Academic entitlement and Ratemyprofessors.com evaluations bias student teaching evaluations: Implications for faculty evaluation and policy-lenient professors’ occupational health

Abstract

This study explored relationships between academic entitlement (AE) and Ratemyprofessors.com (RMP) use. It also investigated, while controlling for AE, if RMP evaluation positivity influences students' intentions to ask for policy exemptions, beliefs professors would provide them, intentions to reward and punish professors contingent upon provision of policy exemptions by improving or lowering their student teaching evaluations, and intentions to evaluate and reenroll with professors. Following exposure to RMP evaluations, participants (n = 320) rated their intentions and beliefs toward a fictional professor. They also completed an AE measure. AE was related to frequency of writing RMP evaluations as well as participants' intentions to ask for exemptions, beliefs they would receive them, and intentions to reward and punish professors. RMP evaluation positivity affected participants' intentions to ask for and beliefs they would receive policy exemptions as well as intention to evaluate and reenroll with professors. Effects did not differ by professor or student gender. Participants reported intention to improve the evaluation of professors who provide any policy exemption. This study's findings suggest that student attitudes related to AE and impacted by RMP evaluations have significant implications for professors' occupational health via requests for policy exemptions and the consequences of professors' responses to them. These findings also contribute to the body of evidence that student teaching evaluations do not exclusively measure teaching effectiveness. Similar to grade leniency, policy leniency may bias student teaching evaluations. These contribute to the ongoing discussion of the use of student teaching evaluations in faculty personnel decisions and underscore the need for robust approaches to professor evaluation.

Academic entitlement (AE), which includes entitled expectations and externalized responsibility, is the attitude that academic success should be given, not earned [1]. Entitled expectations refer to the belief that one should receive high grades and accommodations from professors. Externalized responsibility, on the other hand, refers to the attribution of responsibility for academic failures to professors, rather than answering for them oneself. In sum, academically entitled individuals want good grades but feel no obligation to invest effort toward that outcome. Moreover, they expect professors to provide the accommodations they desire [[1], [2], [3], [4]].

Several scholars believe that AE is, at least in part, based in students' consumerist views toward university education [5,6]. Students with a consumerist view see a university degree as a good they purchase with their tuition. To these students, learning is undervalued; grades are what they appreciate and value instead [7]. It seems logical that academically entitled students who view themselves as university customers may turn to Ratemyprofessors.com (RMP), an anonymous, online professor-evaluation website, to shop for professors they believe will provide them with the good grade that they desire. Thus, this study investigated the relationship between AE and RMP use. Given that RMP content also influences students' expectations about professors and coursework (e.g., Refs. [[8], [9], [10]]), the study also investigated if RMP content influenced students' intentions and beliefs toward professors. Will RMP content engender students' intentions to ask for policy exemptions and beliefs that they will receive them? Moreover, will they intend to reward and punish professors, contingent on their acquiescence to these requests? If RMP content does shape these intentions and beliefs, it has significant implications for professors’ financial well-being and occupational health. These questions extend those studied by Boswell [11] and investigate them in a more diverse sample; additional detail is addressed below.

1. Literature review

RMP is a website in which students provide anonymous evaluations of their professors. RMP is not the only professor-rating website; however, with its 4 million monthly users and 20 million-and-growing reviews, it is the largest in the world [10,12]. Students rate their professors’ difficulty as well as their overall quality based on the how clearly the professor teaches course content and their helpfulness inside class and availability to help outside of class [13]. Given that every professor profile on RMP features an average difficulty score, the site may be the ideal target for academically entitled students looking for an easy course. With their propensity for revenge [14], the website may also be a destination for academically entitled students who are angry over unmet expectations. They may post evaluations to the website to disparage professors who failed to provide an easy, accommodating course.

While RMP may attract individuals who are already high in AE, it is also possible that its reviews may create expectations of good grades and accommodations. Prior research has found that RMP content influences several aspects of students' thinking. RMP evaluation positivity influences students' expectations about their grade in a professor's course [9,15], how much they look forward to a professor's course [9], as well as their self-efficacy for the course [8]. RMP evaluation positivity also influences students' perceptions of the professor; participants exposed to professors' positive RMP evaluations expect the professor to be more knowledgeable, organized, and enthusiastic [10]. Moreover, RMP evaluation positivity also influences students' intentions about their interpersonal behavior toward a professor. Students exposed to positive RMP evaluations report greater confidence in their ability to form a relationship with the professor and ask for help [8]. Given that RMP evaluation positivity influences this breadth of students' thinking, it is possible that it could also shape students' beliefs about policy exemptions professors provide (i.e., professor does not apply a specific course policy to a student, per the student's request). Students exposed to evaluations about professors' helpfulness and availability outside of class may expect that these professors will provide them with policy exemptions and intend to ask for them.

Gender, both professor and student, may also influence students' intentions and beliefs about their professors. The research regarding professor gender provides mixed results, though. Some studies find no effect for professor gender (e.g., Ref. [16]); however, others find that students prefer men (e.g., Ref. [17]). A growing body of research on professor gender focuses on conformity to gender stereotypes. These studies find that students expect women to display more stereotypically feminine characteristics such as being more available, supportive, and accommodating [[18], [19], [20]]. As students anticipate greater leniency from women, they may find themselves making requests for policy exemptions more frequently from them. Student gender may also impact students’ beliefs about receiving policy exemptions as men often report feeling more entitled in academic settings [[21], [22], [23], [24], [25]].

Professors' responses to students who request policy exemptions may have implications for their financial well-being and occupational health. As universities have shifted to a business-model that focuses on customer (i.e., student) satisfaction, students have become more grade- and privilege-focused [26]. If professors deliver the products (i.e., grades and accommodations) that students desire, they may reap rewards. Similar to satisfied customers who demonstrate their appreciation for consumer-service providers who delivered a favorable product or experience [27,28], students may show their appreciation for professors through positive online reviews (i.e., student teaching evaluations) and customer loyalty (i.e., taking another class with the professor). If students are indeed similar to consumers in this way, they will report significant intention to improve professors’ teaching evaluations and take additional courses with professors who provide policy exemptions. Given the role that student teaching evaluations play in faculty employment decisions, professors who provide exemptions stand to reap financial reward in the form of rehire to teach in future semesters. This reward stands to be multiplied when satisfied students enroll in additional courses with these professors, helping to ensure their courses meet minimum enrollment requirements so they will be paid. The reward of favorable student teaching evaluations also bodes favorably for professors applying for tenure or promotion because these evaluations impact career-advancement decisions [29].

If professors decline students' requests for policy exemptions, though, significant, negative consequences for their financial well-being and occupational health may occur. Much like consumers who feel dissatisfied when their purchases fall short of their expectations, students may become dissatisfied when professors fail to meet the expectations they have developed through RMP evaluations or gender-related stereotypes. Similar to dissatisfied customers [30], dissatisfied students may voice their complaints through negative online reviews (i.e., student teaching evaluations) and refusal to do business with the service provider (i.e., professor) again. These punishments – poor student evaluations and no future reenrollment with the professor - may lower the professors’ likelihood of rehire, promotion, and tenure, thus threatening their financial status and career viability. These effects may be compounded for woman professors who, in their refusal of requests for policy exemptions, have behaved inconsistently with gender stereotypes that they should be accommodating.

1.1. The current study

The current study served as a replication of Boswell [11] by exploring the relationship between AE, RMP use, and students' intentions and beliefs about receiving policy exemptions from professors. Moreover, it also investigated if RMP content influenced students' intentions to ask for policy exemptions and beliefs they will receive them as well as intention to lower the evaluations of professors who do not provide policy exemptions. The study also extended Boswell [11] by investigating if RMP content influenced students' intentions to reward professors contingent upon provision of policy exemptions by improving their student teaching evaluations. While Boswell [11] studied the impact of RMP evaluations on intentions to punish professors, it did not study its impact on intention to reward them. If RMP content affects these intentions, it has significant implications for use of student teaching evaluations in personnel decisions about faculty. The study also extended Boswell's [11] study by investigating if RMP content influenced participants' intention to complete the professors' university teaching evaluation and RMP evaluation as well as enroll in another course with the professor. If so, these will suggest additional implications for faculty financial and occupational advancement by shaping which faculty will be rehired, promoted, receive merit pay, and become tenured owing to their favorable student evaluations, reputation, and greater course enrollments. Finally, the study investigated if these effects differed by professor and student gender. Therefore, the study investigated the following research questions.

• 1.Will AE be related to how often students read RMP evaluations, how seriously they consider them during their course decision making, and how often they write RMP evaluations?
• 2.Will AE be related to students' intentions to request policy exemptions, beliefs they will receive these exemptions, intentions to improve the evaluations of professors who provide policy exemptions, and lower the teaching evaluations of professors' who do not?
• 3.Will RMP evaluation positivity, professor gender, and student gender influence students' intentions to request policy exemptions, beliefs they will receive these exemptions, intentions to improve the evaluations of professors who provide policy exemptions, and lower the teaching evaluations of professors who do not?
• 4.Will RMP evaluation positivity, professor gender, and student gender influence students' intentions to complete the professors' university teaching evaluation and RMP evaluation?
• 5.Will RMP evaluation positivity, professor gender, and student gender influence students' intentions to take another class with professors who do and do not provide policy exemptions?

Unlike Boswell [11], this study investigated these questions in a diverse sample recruited from across United States. If this study replicates those results, it will provide evidence of their generalizability to a greater population of university students in the United States. The study also extends Boswell's [11] research questions. The study did so through use of correlational and quasi-experimental research design. Further information about the study's design, results, and implications about use of student teaching evaluations in faculty personnel decision-making are addressed below.

2. Method

2.1. Study design

The study used a 2 (evaluation positivity: positive or negative) x 2 (professor gender: man or woman) experimental, between-groups design. Participants were randomly assigned to one of four experimental conditions created by crossing the two independent variables: 1) negatively-evaluated man professor, 2) positively-evaluated man professor, 3) negatively-evaluated woman professor, and 4) positively-evaluated woman professor. At the time of analysis, the four experimental conditions were crossed with the quasi-independent variable of student gender (man or woman) to create eight groups. Because student gender cannot be experimentally manipulated, crossing this variable with evaluation positivity and professor gender created a 2 x 2 x 2 quasi-experimental design.

2.2. Participants

Three hundred and twenty participants (n = 167, 52.2 % woman; n = 153, 47.8 % man; M_age = 22.25, SD = 6.32) were recruited through Qualtrics Research Panels [31,32]. Qualtrics Research Panels is an internet-based, data collection service that has become increasingly popular in the social and behavioral sciences (e.g., Refs. [33,34]). The service assists researchers by recruiting participants who meet their demographic requirements. Qualtrics Research Panels also provides participants with an incentive (e.g., entry into a sweepstakes) for study participation [35]. In this study, participants were required to be over the age of 18 and currently enrolled as a part-time or full-time undergraduate or graduate student at a university in the United States. The sample was ethnically diverse with participants identifying as White or Caucasian (n = 127, 39.7 %), Black or African America (n = 67, 20.9 %), Asian (n = 56, 17.5 %), Hispanic or Latinx (n = 46, 14.4 %), Native American or Alaska Native (n = 3, 0.9 %), or Native Hawaiian or Pacific Islander (n = 3, 0.9 %). Eleven participants identified as two or more races (3.4 %) and seven identified their race as “other” (2.2 %). The majority of participants identified as a student who is enrolled full-time (n = 278, 86.9 %; part-time student: n = 42, 13.1 %) and at a public university (n = 245, 76.6 %; private university: n = 67, 20.9 %; unknown university type: n = 8, 2.5 %). Finally, the sample was comprised of 74 first-year students (23.1 %), 78 sophomores (24.4 %), 76 juniors (23.8 %), 46 seniors (14.4 %), and 46 graduate students (14.4 %). This study was approved by the Human Subjects Institutional Review Board of University of the Incarnate Word on April 20, 2020, with ethics approval reference 1809003.

3. Materials

Evaluations. Each participant viewed five evaluations about a fictional professor they were told came from RMP. The five evaluations were about either a negatively-evaluated man, positively-evaluated man, negatively-evaluated woman, or positively-evaluated woman. Unlike previous studies that utilized contrived RMP evaluations [[36], [37], [38]], evaluations for this study were copied from RMP. The use of actual RMP evaluations suggests evidence of external validity and generalizability of study results.

RMP categorizes professors into three categories based on their average quality scores. “Poor quality” professors have an average quality score of 1–2.4, “average quality” professors have an average quality score of 2.5–3.4, and “good quality” professors have an average quality score between 3.5 and 5.0 [13]. Evaluations for the positively-evaluated professors were copied from the profiles of “good quality” professors and evaluations for the negatively-evaluated professors were copied from the profiles of “poor quality” professors. To provide evidence of external validity, evaluations' grammatical and spelling mistakes were kept intact. Slight adjustments were made to the wording to eliminate any details that could reveal the course or professor's identity, along with modifying the gender of the professor. In the evaluations of women, the pronouns "she" and "her" were used, while in the evaluations of men, the pronouns "he," "his," and "him" were used. The word count of the evaluations remained consistent across all conditions.

To be selected for inclusion in the study, evaluations had to refer to the professor's helpfulness and clarity. RMP directs students to consider these characteristics when evaluating professors [13]. Moreover, helpfulness and clarity are repeatedly among the characteristics of effective teaching [[39], [40], [41]]. Unhelpfulness and inability to clearly communicate course content, though, are characteristics of ineffective teaching [42]. Positive evaluations, therefore, described the professor as someone who is helpful and able to clearly explain course concepts. Negative evaluations described the professor as someone who is unhelpful and unable to clearly explain course concepts. A sample positive evaluation is, “If you have the chance to take her then do it, you will definitely have a clear understanding of everything. She is super organized & goes through everything step by step!” A sample negative evaluation is, “His teaching style is so abrupt, random, and lacks structure. Don't take his class unless you can teach yourself. He is disorganized and unfocused. He does not explain the assignments well enough.”

Evaluation positivity manipulation check. To determine if participants perceived the positivity of the positive evaluations and negativity of the negative evaluations, they used a 9-point, Likert-type scale that ranged from 1 (very negative) to 9 (very positive) to rate the fictional professor's evaluations. This served as a manipulation check for evaluation positivity.

Intentions and beliefs. Participants rated their intentions to 1) ask for an exemption from the professor's policy, 2) belief the professor would provide the exemption, 3) intention to improve their teaching evaluation if the professor provided the exemption, and 4) intention to lower their teaching evaluation if the professor did not provide the exemption. They used 9-point, Likert-type scales to provide ratings for the following policy exemptions: arrive late to class, leave early from class, miss class (exemptions to the attendance policy), submit a missing assignment, revise an assignment for a better grade (exemptions to the assignment policy), take a missing exam, retake an exam for a better grade (exemption to the exam policy), and receive a better final grade. Ratings for 1) arrive late to class, 2) leave early from class, and 3) miss class were aggregated to form the attendance policy scales (Cronbach's α internal reliabilities: ask for an exemption, α = 0.82; provide an exemption, α = 0.87; improve evaluation, α = 0.84; lower evaluation, α = 0.84). Ratings for 1) submit a missing assignment and 2) revise an assignment for a better grade were aggregated to form the assignment policy scales (Cronbach's α internal reliabilities: ask for an exemption, α = 0.71; provide an exemption, α = 0.83; improve evaluation, α = 0.74; lower evaluation, α = 0.83). Ratings for 1) take a missing exam and 2) retake an exam for a better grade were aggregated to form the exam policy scales (Cronbach's α internal reliabilities: ask for an exemption, α = 0.68; provide an exemption, α = 0.83; improve evaluation, α = 0.83; lower evaluation, α = 0.80). Single items were used to measure intention to ask for an exemption for a better final grade, belief the professor would provide the exemption, and intention to improve the teaching evaluation of the professor who provided the grade policy exemption and lower the teaching evaluation of the professor who did not provide it.

Participants also used 9-point, Likert-type scales to rate their intention to write a university teaching evaluation and RMP evaluation for the professor. Finally, they used the same Likert-type scales to rate their intention to take another class with the professor if the professor does and does not provide policy exemptions.

RMP use. Participants used a 9-point, Likert-type scale ranging from 1 (never) to 9 (always) to indicate the frequency at which they read RMP evaluations during their course decision making, how seriously they consider them, and frequency at which they have written or plan to write RMP evaluations.

A 9-point, Likert-type scale was chosen for the manipulation check, intentions and beliefs, and RMP use rating scales for multiple reasons. A 9-point scale provides a midpoint, which is customary in attitudinal measures, and is often preferred by participants because it reduces confusion by providing a neutral option. Moreover, a 9-point scale, rather than a shorter 5-point or 7-point scale, is often preferred by participants because it provides them with a greater variety of response options [43,44]. A longer scale also provides measurement advantages for researchers by providing increased sensitivity and greater discrimination of participants’ attitudes [44]. All rating scales are presented in the Appendix.

AE. To measure AE, participants completed Kopp et al.’s [2] AE Questionnaire. Participants used 7-point, Likert-type scales ranging from 1 (strongly disagree) to 7 (strongly agree) to respond to the measure's eight items. The AE Questionnaire has demonstrated reliability and validity in previous studies [2,45]. The measure's Cronbach's α internal reliability in the current study was 0.84.

Demographic questionnaire. Participants completed a demographic questionnaire that contained closed-ended items about gender identity, ethnic and/or racial identity, age, student status (part-time or full-time), university type (public or private), and college class.

3.1. Procedure

Data collection occurred online, via Qualtrics. Participants were presented with a written informed consent document and after providing informed consent, participants were randomly assigned to one of the four experimental conditions: negatively-evaluated man, positively-evaluated man, negatively-evaluated woman, and positively-evaluated woman. Participants viewed five evaluations about the assigned fictional professor and were instructed to imagine that they would have a class with the individual. They then completed the rating scales about their intentions and beliefs about the fictional professor. Participants also completed the AE scale, items about RMP use, and the demographic questionnaire.

4. Results

4.1. Research question 1

4.1.1. Bivariate relationships between AE and RMP use

Bivariate correlational analyses tested for a relationship between AE and each of the RMP use variables. AE did not have significant relationships with either how often participants read RMP evaluations (r = 0.06, p = 0.32) or how seriously they consider them (r = 0.06, p = 0.29) during their course decision making. AE was significantly related, however, to how often participants write or plan to write RMP evaluations, (r = 0.41, p < 0.001). Participants reporting greater AE also reported more frequently writing, or planning to write, evaluations of their professors on RMP.

4.2. Research question 2

4.2.1. Bivariate relationships between AE and intentions and beliefs

Bivariate correlational analyses tested for a relationship between AE and participants' intentions and beliefs toward professors. AE was significantly related to all policy exemption intentions and beliefs. AE was positively correlated with intention to ask for exemptions from the attendance (r = 0.41, p < 0.001), assignment (r = 0.43, p < 0.001), exam (r = 0.48, p < 0.001), and better final grade policies (r = 0.47, p < 0.001). AE also positively correlated with belief professors would provide attendance (r = 0.36, p < 0.001), assignment (r = 0.41, p < 0.001), exam (r = 0.47, p < 0.001), and better final grade (r = 0.48, p < 0.001) policy exemptions. Participants who were higher in AE also reported higher intention to improve professors’ evaluations when they granted exemptions to the attendance (r = 0.23, p < 0.001), assignment (r = 0.18, p < 0.001), exam (r = 0.14, p = 0.01), and better final grade (r = 0.14, p = 0.01) policies. Finally, participants who reported greater AE also reported greater intention to lower the teaching evaluations of professors who did not provide attendance (r = 0.46, p < 0.001), assignment (r = 0.48, p < 0.001), exam (r = 0.46, p < 0.001), and better final grade (r = 0.51, p < 0.001) policy exemptions.

4.3. Research question 3

4.3.1. Evaluation positivity manipulation check

One-way ANOVA tested if participants perceived the evaluation positivity manipulation. The ANOVA was significant, F (1, 318) = 546.67, p < 0.001, indicating that participants perceived the manipulation of evaluation positivity. Participants perceived the positively-evaluated professors' evaluations (M = 7.37) to be significantly more positive than the negatively-evaluated professors' evaluations (M = 2.92). Next, a one-sample t-test tested if the positively-evaluated professors' mean positivity rating (7.37) was significantly higher than 5, the objective midpoint of the rating scale. The test was significant, t (160) = 19.15, p < 0.001, indicating that participants perceived the positivity of the positively-evaluated professors' evaluations. Finally, a one-sample t-test tested if the negatively-evaluated professors' mean positivity rating (2.92) was significantly lower than 5, the objective midpoint of the rating scale. The test was significant, t (160) = −14.36, p < 0.001, indicating that participants perceived the negativity of the negatively-evaluated professors’ evaluations. These results provide construct-related evidence for the manipulation of evaluation positivity.

4.3.2. Multivariate analyses

A 2 x 2 x 2 MANCOVA with AE as the covariate was used to examine the effect of evaluation positivity (coded as positive or negative), professor gender (coded as man or woman) and student gender (coded as man or woman) on participants' intentions to ask for policy exemptions, beliefs that they would receive them, and intentions to improve the teaching evaluation of the professor who provided the policy exemption and lower the teaching evaluation of the professor who did not. Pillai's trace was interpreted because Box's test of equality of covariance matrices was significant.

The MANCOVA found significant multivariate main effects for the AE covariate and evaluation positivity, both with a large effect size. The MANCOVA did not find significant multivariate main effects for professor gender or student gender. Moreover, it did not find any significant multivariate interaction effects for any of the interactions of evaluation positivity, professor gender, and student gender. Table 1 displays the MANCOVA's results. Table 2 displays the variables' adjusted means and standard errors from the MANCOVA.

Table 1.

MANCOVA for intention and expectation.

Variable	F	p	ɳp2	Power
Academic Entitlement Covariate	13.86	<0.001	0.43	1.00
Evaluation Positivity	9.03	<0.001	0.33	1.00
Professor Gender	1.65	0.06	0.08	0.92
Student Gender	0.99	0.47	0.05	0.67
Evaluation Positivity X Professor Gender	1.33	0.18	0.07	0.83
Evaluation Positivity X Student Gender	1.05	0.41	0.05	0.70
Professor Gender X Student Gender	1.14	0.32	0.06	0.75
Evaluation Positivity X Professor Gender X Student Gender	0.84	0.64	0.04	0.58
df = 16, 296

Table 2.

Intention and expectation adjusted means and standard errors.

Variable	Negatively Evaluated Man Professor				Positively Evaluated Man Professor				Negatively Evaluated Woman Professor				Positively Evaluated Woman Professor
	Student Gender				Student Gender				Student Gender				Student Gender
	Man		Woman		Man		Woman		Man		Woman		Man		Woman
	M	SE	M	SE	M	SE	M	SE	M	SE	M	SE	M	SE	M	SE
Ask Attendance Exemption	9.90	0.92	9.25	0.87	14.47	0.91	14.37	0.83	10.45	0.89	11.08	0.88	14.22	0.92	13.50	0.89
Provide Attendance Exemption	9.10	0.89	9.84	0.84	16.05	0.88	15.96	0.80	10.20	0.85	9.49	0.85	15.54	0.88	15.76	0.85
Improve Evaluation Attendance Exemption	13.79	0.92	14.92	0.87	18.59	0.91	19.24	0.83	15.81	0.89	14.74	0.88	17.26	0.91	18.56	0.89
Lower Evaluation Attendance Exemption	11.42	0.92	14.44	0.88	12.26	0.91	12.35	0.84	11.54	0.89	11.17	0.88	12.98	0.91	12.29	0.89
Ask Assignment Exemption	7.56	0.65	6.88	0.61	9.86	0.64	9.84	0.59	7.21	0.62	8.38	0.61	10.29	0.64	10.00	0.62
Provide Assignment Exemption	6.04	0.61	6.34	0.58	10.71	0.61	10.90	0.56	6.76	0.59	6.35	0.58	11.11	0.61	10.24	0.59
Improve Evaluation Assignment Exemption	10.39	0.67	11.88	0.64	12.20	0.67	12.53	0.61	11.26	0.65	10.83	0.64	12.28	0.66	12.09	0.65
Lower Evaluation Assignment Exemption	9.10	0.67	9.09	0.63	8.22	0.66	7.73	0.60	7.81	0.64	6.66	0.63	8.61	0.66	8.24	0.64
Ask Exam Exemption	8.43	0.67	7.82	0.63	10.08	0.66	9.62	0.60	7.56	0.64	8.38	0.63	9.90	0.66	9.99	0.64
Provide Exam Exemption	6.48	0.59	6.65	0.56	10.88	0.59	10.13	0.54	6.87	0.57	6.23	0.57	9.86	0.59	9.61	0.57
Improve Evaluation Exam Exemption	10.99	0.73	12.84	0.70	11.76	0.73	12.42	0.66	11.85	0.70	10.91	0.70	12.42	0.72	12.25	0.70
Lower Evaluation Exam Exemption	9.07	0.67	8.75	0.64	8.20	0.67	8.13	0.61	8.03	0.65	6.59	0.64	7.37	0.66	7.80	0.65
Ask Grade Exemption	4.08	0.37	3.43	0.35	5.00	0.37	4.97	0.34	3.76	0.36	3.86	0.35	5.12	0.37	4.17	0.36
Provide Grade Exemption	3.21	0.32	3.41	0.31	5.65	0.32	5.08	0.29	3.05	0.31	3.09	0.31	4.64	0.32	4.06	0.31
Improve Evaluation Grade Exemption	5.60	0.40	6.28	0.38	6.20	0.40	6.48	0.36	6.29	0.38	5.51	0.38	6.18	0.40	6.01	0.38
Lower Evaluation Grade Exemption	4.49	0.36	4.38	0.34	4.18	0.35	4.11	0.32	3.54	0.34	3.13	0.34	3.42	0.35	3.93	0.34

Note. Covariate: AE = 29.31; M = mean; SE = standard error.

4.3.3. Univariate tests

Each significant multivariate main effect was followed with a series of Bonferroni-corrected ANCOVA tests (alpha = 0.003); Table 3 displays the results of these univariate tests. All univariate follow-up tests to the AE covariate effect were significant with the exception of intention to improve the teaching evaluation of professors who provide exemptions to the exam and final grade policies. Univariate follow-up tests to the evaluation positivity effect produced several significant results. Participants reported higher intentions ask for all policy exemptions and beliefs they would receive them from the positively-evaluated professor. Participants also reported greater intention to improve the teaching evaluation of the positively-evaluated professor who provided exemptions to the attendance policy. Table 4 presents the adjusted means, mean differences, and confidence intervals for the follow-up tests of these variables. Evaluation positivity did not, however, affect intention to improve the teaching evaluation of professors who provide assignment, exam, and final grade policy exemptions. Nor did evaluation positivity affect intention to lower the teaching evaluation of professors who did not provide any of the policy exemptions.

Table 3.

ANCOVA for intention and expectation.

Variable	AE		EP		PG		SG		EP X PG		EP X SG		PG X SG		EP X PG X SG
	F	ɳp2	F	ɳp2	F	ɳp2	F	ɳp2	F	ɳp2	F	ɳp2	F	ɳp2	F	ɳp2
Ask Attendance Exemption	62.74*	0.17	40.02*	0.11	0.25	0.00	0.11	0.00	1.95	0.01	0.10	0.00	0.07	0.00	0.57	0.00
Provide Attendance Exemption	48.93*	0.14	103.63*	0.25	0.00	0.00	0.00	0.00	0.36	0.00	0.00	0.00	0.23	0.00	0.54	0.00
Improve Evaluation Attendance	16.80*	0.05	32.75*	0.10	0.00	0.00	0.63	0.00	2.38	0.01	0.56	0.00	0.38	0.00	1.29	0.00
Lower Evaluation Attendance Exemption	79.00*	0.20	0.28	0.00	0.97	0.00	0.67	0.00	2.29	0.01	1.66	0.01	2.75	0.01	1.07	0.00
Ask Assignment Exemption	71.65*	0.19	31.92*	0.09	1.01	0.00	0.01	0.00	0.10	0.00	0.20	0.00	0.80	0.00	1.45	0.00
Provide Assignment Exemption	68.31*	0.18	108.73*	0.26	0.08	0.00	0.22	0.00	0.35	0.00	0.12	0.00	1.12	0.00	0.04	0.00
Improve Evaluation Assignment Exemption	9.23*	0.03	6.63	0.02	0.09	0.00	0.43	0.00	0.01	0.00	0.26	0.00	1.77	0.01	0.57	0.00
Lower Evaluation Assignment Exemption	89.56*	0.22	0.01	0.00	2.43	0.01	1.24	0.00	6.49	0.02	0.03	0.00	0.31	0.00	0.47	0.00
Ask Exam Exemption	93.57*	0.23	16.56*	0.05	0.00	0.00	0.01	0.00	0.07	0.00	0.10	0.00	1.20	0.00	0.24	0.00
Provide Exam Exemption	92.49*	0.23	77.23*	0.20	0.94	0.00	0.82	0.00	0.87	0.00	0.11	0.00	0.04	0.00	0.67	0.00
Improve Evaluation Exam Exemption	5.44	0.02	1.29	0.00	0.08	0.00	0.47	0.00	0.61	0.00	0.05	0.00	3.33	0.01	0.96	0.00
Lower Evaluation Exam Exemption	80.77*	0.21	0.27	0.00	5.66	0.02	0.57	0.00	1.25	0.00	1.35	0.00	0.11	0.00	0.77	0.00
Ask Grade Exemption	83.36*	0.21	16.52*	0.05	0.32	0.00	2.26	0.01	0.60	0.00	0.19	0.00	0.03	0.00	2.76	0.01
Provide Grade Exemption	94.76*	0.23	56.78*	0.15	8.15	0.03	1.09	0.00	3.11	0.01	2.46	0.01	0.04	0.00	0.03	0.00
Improve Evaluation Grade Exemption	5.53	0.02	1.19	0.00	0.27	0.00	0.00	0.00	0.13	0.00	0.04	0.00	3.07	0.01	0.87	0.00
Lower Evaluation Grade Exemption	108.40*	0.26	0.01	0.00	10.29*	0.03	0.01	0.00	1.69	0.01	0.96	0.00	0.08	0.00	0.81	0.00

Note. Covariate: AE = 29.31. AE = academic entitlement; EP = evaluation positivity; PG = professor gender; SG = student gender.

Table 4.

Adjusted means, mean differences, and confidence intervals.

	Meanadj
Variable	Positive	Negative	Difference	95 % CI
Ask, Attendance Exemption	14.14	10.17	3.97	[2.74, 5.21]
Provide, Attendance Exemption	15.83	9.66	6.17	[4.98, 7.36]
Improve Evaluation, Attendance Exemption	18.41	14.81	3.60	[2.36, 4.83]
Lower Evaluation, Attendance Exemption	12.47	12.14	0.33	[-0.91, 1.57]
Ask, Assignment Exemption	10.00	7.51	2.49	[1.62, 3.36]
Provide, Assignment Exemption	10.74	6.37	4.37	[3.54, 5.19]
Improve Evaluation, Assignment Exemption	12.28	11.09	1.19	[0.28, 2.09]
Lower Evaluation, Assignment Exemption	8.20	8.16	0.04	[-0.86, 0.93]
Ask, Exam Exemption	9.90	8.05	1.85	[0.96, 2.75]
Provide, Exam Exemption	10.12	6.56	3.56	[2.77, 4.36]
Improve Evaluation, Exam Exemption	12.21	11.65	0.56	[-0.42, 1.55]
Lower Evaluation, Exam Exemption	7.87	8.11	−0.24	[-1.14, 0.67]
Ask, Grade Exemption	4.81	3.78	1.03	[0.53, 1.53]
Provide, Grade Exemption	4.86	3.19	1.67	[1.23, 2.10]
Improve Evaluation, Grade Exemption	6.22	5.92	0.30	[-0.24, 0.84]
Lower Evaluation, Grade Exemption	3.91	3.88	0.03	[-0.46, 0.51]

A series of one-sample t-tests were performed to test if participants intended to improve the teaching evaluation of professors who provide policy exemptions and lower the teaching evaluations of professors who do not, regardless of evaluation valence. Averages of each of the improve evaluation and lower evaluation scales (attendance, assignment, exam) along with the improve evaluation and lower evaluation items for a better final grade were tested to determine if they differed significantly from 5, the objective midpoint of the rating scale. Average ratings for participants' intentions to improve the teaching evaluation of professors who provide policy exemptions were all significantly higher than 5; this suggests that participants do intend to reward professors who provide policy exemptions with higher evaluations. Participants’ ratings of their intention to lower the teaching evaluations of professors who do not provide policy exemptions were significantly lower than 5, suggesting that they do not intend to punish professors who adhere to their policies. Table 5 displays these results of these one sample t-tests as well as descriptive statistics.

Table 5.

One-Sample t-Tests of Intention to Improve and Lower Professor's Teaching Evaluations.

Variable	Range	Median	Mean	Standard Deviation	t (319)	p
Improve Evaluation Attendance Exemption	1–9	5.50	5.55	2.01	4.91	<0.001
Lower Evaluation Attendance Exemption	1–9	4.33	4.10	2.11	−7.60	<0.001
Improve Evaluation Assignment Exemption	1–9	6.00	5.85	2.08	7.30	<0.001
Lower Evaluation Assignment Exemption	1–9	4.00	4.08	2.32	−7.09	<0.001
Improve Evaluation Exam Exemption	1–9	6.00	5.97	2.24	7.75	<0.001
Lower Evaluation Exam Exemption	1–9	4.00	3.99	2.31	−7.82	<0.001
Improve Evaluation Grade Exemption	1–9	6.00	6.08	2.45	7.88	<0.001
Lower Evaluation Grade Exemption	1–9	4.00	3.89	2.56	−7.75	<0.001

4.4. Research question 4

To test the effect of evaluation positivity (coded as positive or negative), professor gender (coded as man or woman) and student gender (coded as man or woman) on students' intentions to write a university teaching evaluation and RMP evaluation for the professor, a 2 x 2 x 2 MANCOVA with AE as the covariate was used. The MANCOVA found significant multivariate main effects for the AE covariate, F (2, 278) = 7.26, p < 0.001, partial η² = 0.05, and evaluation positivity, F (2, 278) = 4.16, p = 0.03, partial η² = 0.03. No other multivariate main effects or interaction effects were significant. Bonferroni-corrected (alpha = 0.025) univariate ANCOVA tests followed the significant MANCOVA covariate effect. The AE covariate affected only their intention to write the professor's university teaching evaluation, F (1, 249) = 10.36, p < 0.001, partial η² = 0.04. Follow-up, Bonferroni-corrected (alpha = 0.025) ANCOVAs to the significant evaluation positivity effect found that participants reported greater intention to write the positively-evaluated professor's RMP evaluation, F (1, 249) = 7.27, p = 0.007, partial η² = 0.03 (positively evaluated M_adj = 6.14; negatively evaluated M_adj = 5.20; a difference of 0.94, 95 % CI, 0.25 to 1.62).

4.4.1. Research question 5

A 2 x 2 x 2 MANCOVA with AE as the covariate was used to examine the effect of evaluation positivity (coded as positive or negative), professor gender (coded as man or woman) and student gender (coded as man or woman) on participants’ intentions to take another class with professors who provide or do not provide policy exemptions. The MANCOVA found a significant multivariate main effect for evaluation positivity, F (2, 278) = 27.46, p < 0.001, partial η² = 0.17. No other multivariate main effects or interaction effects were significant. A series of Bonferroni-corrected (alpha = 0.025) univariate ANCOVA tests followed and found that participants reported significantly greater intention to take another class with the positively-evaluated professor who provides policy exemptions (positively-evaluated M_adj = 6.72; negatively-evaluated M_adj = 5.20; a difference of 1.52, 95 % CI, 0.87 to 2.17), F (1, 279) = 21.42, p < 0.001, partial η² = 0.07. Participants also reported significantly greater intention to take another class with the positively-evaluated professor who does not provide policy exemptions (positively-evaluated M_adj = 5.29; negatively-evaluated M_adj = 3.25; a difference of 2.04, 95 % CI, 1.47 to 2.60), F (1, 279) = 50.34, p < 0.001, partial η² = 0.13.

5. Discussion

This study investigated AE's relationships with RMP use as well as its relationships with intentions regarding policy exemptions from professors. The study also investigated if RMP evaluation positivity, professor gender, and student gender influenced participants' intentions to request policy exemptions and beliefs they would receive them, as well as students' intentions to reward and punish professors based on their policy exemption decisions.

5.1. Relationships between AE and RMP use

Interestingly, those with higher levels of AE were no more inclined to read or seriously consider professors' RMP evaluations during their course decision making than their less entitled counterparts. Online reviews have increasingly played a role in online and offline consumer decision making, with the majority of individuals consulting these reviews prior to purchases [46]. This crowdsourcing of consumer decision making has in recent years extended from selection of consumer goods to that of professional services. It is, for example, increasingly common for individuals to consult online reviews when making consequential decisions, such as choosing a healthcare provider [47] or hospital [48]. Just as it has become normative for individuals to consult online reviews when selecting a physician [49], consulting RMP may be customary for contemporary students hoping to inform their decisions about their education.

While AE was not related to mere reading and consideration of RMP evaluations in one's decision making, it was related to frequency of evaluating or planning to evaluate professors on RMP. While it is possible that academically entitled individuals turn to RMP to praise professors who have provided them with the treatment that they believe they deserve, AE's large relationship with intentions to lower the evaluation of professors who do not provide policy exemptions suggests a grimmer reason for the relationship. Just as entitled employees may engage in retaliatory behavior when they believe their employers have not treated them as they should [50,51], academically entitled students may similarly seek to punish their professors by publicly criticizing them on RMP. Academically entitled students' tendency to vengefully trash-talk their professors over disagreements [14] suggests that this may be the case; RMP may provide the vehicle to accomplish that goal. Many scholars have expressed this concern that RMP represents “revenge reviews” and extreme viewpoints (e.g., Refs. [52,53]) and recent evidence about the valence of RMP evaluations lends support to this argument. Gao and Katrompas [54] and Katrompas and Metsis [55] found that RMP evaluations are negatively biased, particularly for professors teaching challenging courses. Rybinski [12] found a similar pattern in which numerical ratings were biased significantly against professors of rigorous courses. Moreover, students' emotions were significantly more negative in their written evaluations of these professors. Katrompas and Metsis [55] suggest that these harsh evaluations may be rooted more in the evaluators' dislike of challenging coursework rather than the professors' teaching competency. Pinning the blame on professorial ineffectualness rather than personal challenges managing rigorous coursework may provide students a sense of control. This pattern is consistent with the self-protective function of AE's externalization of responsibility [1]; condemning and disparaging the professor shifts responsibility for unfavorable academic performance outward.

5.2. Relationships between AE, intentions, and beliefs

Individuals with higher levels of AE reported greater intention to ask for all types of policy exemptions and greater belief that they would receive them. This is logical because academically entitled students expect that they should be treated as they wish [56]. Individuals high in AE also reported greater intention to improve the evaluations of professors who provide policy exemptions. Even though academically entitled individuals are not officially considered university customers, they tend to view themselves as such [57,58] and therefore their behavior may be consistent with that of entitled consumers. When entitled consumers receive the treatment that they believe they deserve, they typically remain loyal to the customer service provider [59]. This relationship extends to the workplace; highly entitled employees tend to be satisfied with their employers when those employers treat them favorably [60]. Similarly, academically entitled students likely feel satisfied with and intend to reward professors who provide them with policy exemptions.

Academically entitled participants intended to behave differently, however, toward professors who do not provide them with policy exemptions. They reported significant intentions, with large effect size, to lower evaluations of professors who do not provide policy exemptions; simply put, they aimed to punish them. This is an additional manner in which academically entitled students exhibit behavior aligning with that of entitled consumers. When entitled consumers have unmet expectations, they often experience a sense of injustice or betrayal that sparks a desire for revenge against the service provider [61]. This revengefulness is based in an entitled belief that individuals have the right to harm others who have insulted them [62]; for consumers, revenge often comes in the form of negative word of mouth [63,64]. Similarly, academically entitled students are more likely to report a desire to seek revenge and undermine their professors' credibility in response to perceived injustice, for example, an undesirable grade or class policy [14]. Torpedoing a professor's student teaching evaluation may be the mechanism an academically entitled student utilizes to achieve that goal. Given the role that student teaching evaluations play in faculty evaluation and personnel decisions, these findings suggest that professors could face threat to their career and financial livelihoods at the hands of disgruntled, academically entitled students.

5.3. Effects of RMP evaluation positivity

Even after controlling for AE, RMP evaluation positivity significantly affected participants’ intentions to ask for all types of policy exemptions as well as their beliefs that they would receive them. Participants reported greater intention to ask for policy exemptions and greater belief that they would receive them from positively-evaluated professors. Moreover, they reported greater intention to write an RMP evaluation for positively-evaluated professors and take additional classes with them. In sum, these results suggest that when compared to their less favorably-reputed colleagues, professors who are positively evaluated on RMP can expect to receive a greater number of requests for policy exemptions and be expected to provide them. They can also expect to receive a greater number of repeat students as well as more RMP evaluations. Taken together, these have the potential to create a feed-forward cycle of occupational stress for positively-evaluated professors. This dynamic is addressed below.

RMP evaluation positivity affected participants' intentions to improve the evaluations of positively-evaluated professors who provide attendance policy exemptions. It did not, however, affect participants’ intentions to improve the teaching evaluations for any other type of policy exemption. Further analysis revealed, however, that participants intended to improve the teaching evaluation of professors who provide any policy exemption, regardless of RMP evaluation positivity. Students value flexibility for attendance, assignment, exam, and final grade policies [[65], [66], [67]]; these findings suggest that one way students may demonstrate their appreciation for such flexibility is by awarding higher evaluations to professors who provide it. RMP evaluation positivity did not affect intention to lower the evaluation of professors who did not provide policy exemptions. Moreover, participants did not report intention to lower the evaluations of professors who do not provide policy exemptions, regardless of RMP evaluation positivity. Implications of these dynamics are addressed below.

5.4. Effects of professor gender and student gender

Professor gender did not impact participants' intentions or beliefs. Given that students expect woman professors to be more warm, accommodating, and lenient [19,[68], [69], [70], [71]], this result was surprising. Recent evidence suggests that students' biases about woman professors are complex, though, and related to how well they conform to gender stereotypes [[18], [19], [20]]. Students expect woman professors, for example, to provide them with more inspiration about course content and communicate more passion for it [20]. In the current study, participants in both the man and woman professor conditions were both presented with information about their helpfulness and clarity. This information may not, though, have communicated information about how the professors’ behaviors confirmed or disconfirmed expectations stereotyped for their gender. The absence of a professor gender influence on student intentions and beliefs may be attributable to this methodological issue.

Finally, student gender was unrelated to participants' intentions and beliefs. This adds to an increasingly complex literature on the relationship between gender and AE. Several studies have found men to report higher AE [1,[22], [23], [24],72]; however, others have found the relationship to be more nuanced. Turnipseed and Cohen [73], for example, found gender was related to AE's externalized responsibility. Men reported lower personal accountability for their academic outcomes when compared to women. Student gender, however, was unrelated to AE's entitled expectations – neither men nor women reported feeling more deserving of policy exemptions than the other. In their study, Lemke et al. [74] found that gendered differences in AE disappeared over time. Men reported greater AE in 2009, but not in 2017; the closing of this gender gap was due to lower levels of men's AE in 2017, rather than higher levels of women's. Lemke et al. [74] attributed men's lower levels of AE to a more challenging job market for college graduates.

6. Implications

6.1. Professors’ occupational health

Individuals are increasingly turning to online reviews to inform their decision making about product purchases, hospitality experiences, and professional services (e.g., Refs. [[75], [76], [77]]). Students are following suit, commonly turning to RMP for peer feedback to inform their course decision making [78,79]. This study's results suggest that the impact of RMP evaluation content extends beyond the decision about which courses to take, though. RMP evaluations influence students' intentions toward their professors, including in ways that could increase professors' occupational stress and impair student learning.

Exposure to positive RMP evaluations significantly increased participants' intentions to ask for policy exemptions from their professors. These requests for policy exemptions present professors with dilemmas. When making decisions about how to respond to exemption requests, professors must contemplate, for example, if providing an exemption will create an unfair advantage by providing a particular student with more time or resources [80]. Moreover, they must determine if they will evaluate differently any work submitted as its result (e.g., a late assignment) as well as potential inequity issues that this may cause [81]. These dilemmas have potentially damaging impacts on professors' occupational health [82,83]. Moreover, when well-intentioned professors provide policy exemptions aiming to support student success (e.g., assignment due date extension due to extenuating circumstances), this may lead to multiple unwanted outcomes. First, providing policy exemptions creates additional workload for professors. Time must be invested into creating different versions of exams and rubrics for different essays; professors must stay on top of new assignment due dates [80]. Second, students may come to believe that policies do not apply to them and in turn, ask for more exemptions [84] and seek out flexible professors to exploit [80]. One may argue that the simple solution to this problem is to decline students' requests for policy exemptions, however, this too has implications for professors' occupational health and livelihood. Professors who deny the policy exemptions may be criticized in their teaching evaluations for their lack of compassion [84]. Given the weight that students' teaching evaluations hold in decisions to hire, rehire, promote, and award tenure and merit pay [29,85,86], professors may put their career and financial bottom line at risk by saying “no” to policy exemption requests. This may leave them feeling obligated to fulfill students’ requests [84].

This cycle also has implications for student learning. When students request policy exemptions, it creates stressful dilemmas for professors. This may be compounded when those requests come from students who are high in AE. From those students, requests for exemptions from course policies are more likely framed as demands [3] and over time, contending with these demands contributes to professor burnout [87]. At a time when professors already face several other pro-burnout influences, for example, competition for students and resources, additional sources of occupational stress stand to strain the professor-student relationship and when this occurs, student learning suffers [88].

Participants reported significantly greater intentions to take another class with positively-evaluated professors, regardless of whether they say yes or no to policy exemption requests. They also reported greater intention to write RMP evaluations for positive-evaluated professors. Repeat students and additional positive RMP evaluations and may create a double-edged sword for these professors. Financially, these effects could be a boon by increasing enrollment in professors' future classes, increasing the likelihood that they will meet minimum enrollment standards and get paid. This study's findings suggest, however, that those students will come to the professors' classes with greater intention to ask for policy exemptions. In turn, financial gain may come at the cost of professors' occupational health.

6.2. Student teaching evaluations

Student teaching evaluations are intended as an objective measure of teaching effectiveness and some scholars suggest that they are valid as such (e.g., Refs. [89,90]). Others, though, find no evidence of a relationship between student teaching evaluations and student learning, suggesting that they are not an appropriate measure of teaching effectiveness (e.g., Ref. [91]). Additionally, ample evidence suggests that they are fraught with bias by factors unrelated to teaching competency such as gender and nonconformity to gender stereotypes, race and ethnicity, age, attractiveness, vocal tone, and even attire (e.g., Refs. [18,92,93]).

Another factor that may bias student teaching evaluations is student grades. Under financial pressures, universities have become more business-oriented. Scholars suggest that competition for and retention of satisfied customers (i.e., students) has become the focus and professors face pressure to provide their customers with want they want: good grades [[94], [95], [96]]. When professors deliver the product that their customers want, they are rewarded with higher student teaching evaluations. Indeed, several studies find that students award higher teaching evaluations to professors who are lenient graders [[97], [98], [99]]. Given that student evaluations are at times the only source of information used in employment decisions, professors face a dilemma: grade leniently to secure high teaching evaluations or challenge students intellectually and risk losing their jobs. The current study suggests that the effect of grade leniency extends to policy leniency. Participants reported intentions to improve the teaching evaluation of professors who provide any of the policy exemptions in the study: attendance, assignment, test, and grade.

Policy leniency's effect on participants' evaluations has implications for the ongoing conversation about the use of student teaching evaluations in faculty personnel decisions. This finding provides further evidence that student teaching evaluations are biased and do not exclusively measure teaching effectiveness. They also measure, at least in part, reward for professors who are policy lenient and provide policy exemptions to students who ask for them. These findings suggest that near-exclusive reliance on student teaching evaluations will continue to pressure faculty to lower their academic standards and be lenient with students, possibly perpetuating inequities in university education. This finding underscores the need for a more diverse approaches to faculty evaluation including, for example, peer and supervisor observation and teaching portfolio evaluation.

6.3. Strengths and limitations

Experiences in university education, including attitudes toward professors, can vary by student demographic variables. Therefore, the constituency of this study's sample was a strength. Participants were diverse in race, ethnicity, gender, and college classification. They were also diverse in enrollment status (part-time and full-time) as well as the type of university they attend (public or private). Finally, participants were recruited from across the United States, rather than one region. The diverse characteristics of the sample suggest that the study's results are representative of students, at least in the United States. Other researchers' works bolster this contention as they have found that samples recruited via Qualtrics Research Panels are demographically and attitudinally representative of the United States population [100], produce data of similar validity to more customary approaches to data collection and are appropriate for the study of psychological variables [101], and increase generalizability of research findings [102]. The sample's diversity also amplifies another strength of the study. The study replicated Boswell's [11] findings about the relationship between AE and RMP use as well as the effect of evaluation positivity on students' intentions and beliefs about policy exemptions. Replication of these results alone would suggest evidence of their external validity. Replication of these results in a more diverse sample, though, further suggests that they will generalize and are representative of the attitudes of many university students in the United States. While Qualtrics Research Panels participants are incentivized for their participation, university students recruited through more customary approaches (e.g., university participant pools) typically are as well (e.g., course credit, extra credit); given this, any effect of incentive on participation may be equivalent between the two groups. Research suggests, though, that incentives may have no effect on participation. Some research finds that these incentives increase motivation for participation; other studies, however, find that they do not [103].

While the study had strengths, it also has limitations. Participants were individuals who opted into the study. These individuals may differ from the larger population of United States university students, stemming from their choice to participate in research. Individuals who opt into online studies are likely more curious and engaged when compared to the general population [103].

The study also relied upon self-report data about participants' intentions and beliefs, rather than observations of participants' actual behavior. While intentions and beliefs do not perfectly translate into behavior, they are its strongest predictors [104,105]. In sum, when individuals intend to engage in a behavior, the behavior typically follows. Intention and the likelihood of its behavioral occurrence are strengthened when individuals believe that the behavior will be successful [106]. Intention to engage in a behavior is also influenced by social information; the impact of social information on beliefs is most pronounced when individuals possess minimal information regarding their ability to perform in a specific context [107]; for instance, when considering how one will act toward a new instructor. In the current study, participants received social information about a novel professor in the form of RMP evaluations from the professors' purported former students. They used this information to form beliefs about the professors’ likelihood of granting policy exemptions (i.e., likelihood their requests would be successful). Based on these beliefs, participants in turn formed intentions to request exemptions. Intentions and behaviors, though, are not the same.

It is also possible that participants' intentions and beliefs toward their professors could change following a series of real-life interactions during the semester. Indeed, real-life interactions often do change individuals' explicit attitudes toward others. This likelihood seems highest in small, face-to-face classes in which professors and students have a higher degree of interaction. Real-life interactions do not always change individuals' implicit first impressions, though, because people sometimes cling to their initial impressions even in the face of contradictory evidence [108]. It is possible, then, that the intentions and beliefs students formed based on professors' RMP evaluations may not change, even after several interactions with the professor. Indeed, studies have found that a semester's worth of interaction with professors is not adequate to override initial impressions based on peers' word-of-mouth. Compared to students who did not receive peers' negative word-of-mouth about a professor before the start of the semester, students who did evaluated the professor significantly more negatively, even after a semester's worth of interactions to disconfirm the professor's negative reputation. Moreover, they underestimated how much they learned in the professor's course [109]. This effect extends to positive word-of-mouth as well; students who received their peers' pre-semester, favorable word-of-mouth about a new professor overinflated their estimates of what they learned in the course. Additionally, they overinflated their student teaching evaluations of the professor [110]. Even though participants in the current study did not have real-life interactions with the professors, McNatt's [[109], [110]] findings suggest that the intentions and beliefs that they formed from exposure to RMP evaluations should be regarded seriously.

While the study's sample may adequately represent the diversity of university students in the United States, its findings may not generalize well to students from other countries. Although AE was originally studied in Western samples, there is a growing body of research investigating AE research in non-Western countries [[111], [112], [113]]. These studies find some differences in AE's correlates and gendered-patterns compared to samples collected in Western countries. Given RMP's international use (United States, Canada, and United Kingdom), as well as use of similar websites in other countries (e.g., Campus.nikki.ne.jp Japan, MeinProf.de Germany, and Mis Profesores Mexico), caution should also be exercised in generalizing this study's findings to samples taken outside the United States.

7. Conclusion

This study's findings suggest that student attitudes related to AE and impacted by RMP evaluations have significant implications for professors' occupational health via requests for policy exemptions and the consequences of professors' responses to them. These findings also contribute to the body of evidence that student teaching evaluations do not exclusively measure teaching effectiveness. Similar to grade leniency, policy leniency may also bias student teaching evaluations. These underscore the need for robust approaches to professor evaluation.

Data availability statement

The author does not have participants’ permission to share data.

Ethics statement

The study was reviewed and approved by the University of the Incarnate Word Human Subjects Institutional Review Board on April 20, 2020, approval 1,809,003. All participants provided informed consent to participate in the study.

CRediT authorship contribution statement

Stefanie S. Boswell: Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The author has served as an article peer-reviewer for Heliyon.

Appendix.

Rating Scales.

Participants used 9-point, Likert-type scales to rate the following.

• 1.How would you describe this professor's evaluations?
• 2.
  How likely is it that you would ask the professor make an exception to:

  • a.
    His/her attendance policy so you could arrive late to class?
  • b.
    If you were to ask, how likely is it that he/she would say yes?
  • c.
    If the professor said yes, how much would this improve your evaluation of him/her?
  • d.
    If the professor said no, how much would this lower your evaluation of him/her?
• 3.
  How likely is it that you would ask the professor make an exception to:

  • a.
    His/her attendance policy so you could leave early from class?
  • b.
    If you were to ask, how likely is it that he/she would say yes?
  • c.
    If the professor said yes, how much would this improve your evaluation of him/her?
  • d.
    If the professor said no, how much would this lower your evaluation of him/her?
• 4.
  How likely is it that you would ask the professor make an exception to:

  • a.
    His/her attendance policy so you could miss class?
  • b.
    If you were to ask, how likely is it that he/she would say yes?
  • c.
    If the professor said yes, how much would this improve your evaluation of him/her?
  • d.
    If the professor said no, how much would this lower your evaluation of him/her?
• 5.
  How likely is it that you would ask the professor make an exception to:

  • a.
    His/her make-up/late policy so you could turn in an assignment late if you missed the deadline?
  • b.
    If you were to ask, how likely is it that he/she would say yes?
  • c.
    If the professor said yes, how much would this improve your evaluation of him/her?
  • d.
    If the professor said no, how much would this lower your evaluation of him/her?
• 6.
  How likely is it that you would ask the professor make an exception to:

  • a.
    His/her grading policy so you could resubmit an assignment for a better grade?
  • b.
    If you were to ask, how likely is it that he/she would say yes?
  • c.
    If the professor said yes, how much would this improve your evaluation of him/her?
  • d.
    If the professor said no, how much would this lower your evaluation of him/her?
• 7.
  How likely is it that you would ask the professor make an exception to:

  • a.
    His/her make-up/late policy so you could take an exam late if you missed it?
  • b.
    If you were to ask, how likely is it that he/she would say yes?
  • c.
    If the professor said yes, how much would this improve your evaluation of him/her?
  • d.
    If the professor said no, how much would this lower your evaluation of him/her?
• 8.
  How likely is it that you would ask the professor make an exception to:

  • a.
    His/her grading policy so you could retake an exam for a better grade?
  • b.
    If you were to ask, how likely is it that he/she would say yes?
  • c.
    If the professor said yes, how much would this improve your evaluation of him/her?
  • d.
    If the professor said no, how much would this lower your evaluation of him/her?
• 9.
  How likely is it that you would ask the professor make an exception to:

  • a.
    His/her grading policy so you could get a better final grade?
  • b.
    If you were to ask, how likely is it that he/she would say yes?
  • c.
    If the professor said yes, how much would this improve your evaluation of him/her?
  • d.
    If the professor said no, how much would this lower your evaluation of him/her?
• 10.If professor did give you an exception to a policy, how likely is it that you'd take another class with him/her?
• 11.If the professor did not give you an exception to a policy, how likely it that you'd take another class with him/her?
• 12.How likely is it that you would complete the university's end-of-semester teaching evaluation of him/her?
• 13.How likely is it that you would rate him/her on Ratemyprofessors.com?
• 14.How often do you write anonymous, online evaluations at websites like Ratemyprofessors.com? If this is your first semester in college and you have not yet had the opportunity to write anonymous, online evaluations at Ratemyprofessors.com, how often plan to do them?
• 15.How often do you read anonymous, online student evaluations at websites like Ratemyprofessors.com when making decisions about which teachers or courses to take? If this is your first semester at in college and you have not yet had the opportunity to read evaluations at Ratemyprofessors.com, how often plan to read them?
• 16.How seriously would you consider other students' anonymous, online comments on Ratemyprofessors.com when making decisions about which teacher or course to take?