Exhibit 1.2
Ethical Belief Statement
| Directions: Please check your opinion on how the following statements reflect your institution | Strongly Disagree | Neutral | Strongly Agree | No Opinion |
| Golden Rule: Do unto others, as you would have them do unto you | 1 | 2 3 4 | 5 |
Exhibit 1.3
Personal Belief Statement
| Directions: Please indicate the following information for yourself | Strongly Disagree | Neutral | Strongly Agree | No Opinion |
| The more I win, the more power I feel | 1 | 2 3 4 | 5 |
The surveyed data was collected and tabulated into an SPSS spreadsheet. This allowed for accurate and precise data analysis. The questionnaire that was used to collect this data is included in the appendix of this report.
Data Analysis
Sample Profile:
The total sample size consisted of 191 participants. Approximately 36% of the respondents were marketing majors and approximately 60% were non-marketing majors. Table 1.1 represents the frequency count in percentages of the respondent’s majors between marketing and non-marketing. The table also displays that approximately 4% of the participants did not specify their major.
Table 1.1
Marketing and Non-Marketing Majors Frequency Count
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | Marketing | 69 | 36.1 | 37.7 | 37.7 |
| Non-Marketing | 114 | 59.7 | 62.3 | 100.0 | |
| Total | 183 | 95.8 | 100.0 | ||
| Missing | System | 8 | 4.2 | ||
| Total | 191 | 100.0 | |||
The sample consisted of a mix of other business majors because the surveyed Marketing Principles class is a business core requirement. Table 1.2 displays the percentage of males and females that participated in the survey. Approximately 51% were male and 49% were female out of a total sample size of 191.
Table 1.2
Gender Frequency Count
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | Male | 97 | 50.8 | 50.8 | 50.8 |
| Female | 94 | 49.2 | 49.2 | 100.0 | |
| Total | 191 | 100.0 | 100.0 | ||
Descriptive Statistics:
The cross-tabulation of reported behavior of academic dishonesty between marketing and non-marketing majors provides interesting information. Table 1.3 and Exhibit 2 indicate the exact percentages of acts of academic dishonesty between marketing and non-marketing majors. This data displays that the percentage of non-marketing majors is relatively higher than marketing majors for 0 to 3 acts of academic dishonesty. However, for acts of academic dishonesty totaling 4 or more, the data shows higher percentages for marketing majors than non-marketing majors.
Table 1.3
Reported Behavior of Academic Dishonesty between Marketing and Non-Marketing Majors
| Acts of Academic Dishonesty | Total | ||||||
| 0 | 1-3 | 4-6 | 7-9 | 10+ | |||
| Marketing% | 1321.0% | 2337.1% | 1219.4% | 1016.1% | 46.5% | 62 | |
| Non-Marketing% | 3936.1% | 4945.4% | 98.3% | 54.6% | 65.6% | 108 | |
| Total | 52 | 72 | 21 | 15 | 10 | 170 | |
Exhibit 2
Reported Behavior of Academic Dishonesty between Marketing and Non-Marketing Majors
Exhibit 3 displays the measure of central tendency between acts of academic dishonesty and the entire sample. The mean response patterns for the sample concerning acts of academic dishonesty were approximately 2.1. This implies that on average, the entire sample cheated between 1 and 3 times. Exhibit 3 also indicates that 177 out of 191 participants answered the question regarding academic dishonesty.
Exhibit 3
Reported Acts of Academic Dishonesty for the Entire Sample
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Confidence Intervals:
Using a 95% confidence interval it appears that marketing students cheat more than non-marketing majors. This assumption is based purely on the face value of the confidence intervals conducted. The reason it appears that marketing majors are more academically dishonest than non marketing majors is because the confidence intervals for marketing majors are farther away from 1 (no cheating), than non-marketing majors. The confidence intervals for marketing majors for the lower and upper bound are 2.1993 and 2.8007 respectively. The confidence intervals for non-marketing majors for the lower and upper bound are 1.7778 and 2.1851 respectively.
Table 1.4
Confidence Intervals: Marketing and Non-Marketing
| Major | Mean | z-value | Standard Error | Lower Bound | Upper Bound |
| Marketing | 2.5 | 1.96 | 0.15036 | 2.1993 | 2.8007 |
| Non-Marketing | 1.9815 | 1.96 | 0.10273 | 1.7778 | 2.1851 |
Using a 95% confidence interval it appears that males are more academically dishonest than females because the confidence intervals for males are farther away from 1 (no cheating), than females. The confidence intervals for males for the lower and upper bound are 2.1374 and 2.6716 respectively. The confidence intervals for females for the lower and upper bound are 1.7451 and 2.1186 respectively.
Table 1.5
Confidence Intervals: Gender
| Gender | Mean | z-value | Standard Error | Lower Bound | Upper Bound |
| Male | 2.4045 | 1.96 | 0.13438 | 2.1374 | 2.6716 |
| Female | 1.9318 | 1.96 | 0.09396 | 1.7451 | 2.1186 |
Statistical Tests of Significance
Hypothesis 1:
There is a difference in cheating behavior between marketing and non-marketing majors.
In order to determine whether there is a significant difference in cheating behavior between marketing and non-marketing majors, independent sample t-tests were used. This statistical test compared the means of both marketing and non-marketing majors regarding their actual incidences of academic dishonesty. This process was done with the aid of SPSS. Table 2.1 displays the group statistics for the independent variable (major) and dependent variable (academic dishonesty).
Table 2.1
Group Statistics Comparing Major and Acts of Academic Dishonesty
| N | Mean | Std. Deviation | Std. Error Mean | |
| Marketing | 62 | 2.5 | 1.18391 | 0.15036 |
| Non-Marketing | 108 | 1.9815 | 1.06763 | 0.10273 |
The computed t-value between marketing and non-marketing majors is 2.928. The p-value between the two groups was 0.004 which shows significance at an alpha 0.05. This is displayed in Table 2.2.
Table 2.2
Independent Sample t-test for Marketing and Non Marketing Majors
| t-value | df | p-value (Sig.) | Mean Difference | Std. Error Difference | |
| Equal Variances Assumed | 2.928 | 168 | 0.004 | 0.51852 | 0.17706 |
Based on the p-value of 0.004, the null hypothesis (there is no significant difference in cheating behavior between marketing and non-marketing majors) is rejected. This means that there is evidence to indicate that there is a significant difference in the frequency of cheating between marketing majors and non-marketing majors. The confidence intervals that were previously computed also reveal that marketing majors are more likely to cheat than non-marketing majors.
Hypothesis 2:
There is a difference in cheating behavior between males and females.
In order to determine whether there is a significant difference in cheating behavior between males and females independent sample t-tests were used. This statistical test compared the means of both males and females regarding to their actual incidences of academic dishonesty. This process was done with the aid of SPSS. Table 2.3 displays the group statistics for the independent variable (gender) and dependent variable (academic dishonesty).
Table 2.3
Group Statistics Comparing Major and Acts of Academic Dishonesty
| N | Mean | Std. Deviation | Std. Error Mean | |
| Males | 89 | 2.4045 | 1.26777 | 0.13438 |
| Females | 88 | 1.9318 | 0.88142 | 0.09396 |
The computed t-value between males and females is 2.877. The p-value between the two groups was 0.005 which shows significance at an alpha of 0.05. This is displayed in Table 2.4.
Table 2.4
Independent Sample t-test for Gender and Acts of Academic Dishonesty
| t-value | df | p-value (Sig.) | Mean Difference | Std. Error Difference | |
| Equal Variances Assumed | 2.877 | 175 | 0.005 | 0.47268 | 0.1643 |
Based on the p-value of 0.005, the null hypothesis (there is no significant difference between cheating behavior and gender) is rejected. This means that there is evidence to indicate that there is a significant difference in the frequency of cheating between males and females. The confidence intervals that were previously computed also reveal that males are more likely to cheat than females.
Hypothesis 3:
Part 1: There is a correlation between ethical and personal beliefs and academic dishonesty
Bivariate correlations were used to test the many different ethical and personal belief statements and acts of academic dishonesty using SPSS. The only statement that showed significance at the 0.05 level was the ethical belief statement regarding the “Golden Rule.” All of the other ethical and personal beliefs statements did not show significance. Table 2.5 and 2.6 display the results from the bivariate correlations calculated for both the ethical and personal belief statements. Both tables display the Pearson correlation, coefficient of determination (R Squared), and the computed p-value.
Table 2.5
Correlations between Acts of Academic Dishonesty and Ethical Belief Statements
| Belief Statements | Academic Dishonesty | ||
| Ethical Beliefs: | Pearson Correlation | R Squared | Significance (p-value) |
| An action is ethical if it produces a greater positive consequence than other alternatives | -0.052 | 0.002704 | 0.491 |
| The inherent goodness or badness of an action is the basis of ethical behavior | 0.065 | 0.004225 | 0.386 |
| Golden Rule: Do unto others, as you would have them do unto you | -0.189* | 0.035721 | 0.012 |
*Correlation is significant at the 0.05 level (2-tailed)
Table 2.6
Correlations between Acts of Academic Dishonesty and Personal Belief Statements
| Belief Statements | Academic Dishonesty | ||
| Personal Beliefs: | Pearson Correlation | R Squared | Significance (p-value) |
| Achievement: Do whatever it takes to get a head in a stressful competitive environment | 0.14 | 0.0196 | 0.062 |
| Opportunism: Self seeking behavior with guile is necessary to succeed | 0.139 | 0.019321 | 0.066 |
| Competitiveness: I feel pressure/stress to outperform my peers to succeed in life | 0.083 | 0.006889 | 0.27 |
| The more I win, the more power I feel | 0.141 | 0.019881 | 0.062 |
| My religious beliefs guide my actions | -0.083 | 0.006889 | 0.27 |
| Good business ethics lead to financial success | -0.131 | 0.017161 | 0.082 |
The “Golden Rule” statement had a p-value of .012, which shows significance, and a Pearson correlation of (-.189). The beliefs in the “Golden Rule” and acts of academic dishonesty are negatively correlated. This means that students who believe in the “Golden Rule” statement are less likely to be academically dishonest.
It is important to note that the coefficient of determination .036. This is computed simply by squaring the Pearson correlation. The coefficient of determination measures the portion of the total variation that is accounted for by knowing the independent variable. This means that only 3.6% of the variation in acts of academic dishonesty can be explained by knowing whether or not the participants believed in the “Golden Rule” statement. Unfortunately, the “Golden Rule Statement” does not account for much of the variation in acts of academic dishonesty and therefore, alone, it is not a good predictor for academic dishonesty. These correlations indicate that ethical and personal beliefs are not good predictors of academic dishonesty.
Hypothesis 3:
Part 2: Marketing majors have a different set of ethical and personal beliefs than non-marketing majors
In order to determine whether marketing majors have a different set of ethical and personal beliefs as opposed to non-marketing majors, independent sample t-tests were used. This statistical test compared the means of both marketing and non-marketing majors regarding the responses to each of the ethical and personal belief statements. This process was done with the aid of SPSS. Surprisingly, the results displayed in Table 2.7 and 2.8 show that the only significant difference between marketing and non-marketing majors was their personal beliefs about competition.
Table 2.7
Independent Sample t-test for:
Marketing and Non-Marketing Majors and Ethical Belief Statements
| Belief Statements | Marketing and Non-Marketing Majors | ||||
| Ethical Beliefs: | t – value | df | Significance (p-value) | Mean Difference | Std. Error Difference |
| An action is ethical if it produces a greater positive consequence than other alternatives | -0.189 | 181 | 0.851 | -0.04081 | 0.21627 |
| The inherent goodness or badness of an action is the basis of ethical behavior | 1.615 | 181 | 0.108 | 0.27193 | 0.16838 |
| Golden Rule: Do unto others, as you would have them do unto you | -1.237 | 180 | 0.218 | -0.17827 | 0.14408 |
Table 2.8
Independent Sample t-test for:
Marketing and Non-Marketing Majors and Personal Belief Statements
| Belief Statements | Academic Dishonesty | ||||
| Personal Beliefs: | t – value | df | Significance (p-value) | Mean Difference | Std. Error Difference |
| Achievement: Do whatever it takes to get a head in a stressful competitive environment | -0.929 | 181 | 0.354 | -0.18688 | 0.20125 |
| Opportunism: Self seeking behavior with guile is necessary to succeed | 0.232 | 180 | 0.816 | 0.04861 | 0.20911 |
| Competitiveness: I feel pressure/stress to outperform my peers to succeed in life | 2.33 | 181 | 0.021* | 0.42067 | 0.18056 |
| The more I win, the more power I feel | 1.887 | 181 | 0.061 | 0.31655 | 0.16774 |
| My religious beliefs guide my actions | 0.191 | 181 | 0.849 | 0.04272 | 0.22417 |
| Good business ethics lead to financial success | 0.889 | 181 | 0.375 | 0.14035 | 0.15781 |
* Significant at the 0.05 level (2-tailed)
In order to determine the strength of effect regarding the “Competitiveness” statement between marketing and non-marketing majors, confidence intervals must be performed. Using a 95% confidence interval it appears that marketing students feel more pressure and/or stress to outperform their peers to succeed in life. The reason it appears that marketing majors are more competitive than non marketing majors is because the confidence intervals for marketing majors are farther away from the hypothesized mean of 0. The confidence intervals for marketing majors for the lower and upper bound are 3.7881 and 4.0435 respectively. The confidence intervals for non-marketing majors for the lower and upper bound are 3.3907 and 3.8549 respectively. The results for the computed confidence intervals are displayed in Table 2.9.
Table 2.9
Confidence Intervals: “Competitiveness” Statement
| Major | Mean | z-value | Standard Error | Lower Bound | Upper Bound |
| Marketing | 4.0435 | 1.96 | 0.12800 | 3.7881 | 4.2989 |
| Non-Marketing | 3.6228 | 1.96 | 0..11715 | 3.3907 | 3.8549 |
Conclusions and Implications for Marketing Management
Conclusion:
The result of the first hypothesis, that marketing majors cheat more than non-marketing majors, is consistent with the journal article written by Kenneth J. Chapman. This finding is supported by the statistical computations and subsequent t-tests computed above. This problem is a concern for the Bloomsburg University marketing faculty. The second hypothesis reveals that there is a difference in cheating behavior between males and females. Male students displayed more instances of academic dishonesty as compared to their female cohorts.
The third research objective was to develop important predictor variables based on the many different ethical and personal belief statements. Only one ethical belief statement was significant in determining academic dishonesty. This ethical belief statement was the “Golden Rule.” However, the “Golden Rule” statement only accounted for 3.6% of the variation in acts of academic dishonesty. Because all the other ethical and personal belief statements were not significant and the “Golden Rule” statement only accounted for 3.6% of the variation, it is safe to say that ethical and personal beliefs are not good predictors of academic dishonesty. Other extraneous variables have greater influence in determining academic dishonesty. Further investigation of such variables, such as situational variables or economic status, should be researched. Exploratory research would determine whether these variables are valid for predicting academic dishonesty. These variables may also lead the researcher to more predictor variables of behavior.
The second part of the third hypothesis was to determine if marketing majors had a different set of personal beliefs than that of non-marketing majors. The only difference found between the two groups was that marketing majors personally felt more pressure and/or stress to out-perform their peers to succeed in life. Even though there was a difference in the personal belief about competition between marketing and non-marketing majors, this information is not useful for determining why marketing majors cheat more often. The “Competitiveness” belief statement was not a predictor variable for academic dishonesty. The results of these findings cannot be projected across all other university populations, because it lacks external validity.
Implications for Marketing Management:
A marketing manager will find this research informative in the business environment. With the knowledge of potential academic dishonesty by their employees, particularly close attention will have to be paid to a firm’s marketing department, and marketing practices. This includes all aspects that have to deal with the marketing mix, such as sales management, advertising management, and marketing management. Insight into an employees’ value structure can be insightful when a marketing manager is developing their productivity. However, this value structure will not predict an employee’s thoughts or willingness toward acting in a dishonest manner.
Appendix
Questionnaire:
| Directions: Please check your opinion on how the following statements reflect your institution | Strongly Disagree | Neutral | Strongly Agree | No Opinion | ||||||||||
| Ethical Beliefs1) An action is ethical if it produces a greater positive consequence than other alternatives | 1 | 2 3 4 | 5 | |||||||||||
| 2)The inherent goodness or badness of an action is the basis of ethical behavior | 1 | 2 3 4 | 5 | |||||||||||
| 3) Golden Rule: Do unto others, as you would have them do unto you | 1 | 2 3 4 | 5 | |||||||||||
| Personal Beliefs | ||||||||||||||
| Directions: Please indicate the following information for yourself | Strongly Disagree | Neutral | Strongly Agree | No Opinion | ||||||||||
| 1)Achievement: Do whatever it takes to get a head in a stressful competitive environment | 1 | 2 3 4 | 5 | |||||||||||
| 2) Opportunism: Self seeking behavior with guile is necessary to succeed | 1 | 2 3 4 | 5 | |||||||||||
| 3) Competitiveness: I feel pressure/stress to outperform my peers to succeed in life | 1 | 2 3 4 | 5 | |||||||||||
| 4) The more I win, the more power I feel | 1 | 2 3 4 | 5 | |||||||||||
| 5) My religious beliefs guide my actions | 1 | 2 3 4 | 5 | |||||||||||
| 6) Good business ethics lead to financial success | 1 | 2 3 4 | 5 | |||||||||||
| About how many acts of academic dishonesty (if any) have you participated in during your college education? | 0 | 1-3 | 4-6 | 7-9 | 10+ | |||||||||
Source: Batory, S. (n.d.). Section 1: Institutional characteristics. Retrieved September 22, 2005, from http://cob.bloomu.edu/afundaburk/batorysurvey.asp.
References
Batory, S. (n.d.). Section 1: Institutional characteristics. Retrieved September 22, 2005, from http://cob.bloomu.edu/afundaburk/batorysurvey.asp.
Chapman, K. J., Davis R., Toy D., and Wright, L. (2004). Academic integrity in the business school environment: I’ll get by with a little help from my friends. Journal of Marketing Education, 26(3), 236-249.
Storch, E. A., & Storch, J. B. (2002). Fraternities, sororities, and academic dishonesty. College Student Journal, 36(2), 247-253.