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Empirically Assessing the Importance of Characteristics of Accounting Students.

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Title: Empirically Assessing the Importance of Characteristics of Accounting Students.
Language: English
Authors: Baker, William M., McGregor, Calvert C.
Source: Journal of Education for Business. Jan-Feb 2000 75(3):149-157.
Peer Reviewed: Y
Page Count: 9
Publication Date: 2000
Document Type: Journal Articles
Reports - Research
Descriptors: Accounting, Employer Attitudes, Employment Qualifications, Higher Education, Integrity, Personnel Selection, Student Attitudes, Teacher Attitudes
ISSN: 0883-2323
Abstract: Three employer groups (n=117), 47 accounting faculty, and 63 students rated the following characteristics of potential employees: master's degree, overall and accounting grade point average, personal integrity, communication skills, energy/drive/enthusiasm, and appearance. Employers and faculty considered integrity extremely important; students rated it significantly less important. Only faculty believed overall grade average important. (SK)
Journal Code: CIJSEP2000
Entry Date: 2000
Accession Number: EJ602899
Database: ERIC
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  Value: <anid>AN0002942410;JEB01JAN.00;2000Apr10.17:12;v4.1</anid> <title id="AN0002942410-1">EMPIRICALLY ASSESSING THE IMPORTANCE OF CHARACTERISTICS OF ACCOUNTING STUDENTS </title> <p>ABSTRACT. Using conjoint analysis, this study empirically assessed the importance of master's degrees; overall GPA, accounting GPA; communication skills; personal integrity; energy, drive, and enthusiasm; and appearance to nationwide samples of three employer groups, accounting faculty, and students. All three employer groups, and accounting faculty, found personal integrity to be an exceedingly important characteristic for potential hires. Accounting students indicated personal integrity to be significantly less important than did their real-world counterparts. This and similarly unexpected findings suggest that many accounting students and accounting educators do not know which attributes are most important to potential employers. </p> <p>Legal and ethical concerns over the past 20 years have forced the accounting profession to recognize the overwhelming importance of personal integrity. Duane Kullberg, then managing partner and chief executive officer of Arthur Andersen, summarized this importance: </p> <p>[T]here is one tradition--one value--that neither we nor any other accounting firm could have survived without: integrity. Integrity is the one constant more lasting than any technique, service, or law. It must color everything we do because it is the key to our future. </p> <p>In view of the scandals that have taken place recently in the business community, and the accompanying focus on our need for business ethics, I can't emphasize too much that integrity is not simply important. It forms the very reason for our existence as a profession. Our collective integrity is the root of our professionalism, not the performance of audits or any other particular service with which we have become associated [emphasis in original]. (Kullberg, 8-9) </p> <p>Kullberg went on to define integrity as "honesty in action." This is a definition that many employers have adopted when interviewing business students. Well-researched scientific approaches to assessing integrity have evolved (Elliott-Howard, DuFreme, & Daniel, 1997; Hollwith & Pawlowski, 1997; Sackett & Warek, 1996), and interviews now contain assessments of honesty and integrity. These assessments consist of either (a) a questionnaire designed to assess an interviewee's attitude toward honesty, or (b) a presentation of complex and potentially unethical scenarios followed by a solicitation of the interviewee's reactions. But do students and faculty fail to understand the importance of integrity? </p> <p>The Accounting Education Change Commission (AECC) has studied this concern. They concluded that "to become successful professionals, accounting graduates must possess communication skills, intellectual skills, and interpersonal skills" (1990, p. 307). That study examined seven characteristics: (a) accounting degree, (b) overall grade-point average (GPA), (c) accounting GPA, (d) communication skills, (e) personal integrity, (f) energy, drive, and enthusiasm, and (g) appearance. These characteristics all reflect the "skills set" that the AECC demands. Research (Friedlan, 1995) suggests that accounting educators have strong influences on students' perceptions of which characteristics are important in practice. It is imperative that researchers determine the relative importance of these seven characteristics so that accounting educators can share this importance with their students. </p> <p>In the past decade, accounting educators have been accused of failing to understand business and the accounting profession, because students graduating from accounting programs do not, as a group, possess all of the skills and attributes that employers in the accounting profession desire. In April 1989, the managing partners of the Big Eight accounting firms described the capabilities they believed to be necessary for success in the profession (Big Eight Accounting Firms, 1989). The combination of this White Paper, the Bedford Committee Report (AAA Bedford Committee Report, 1986), a quickly changing and expanding accounting profession (Big Eight Accounting Firms, 1989), and the then new requirement adopted by the AICPA requiring 150 semester hours for membership by the year 2000 (AICPA, 1986, 1988) created excitement and perceived demand for changing accounting curricula nationwide. </p> <p>Concerns for ethics, communication skills, and other personal factors, in addition to academic performance, have developed under assumptions and assertions that such characteristics are important to the success of accounting students once they join the accounting profession (AICPA, 1988; Big Eight Accounting Firms, 1989; IMA, 1994). The AECC was created to react to these concerns. The commission has issued several position statements and issues statements, all of which revolve around their first position statement: Objectives of Education for Accountants (AECC, 1990). In conjunction with the AECC, researchers continue to search for ways to develop accounting education models (Frederickson & Pratt, 1995; Novin, 1997; Williams, Tiller, Herring, & Scheiner, 1988) and create meaningful changes in accounting education (Nelson, Bailey, & Nelson, 1998). </p> <p>In Objectives of Education for Accountants, desired characteristics of accounting graduates were identified. Little emphasis has been placed on the relative importance of these characteristics to employers of accounting students. </p> <p>Yunker, Sterner, and Costigan (1986) examined the perceptions of public accounting firm and industry recruiters, along with those of accounting majors, regarding positions in accounting. They justified their study by indicating that accounting professors and accounting student organizations provide a mixed bag of information for graduating accounting majors. The most consistent source of confusion was related to the importance of obtaining a graduate degree. They indicated that the importance of a graduate degree is either ineffectively communicated (for those going into public accounting) or adversely communicated (for those going into industry). </p> <p>In the first study of the importance of accounting student characteristics, the Delphi method was used (Dinius & Rogow, 1988) to glean measures of importance for 49 variables (including 16 personality characteristics) that might be important in the recruiting process. Importance was measured for a panel of Big Eight accounting firm partners. The study found accounting GPA to be most important, although no consensus could be reached as to how grades should be interpreted. The 23 most important characteristics in the study had scores of 5.0 or greater on a 7-point scale ranging from 1 (least important) to 7 (most important). Of these 23 characteristics, 12 were personality characteristics. These results show that personality characteristics such as personal integrity and energy, drive, and enthusiasm are important to the employers of accounting students. </p> <p>Other research has focused on the perceptions of accounting students, educators, and practitioners. For example, Novin, Fetyko, and Tucker (1997) examined differences in perceptions between practitioners and educators regarding the composition of 150-hour programs. They sought to provide rationale for the differences they discovered. Palmer, Gilfillan, Palmer, and Adrian (1997) found that necessary knowledge and skill areas differ for nonpublic and public accounting employers. Usoff and Feldmann (1998) examined the skills that accounting students perceived to be important for career success. They found that "students still do not appreciate the value that employers place on nontechnical skills" (p. 219), such as communication skills; energy, drive, and enthusiasm; and personal integrity. The researchers found, however, that students attribute increasing importance to these nontechnical skills as they progress through their education. </p> <p>DeZoort, Lord, and Cargile (1997) compared the perceptions of students and educators regarding the work environment in public accounting. They analyzed differences among juniors, seniors, and faculty for three general concerns: (a) job duties and responsibilities; (b) advancement, training, and supervision; and (c) personal concerns. Though there were few differences between the perceptions of junior and senior accounting students, there were many differences between both student groups and faculty. DeZoort et al. indicated that their results "support the AECC's emphasis on professors taking more active roles in informing students about what they can expect in practice" (p. 281). They suggested that additional research should compare the perceptions of accounting educators with those of accounting practitioners. </p> <p>Numerous characteristics have been mentioned in the reports discussed above. It would be impossible to examine the importance of all of them in one study. We used four criteria to select the seven characteristics examined in our study. The foremost criterion was that each characteristic had to be addressed by the AECC. Other criteria were then necessary to limit the number of characteristics to a manageable number. Each characteristic chosen was also (a) of concern to groups such as the AICPA and the IMA, (b) one of the top 23 characteristics in the Dinius and Rogow (1988) study of the importance of student characteristics, and (c) a characteristic that faculty members and students might have some ability to influence during an accounting degree program. One exception was the accounting degree variable. Whereas the AECC, AICPA, and IMA have all placed overwhelming emphasis on research concerning the merits of 150-hour programs, and Yunker et al. (1986) found that practitioners and educators are confused about the importance of graduate degrees, Dinius and Rogow did not examine graduate degrees. Also, the 12 personality characteristics that scored higher than 5.0 in the Dinius and Rogow study were combined into "personal integrity" and "energy, drive, and enthusiasm," so that the number of variables would be manageable and the terminology used by the AECC, AICPA, and IMA could be applied. </p> <hd id="AN0002942410-2"> Research Questions </hd> <p>Foremost among the questions examined in this study is the following: </p> <p>RQ1. How important are each of the seven characteristics to employers who hire accounting students, and how important do faculty and students perceive these characteristics to be? </p> <p>In addition to students and faculty, three employer groups that hire substantial numbers of accounting graduates were examined: Big CPA firms, local CPA firms, and industry. As first discerned by Yunker et al. (1986), some groups may focus more on certain characteristics than others, or some groups may totally ignore (receive no utility from) some characteristics. The following research question is thus posed: </p> <p>RQ2. Does the relative importance of the characteristics of accounting students vary among (a) the three types of employers, (b) faculty, and (c) students? </p> <p>An understanding of the importance of these characteristics to employers should reveal an employer's preferences for hiring particular students. For example, if communication skills and accounting GPA are the most important variables for CPA firms, then faculty should cultivate these characteristics in students. Further, it is important to note whether faculty and student perceptions of the importance of characteristics are the same as those of employer groups. This would indicate whether faculty and students understand which characteristics are important to employers, and how important they are. These measures of importance would be even more valid if they could be used to ascertain hiring preferences; this leads to the following question: </p> <p>RQ3. Once knowledge about importance of characteristics exists, can hiring preferences be reliably predicted? </p> <p>The ability to predict hiring preferences accurately would go far toward establishing the importance of the conjoints as measures of importance. With such validity, these measures would be of great value in counseling students in preparation for their entry into the job market. The recruiting process could be made more effective and efficient for both students and their potential employers. A better matching of students and employers would be likely to occur. </p> <hd id="AN0002942410-3"> Method </hd> <p>Subjects </p> <p>Subjects were selected from five distinct groups: </p> <olist> <item> Big Six CPA firms 2. Local CPA firms 3. Industry 4. Accounting faculty members 5. Accounting students </item> </olist> <p>The three employer groups and the accounting faculty members were randomly chosen national samples. Responses for these groups were solicited through mailed instruments. One hundred fifty instruments were mailed for each group. The 150 Big Six recipients were randomly chosen from lists of United States offices.(<reflink idref="bib1" id="ref1">n1</reflink>) Faculty were chosen from the Hasselback Accounting Faculty Directory, and local CPA firms were chosen from the Directory of Member Firms in the Division for CPA Firms of the AICPA. The 150 industry companies were chosen from Business Week's "Top 1,000: America's Most Valuable Companies." Business Week ranked companies based on market value. A summary of responses is provided in Table 1. Demographic information obtained from the subjects is also presented in Table 1; none of the demographic variables significantly affected the results (p values for all tests are > 0.6). </p> <p>Unlike the remaining four groups, students were not chosen randomly. Students who took part in the study were volunteers from undergraduate and graduate accounting courses in a major university in the southeastern United States. Students and faculty were instructed to play the role of an employer of accounting students. </p> <p>Research Design </p> <p>The levels of the variables (student characteristics) under study are described as follows: Bachelor's and master's degrees were used for the degree variable. For each of the six remaining variables, three levels were used: below average, average, and above average. For overall GPA and accounting GPA, the terms below average, average, and above average might be considered imprecise. Therefore, numerical measures were derived using responses from students, faculty, and practicing accountants. Each respondent was given a list of grade-point averages, from 2.0 to 4.0, in increments of 0.1. The list was randomly sorted. The respondents then described each value as below average, average, or above average. Undergraduate and graduate values were derived independently. Modal responses were observed for each category, and then values were assigned such that "above average" and "below average" were equidistant from "average." The below-average values used were 2.5 for bachelor's degrees and 3.0 for master's degrees. At many schools, these are the minimum GPAs necessary for graduation. For average values, 3.1 was used for bachelor's degrees and 3.4 was used for master's degrees. For above average, 3.7 and 3.8 were used. </p> <p>Hypothetical student descriptions (cards) were created by varying these characteristics on the levels indicated. A full replication of the seven characteristics would have necessitated the creation of 1,458 cards (2 x 3 x 3 x 3 x 3 x 3 x 3). Instead, an orthogonal array was developed for the seven student characteristics that required only 18 cards (Addelman, 1962). The correlations between the characteristics in the array were all zero, so that the effects of the characteristics can be examined without full replication. </p> <p>Task </p> <p>Subjects were initially instructed to divide the 18 cards, each card describing one hypothetical student in terms of the student characteristics, into better and worse students. Then, each subject was asked to rank the hypothetical students from 1 (most desirable) to 18 (least desirable) as potential employees. Each card had a two-digit student number that the subject could write next to the appropriate rank. </p> <p>Next, each subject examined a second set of four cards with three-digit student numbers. These four cards contained randomly assigned characteristic levels, and were used to determine whether the results from the first 18 could be relied on for predicting hiring preferences. These four were different from the first 18 cards. </p> <p>Conjoint Analysis Approach </p> <p>The responses for the 18 cards were examined through conjoint analysis (Green & Srinivasan, 1978; Green & Wind, 1975). Conjoint analysis develops measures of utility that represent the importance of the various levels of the independent variables. The analysis was performed using ordinary least squares regression.(<reflink idref="bib2" id="ref2">n2</reflink>) The regression was designed such that the seven characteristics were independent variables and the responses were dependent variables (see the Appendix, which also includes the data-gathering instrument). The beta coefficients derived from the regression were measures of utility. </p> <p>The characteristics were coded beginning with the levels of the characteristics that were perceived to be least desirable. A priori, the least desirable level for six of the characteristics was the "below average" level, and the bachelor's degree was the base level for the degree characteristic. These levels served as the base for coding the orthogonal array. For the degree characteristic, one dummy variable was necessary, with a value of zero for the bachelor's degree and one for the master's degree. </p> <p>Each of the six remaining characteristics was coded using two variables to represent the three levels: "below average," "average," and "above average." "Below average" was coded with zeroes for both dummy variables. "Average" was coded with a one for the first dummy variable and a zero for the second. "Above average" was coded using a zero for the first dummy variable and a one for the second variable. Thus, the regression data were coded as follows: A value of one was entered when the level was present; otherwise the value entered was a zero. Conjoints (beta coefficients) were then derived for each level of each characteristic. They measure the utility of any particular level relative to the base (below average or bachelor's degree) level. Utility was not measured for the base (a priori, least) level. Base levels are essentially starting points from which utility can be measured. </p> <p>The non-base levels of each characteristic will have a measure of utility, and the sum of these represents a total measure of utility for the seven characteristics. The measures for some levels will contribute more to this total utility than others. Heuristically, if one level contributed more to total utility than another, that level was more important than the other level. Further, if the conjoint was negative, then the level was less important than the base level.(<reflink idref="bib3" id="ref3">n3</reflink>) Thus conjoints provided measures of importance. There were 13 conjoints in this study. Conjoint analysis can be applied to a group of persons, or even to a single individual, depending on whose utility is to be measured. </p> <p>In this study, we examined the utility of the seven characteristics for each of the five groups to answer the first research question. Much of the analysis focused on the conjoints for these groups. Discriminant analyses and multidimensional scaling also were used to discern and demonstrate differences among the five subject groups and answer the second research question. These analyses were performed using the 13 conjoints of each individual as input. Individual utilities were also used to examine the ability of the conjoints to predict hiring preferences. Each subject ranked a second set of four cards. Each of those cards, representing one of four hypothetical students, was represented by a specific array of ones and zeroes. The conjoints for each individual were multiplied by the appropriate array of ones and zeroes for each of the four cards. As a result, a total utility score was derived for each card. The scores were ranked to create predicted ranks for the four students. To address the third research question, the predicted ranks were correlated with subjects' actual ranks. </p> <hd id="AN0002942410-4"> Results </hd> <p>Conjoint analysis was used to address RQ1. MANOVA was used to determine whether groups differed significantly. Univariate ANOVAs were then used to determine which characteristic levels the groups differed on and to address RQ2. Multiple comparisons were performed for significant ANOVAs and tested using Tukey's Studentized Range Test. RQ2 was again addressed for the three employer groups only, using discriminant analysis. The purpose of the discriminant analysis was to show how the three employer groups differed. Finally, the predictive ability of the conjoints was examined (RQ3). Comparisons between actual and predicted results were tested using Kendall's Tau statistic. </p> <p>Utility of the Characteristics in Employment Decisions </p> <p>RQ1 considered the importance of each of the seven characteristics to the five groups. We show the results of the conjoint analysis in Table 2.4 Except for overall GPA, all of the characteristics were important to all five groups. Though all five groups insisted that overall GPA be included, they obtained utility relating to grades from accounting GPA. Only the faculty group obtained utility from overall GPA at both levels.(<reflink idref="bib5" id="ref4">n5</reflink>) Big Six firms obtained utility from average overall GPA, but not above-average GPA. For all other instances, none of the conjoints were significantly greater than zero; thus information about overall GPA was not used by the groups. Accounting GPA was probably used instead. Accounting GPA was important to all groups, although average accounting GPA provided no more utility to local CPA firms than below-average accounting GPA. </p> <p>All five of the remaining characteristics were important at all levels to all five groups. The conjoints for degree and appearance were about the same as those for accounting GPA, suggesting that degree and appearance are just as important as accounting GPA. The three remaining characteristics, personal integrity; communication skills; and energy, drive, and enthusiasm, had the highest conjoints. Thus, they were the most important characteristics for all five groups. For nonstudent groups, the conjoint derived for personal integrity was significantly higher than that derived for any other characteristic. Thus, personal integrity was the most important characteristic for every group except students. For students, personal integrity; communication skills; and energy, drive, and enthusiasm had similar conjoints, and were of similar importance. </p> <p>Students gave the greatest importance to energy, drive, and enthusiasm. Unlike other groups, they also placed greater emphasis on the above-average level than on the average level. Surprisingly, the other four groups perceived above-average energy, drive, and enthusiasm as lower in utility than average energy, drive, and enthusiasm. This is most noticeable for the Big Six group. </p> <p>How Utility Varies From Group to Group </p> <p>Within groups, there was a high level of consistency in responses. The adjusted R<sups>2</sups> values ranged from 0.5620 to 0.7558 (see Table 2). RQ2 considered how the importance of the seven characteristics varied across groups. Respondents were somewhat consistent across groups. Students and faculty were, at times, ineffective when trying to play the roles of employers, and the statistical noise that resulted made it difficult to find differences. Although there was significant variation among the groups (Wilks' Criterion = 0.5392, p value = 0.0001), no significant differences across the three employer groups could be found when all five groups were considered. Further, there were no significant differences at any level for the accounting GPA, degree, or communication skills characteristics. </p> <p>Thus, two approaches were taken to address RQ2. First, each conjoint was examined to determine whether significant differences existed. Results of ANOVAs for each level of each characteristic are provided in Table 3. Then, Tukey's Studentized Range Tests were performed on the levels that had significant ANOVAs. The significant findings in those tests appear in Table 4. </p> <p>Perhaps the most startling result in this study concerns students and personal integrity. Students did glean utility from personal integrity. Yet, in playing the role of employer, students vastly underestimated the importance of both average and above-average personal integrity (see Figure 1). Even though students did realize that personal integrity is important, accounting educators are more aware of just how important it is, and they should teach their students that the importance of personal integrity and ethics in the accounting-related professions cannot be overemphasized. </p> <p>For the most part, the remaining significant differences among the groups were a result of faculty's attempting to play the role of employer (see Table 4). Faculty derived significantly more utility than did local CPA firms from above-average overall GPA. Faculty members also attributed less importance than some groups to energy, drive, and enthusiasm. For average energy, drive, and enthusiasm, faculty derived less importance than did industry. Both faculty and Big Six firms derived less importance from above-average energy, drive, and enthusiasm than did students. It is again pertinent to note that students were the only group that derived more utility from above-average energy, drive, and enthusiasm than average energy, drive, and enthusiasm. Further, faculty derived significantly less utility from above-average appearance than either students or local CPA firms. </p> <p>It is clear that neither students nor faculty fully understand the role of energy, drive, and enthusiasm in employment decisions. Students and faculty were less consistent within groups than the three employer groups. Further, all significant differences between groups can be attributed to students or faculty. The inability of these two groups to perform like the three employer groups prevented the discovery of any significant differences among the employer groups. Thus, another analysis was performed on the three employer groups only. Discriminant analysis was used. </p> <p>Discriminant analysis seeks linear combinations of the conjoints that maximize separation of the groups.(<reflink idref="bib6" id="ref5">n6</reflink>) Of the 13 conjoints, only 2 contributed significantly to the separation of the three employer groups. The loadings of those two variables are presented in Panel A of Table 5. In Figure 2, these two variables are presented as vectors in the direction in which they affect the analysis. The length of each vector is a function of how important the variable is to the separation. Group centroids are presented in Panel B of Table 5. They represent the location of the three groups, as indicated in Figure 2. </p> <p>The three employer groups are remarkably similar. Only two conjoints, above-average energy, drive, and enthusiasm, and above-average appearance, were significant. Thus the employer groups differed in two respects: (a) Big Six CPA firms placed less importance on above-average energy, drive, and enthusiasm than either local CPA firms or industry, and (b) industry placed less importance on appearance than did Big Six or local CPA firms. </p> <p>Using Conjoints to Predict Hiring Preferences </p> <p>Each subject ranked a second group of four hypothetical students, whose characteristic levels were entirely different from any of the students in the first group. Using the conjoints for the appropriate characteristic levels, a predicted utility score was developed for each subject. These scores were ranked from 1 (best) to 4 (worst). These predicted ranks were compared with the four actual ranks that the subjects had provided. Kendall's Tau was used to assess the correlation between the predicted and actual ranks. These are reported in Table 6. The correlation was significant for all groups except students. Thus, for all nonstudent groups, conjoint models were consistent with the actual ranks assigned to a second group of students. For all four of these groups, the conjoints can be used to predict hiring preferences. </p> <hd id="AN0002942410-5"> Summary and Conclusions </hd> <p>Of the seven characteristics examined in this study, only overall GPA failed to provide utility to all five groups in making hiring-preference decisions. Of students, faculty, industry, local CPA firms, and Big Six CPA firms, only faculty believed that overall GPA was important. Except for average accounting GPA for local CPA firms, all other characteristics were important in making decisions at all levels for all five groups. Generally, the groups were also quite consistent with regard to the magnitude of importance. </p> <p>A few significant differences across groups appeared. All of these differences involved attempts by faculty and students to play the role of employer and make preference decisions. Students and faculty do not fully understand the importance of these seven characteristics to employers. The widest disparity was that between students and all four other groups for both average and above-average personal integrity. Though students certainly indicated that employers place importance on personal integrity, they vastly underestimated the magnitude of the utility that employers derive from the personal integrity characteristic. Now that employers are making strong efforts to evaluate personal integrity at the interview stage, it is extremely important that accounting educators emphasize to their students just how important personal integrity is to the accounting profession. </p> <p>Because students and faculty seemingly were unable to play the role of employer, another analysis was performed considering only the three employer groups. Only two significant differences were found. Industry placed less emphasis on above-average appearance than CPA firms, and Big Six firms placed less emphasis on above-average energy, drive, and enthusiasm than either local CPA firms or industry. In addition, the conjoints derived for the four nonstudent groups, on an individual basis, provide significant power for predicting hiring preferences. </p> <p>As with most experimental exercises, this study may be limited in its generalizability. This possibility has been addressed in several ways, including the random selection of nationwide samples of employers of accounting graduates and structuring the exercise to be as realistic as possible. Therefore, the generalizability and external validity of this study are relatively high with respect to the variables of interest and the employer groups represented in the study. </p> <p>The students were not selected randomly. The graduate and undergraduate students in the study responded similarly, and the graduate students in the sample came from a variety of backgrounds and from various parts of the country. Also, the significant differences between these students and the various employer groups, especially regarding the personal integrity. </p> <p>Characteristic, lead one to believe that these differences also would be found among many, if not most, other accounting students. </p> <p>Accounting educators and employers can benefit tremendously from the results of this research. Educators can use the results to better understand the importance of these characteristics to employers. Then, faculty and employers can effectively inform students what characteristics are important to employers, and how important they are. Further, accounting educators should consider showing students how personal integrity is evaluated in the interview process. Finally, accounting educators can assure students that they do not need to develop and emphasize different characteristics for different career paths, because there are very few differences among employer groups. </p> <ref id="AN0002942410-6"> <title>NOTES</title> <blist> <bibl id="bib1" idref="ref1" type="bt">(n1.)</bibl> <bibtext>Mailings were conducted before merger activity "resulted" in the existence of the Big 5 accounting firms. </bibtext> </blist> <blist> <bibl id="bib2" idref="ref2" type="bt">(n2.)</bibl> <bibtext>Ordinary least squares regression is but one of the several commonly used approaches for obtaining conjoints. Ordinary least squares is as robust as any of the other methods (Green & Srinivasan, 1978, p. 114; Jain et al., 1979, p. 318). Researchers are continuing to search for more robust methods of deriving conjoints (Green & Helsen, 1989, p. 349; Green & Srinivasan, 1989). Least squares has proven to be as robust as any other approach. </bibtext> </blist> <blist> <bibl id="bib3" idref="ref3" type="bt">(n3.)</bibl> <bibtext>Because "1" represented the best response and "18" the worst, the conjoints (beta coefficients) were expected to be negative. The signs are changed to promote understanding. </bibtext> </blist> <blist> <bibl id="bib4" type="bt">(n4.)</bibl> <bibtext>A test for nonresponse bias was conducted by comparing the first 25% of responses with the last 25% of responses. The responses of the two groups did not differ significantly (p value = 0.7862). Thus, nonresponse bias was deemed to be insignificant. </bibtext> </blist> <blist> <bibl id="bib5" idref="ref4" type="bt">(n5.)</bibl> <bibtext>Although the conjoints for above-average overall GPA for the CPA firm groups were both negative, which would imply less utility than the below-average overall GPA, they were not significantly different from 0, which means that no difference between above-average and below-average was found. </bibtext> </blist> <blist> <bibl id="bib6" idref="ref5" type="bt">(n6.)</bibl> <bibtext>The geometric representation derived using discriminant analysis is essentially equivalent to a simultaneous presentation of multiple univariate analyses and multivariate multiple comparisons of means. Such a representation for all five groups would be unwieldy for two reasons: (a) Six conjoints were significant (see Table 3), which would result in the presentation of six vectors, and (b) the two personal integrity conjoints dominated the presentation because of the (shortage of) utility, to students, of personal integrity. </bibtext> </blist> </ref> <hd id="AN0002942410-7">TABLE 1. Summary of Responses, Response Rates, and Demographics</hd> <ct id="AN0002942410-8"> Legend for Chart: B - Students C - Faculty D - Industry E - Local CPA F - Big 6 G - Total A B C D E F G Mailings N/A 150 150 150 150 600 Usable responses N/A 47 49 28 40 164 Percentage N/A 31 33 19 27 27 Volunteers 63 N/A N/A N/A N/A 63 Subjects 227 Average age (yrs.) 24 45 42 39 38 37 Certification (%) None 0 9 17 4 17 7 CPA 0 77 83 96 80 60 CMA 0 6 0 0 3 2 CPA/CMA 0 2 0 0 0 0 CPA/CIA 0 4 0 0 0 1 CPA/CMA/CIA 0 2 0 0 0 0 Gender (%) Female 51 15 19 11 18 26 Male 49 85 81 89 82 74 Education (%) No degree 57 0 0 0 0 16 Bachelor's 43 2 50 82 77 47 Master's 0 9 50 14 20 18 Doctorate 0 89 0 4 3 19</ct> <hd id="AN0002942410-9">TABLE 2. How Important Are the Seven Characteristics? (RQI)</hd> <ct id="AN0002942410-10"> Legend for Chart: A - Variable B - Students C - Faculty D - Industry E - Local CPA F - Big 6 A B C D E F Degree[a] Master's 2.45[*] 1.73[*] 1.99[*] 1.31[*] 2.10[*] Accounting GPA[b] Average 1.21[*] 0.89[*] 1.05[*] 0.41 1.23[*] Above average 2.06[*] 2.14[*] 1.90[*] 1.36[*] 2.56[*] Overall GPA[b] Average 0.19 0.58[*] 0.36 0.25 0.50[*] Above average 0.08 0.98[*] 0.33 -0.14 -0.08 Communication skills[b] Average 3.43[*] 2.99[*] 3.40[*] 3.56[*] 3.71[*] Above average 4.15[*] 3.81[*] 4.18[*] 4.46[*] 4.25[*] Personal integrity[b] Average 3.43[*] 6.00[*] 5.85[*] 6.11[*] 5.55[*] Above average 4.75[*] 7.78[*] 7.38[*] 7.93[*] 7.50[*] Energy, drive, and enthusiam[b] Average 4.21[*] 3.26[*] 4.46[*] 3.91[*] 4.03[*] Above average 4.82[*] 3.06[*] 4.30[*] 3.68[*] 3.15[*] Appearance[b] Average 2.16[*] 1.33[*] 1.81[*] 2.35[*] 1.93[*] Above average 2.68[*] 1.59[*] 1.86[*] 2.60[*] 2.31[*] Adjusted R<sups>2</sups> 0.5620 0.6646 0.7362 0.7558 0.7314 [a] Base level (zero) is bachelor's degree. [b] Base level (zero) is below average. [*] Significantly different from the base level (zero) at the .05 level.</ct> <hd id="AN0002942410-11">TABLE 3. Univariate ANOVAs for Determining Differences Among Groups</hd> <ct id="AN0002942410-12"> Legend for Chart: A - Characteristic level (conjoint) B - F C - P value A B C Master's degree 1.41 0.2330 Average accounting GPA 1.30 0.2703 Above-average accounting GPA 0.97 0.4272 Average overall GPA 0.27 0.8944 Above-average overall GPA 2.57 0.0388[a] Average communication skills 0.87 0.4822 Above-average communication skills 0.65 0.6304 Average personal integrity 10.51 0.0001[a] Above-average personal integrity 13.17 0.0001[a] Average energy, drive, and enthusiasm 2.52 0.0419[a] Above-average energy, drive, and enthusiasm 6.14 0.0001[a] Average appearance 2.23 0.0670 Above-average appearance 4.29 0.0023[a] [a] Significant differences (at .05 level) exist for these characteristic levels. These ANOVAs are a prelude to answering RQ2. Results of Tukey's Studentized Range Tests for characteristics where</ct> <hd id="AN0002942410-13">TABLE 4. Characteristics Where the Five Groups Differ (RQ2)</hd> <ct id="AN0002942410-14"> For above-average overall GPA-- Faculty derived more utility than local CPA firms. For average personal integrity-- Students derived less utility than all four other groups. For above-average personal integrity-- Students derived less utility than all four other groups. For average energy, drive, and enthusiasm-- Industry derived more utility than faculty. For above-average energy, drive, and enthusiasm-- Students derived more utility than Big-Six CPA firms and faculty. Note. The differences reported in this table are those deemed significant using Tukey's Studentized Range Test.</ct> <hd id="AN0002942410-15">TABLE 5. Discriminant Analysis for Employer Groups Only (RQ2)</hd> <ct id="AN0002942410-16"> Legend for Chart: A - Conjoint/group B - Factor 1 C - Factor 2 A B C Panel A Loadings for significant conjoints Above-average energy, drive, and enthusiasm -0.48 -0.14 Above-average appearance 0.36 -0.55 Panel B Group centroids Industry 0.59 -0.09 Local CPA -0.19 0.44 Big 6 -0.55 -0.20</ct> <hd id="AN0002942410-17">TABLE 6. Can the Conjoints Successfully Predict Hiring Priority? (RQ3)</hd> <ct id="AN0002942410-18"> Legend for Chart: A - Group B - Kendall's Tau C - p value A B C Industry 0.781 0.031 Local CPA 0.835 0.038 Big 6 0.796 0.040 Faculty 0.701 0.041 Students 0.620 0.060</ct> <p>GRAPH: FIGURE 1. Conjoints for Personal Integrity </p> <p>DIAGRAM: FIGURE 2. Geometric Representation of employer Group Discriminant Solution </p> <hd id="AN0002942410-19">REFERENCES</hd> <p>Accounting Education Change Commission (AECC). (1990). Objectives of education for accountants: Position Statement Number One. Issues in Accounting Education, 5, 307-312. </p> <p>Addelman, S. (1962). Orthogonal main-effect plans for asymmetrical factorial experiments. Technometrics, 4(<reflink idref="bib2" id="ref6">2</reflink>), 21-46. </p> <p>American Accounting Association, Committee on the Future Structure, Content, and Scope of Accounting Education. (1986). Future accounting education: preparing for the expanding profession. Issues In Accounting Education, 1(<reflink idref="bib1" id="ref7">1</reflink>), 168-195. </p> <p>American Institute of Certified Public Accountants, Division for CPA Firms. (1989). Directory of member firms. New York: AICPA. </p> <p>American Institute of Certified Public Accountants, Education Executive Committee. (1988). Education requirements for entry into the accounting profession. New York: AICPA. </p> <p>American Institute of Certified Public Accountants, Special Committee on Standards of Professional Conduct for Certified Public Accountants. (1986). Restructuring professional standards to achieve professional excellence in a changing environment. New York: AICPA. </p> <p>Big Eight Accounting Firms: Arthur Andersen & Company, Arthur Young, Coopers & Lybrand, Deloitte Haskins & Sells, Ernst & Whinney, Peat Marwick Main & Co., Price Waterhouse, & Touche Ross. (1989). Perspectives on education: Capabilities for success in the accounting profession (commonly referred to as The White Paper). New York: Author. </p> <p>DeZoort, F. T., Lord, A. T, & Cargile, B. R. (1997). A comparison of accounting professors' and students' perceptions of the public accounting work environment. Issues in Accounting Education, 12(<reflink idref="bib2" id="ref8">2</reflink>), 281-298. </p> <p>Dinius, S., & Rogow, R. (1988). Application of the delphi method in identifying characteristics big eight firms seek in entry-level accountants. Journal of Accounting Education, 7(<reflink idref="bib1" id="ref9">1</reflink>), 83-101. </p> <p>Elliott-Howard, F. E., DuFrene, D. D., & Daniel, L. G. (1997). The ethical issues rating: An instrument for measuring ethical orientation of college students toward various business practices. Education and Psychological Measurement, 57, 515-526. </p> <p>Frederickson, J. R., & Pratt, J. (1995). A model of the accounting education process. Issues in Accounting Education, 10(<reflink idref="bib2" id="ref10">2</reflink>), 229-246. </p> <p>Friedlan, J. M. (1995). The effects of different teaching approaches on students' perceptions of the skills needed for success in accounting courses and by practicing accountants. Issues in Accounting Education, 10(<reflink idref="bib1" id="ref11">1</reflink>), 47-64. </p> <p>Green, P. E., & Helsen, K. (1989). Cross-validation assessment of alternatives to individual-level conjoint analysis: A case study. Journal of Marketing Research, 26(<reflink idref="bib3" id="ref12">3</reflink>), 346-350. </p> <p>Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(<reflink idref="bib2" id="ref13">2</reflink>), 103-123. </p> <p>Green, P. E., & Srinivasan, V. (1989). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 53(<reflink idref="bib4" id="ref14">4</reflink>), 3-19. </p> <p>Green, P. E., & Wind, Y. (1975). New way to measure consumers' judgments. Harvard Business Review, 53(<reflink idref="bib4" id="ref15">4</reflink>), 107-117. </p> <p>Hasselback, J. R. (Compiler). (1989). 1989 Accounting faculty directory. Englewood Cliffs, NJ: Prentice-Hall. </p> <p>Hollwitz, J. C., & Pawlowski, D. R. (1997). The development of a structured ethical integrity for pre-employment screening. The Journal of Business Communication, 34(<reflink idref="bib2" id="ref16">2</reflink>), 203-219. </p> <p>Institute of Management Accountants (IMA). (1994). Colleges are not adequately preparing accounting graduates for first jobs, say corporate executives. Management Accounting, 76(<reflink idref="bib3" id="ref17">3</reflink>), 24. </p> <p>Jain, A. K., Acito, F., Malhotra, N. K., & Mahajan, V. (1979). A comparison of the internal validity of alternative parameter estimation methods in decompositional multivariate preference models. Journal of Marketing Research, 16(<reflink idref="bib3" id="ref18">3</reflink>), 313-322. </p> <p>Kullberg, D. R. (1988). Integrity: The key to the future of the accounting profession. (Keynote Address). In B. E. Needles (Ed.), A profession in transition: The ethical and legal responsibilities of accountants. Chicago: Depaul University. </p> <p>Nelson, I. T., Bailey, J. A., & Nelson, A. T. (1998). Changing accounting education with purpose: Market-based strategic planning for departments of accounting. Issues in Accounting Education, 13(<reflink idref="bib2" id="ref19">2</reflink>), 301-326. </p> <p>Novin, A. M. (1997). Education for careers in management accounting, auditing, and tax: A comparison. Journal of Education for Business, 73(<reflink idref="bib1" id="ref20">1</reflink>), 29-34. </p> <p>Novin, A. M., Fetyko, D. F., & Tucker, J. M. (1997). Perceptions of accounting educators and public accounting practitioners on the composition of 150-hour accounting programs: A comparison. Issues in Accounting Education, 12(<reflink idref="bib2" id="ref21">2</reflink>), 331-352. </p> <p>Palmer, K. N., Gilfillan, S. W., Palmer, G. D., & Adrian, C. M. (1997). Meeting the needs of nonpublic and public accountants in a 4-year program. Journal of Education .for Business, 72(<reflink idref="bib5" id="ref22">5</reflink>), 267-272. </p> <p>Sackett, P. R., & Warek, J. E. (1996). New developments in the use of measures of honesty, integrity, conscientiousness, dependability, trustworthiness, and reliability for personnel selection (Pt 4). Personnel Psychology, 49(<reflink idref="bib4" id="ref23">4</reflink>), 787-829. </p> <p>Usoff, C., & Feldmann, D. (1998). Accounting students' perceptions of important skills for career success. Journal of Education for Business, 73(<reflink idref="bib4" id="ref24">4</reflink>), 215-220. </p> <p>Williams, J. R., Tiller, M. G., Herring, H. C., III, & Scheiner, J. H. (1988). A framework for the development of accounting education research. Sarasota, FL: American Accounting Association. </p> <p>Yunker, P., Sterner, J., & Costigan, M. (1986). Employment in accounting: A comparison of recruiter perceptions with student expectations. Journal of Accounting Education, 5(<reflink idref="bib1" id="ref25">1</reflink>), 95-112. </p> <hd id="AN0002942410-20">APPENDIX. Conjoint Analysis Model</hd> <ct id="AN0002942410-21"> Rank = Beta<subs>0</subs> + Beta<subs>1</subs>d<subs>1</subs> + Beta<subs>2</subs> d<subs>2</subs> + Beta<subs>3</subs>d<subs>3</subs> + Beta<subs>4</subs> d<subs>4</subs> + B Beta<subs>5</subs>d<subs>5</subs> + B Beta<subs>6</subs> d<subs>6</subs> + Beta<subs>7</subs>d<subs>7</subs> + B Beta<subs>8</subs> d<subs>8</subs> + Beta<subs>9</subs>d<subs>9</subs> + Beta<subs>10</subs> d<subs>10</subs> + Beta<subs>11</subs>d<subs>11</subs> + Beta<subs>12</subs> d<subs>12</subs> + Beta<subs>13</subs>d Beta<subs>13</subs> + Epsilon Where Rank = the dependent variable, as affected by the characteristic levels and their conjoints. d<subs>1</subs> = 1 if student has a master's degree and 0 otherwise. d<subs>2</subs> = 1 if student has average accounting GPA and 0 otherwise. d<subs>3</subs> = 1 if student has above-average accounting GPA and 0 otherwise. d<subs>4</subs> = 1 if student has average overall GPA and 0 otherwise. d<subs>5</subs> = 1 if student has above-average overall GPA and 0 otherwise. d<subs>6</subs> = 1 if student has average communication skills and 0 otherwise. d<subs>7</subs> = 1 if student has above-average communication skills and 0 otherwise. d<subs>8</subs> = 1 if student has average personal integrity and 0 otherwise. d<subs>9</subs> = 1 if student has above-average personal integrity and 0 otherwise. d<subs>10</subs> = 1 if student has average energy, drive, and enthusiasm and 0 otherwise. d<subs>11</subs> = 1 if student has above-average energy, drive, and enthusiasm and 0 otherwise. d<subs>12</subs> = 1 if student has average appearance and 0 otherwise. d<subs>13</subs> = 1 if student has above-average appearance and 0 otherwise. Epsilon = unexplained error. Beta<subs>0</subs> = a parameter that adjusts the remainder of the model to the ranking scheme Beta<subs>i</subs> = the ith parameter (where i = 1 - 13, Beta<subs>i</subs> is the conjoint corresponding to the "ith" variable above).</ct> <aug> <p>By William M. Baker, Appalachian State University Boone, North Carolina and Calvert C. McGregor, Elon College, Elon College, North Carolina </p> </aug>
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  Data: <searchLink fieldCode="DE" term="%22Accounting%22">Accounting</searchLink><br /><searchLink fieldCode="DE" term="%22Employer+Attitudes%22">Employer Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Employment+Qualifications%22">Employment Qualifications</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Integrity%22">Integrity</searchLink><br /><searchLink fieldCode="DE" term="%22Personnel+Selection%22">Personnel Selection</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Attitudes%22">Teacher Attitudes</searchLink>
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  Data: Three employer groups (n=117), 47 accounting faculty, and 63 students rated the following characteristics of potential employees: master's degree, overall and accounting grade point average, personal integrity, communication skills, energy/drive/enthusiasm, and appearance. Employers and faculty considered integrity extremely important; students rated it significantly less important. Only faculty believed overall grade average important. (SK)
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