Prior studies found that female students outperform male students when the instructor's gender is female, and male students outperform female students when the instructor's gender is male (Mutchler et al., 1987; Lipe, 1989). This study explains the findings of Mutcher et al. (1987) and Lipe (1989) and makes suggestions to counteract the effect of instructor gender on student performance. It proposes that the observations of Mutchler et al. and Lipe may be attributed to inherent differences, specifically differences in risk-taking propensity between the genders. This study speculates that the observed effect of instructor gender on students' performance is due to a positive (negative) bias when student gender and instructor gender are matched (mismatched). To demonstrate this, we made use of a case study involving the assessment of risk and hypothesised that there will be a match between the responses of female students and female instructors and between the responses of male students and male instructors. However, no match between the responses of male and female students and between the responses of male and female instructors is expected. An experiment with 117 students and 23 instructors was carried out. The results support our hypotheses.
Inherent differences between the genders comprise many dimensions. One dimension examined in the psychology and marketing studies is risk-taking propensity (Darley and Smith, 1995; Lenney, 1983; Meyers-Levy, 1986). These studies found that males tend to take more risk, while females tend to be risk-averse. The practice of accounting frequently involve risky judgments (such as capitalisation of expenses and treatment of leases), and these are included in the curricula of many accounting courses. In class assignments or assessments, students may be given a particular treatment of leases (e.g., classification of financing or operating leases), and asked to decide on the validity of the treatment. Similarly in auditing, risk evaluation forms a substantial part of the course. These include topics such as audit risk, control risk, inherent risk and going-concern risk. When a female (male) instructor constructs a going-concern class assignment or an examination question, she (he) assembles together various information cues about a hypothetical (and sometimes, actual) company and determines what the acceptable answer is. Typical requirements of such questions are for students to identify the various going-concern problem indicators and mitigating factors and make an overall going-concern judgment. Due to inherent differences between the genders, the female (male) instructor's predetermined answer would match the female (male) students' answers, whereas the male (female) students' answers would not. This bias results in female (male) students obtaining higher scores than male (female) students. Thus, inherent differences between the genders may result in a positive bias when student gender and instructor gender are matched, and a negative bias when student gender and instructor gender are mismatched. This may be an explanation of Lipe's and Mutchler et al.'s observations. The purpose of this study is to examine whether inherent differences between the genders is an alternative explanation for these findings.
In attempting to explain the observed gender difference(s), prior literature examined the information processing styles of males and females. These studies consistently found that males and females process information differently (Meyers-Levy, 1986; Gilligan, 1982). Males encode fewer details while females pay more attention to details (Meyers-Levy, 1986; Gilligan, 1982). Because females process more details than males, therefore males must use heuristics in their processing of information (Meyers-Levy, 1986; Meyers-Levy and Maheswaran, 1991). The tendency of males to use heuristics in information processing could be explained in two ways. First, the amount of information that is stored in long term memory is dependent upon the amount of information processed. As males encode fewer details, they would recall less information for decision-making. Hence they would have to resort to using heuristics to compensate for the lack of stored information (Reder, 1987). Second, the presence of contrasting information in the data set causes cognitive strain (Kahneman, 1973). When faced with contrasting information, normative decision theories suggest that people should pay greater attention to details in order to reconcile the contrasting information. As females pay greater attention to details than males do (Meyers-Levy, 1986), this suggests that males and females do not react to contrasting information in the same way. Females deal with contrasting information by increasing processing effort whereas males make greater use of heuristics.
A heuristic often cited in the marketing literature to explain the differences between males' and females' risk-taking propensity is the "cue sensitivity thresholds" (see Meyers-Levy ). Cue sensitivity threshold theory states that a person's sensitivity to the nature of the cues contained in a data set determines the judgment outcome. Meyers-Levy (1986) suggests that males' more risky judgment is a result of their lack of sensitivity to risk cues. Other studies also found that males are less sensitive to risk cues compared to females (e.g., Darley and Smith 1995; Meyers-Levy, 1985). Meyers-Levy (1985) gave both males and females descriptions of a new product which contained a risky attribute. The salience of the risky attribute was given at two levels - weak and moderate. Female participants were able to detect the risky attribute when its description was moderately salient. However, male participants were unable to detect it at both levels of salience. The males in both levels of salience and the females in the weak salience condition appeared to rely on other cues. Darley and Smith (1995) found similar gender differences in an experiment which manipulated two consumer products (risky and non-risky) at two levels of risk (low and moderate) and two types of product claims (objective and subjective). The results showed that when risk level increased, females changed their processing strategy from evaluating both objective and subjective claims to evaluating mainly objective claims. Males, however, did not change their processing strategy when the risk level was increased. Like Meyers-Levy, Darley and Smith concluded that males are less sensitive to risk cues than females are.
Goldhaber and deTurck (1988) found that males are more likely to ignore "No Diving" signs, and dive into the swallow end of a pool. Like Meyers-Levy (1986) and Darley and Smith (1995), deTurck and Goldhaber (1989) and Goldhaber and deTurck (1988) attributed this finding to differences in their information processing styles. Females, as detailed processors are more persuaded by the information contained in the data set than males are. Males tend to process only a portion of the information before forming their judgments. When the data set contains risk cues, the males would register the presence of these cues but would not read their details (Goldhaber and deTurck, 1988). Females, however, would read the cues in detail before making their final judgment (Goldhaber and deTurck, 1988). Because females process risk cues in greater detail, they are more aware of the dangers implied by these cues, and this leads to more risk-averse judgments (Goldhaber and deTurck, 1988). Consequently, the difference between the genders' risky judgments is a result of the difference in their information processing styles (Darley and Smith, 1995; Goldhaber and deTurck, 1989; deTurck and Goldhaber, 1988; Meyers-Levy, 1986). Similarly, in a class assignment involving a risky task, male students' encoding of the risk cues would be at a lower level compared to female students' encoding of these cues. Because female students process risk cues in greater details, they would make more risk-averse judgments compared to male students.
In this study, a going-concern task was used. In this task, the going-concern problem indicators are risk cues whereas the mitigating factors are non-risk cues. The cues were preselected and premeasured, and participants were required to evaluate and combine the cues into a global going-concern judgment. Female students are expected to make more risk-averse judgments compared to male students. That is, female students would judge the company as more likely to fail and the male students would judge the company as less likely to fail. Similarly, male instructors' judgments will be more risky compared to female instructors' judgments. The literature does not suggest that such attitudes towards risk-taking diminish with age and experience. In fact, studies seem to suggest that it does not (Sexton and Bowman-Upton, 1990). This suggests that the risk-taking propensity of males and females is not affected by age (general life experience) and domain-specific experience. It is expected that there will be no significant difference between the judgments of male instructors and male students, as well as between female instructors and female students. However, significant differences are expected when student and instructor gender are mismatched. Therefore, the following hypotheses state:
|H1||Male instructors' judgments will not be different from male students' judgments.|
|H2||Female instructors' judgments will not be different from female students' judgments.|
|H3||Male instructors' judgments will be more risky compared to female students' judgments.|
|H4||Female instructors' judgments will be less risky compared to male students' judgments.|
The experiment for the students was conducted during class time. The participants were given some introductory information which explained the purpose of the study. This was to examine going-concern judgments and an assurance of confidentiality was also provided in the introductory information. The students read the financial statements and made an initial going-concern judgment on a nine-point Likert-type scale anchored by "will fail within the next 12 months" (1) and "will continue in operation for the next 12 months" (9). In the next task, the participants were provided with the eight information cues. These were provided in random order. After evaluating these cues, the students made a final going-concern judgment on a similar scale. The eight information cues are presented in Appendix A. The participants then provided some demographic information. The procedures for the instructors were similar to those for the students except the instructors completed the tasks at their own pace.
The student participants were 117 final year accounting majors enrolled in an auditing course, and comprised 61 males and 56 females. At the time of the experiment, they had attended two three-hour classes on risk evaluation including going-concern risk. They had also completed a comprehensive class assignment on going-concern risk evaluation which was compulsory. The average age of the students was 25.3 years (sd 7.4). Eight of the students had an average of 10.5 (sd 11) months of audit experience while the rest did not have any audit experience. Each student was paid A$10 for their efforts. The 23 instructors (12 males and 11 females) comprised both full-time (15) and part-time (8) accounting instructors. The part-time instructors were in full-time employment as accountants/auditors, and had at least one year teaching experience. The full-time instructors had teaching experience ranging from four to twenty years. The instructors were not paid.
|Panel A - Male Students and Male Instructors|
|Source of Variation||MS||df||F||p|
|Covariate: Initial Judgment||164.81||1||69.59||.00|
|Main effect - Student/Instructor||3.46||1||1.46||.23|
|Panel B - Female Students and Female Instructors|
|Source of Variation||MS||df||F||p|
|Covariate - Initial judgment||63.03||1||29.39||.00|
|Main effect - Student/Instructor||.36||1||.17||.68|
|Panel C - Male Instructors and Female Students|
|Source of Variation||MS||df||F||p|
|Covariate - Initial judgment||115.55||1||54.06||.00|
|Main effect - Student/Instructor||16.04||1||7.50||<.01|
|Panel D - Female Instructors and Male Students|
|Source of Variation||MS||df||F||p|
|Covariate - Initial judgment||112.00||1||47.00||.00|
|Main effect - Student/Instructor||6.61||1||3.13||.08|
H3 predicts a significant difference between the judgments of female students and male instructors. ANCOVA analysis between the responses of male instructors and female students was carried out. Significant differences between their mean scores were noted (F = 7.50, p < .01) (panel C, Table 1). The male instructors' judgments were more risky compared to the female students' judgments. In H4, female instructors' judgments are expected to be less risky compared to the judgments of male students. Significant differences between the female instructors' and male students' judgments were observed (F = 3.13, p = .08) (panel D, Table 1). The male students' judgments were more risky compared to the female instructors' judgments. These results lend further support to our proposition that inherent gender differences may be an explanation for the findings of Mutchler et al. (1987) and Lipe (1989). Significant differences were noted when student gender and instructor gender were mismatched. Translated into a classroom situation, students of the same gender as the instructor would have answers similar to the instructor's answers. Consequently, they would be awarded more credit compared to students whose answers are dissimilar from the instructor's. These latter students are usually students of the opposite gender.
n = 61
n = 12
n = 73
n = 56
n = 11
n = 67
n = 23
n = 140
This problem could be redressed in two ways. The first is to be aware of the problem as awareness is the first step towards solving it. Second, when the gender of the students enrolled in a class is mixed, it is beneficial to obtain the input of instructors of both genders when designing and writing class assignments and assessments. This ensures that the effect of the instructor gender on student performance is neutralised.
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|Risk Cues:||1.||The competence of the company's management has been questioned by outside observers.|
|2.||It appears that if needed it will be difficult to obtain additional debt capital.|
|3.||Discussions with management indicate that a material liability from litigation is likely this year.|
|4.||Management believes there is a good chance of losing a major customer.|
|Non-risk Cues:||5.||The company's major service is generally considered to be of good quality.|
|6.||Management states that it is possible that a contract with a major hospital may be obtained in the near future.|
|7.||This year the company reported a positive cash flow from operations.|
|8.||In general, suppliers of the company indicate that usual trade credit to the company will be available.|
|Please cite as: Chung, J. and Tang, K. (1998). Inherent gender differences as an explanation of the effect of instructor gender on accounting students' performance. In Black, B. and Stanley, N. (Eds), Teaching and Learning in Changing Times, 72-79. Proceedings of the 7th Annual Teaching Learning Forum, The University of Western Australia, February 1998. Perth: UWA. http://lsn.curtin.edu.au/tlf/tlf1998/chung.html|