- Email: [email protected]
Effort-expectation and academic performance in managerial cost accounting
Joarrm4 of Accounring Educatim, WI. 7, pp. 57-68, Printed in the USA. AI1 rights reserved.
1989 Copyright
0 I989 Maxwell
0748-5751189 $3.oo+.w Pergamon Macmillan plc
EFFORT-EXPECTATION AND ACADEMIC PERFORMANCE IN MANAGERIAL COST ACCOUNTING Mohamed E. Ibmhim UNIVERSITY OF MANITOBA Abstract: An area of research in accounting education has been concerned with the academic ~rformance of students in accounting courses. The major thrust of this research has attempted to identify or assess the effect of specific variables (or predictors) on students’ performance. Little attention has been given to students’ expectations and effort as predictors of performance. This study examines the relationship between effort-expectation and students’ academic performance in managerial cost accounting. The reported correlation and regression results indicate that students’ effort levels have a significant relationship with actual performance. In addition, students’ expected performance and overall GPA were sig~ficantly correIated with actual performance. However, overall GPA was more significant than students’ expected performance with the exception of the first exam in the course.
Variables affecting students’ academic performance have been a subject of research interest for many years. Recently, a sizable amount of research in accounting education has been devoted to this particular issue (e.g., Baldwin & Howe, 1982; Bergin, 1983; Eskew & Faley, 1988; Hicks & Richardson, 1984; Ingram & Peterson, 1987; Porcano, 1984; Vruwink & Otto, 1987). The major thrust of these studies was to identify or assess the relationship between (or the effect of) specific variables or predictors and student performance in accounting courses. Little attention, however, has been given to the role of students’ expectations and levels of effort in their performance in accounting courses. According to Smead and Chase (1981), individual achievement expectations were found to be significantly related to eighth grade students’ subsequent achievement in mathematics during the year. This article presents the results of examining the differential effects of students’ expectations and effort levels on students’ actual performance in a managerial cost accounting course. RESEARCH DESIGN Data Collection
The data were collected on an initial sample of 107 undergraduate accounting students enrolled in three sections of managerial cost accounting in the fall of 1984. However, the sample size decreased over the semester to
M. E. Ibrahim
58
86 students because of the withdrawal of some students either from the course or from the study. Four short questionnaires were used to collect the research data. Each questionnaire included directions informing students that their participation was voluntary. The instructions emphasized the need for answering the questions honestly and assured the students of the confidentiality of their responses and that their participation in the study would not influence their actual course grade. The first questionnaire was distributed to 107 students at the end of the second week of the semester. Students were asked to assess their expected performance on each exam and the expected overall course performance. The second week of the semester was chosen to distribute the first questionnaire in order to give students an opportunity to formulate ideas about the contents of the course and the grading system. A second questionnaire was distributed to the same 107 students after the announcement of their actual performance on the first exam. A third questionnaire was distributed to 103 students after the announcement of the actual results of the second exam, with a final questionnaire distributed to 96 students prior to the final examination. The students were instructed to return the completed questionnaire after the final examination but before the announcement of their actual course performance. On each of the last three questionnaires students were asked to again assess their expected performance on each of the upcoming exams and the average number of actual weekly hours devoted to the course. Screening returned questionnaires for completeness resulted in excluding several questionnaires from the analysis because of incomplete responses. In addition, five responses to the last questionnaire were also excluded because of their arrival after the announcement of the final examination scores and course grades. Research Variables Dependent variables. The study used students’ actual performance on examinations as the dependent variables. Four performance measures were used: actual performance on each of the three exams and overall performance in the course. Overall performance was calculated by averaging the actual score on the three exams. Independent variables. Expected performance, effort level, and the overall grade-point average (GPA) constituted the independent variables that were employed in the study. A description of each of these variables and its measurement follows: 1. Expected performance, Previous
research has reported evidence to suggest an existing link between actual performance and performance expectations (Smead & Chase, 1981). Although such expectations are usually derived
Effort-Expectation
and Academic Performance
from past performance, they may serve as a motivational anchor for the initial level of effort toward actual performance. This postulated link implies a probable relationship between expected performance and effort level. On the other hand, one may argue that since expectations are derived from past performance, one might expect performance expectations to reflect the overall GPA. This means that a potential multicollinearity problem may exist between expected performance and the other two independent variables (i.e., effort level and overall GPA). To overcome the analytical problem of interrelated independent variables, a sequential (hierarchical) regression strategy was used. As explained in the data analysis section, such a strategy allows for statistically controlling the effects of interrelated variables and revealing the incremental contribution of each independent variable to the explanatory power of the regression model. The course outline distributed at the beginning of the semester informed students that three equally weighted exams would be given and the final course grade was to be based on cumulative points scored on examinations. The first questionnaire was used to initially assess students’ expected performance for each of the three exams and the overall course performance. Thus, for each student in the sample four scores of expected performance were collected, one of which represents the overall performance in the course. Although expected overall performance in the course was measured in terms of percentage of maximum points available, this measure was transformed to points in the process of data analysis. One reason for this transformation is the fact that students were asked to assess their expected scores on the individual exams in terms of points. 2. Effort level. This variable measures the quantity of input (weekly hours) devoted by a student to achieve his/her output (performance) on each exam and in the course as a whole. The inclusion of effort level as an independent variable is based on the postulated link between effort level and performance in the industrial psychology literature. According to expectancy theory (e.g., Vroom 1964), there is a monotonical functional relationship between effort level and desired level of performance. This means that a person will select the behavior to engage in and the level of effort to be exerted toward an outcome on the basis of a subjective estimation of the probability that the effort will lead to a set of desired outcomes. Thus, a high level of effort is expected to lead to a better performance as long as the performer believes in such a relationship. This postulated relationship between effort and performance was used in the measurement of effort level. Each student in the sample was asked, after each exam, to indicate the average number of weekly hours that had been actually devoted to the course. Three data points of effort level for each student in the sample were collected to match the three exams. 3. Overd CPA. Many educators believe that overall GPA reflects the general ability of a student to perform in accounting courses and that it serves as a significant predictor of students’ performance. In fact, the education literature contains some evidence to support this claim. Hicks and Richardson (1984), for example, assessed the relationship between student performance in first-quarter intermediate accounting and scores on an entry examination
59
60
M. E. Ibrahim
and two measures of GPA (an overall GPA measure and a subject related principles of accounting GPA). Their results indicate that both measures of GPA were significant although the subject related GPA measure was more highly correlated with students’ performance than the overall GPA measure. This study used the overall GPA as a general measure of students’ ability to perform rather than a subject related GPA measure to overcome the problem of course credit transfer (some of the subjects studied principles of accounting at other institutions). Theoretical justification for the inclusion of a measure of ability (e.g., overall GPA) to study academic performance of students in accounting courses can also be found in the expectancy theory literature where performance was conceived as a function of effort and ability (e.g., Ferris, 1977; Vroom, 1964). This functional relationship may take a multiplicative or an additive form. In a multiplicative form, performance is regarded as an outcome of the interaction between (or joint effect of) effort and ability (i.e., P=FxA).In the alternate form, performance is regarded as an outcome of the additive function of effort and ability (i.e., P=F+A). Although there is no inclusive empirical evidence to suggest superiority of one functional form over the other, this study used the notion of performance as an additive function of effort and ability to justify the inclusion of GPA as a proxy measure of individual ability to perform. Such a choice is more consistent with the objective of studying the incremental effects of effort and GPA on students’ academic performance. Furthermore, one may argue that, under a multiplicative form, the overall GPA is a measure of performance and theoretically would represent a joint measure of effort and ability. Research Hypotheses The focus of this study was the relationship between effort-expectation and academic performance of undergraduate students in managerial cost accounting. It is hypothesized that actual performance is significantly related to students’ expectations. If actual performance on one exam turns out to be different from what was expected, students would tend to modify their levels of effort and/or their expectations for the upcoming exams. This general hypothesis also implies a relationship between effort level and actual performance on one hand, and a relationship between performance variances (expected minus actual performance) and changes in effort levels, on the other hand. Thus, this study deals with three hypotheses which are stated in a null form as follows: HO,: There is no significant relationship between expected performance and actual performance. H02: There is no significant relationship between effort level and actual performance. H03: There is no significant relationship between performance variances and changes in effort levels.
Effort-Expectation
and Academic Performance
61
Data Analysis
The data collected from the participants’ responses to the four questionnaires were analyzed through the use of correlation and multiple regression techniques. The multiple regression technique was used in a sequential form (hierarchical regression) for the task of statistically controlling the effect of interrelated variables and revealing the partial contribution of each independent variable to the explanatory power of the model. According to Cohen and Cohen (1975), the sequential regression strategy is similar to the known step-wise strategy, except that the researcher determines the order in which the independent variables are entered into the regression process. Therefore, a sequential strategy allows the regression model to be studied incrementally and overcomes some of the analytical problems associated with multicollinearity. However, to arrive at a final regression equation for prediction purposes, it is necessary to examine the final step simultaneous model which isolates the independent effects of each explanatory variable and fully shows the final effects of any supression encountered. The general regression model was formulated as follows: A,=a+b,E;+b
,F;+&GPA
where, A ;= actual performance on exam i, E,= expected performance on exam i, Fi=actual effort exerted toward exam i, GPA = overall Grade Point Average, a, 6,=the regression parameters. This regression model was applied to the results of the three exams and the calculated averages for the course. The sequential strategy was used to examine the incremental contribution of each independent variable to the explanatory power of the model at the time of entering the regression, with the effects of previously entered variables partialled out. The final step simultaneous model was used to measure the final contribution of each independent variable after isolating the independent effects of each of the independent variables. The use of sequential regression requires the researcher to determine the order in which the independent variables would enter the regression. Such an ordering system is usually established either according to a theoretical framework that specifies the structural relationships among the variables to be studied or according to intuition. Since there is no theoretical framework that specifies the structural relationships among effort, expectations and overall GPA, a rotating strategy was used. Under such a strategy, each variable was given the chance to enter the regression model once as the first variable and again as the last variable in the sequence so that the total and
62
M. E. Ibrahim
the net contribution of each variable to the model’s R square could be measured before and after partialling out the effects of the interrelationships of the other variables. Examination of the regression processes that produced the total and net contribution of each variable helps to identify the redundancy or supression effects among the variables.
RESULTS Table 1 presents the basic descriptive statistics for each of the research variables. Table 2 presents the correlation coefficients obtained from the univariate and multiple correlation analyses between initial expected performance, actual effort levels during the semester, and overall GPA and actual performance on the three exams and the course as a whole. As shown in Table 2, the three predictor variables were significantly correlated with actual performance. Effort level had higher correlation coefficients with actual performance than the other two variables with the exception of exam 3, where GPA had the higher coefficient of correlation. Examination of correlation coefficients in Table 2 shows an increase over
Table 1. Variable descriptive Variable
Mean
statistics
Standard
Deviation
Sample
Dependent Variables:
Actual Performance: Score on Exam 1 (Yl) Score on Exam 2 (Y2) Score on Exam 3 (Y3) Course Score (Y4)
72.75 73.29 74.64 75.01
9.42 10.04 5.93 6.47
107 107 98 86
Expected Performance (Xl): Expected Score on Exam 1 Expected Score on Exam 2 Expected Score on Exam 3 Expected Course Score
74.51 77.58 76.93 76.34
7.07 7.77 7.08 6.88
107 107 107 107
Effort Level (X2): Weekly His Toward Exam 1 Weeklv Hrs Toward Exam 2 Weekly Hrs Toward Exam 3 Course Weekly Hrs
7.12 8.03 8.21 8.18
1.31 1.30 1.43 1.18
107 101 86 86
Overall GPA (X3): For Attendees of Exam 1 For Attendees of Exam 2 For Attendees of Exam 3
3.12 3.12 3.28
.43 .43 .51
107 107 86
Size
Effort-Expectation
63
and Academic Performance
Table 2. Correlation coefficients between actual performance and performance predictors Actual Performance Variables
Exam 1
Exam 2
Exam 3
Overall
Expected Performance Effort level Overall GPA
.66 .72 .54
.47 .74 58
.56 .61 .65
.67’ .?6’ .58*
Multiple R
.a3
.78
.7f
.84*
‘Significant at the .OOOl level.
in the GPA coefficient and a decrease in the coefficient of expected performance after the first examination. This decrease indicates that students were unable to predict, with a relative degree of accuracy, their actual performance on multiple examinations beyond the first examination. To partially confirm these results and this interpretation, correlation coefficients between actual performance and the revised expected performance for the second and third exams were calculated again. The obtained correlation coefficients were .72 and .68 for the two exams, respectively. These correlation coefficients were significant at the .005 level. A second correlation analysis was performed using changes in effort levels and performance variances (expected minus actual performance). If the reported correlation coefficients between effort and actual performance (as in Table 2) incorporate the effect of performance feedback, one would expect changes in effort levels to be significantly correlated with the performance variance. Such a relationship is based on the achievement-motivation hypothesis where a performance variance would produce pressure on students to modify their levels of effort to achieve expected performance or to change their performance expectations and exert the same level of effort. Table 3 presents the correlation results between performance variance and changes in effort level. As shown in Table 3, there is a significant correlation between performance variance and changes in effort level toward exam 2. However, the correlation coefficient between performance variance on exam 2 and changes in effort level toward exam 3 was not significant. One possible explanation for the difference in the above reported correlation coefficients is that students have different utility functions toward examination scores to the extent that the expected return on effort toward exam 3 was not as attractive as the return on effort toward exam 2. However, if one had presumed that students would have learned the relationship between effort and performance in previous courses, the difference in the relationships between performance variance and changes in effort levels may simply be due to a
time
M. E. Ibrahim
64
Table 3. Correlation coefficients between pe~ormanca variance and changes in effort level Pertormance Changes in Effort Lsvef
Exam 1 (E-A)
Toward Exam 2 (FZ-f 1f
Exam 2 (E-A)
.38 (.C%XX~
Toward Exam 3 (F3-F2)
Key: A = Actual Performance, E= Expected Performance, F = Effort Level.
Variance
.I4 (.2130)
and
common perception that the material on exam 3 was easier than that on exam 2. Evaluation of the difference in actual performance between the two exams (difference in sample average of the two exams and paired samples ttest) indicates, however, that the difference was not significant @= .32), This result may indicate that a learned relationship between effort and performance is conditioned by course level and its content, among other factors. RegressionResults In reporting the findings of the regression analysis, an incremental analysis table is presented, followed by the results of the simultaneous regression analysis. As explained in the data analysis section, the incremental analysis considers only the incremental effect of the added variable in the regression model at the time of its entrance. Table 4 presents the results obtained from the incremental regression analysis and Table 5 presents the results of the simuftaneous regression analysis. Table 4 shows that the total contribution of each of the three variables to the model’s R square is significant at the .OOfXlevel when the variable entered the regression as the first in the sequence. Effort Ievel has the largest value of total contribution to the modeI’s R square, except on exam 3 where effort level is ranked second tv GPA. When the variables were rotated and each one was entered in the regression as the last variable in the sequence, however, only effort level was sig~ficant at the .OMH level for each exam. The significance leve1 of the GPA and expected performance variables was Lower. Examination of the regression process reveals that there were no suppressian effects on the model’s R square but there were different levels of partiailing out effects among the three variables because of redundancy (multicollinearity)). However, only GPA and expected performance variables were subject to a heavy partialling out process. Table 5 shows the resulrs of the final step simultaneous regression mvdel,
Effort-Expectation
65
and Academic Performance
Table 4. Variable total and net incremental effects on actual performance When Entered First Variable
RSquare
When Entered Last RSquare t Sig.
t
Sig.
.290
6.54
.OOOl
,014
2.10
.0394
Effort
.440 546
9.09 11.21
.OOOl .0001
,086 ,192
5.20 7.89
.OOOl .OOOl
Second Exam GPA Expected Performance Effort
,333 ,219 ,518
7.23 5.42 10.31
.OOOl .OOOl .OOOl
,060 ,020 ,176
3.70 1.94 6.55
.0005 .0563 .OOOl
Third Exam GPA Expected Performance Effort
,426 ,317 ,367
6.44 6.67 6.96
.OOOl .OOOl .OOOl
,187 ,010 .069
5.76 1.41 4.01
.OOOl .1512 .OOOl
Overa// Performance GPA Expected Performance Effort
335 .454 ,578
6.95 8.93 10.72
.OOOl .OOOl .OOOl
.072 .029 ‘139
4.73 2.81 6.60
.OOOl .0064 .OOOl
First Exam GPA Expected Performance
Examination of these results reveals that with the exception of expected performance in the second and third model, the three independent variables are significant at the .05 level, However, according to the standardized Beta measures, it appears that effort level represents the major variable that affects actual performance, except on the first exam where expected performance was second to effort level. While these results suggest the rejection of the second null hypothesis which states that there is no significant relationship between effort level and actual performance, the results provide mixed evidence regarding the effects of expectations on actual performance. SUMMARY AND CONCLUSION This study examined the relationship between effort-expectation and academic performance of students in managerial cost accounting. The reported correlation and regression results indicate that effort level is the major variable affecting actual performance. Although performance expectations and overall GPA were significantly related to actual overall performance, their levels of significance varied among the course exams, indicating the existence of complex relationships. The implications of these results are motivational ones. Educators could utilize performance feedback as a tool to motivate their students to work harder in order to achieve targeted performance. However, one should be
66
M. E. Ibrahim
Table 5. Simultaneous regression results of actual performance First Exam Variable Expected Performance Effort level GPA Intercept
Coefficient
Std. Error
,469 3.669 3.054 2.178
0.090 0.465 1.457
t
Sig.
5.20 7.89 2.10
.OOOl .OOOl .0394
Mean-Square
f-Ratio
P
2148.269 28.674
74.921
.OOOl
Std. Coeff.
t
Sig.
1.94 6.55 3.70
.0563 .OOOl .0005
Mean-Square
F-Ratio
P
1761.354 36.252
48.586
.OOOl
Std. Coeff.
t
Sig.
1.45 4.01 5.78
.1512 .OOOl .OOOl
F-Ratio
P
39.512
.OOOl
Std. Coeff. .352 512 .139
No. of Observations: 107 Squared ~ultipie R: ,686 Analysis of Variance Source Regression Residual
Sum-of-Squares 6444.806 2953.381
DF 3 103
Second Exam Variable Expected Performance Effort level GPA Intercept
Coefficient
Std. Error
0.180 3.786 5.927 11.023
0.093 0.578 1.604 6.607
.148 S23 ,268
No. of Observations: 101 Squared Multiple R: .60 Analysis of Variance Source Regression Residual
Sum-of-Squares 5284.062 3516.453
DF 3 97
Third Exam Variable Expected Pe~ormance Effort level GPA Intercept
Coefficient 0.115 1.423 7.125 32.148
Std. Error 0.079 0.355 1.232 5.038
.128 .346 .469
No. of Observations: 86 Squared Multiple R: ,591 Analysis of Variance Source Regression Residual
Sum-of-Squares 1749.958 1210.565
DF 3 82
Mean-Square 583.319 14.763
Effort-Expectation
and Academic
Performance
Table 5. Continued Overall Performance Variable Expected Performance Effort level GPA Intercept
Coefficient 0.218 2.736 4.994 20.811
Std. Error 0.078 0.414 1.109 4.900
Std. Coeff. ,215 .506 .302
t
Sig.
2.81 7.39 4.73
.0064 .OOOl .OOOl
F-Ratio
P
65.260
.OOOl
No. of Observations: 86 Squared Multiple R: ,705 Analysis of Variance Source Regression Residual
Sum-of-Squares 2420.065 1013.610
DF 3 82
Mean-Square 806.688 12.361
that there are some limitations on the generalizability of the results. First, there may be some other important variables affecting actual performance that are not included in the model. Second, although students were asked to respond to the questions honestly, their actual responses may not have been accurate nor honest. Third, cultural and environmental factors may motivate students in different ways.
aware
Acknowledgmenls-The author would like to thank Edwin Cheng, krishnan and two anonymous reviewers for their helpful comments article.
Nabil Elias, V. Gopalaon earlier drafts of this
REFERENCES Baldwin, B. A., & Howe, K. J. (1982). Secondary-level study of accounting and subsequent performance in the first college course. The Accounting Review, 57(3), 619-626. Bergin, J. L. (1983). The effects of previous accounting study on student performance in the first college-level financial accounting course. Issues in Accounting Educution, 19-28. Cohen, J., & Cohen, P. (1975). Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers. Eskew, T. P., & Faley, R. H. (1988). Some determinants of students’ performance in the first college-level financial accounting course. The Accounting Review, 63(l), 137-147. Ferris, K. R. (1977). A test of the expectancy theory of motivation in an accounting environment. The Accounting Review, 52(3), 605-615. Hicks, D. W., & Richardson, F. M. (1984). Predicting early success in intermediate accounting: The influence of entry examination and GPA. Issues in Accounting Education, 61-67. Ingram, R. W., & Peterson, R. J. (1987). An evaluation of AICPA tests for predicting the performance of accounting majors. The Accounting Review, 62(l), 215-223. Porcano, T. M. (1984). An empirical analysis of some factors affecting students performance. Journal of Accounting Education, 2(2), 111-126.
68
M. E. Ibrahim
Smead, V. S., & Chase, C. I. (1981). Students’ expectations as they relate to achievement in eighth grade mathematics. Journal ofL?ducutional Research, 75(2), 112-l 15. Vroom, V. H. (1964). Work andmotivation. New York: John Wiley and Sons, Inc. Vruwink, D. R., & Otto, J. R. (1987). Evaluation of teaching techniques for introductory accounting courses. The Accounting Review, 62(2), 402-408.
1989 Copyright
0 I989 Maxwell
0748-5751189 $3.oo+.w Pergamon Macmillan plc
EFFORT-EXPECTATION AND ACADEMIC PERFORMANCE IN MANAGERIAL COST ACCOUNTING Mohamed E. Ibmhim UNIVERSITY OF MANITOBA Abstract: An area of research in accounting education has been concerned with the academic ~rformance of students in accounting courses. The major thrust of this research has attempted to identify or assess the effect of specific variables (or predictors) on students’ performance. Little attention has been given to students’ expectations and effort as predictors of performance. This study examines the relationship between effort-expectation and students’ academic performance in managerial cost accounting. The reported correlation and regression results indicate that students’ effort levels have a significant relationship with actual performance. In addition, students’ expected performance and overall GPA were sig~ficantly correIated with actual performance. However, overall GPA was more significant than students’ expected performance with the exception of the first exam in the course.
Variables affecting students’ academic performance have been a subject of research interest for many years. Recently, a sizable amount of research in accounting education has been devoted to this particular issue (e.g., Baldwin & Howe, 1982; Bergin, 1983; Eskew & Faley, 1988; Hicks & Richardson, 1984; Ingram & Peterson, 1987; Porcano, 1984; Vruwink & Otto, 1987). The major thrust of these studies was to identify or assess the relationship between (or the effect of) specific variables or predictors and student performance in accounting courses. Little attention, however, has been given to the role of students’ expectations and levels of effort in their performance in accounting courses. According to Smead and Chase (1981), individual achievement expectations were found to be significantly related to eighth grade students’ subsequent achievement in mathematics during the year. This article presents the results of examining the differential effects of students’ expectations and effort levels on students’ actual performance in a managerial cost accounting course. RESEARCH DESIGN Data Collection
The data were collected on an initial sample of 107 undergraduate accounting students enrolled in three sections of managerial cost accounting in the fall of 1984. However, the sample size decreased over the semester to
M. E. Ibrahim
58
86 students because of the withdrawal of some students either from the course or from the study. Four short questionnaires were used to collect the research data. Each questionnaire included directions informing students that their participation was voluntary. The instructions emphasized the need for answering the questions honestly and assured the students of the confidentiality of their responses and that their participation in the study would not influence their actual course grade. The first questionnaire was distributed to 107 students at the end of the second week of the semester. Students were asked to assess their expected performance on each exam and the expected overall course performance. The second week of the semester was chosen to distribute the first questionnaire in order to give students an opportunity to formulate ideas about the contents of the course and the grading system. A second questionnaire was distributed to the same 107 students after the announcement of their actual performance on the first exam. A third questionnaire was distributed to 103 students after the announcement of the actual results of the second exam, with a final questionnaire distributed to 96 students prior to the final examination. The students were instructed to return the completed questionnaire after the final examination but before the announcement of their actual course performance. On each of the last three questionnaires students were asked to again assess their expected performance on each of the upcoming exams and the average number of actual weekly hours devoted to the course. Screening returned questionnaires for completeness resulted in excluding several questionnaires from the analysis because of incomplete responses. In addition, five responses to the last questionnaire were also excluded because of their arrival after the announcement of the final examination scores and course grades. Research Variables Dependent variables. The study used students’ actual performance on examinations as the dependent variables. Four performance measures were used: actual performance on each of the three exams and overall performance in the course. Overall performance was calculated by averaging the actual score on the three exams. Independent variables. Expected performance, effort level, and the overall grade-point average (GPA) constituted the independent variables that were employed in the study. A description of each of these variables and its measurement follows: 1. Expected performance, Previous
research has reported evidence to suggest an existing link between actual performance and performance expectations (Smead & Chase, 1981). Although such expectations are usually derived
Effort-Expectation
and Academic Performance
from past performance, they may serve as a motivational anchor for the initial level of effort toward actual performance. This postulated link implies a probable relationship between expected performance and effort level. On the other hand, one may argue that since expectations are derived from past performance, one might expect performance expectations to reflect the overall GPA. This means that a potential multicollinearity problem may exist between expected performance and the other two independent variables (i.e., effort level and overall GPA). To overcome the analytical problem of interrelated independent variables, a sequential (hierarchical) regression strategy was used. As explained in the data analysis section, such a strategy allows for statistically controlling the effects of interrelated variables and revealing the incremental contribution of each independent variable to the explanatory power of the regression model. The course outline distributed at the beginning of the semester informed students that three equally weighted exams would be given and the final course grade was to be based on cumulative points scored on examinations. The first questionnaire was used to initially assess students’ expected performance for each of the three exams and the overall course performance. Thus, for each student in the sample four scores of expected performance were collected, one of which represents the overall performance in the course. Although expected overall performance in the course was measured in terms of percentage of maximum points available, this measure was transformed to points in the process of data analysis. One reason for this transformation is the fact that students were asked to assess their expected scores on the individual exams in terms of points. 2. Effort level. This variable measures the quantity of input (weekly hours) devoted by a student to achieve his/her output (performance) on each exam and in the course as a whole. The inclusion of effort level as an independent variable is based on the postulated link between effort level and performance in the industrial psychology literature. According to expectancy theory (e.g., Vroom 1964), there is a monotonical functional relationship between effort level and desired level of performance. This means that a person will select the behavior to engage in and the level of effort to be exerted toward an outcome on the basis of a subjective estimation of the probability that the effort will lead to a set of desired outcomes. Thus, a high level of effort is expected to lead to a better performance as long as the performer believes in such a relationship. This postulated relationship between effort and performance was used in the measurement of effort level. Each student in the sample was asked, after each exam, to indicate the average number of weekly hours that had been actually devoted to the course. Three data points of effort level for each student in the sample were collected to match the three exams. 3. Overd CPA. Many educators believe that overall GPA reflects the general ability of a student to perform in accounting courses and that it serves as a significant predictor of students’ performance. In fact, the education literature contains some evidence to support this claim. Hicks and Richardson (1984), for example, assessed the relationship between student performance in first-quarter intermediate accounting and scores on an entry examination
59
60
M. E. Ibrahim
and two measures of GPA (an overall GPA measure and a subject related principles of accounting GPA). Their results indicate that both measures of GPA were significant although the subject related GPA measure was more highly correlated with students’ performance than the overall GPA measure. This study used the overall GPA as a general measure of students’ ability to perform rather than a subject related GPA measure to overcome the problem of course credit transfer (some of the subjects studied principles of accounting at other institutions). Theoretical justification for the inclusion of a measure of ability (e.g., overall GPA) to study academic performance of students in accounting courses can also be found in the expectancy theory literature where performance was conceived as a function of effort and ability (e.g., Ferris, 1977; Vroom, 1964). This functional relationship may take a multiplicative or an additive form. In a multiplicative form, performance is regarded as an outcome of the interaction between (or joint effect of) effort and ability (i.e., P=FxA).In the alternate form, performance is regarded as an outcome of the additive function of effort and ability (i.e., P=F+A). Although there is no inclusive empirical evidence to suggest superiority of one functional form over the other, this study used the notion of performance as an additive function of effort and ability to justify the inclusion of GPA as a proxy measure of individual ability to perform. Such a choice is more consistent with the objective of studying the incremental effects of effort and GPA on students’ academic performance. Furthermore, one may argue that, under a multiplicative form, the overall GPA is a measure of performance and theoretically would represent a joint measure of effort and ability. Research Hypotheses The focus of this study was the relationship between effort-expectation and academic performance of undergraduate students in managerial cost accounting. It is hypothesized that actual performance is significantly related to students’ expectations. If actual performance on one exam turns out to be different from what was expected, students would tend to modify their levels of effort and/or their expectations for the upcoming exams. This general hypothesis also implies a relationship between effort level and actual performance on one hand, and a relationship between performance variances (expected minus actual performance) and changes in effort levels, on the other hand. Thus, this study deals with three hypotheses which are stated in a null form as follows: HO,: There is no significant relationship between expected performance and actual performance. H02: There is no significant relationship between effort level and actual performance. H03: There is no significant relationship between performance variances and changes in effort levels.
Effort-Expectation
and Academic Performance
61
Data Analysis
The data collected from the participants’ responses to the four questionnaires were analyzed through the use of correlation and multiple regression techniques. The multiple regression technique was used in a sequential form (hierarchical regression) for the task of statistically controlling the effect of interrelated variables and revealing the partial contribution of each independent variable to the explanatory power of the model. According to Cohen and Cohen (1975), the sequential regression strategy is similar to the known step-wise strategy, except that the researcher determines the order in which the independent variables are entered into the regression process. Therefore, a sequential strategy allows the regression model to be studied incrementally and overcomes some of the analytical problems associated with multicollinearity. However, to arrive at a final regression equation for prediction purposes, it is necessary to examine the final step simultaneous model which isolates the independent effects of each explanatory variable and fully shows the final effects of any supression encountered. The general regression model was formulated as follows: A,=a+b,E;+b
,F;+&GPA
where, A ;= actual performance on exam i, E,= expected performance on exam i, Fi=actual effort exerted toward exam i, GPA = overall Grade Point Average, a, 6,=the regression parameters. This regression model was applied to the results of the three exams and the calculated averages for the course. The sequential strategy was used to examine the incremental contribution of each independent variable to the explanatory power of the model at the time of entering the regression, with the effects of previously entered variables partialled out. The final step simultaneous model was used to measure the final contribution of each independent variable after isolating the independent effects of each of the independent variables. The use of sequential regression requires the researcher to determine the order in which the independent variables would enter the regression. Such an ordering system is usually established either according to a theoretical framework that specifies the structural relationships among the variables to be studied or according to intuition. Since there is no theoretical framework that specifies the structural relationships among effort, expectations and overall GPA, a rotating strategy was used. Under such a strategy, each variable was given the chance to enter the regression model once as the first variable and again as the last variable in the sequence so that the total and
62
M. E. Ibrahim
the net contribution of each variable to the model’s R square could be measured before and after partialling out the effects of the interrelationships of the other variables. Examination of the regression processes that produced the total and net contribution of each variable helps to identify the redundancy or supression effects among the variables.
RESULTS Table 1 presents the basic descriptive statistics for each of the research variables. Table 2 presents the correlation coefficients obtained from the univariate and multiple correlation analyses between initial expected performance, actual effort levels during the semester, and overall GPA and actual performance on the three exams and the course as a whole. As shown in Table 2, the three predictor variables were significantly correlated with actual performance. Effort level had higher correlation coefficients with actual performance than the other two variables with the exception of exam 3, where GPA had the higher coefficient of correlation. Examination of correlation coefficients in Table 2 shows an increase over
Table 1. Variable descriptive Variable
Mean
statistics
Standard
Deviation
Sample
Dependent Variables:
Actual Performance: Score on Exam 1 (Yl) Score on Exam 2 (Y2) Score on Exam 3 (Y3) Course Score (Y4)
72.75 73.29 74.64 75.01
9.42 10.04 5.93 6.47
107 107 98 86
Expected Performance (Xl): Expected Score on Exam 1 Expected Score on Exam 2 Expected Score on Exam 3 Expected Course Score
74.51 77.58 76.93 76.34
7.07 7.77 7.08 6.88
107 107 107 107
Effort Level (X2): Weekly His Toward Exam 1 Weeklv Hrs Toward Exam 2 Weekly Hrs Toward Exam 3 Course Weekly Hrs
7.12 8.03 8.21 8.18
1.31 1.30 1.43 1.18
107 101 86 86
Overall GPA (X3): For Attendees of Exam 1 For Attendees of Exam 2 For Attendees of Exam 3
3.12 3.12 3.28
.43 .43 .51
107 107 86
Size
Effort-Expectation
63
and Academic Performance
Table 2. Correlation coefficients between actual performance and performance predictors Actual Performance Variables
Exam 1
Exam 2
Exam 3
Overall
Expected Performance Effort level Overall GPA
.66 .72 .54
.47 .74 58
.56 .61 .65
.67’ .?6’ .58*
Multiple R
.a3
.78
.7f
.84*
‘Significant at the .OOOl level.
in the GPA coefficient and a decrease in the coefficient of expected performance after the first examination. This decrease indicates that students were unable to predict, with a relative degree of accuracy, their actual performance on multiple examinations beyond the first examination. To partially confirm these results and this interpretation, correlation coefficients between actual performance and the revised expected performance for the second and third exams were calculated again. The obtained correlation coefficients were .72 and .68 for the two exams, respectively. These correlation coefficients were significant at the .005 level. A second correlation analysis was performed using changes in effort levels and performance variances (expected minus actual performance). If the reported correlation coefficients between effort and actual performance (as in Table 2) incorporate the effect of performance feedback, one would expect changes in effort levels to be significantly correlated with the performance variance. Such a relationship is based on the achievement-motivation hypothesis where a performance variance would produce pressure on students to modify their levels of effort to achieve expected performance or to change their performance expectations and exert the same level of effort. Table 3 presents the correlation results between performance variance and changes in effort level. As shown in Table 3, there is a significant correlation between performance variance and changes in effort level toward exam 2. However, the correlation coefficient between performance variance on exam 2 and changes in effort level toward exam 3 was not significant. One possible explanation for the difference in the above reported correlation coefficients is that students have different utility functions toward examination scores to the extent that the expected return on effort toward exam 3 was not as attractive as the return on effort toward exam 2. However, if one had presumed that students would have learned the relationship between effort and performance in previous courses, the difference in the relationships between performance variance and changes in effort levels may simply be due to a
time
M. E. Ibrahim
64
Table 3. Correlation coefficients between pe~ormanca variance and changes in effort level Pertormance Changes in Effort Lsvef
Exam 1 (E-A)
Toward Exam 2 (FZ-f 1f
Exam 2 (E-A)
.38 (.C%XX~
Toward Exam 3 (F3-F2)
Key: A = Actual Performance, E= Expected Performance, F = Effort Level.
Variance
.I4 (.2130)
and
common perception that the material on exam 3 was easier than that on exam 2. Evaluation of the difference in actual performance between the two exams (difference in sample average of the two exams and paired samples ttest) indicates, however, that the difference was not significant @= .32), This result may indicate that a learned relationship between effort and performance is conditioned by course level and its content, among other factors. RegressionResults In reporting the findings of the regression analysis, an incremental analysis table is presented, followed by the results of the simultaneous regression analysis. As explained in the data analysis section, the incremental analysis considers only the incremental effect of the added variable in the regression model at the time of its entrance. Table 4 presents the results obtained from the incremental regression analysis and Table 5 presents the results of the simuftaneous regression analysis. Table 4 shows that the total contribution of each of the three variables to the model’s R square is significant at the .OOfXlevel when the variable entered the regression as the first in the sequence. Effort Ievel has the largest value of total contribution to the modeI’s R square, except on exam 3 where effort level is ranked second tv GPA. When the variables were rotated and each one was entered in the regression as the last variable in the sequence, however, only effort level was sig~ficant at the .OMH level for each exam. The significance leve1 of the GPA and expected performance variables was Lower. Examination of the regression process reveals that there were no suppressian effects on the model’s R square but there were different levels of partiailing out effects among the three variables because of redundancy (multicollinearity)). However, only GPA and expected performance variables were subject to a heavy partialling out process. Table 5 shows the resulrs of the final step simultaneous regression mvdel,
Effort-Expectation
65
and Academic Performance
Table 4. Variable total and net incremental effects on actual performance When Entered First Variable
RSquare
When Entered Last RSquare t Sig.
t
Sig.
.290
6.54
.OOOl
,014
2.10
.0394
Effort
.440 546
9.09 11.21
.OOOl .0001
,086 ,192
5.20 7.89
.OOOl .OOOl
Second Exam GPA Expected Performance Effort
,333 ,219 ,518
7.23 5.42 10.31
.OOOl .OOOl .OOOl
,060 ,020 ,176
3.70 1.94 6.55
.0005 .0563 .OOOl
Third Exam GPA Expected Performance Effort
,426 ,317 ,367
6.44 6.67 6.96
.OOOl .OOOl .OOOl
,187 ,010 .069
5.76 1.41 4.01
.OOOl .1512 .OOOl
Overa// Performance GPA Expected Performance Effort
335 .454 ,578
6.95 8.93 10.72
.OOOl .OOOl .OOOl
.072 .029 ‘139
4.73 2.81 6.60
.OOOl .0064 .OOOl
First Exam GPA Expected Performance
Examination of these results reveals that with the exception of expected performance in the second and third model, the three independent variables are significant at the .05 level, However, according to the standardized Beta measures, it appears that effort level represents the major variable that affects actual performance, except on the first exam where expected performance was second to effort level. While these results suggest the rejection of the second null hypothesis which states that there is no significant relationship between effort level and actual performance, the results provide mixed evidence regarding the effects of expectations on actual performance. SUMMARY AND CONCLUSION This study examined the relationship between effort-expectation and academic performance of students in managerial cost accounting. The reported correlation and regression results indicate that effort level is the major variable affecting actual performance. Although performance expectations and overall GPA were significantly related to actual overall performance, their levels of significance varied among the course exams, indicating the existence of complex relationships. The implications of these results are motivational ones. Educators could utilize performance feedback as a tool to motivate their students to work harder in order to achieve targeted performance. However, one should be
66
M. E. Ibrahim
Table 5. Simultaneous regression results of actual performance First Exam Variable Expected Performance Effort level GPA Intercept
Coefficient
Std. Error
,469 3.669 3.054 2.178
0.090 0.465 1.457
t
Sig.
5.20 7.89 2.10
.OOOl .OOOl .0394
Mean-Square
f-Ratio
P
2148.269 28.674
74.921
.OOOl
Std. Coeff.
t
Sig.
1.94 6.55 3.70
.0563 .OOOl .0005
Mean-Square
F-Ratio
P
1761.354 36.252
48.586
.OOOl
Std. Coeff.
t
Sig.
1.45 4.01 5.78
.1512 .OOOl .OOOl
F-Ratio
P
39.512
.OOOl
Std. Coeff. .352 512 .139
No. of Observations: 107 Squared ~ultipie R: ,686 Analysis of Variance Source Regression Residual
Sum-of-Squares 6444.806 2953.381
DF 3 103
Second Exam Variable Expected Performance Effort level GPA Intercept
Coefficient
Std. Error
0.180 3.786 5.927 11.023
0.093 0.578 1.604 6.607
.148 S23 ,268
No. of Observations: 101 Squared Multiple R: .60 Analysis of Variance Source Regression Residual
Sum-of-Squares 5284.062 3516.453
DF 3 97
Third Exam Variable Expected Pe~ormance Effort level GPA Intercept
Coefficient 0.115 1.423 7.125 32.148
Std. Error 0.079 0.355 1.232 5.038
.128 .346 .469
No. of Observations: 86 Squared Multiple R: ,591 Analysis of Variance Source Regression Residual
Sum-of-Squares 1749.958 1210.565
DF 3 82
Mean-Square 583.319 14.763
Effort-Expectation
and Academic
Performance
Table 5. Continued Overall Performance Variable Expected Performance Effort level GPA Intercept
Coefficient 0.218 2.736 4.994 20.811
Std. Error 0.078 0.414 1.109 4.900
Std. Coeff. ,215 .506 .302
t
Sig.
2.81 7.39 4.73
.0064 .OOOl .OOOl
F-Ratio
P
65.260
.OOOl
No. of Observations: 86 Squared Multiple R: ,705 Analysis of Variance Source Regression Residual
Sum-of-Squares 2420.065 1013.610
DF 3 82
Mean-Square 806.688 12.361
that there are some limitations on the generalizability of the results. First, there may be some other important variables affecting actual performance that are not included in the model. Second, although students were asked to respond to the questions honestly, their actual responses may not have been accurate nor honest. Third, cultural and environmental factors may motivate students in different ways.
aware
Acknowledgmenls-The author would like to thank Edwin Cheng, krishnan and two anonymous reviewers for their helpful comments article.
Nabil Elias, V. Gopalaon earlier drafts of this
REFERENCES Baldwin, B. A., & Howe, K. J. (1982). Secondary-level study of accounting and subsequent performance in the first college course. The Accounting Review, 57(3), 619-626. Bergin, J. L. (1983). The effects of previous accounting study on student performance in the first college-level financial accounting course. Issues in Accounting Educution, 19-28. Cohen, J., & Cohen, P. (1975). Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers. Eskew, T. P., & Faley, R. H. (1988). Some determinants of students’ performance in the first college-level financial accounting course. The Accounting Review, 63(l), 137-147. Ferris, K. R. (1977). A test of the expectancy theory of motivation in an accounting environment. The Accounting Review, 52(3), 605-615. Hicks, D. W., & Richardson, F. M. (1984). Predicting early success in intermediate accounting: The influence of entry examination and GPA. Issues in Accounting Education, 61-67. Ingram, R. W., & Peterson, R. J. (1987). An evaluation of AICPA tests for predicting the performance of accounting majors. The Accounting Review, 62(l), 215-223. Porcano, T. M. (1984). An empirical analysis of some factors affecting students performance. Journal of Accounting Education, 2(2), 111-126.
68
M. E. Ibrahim
Smead, V. S., & Chase, C. I. (1981). Students’ expectations as they relate to achievement in eighth grade mathematics. Journal ofL?ducutional Research, 75(2), 112-l 15. Vroom, V. H. (1964). Work andmotivation. New York: John Wiley and Sons, Inc. Vruwink, D. R., & Otto, J. R. (1987). Evaluation of teaching techniques for introductory accounting courses. The Accounting Review, 62(2), 402-408.