the influence of computer-mediated word-of-mouth communication on student perceptions of instructors...

The Influence of Computer-MediatedWord-of-Mouth Communication onStudent Perceptions of Instructors andAttitudes Toward Learning CourseContentChad Edwards, Autumn Edwards, Qingmei Qing &Shawn T. Wahl

The purpose of this study was to experimentally test the influence of computer-mediated

word-of-mouth communication (WOM) on student perceptions of instructors (attrac-

tiveness and credibility) and on student attitudes toward learning course content (affective

learning and state motivation). It was hypothesized that students who receive posi-

tive computer-mediated WOM about an instructor would perceive the instructor as more

credible and attractive than students who receive no information or negative information.

It was further hypothesized that students who receive positive computer-mediated WOM

about an instructor would report greater levels of affective learning and state motivation

to learn than students who receive no information or negative information. All hypotheses

were supported. Results are discussed in light of the heuristic�systematic processing model,

and the implications for instructional communication are addressed.

Keywords: Computer-Mediated Communication; Teacher Credibility; Attractiveness;

Affective Learning; State Motivation; RateMyProfessors.com; Student Evaluations

Many studies have shown that word-of-mouth communication (WOM) plays an

important role in shaping people’s attitudes and behaviors (Harrison-Walker, 2001).

Chad Edwards (Ph.D., University of Kansas, 2003) and Autumn Edwards (Ph.D., Ohio University, 2006) are

Assistant Professors of Communication in the School of Communication at Western Michigan University.

Qingmei Qing (BA, Northeast University at Qinhuangdao, 2005) is an M.A. student in the School of

Communication at Western Michigan University. Shawn T. Wahl (Ph.D., University of Nebraska, 2003) is an

Assistant Professor of Communication in the Department of Communication Studies at Texas A&M University

at Corpus Christi. Portions of an earlier draft were presented at the 2007 Central States Communication annual

meeting, Minneapolis, MN. Chad Edwards can be contacted at [email protected]

ISSN 0363-4523 (print)/ISSN 1479-5795 (online) # 2007 National Communication Association

DOI: 10.1080/03634520701236866

Communication Education

Vol. 56, No. 3, July 2007, pp. 255�277

Predominately, this research has focused on the role of WOM in shaping consumer

perceptions of products, services, and organizations (Herr, Kardes, & Kim, 1991).

Increasingly, college students are relying on computer-mediated WOM, through the

use of Internet sites like RateMyProfessors.com and PickAProf.com, to gather

information about prospective professors and make decisions about which courses to

take and what to expect. However, the effects of WOM in the educational context

have gone largely unexplored. Using the heuristic�systematic processing model as a

theoretical frame, the purpose of this study is to examine the influence of computer-

mediated WOM on students’ perceptions of instructors and their attitudes toward

course content. Specifically, Study 1 uses an experimental design to explore the effects

of an online instructor rating system (RateMyProfessors.com) on student perceptions

of instructor credibility and interpersonal attractiveness. Study 2 employs the same

design to investigate effects on affective learning and student state motivation to

learn.

Literature Review

Word-of-Mouth Communication (WOM)

At the broadest level, WOM includes any information about a target object

transferred from one person to another either face to face or via some other

communication medium (Brown, Barry, Dacin, & Gunst, 2005). Harrison-Walker

(2001) defined WOM as ‘‘informal, person-to-person communication between a

perceived noncommercial communicator and a receiver regarding a brand, a product,

an organization, or a service’’ (p. 63). Research on WOM has been long-running and

extensive. As early as 1955, Katz and Lazarsfeld demonstrated that WOM is seven

times more effective than print advertisements, and four times more effective than

personal sales pitches in terms of changing consumer attitudes and behavior

(Harrison-Walker, 2001). Numerous studies since have consistently demonst-

rated the links between WOM and consumer purchasing behavior (e.g., Arndt,

1967, 1968; Brown & Reingen, 1987; Engel, Blackwell, & Kegerreis, 1969; Howard &

Gengler, 2001; Richins, 1983), product success (e.g., Day, 1971; Katz & Lazarsfeld,

1955), satisfaction with experiences (e.g., Burzynski & Bayer, 1977; Harrison-Walker,

2001), diffusion of innovations (Arndt, 1967; Singhal, Rogers, & Mahajan, 1999;

Sultan, Farley, & Lehmann, 1990; Sun, Youn, Wu, & Kuntaraporn, 2006), perception

of risk (Shrum & Bischak, 2001), and persuasion (e.g., Bytwerk, 2005; Carl, 2006;

Compton & Pfau, 2004; Spangenberg & Giese, 1997).

In recent years, the Internet has emerged as a powerful medium for the

transmission of WOM, which is a significant form of online interpersonal

communication and serves as a critical tool for facilitating the diffusion of

information throughout online communities (Sun et al., 2006). According to

Bickart and Schindler (2001), traditional WOM has typically consisted of spoken

words exchanged face to face between friends or relatives. By contrast, computer-

mediated WOM typically involves the technology-facilitated transmission of written

256 C. Edwards et al.

personal opinions and experiences among acquaintances or strangers (Sun et al.,

2006). The growth of the Internet has expanded both the availability and the

importance of WOM in the marketplace (Pitt, Berthon, Watson, & Zinkhan, 2002;

Zinkhan, Kwak, Morrison, & Peters, 2003). Furthermore, because of the relative

speed, convenience, breadth of reach, and lack of face-to-face social pressure,

Phelps, Lewis, Mobilio, Perry, and Raman (2004) have argued that computer-

mediated WOM has surpassed traditional WOM in its influence on information

and decision-making processes.

In the educational context, the role of WOM has gone largely unexplored. In one

of few studies on the topic, Borgida and Nisbett (1977) found that college students

were influenced by WOM about college courses. Specifically, their results demon-

strated that vivid WOM delivered face-to-face by a single student had a greater

influence on student course choice than did extensive written course evaluations

from a large group of students. In the decades following Borgida and Nisbett’s

investigation, the evolution of computer technology has provided students with

expanded opportunities for accessing and participating in WOM about college

courses and instructors (Wilhelm & Comegys, 2004). This is evidenced in the

emergence and growing popularity of online instructor rating systems, which

generally present a collection of anonymous student reviews of faculty and courses

(Lewin, 2003).

Online Instructor Rating Systems

Over the past few years, numerous websites devoted to evaluations of college

instructors and their courses have emerged on the Internet. Included among the most

successful and widely used are sites like RateMyProfessors.com, PickAProf.com, and

ProfessorPerformance.com. Such sites are intended to aid in the process of course

selection by providing students a forum to anonymously post and view quantitative

and open-ended evaluations of teaching effectiveness (Kindred & Mohammed, 2005).

RateMyProfessors.com (RMP), founded in 1999, is arguably the largest and best-

known website of this type (Kindred & Mohammed, 2005). In December of 2006, over

6.2 million ratings had been posted, spanning over 770,000 instructors from 6,000

schools in the country (Statistics, RateMyProfessors.com). Student users may rate

instructors along three dimensions: easiness, helpfulness, and teaching clarity.

Helpfulness and teaching clarity ratings are averaged to provide an overall quality

rating, which is summarized numerically on a 1�5 scale and indicated visually with an

icon of a face displaying one of three expressions: smiling (good quality), neutral

expression (average quality), and frowning (poor quality). Users have the option of

indicating physical attractiveness by putting a ‘‘chili pepper’’ next to the name of the

instructor to signify them as ‘‘hot.’’ In addition, concise open-ended comments about

the instructor and course can be posted (Kindred & Mohammed, 2005). The ratings

and comments become immediately available for viewing by others. Access to the site

is unrestricted insofar as users do not have to pay a fee.

Computer-Mediated WOM and Student Perceptions 257

RMP has been the subject of several empirical investigations. Research has

demonstrated that instructor quality scores on RMP are strongly positively correla-

ted with perceived course easiness and professor sexiness (Felton, Mitchell, & Stinson,

2004, 2005). Kindred and Mohammed (2005) demonstrated that students’ motives

for accessing RMP included information-seeking (to aid in the process of course

selection), convenience, and interpersonal utility (curiosity about peer opinions

of the instructor and course). Additionally, Kindred and Mohammed’s (2005)

investigation found that issues of instructor competence and classroom experience

were the primary focus of comments posted on RMP. To date, however, there

has been no empirical investigation concerning whether and in what ways RMP

ratings actually affect student perceptions of their instructors or their attitudes

toward course content. Given the popularity of RMP and the increasing desire

of students to have information about instructors prior to enrolling in their

courses (Gilroy, 2003), examining the impact of such student ratings is timely

and important. According to RMP founder, John Swapceinski, students expect

more information than in previous times because they regard themselves as

customers seeking to maximize the value of their educational dollars (Gilroy,

2003). If this is the case, RMP, as an instance of computer-mediated WOM, should

influence the perceptions of instructors and college courses held by students.

Moreover, if students are influenced by information appearing on sites like RMP, part

of the explanation likely relates to the ways in which they process these types of

persuasive messages. A considerable body of communication research has examined

the role of information processing on the formation of attitudes and judgments

(Dillard & Pfau, 2002). The following section describes the heuristic�systematic

processing model.

Heuristic-Systematic Processing Model

The heuristic�systematic processing model was originally developed by Chaiken

(1980) and Chaiken and Eagly (1983) as an alternative to the elaboration-likelihood

model of persuasion. According to Eagly and Chaiken (1993), ‘‘the heuristic�systematic model was developed for application to validity-seeking persuasion

settings in which people’s primary motivational concern is to attain accurate

attitudes that square with relevant facts’’ (p. 326). The heuristic�systematic

processing model is a dual-process framework based on the assumption that

‘‘attitudes are formed and modified as people gain information about attitude

objects’’ (Eagly & Chaiken, 1993, p. 257). Heuristic processing refers to the relatively

effortless application of simple decision rules (e.g., expert opinions are correct; mass

consensus is reliable) (Trumbo, 1999, 2002). By contrast, systematic processing

requires greater cognitive effort and resources as individuals carefully examine

arguments and relate them to previously obtained information. Individuals may

engage in one or both types of processing modes to form a judgment about a

particular issue or person (Chaiken, Liberman, & Eagly, 1989). According to the

model, the mode of processing is best predicted by the sufficiency principle, which


maintains that the nature of processing reflects the effort to strike a balance between

minimizing cognitive effort while maximizing judgmental confidence (Chaiken,

1987; Chaiken et al., 1989; Maheswaran & Chaiken, 1991). The consequence of a

mode of processing is that judgments based on heuristic processing tend to be less

stable and less related to subsequent behavior than judgments based on systematic

processing (Trumbo, 1999).

Previous research has indicated that there are three primary antecedent variables

which influence the mode of processing: motivation, self-efficacy, and information

sufficiency (Eagly & Chaiken, 1993; Trumbo, 1999). Motivation refers to the

perceived importance of reaching a judgment and has been positively associated

with greater systematic processing, presumably because highly motivated indivi-

duals are willing to exert greater effort and resources in attaining judgmental

confidence (Trumbo, 1999). Self-efficacy refers to an individual’s self-perceived level

of ability to acquire and use information in a specific situation. According to the

heuristic�systematic processing model, because systematic processing is presumed

to be the more demanding of the two modes, its use should be associated with

situations in which individuals feel highly self-efficacious (Eagly & Chaiken, 1993).

However, previous research has established positive associations between self-

efficacy and both systematic and heuristic processing modes (Trumbo, 1999). The

final antecedent variable, information sufficiency, refers to an individual’s percep-

tion of having enough information to make a judgment and has been positively

associated with the use of the less effortful heuristic processing (Trumbo, 1999).

Using the heuristic�systematic processing model as a theoretical framework for

attitude formation, two studies were conducted to examine the influence of

computer-mediated WOM, contextualized as RMP, on perceptions of instructors

held by students.

Study 1: Student Perceptions of Instructors

The purpose of Study 1 is to experimentally test the influence of computer-mediated

WOM (RMP) on student perceptions of instructors. Among the most important

and likely influenced perceptions of instructors are those of their credibility and

attractiveness.

Credibility

Credibility is defined as ‘‘the attitude toward a source of communication at a given

time by a communicator’’ (McCroskey & Young, 1981, p. 24), and encompasses the

degree to which students consider their instructors to be competent, have character,

and demonstrate caring (McCroskey & Teven, 1999). According to Frymier and

Thompson (1992), whether held consciously or unconsciously, students’ perceptions

of teachers’ credibility can exert a significant influence on their reactions to teachers

and on the effectiveness of teachers in the classroom. Instructors who are perceived

as more credible have students who report greater levels of affective learning (Teven &


McCroskey, 1997) and satisfaction (Teven & Herring, 2005), feel better understood

(Schrodt, 2003), are more willing to talk in class (Myers, 2004), and are more likely to

recommend the instructor to a friend (Nadler & Nadler, 2001).

Previous research has demonstrated that impressions of instructor credibility are

linked to instructor verbal and nonverbal communicative behaviors (Hendrix, 1997;

Myers & Bryant, 2004). Aggressive communication on the part of instructors is

associated with low student perceptions of instructor credibility (Edwards & Myers,

in press), while nonverbally immediate communication on the part of instructors

is associated with high student perceptions of instructor credibility (Thweatt &

McCroskey, 1998). While previous research makes clear that student perceptions of

instructor credibility are influenced by various instructor behaviors, it is likely that

such perceptions are, in part, also shaped by WOM exchanged via online instructor

rating systems. Therefore, the following hypothesis is offered:

H1: Students who receive positive computer-mediated WOM (RMP ratings)

about an instructor will perceive the instructor as more credible (on

the dimensions of competence, character, and caring) than students

who receive no computer-mediated WOM or negative computer-mediated

WOM.

Attractiveness

McCroskey and McCain (1974) identified three dimensions of interpersonal

attraction: task, social, and physical. In order, these dimensions refer to a person’s

desire to work with another person, to socialize with another person, and attraction

to another person based on physical appearance. Individuals rated as highly attractive

(especially on all three dimensions) have been rated as more persuasive and credible

than less attractive individuals (McCroskey, Hamilton, & Weiner, 1974). Further-

more, interpersonal attraction has been linked both to the amount of communica-

tion people engage in and to the quality of their exchanges (McCroskey et al., 1974).

In the instructional communication context, student perceptions of instructor

attractiveness have been positively associated with instructor immediacy and with

perceptions of attitude and background similarity (Edwards & Edwards, 2001; Rocca

& McCroskey, 1999). It is reasonable to propose that student perceptions of

instructor attractiveness are also influenced by computer-mediated WOM. The

current study limits the assessment of instructor attractiveness to the task and social

dimensions because previous research has demonstrated that students’ evaluations of

instructors’ physical attractiveness are not a central and relevant dimension of

teaching effectiveness (cf. Nussbaum, 1992; Rice, Stewart, & Hujber, 2000).1 Thus, we

offer a second hypothesis.

H2: Students who receive positive computer-mediated WOM (RMP ratings)

about an instructor will perceive the instructor as more attractive (task and

social) than students who receive no computer-mediated WOM or negative

computer-mediated WOM.


Method

Participants

The convenience sample was composed of 309 undergraduate students enrolled in

one of three large lecture introductory communication courses at a large Midwestern

university. Participants included 185 females (59.90%), 115 males (37.20%), and

9 individuals who did not indicate sex. The majority self-identified as Caucasian/

White (69.90%, n�216). Participants’ ages ranged from 18 to 48 years, with a mean

of 20.90 (SD�2.66). The largest percentage of participants classified as juniors

(34.00%, n�105), followed by sophomores (23.90%, n�74), seniors (21.70%,

n�67), first-years (15.90%, n�49), and ‘‘others’’ (4.50%, n�14). Participants

received extra credit points in return for taking part in the study.

Procedures

Upon securing institutional review board approval, an experimental design consisting

of two treatment groups (positive and negative RMP ratings) and a control group (no

RMP ratings) was utilized (Kerlinger & Lee, 2000). The groups consisted of three

sophomore-level introductory communication courses of approximately 100 students

each. Each class was randomly assigned a condition (positive treatment, negative

treatment, or control). In all conditions, after reading a consent form, participants

were informed by the first author that they would be asked to view a 12 minute

videotape of an instructor delivering a lecture on the topic of computer-mediated

communication. Furthermore, participants were informed that they would be asked

to evaluate the instructor after viewing the presentation. At this point, students in the

two treatment groups were given a handout. The researcher described the handout as

a printout of the RMP results for the instructor appearing in the taped lecture and

asked participants to read the handout during the moments taken to set up the

videotape presentation. Positive RMP ratings were given to the first treatment group,

while negative RMP ratings were given to the second treatment group. The control

group did not receive a RMP ratings handout, but was given a fictitious name for

both the instructor and his institutional affiliation. At the completion of the

videotaped lecture, participants in all three conditions received a 2-page survey-

questionnaire comprising the following measures: (a) McCroskey and McCain’s

(1974) Measure of Interpersonal Attraction, (b) McCroskey and Teven’s (1999)

Measure of Source Credibility, and (c) a brief demographic survey. Finally,

participants were debriefed and thanked.

Independent Variable

To create the two treatment conditions, two 1-page handouts were produced,

showing simulated RMP ratings. Handouts were produced by using html code to

manipulate the content of an actual RMP results page. Appearing at the top of the

pages were realistic-sounding, but fictitious, names for both the instructor appearing


in the taped lecture and his institutional affiliation. The middle of the pages included

a face displaying an expression (either a smile or frown) and simulated quantitative

summaries of student-raters’ evaluations of the instructor’s easiness, helpfulness,

clarity, and overall quality (each based on a maximum score of 5.0 and a minimum of

1.0). The bottom third of the pages included five fabricated open-ended comments

from students regarding the instructor. These comments were modeled from student

comments appearing on actual RMP results pages. The posted dates assigned to the

five comments spanned the three academic semesters prior to that in which this study

was conducted. By listing the same fabricated course number next to each comment,

it was made to appear that all five comments pertained to the same course.

The positive RMP handout included a ‘‘smiley face’’ to indicate good overall

quality of the instructor. The average easiness rating was listed at 3.2 out of 5.0, with

1.0 representing difficult and 5.0 representing easy.2 The average helpfulness rating

was listed as 5.0, and the average clarity rating was listed as 4.8. These numbers were

averaged to provide an overall quality rating of 4.9 out of 5.0, with 5.0 representing

the highest possible overall quality rating. Five simulated open-ended comments

about the instructor were provided. For example: ‘‘If you want to learn something,

take him if at all possible. Things that I liked: He’s very prepared. He’s very

understanding and nice. He’s very good at using humor.’’

The negative RMP evaluation included a ‘‘frowny face’’ to indicate poor overall

quality of the instructor. The average easiness rating was again listed at 3.2 out of 5.0.

The average helpfulness rating was listed as 2.0, and the average clarity rating was

listed as 1.4, providing an overall quality rating of 1.7.3 Five simulated open-ended

comments about the instructor were provided. For example: ‘‘If you want to learn

something, don’t take him if at all possible. Things that annoyed me: He’s very under

prepared. He’s very rude. He’s very bad at using humor.’’

As evidenced in the sample provided above, negative comments were produced by

reversing the sentiments expressed in each of the five comments used on the positive

RMP evaluation.

Video Stimulus

The fourth author, a person unknown to student participants, developed and

rehearsed a short lecture on the topic of computer-mediated communication. He

was instructed to deliver a teaching performance of ‘‘average quality.’’4 He was kept

blind to the purposes of this study until all data collection was completed. The

resultant 12-minute lecture was recorded in front of a live classroom audience. The

video frame included the instructor from the waist up and a projection screen used to

display a basic PowerPoint outline of the lecture. The student audience was not visible.

Dependent Variables

The Measure of Source Credibility (McCroskey & Teven, 1999) is an 18-item

instrument designed to assess perceptions of an individual’s credibility across the


three dimensions of competence (6 items; e.g., ‘‘intelligent/unintelligent’’), character

(6 items; e.g., ‘‘trustworthy/untrustworthy’’), and caring (6 items; e.g., ‘‘cares about

me/doesn’t care about me’’). Participants were asked to rate the instructor appearing

in the videotaped lecture along a series of 7-point semantic differential scales. Past

studies have reported reliability coefficients ranging from .86 to .95 for the three

dimensions (Brann, Edwards, & Myers, 2005; Teven & McCroskey, 1997). In this

study, reliability coefficients of .87 for competence (M�27.20, SD�7.19), .79 for

character (M�26.48, SD�5.71), and .85 for caring (M�25.18, SD�6.71) were

obtained.

The Measure of Interpersonal Attraction (McCroskey & McCain, 1974) was

designed to assess a person’s attraction to another along three dimensions: task

(5 items; e.g., ‘‘I am confident with his or her capability to get the job done’’), social

(5 items; e.g., ‘‘I think she or he could be a friend of mine’’), and physical attra-

ction (5 items; e.g., ‘‘I find him or her very physically attractive’’). In this study, the

physical attraction dimension was not utilized. Participants were asked to indicate the

degree to which they agreed or disagreed with task and social attraction items on

Likert scales with response options ranging from 1 (strongly disagree) to 5 (strongly

agree). Past studies have shown this measure to be reliable (e.g., task a�.81, social

a�.84, McCroskey & McCain, 1974; task a�.69, social a�.78, Rocca & McCroskey,

1999). In this study, the following reliability coefficients were obtained: task�.82

(M�17.23, SD�4.12) and social�.80 (M�14.08, SD�4.07).

Results

A one-way K-group multivariate analysis of variance (MANOVA) was conducted to

determine the effects of computer-mediated WOM (positive, negative, or no RMP

ratings) on the five dependent variables of the competence, character, and caring

dimensions of credibility and the task and social dimensions of attraction. A

MANOVA was chosen because the dependent variables were related. Table 1 reports

the correlations among the dependent variables.

Significant differences were found among the positive, negative and control

computer-mediated WOM conditions on the dependent measures, Wilks’s l�.680,

F(10, 604)�12.85, p B.001. The multivariate h2 based on Wilks’s lambda was

moderate, .18. Table 2 reports the means and standard deviations on the dependent

variables for the three groups.

Table 1 Correlations Among the Dependent Variables

Competence Character Caring Task Attraction Social Attraction

Competence � .74* .71* .73* .49*Character � � .78* .62* .55*Caring � � � .60* .57*Task Attraction � � � � .50*

*p B.01.


Analyses of variance (ANOVAs) on each dependent variable were conducted as

follow-up tests to the MANOVA. To test the ANOVA assumption of equality of error

variances, Levine’s test was performed on each of the five dependent variables. Results

indicated no violation of the assumption for four of the five dependent variables: task

attraction, F(2, 306)�.94, p�.39; social attraction, F(2, 306)�2.65, p�.07;

competence, F(2, 306)�.18, p�.84; and caring, F(2, 306)�.06, p�.94. Using the

Bonferroni method (.05/5) to control for a Type I error, each of these ANOVAs was

tested at the .01 level. The ANOVAs were significant for task attraction [F(2, 306)�37.88, p B.001, h2�.20], social attraction [F(2, 306)�24.91, p B.001, h2�.14],

competence [F(2, 306)�50.10, p B.001, h2�.25], and caring [F(2, 306)�48.04,

p B.001, h2�.24]. Because character showed a violation of the ANOVA assumption

of homogeneity of variance [F(2, 306)�12.11, p B.001], the more robust Brown�Forsythe test was used to determine whether the three groups differed on this variable

(Green & Salkind, 2003). Results demonstrated a significant difference, F(2,

203.15)�49.78, p B.001, h2�.25.

Post hoc analyses to the ANOVAs for task attraction, social attraction, competence,

and caring consisted of pairwise comparisons using Tukey’s HSD. Post hoc analyses

to the ANOVA for character consisted of Dunnett’s C, which is more robust to

violations of homogeneity of variance (Green & Salkind, 2003). Results demonstrated

that the group receiving positive computer-mediated WOM (RMP ratings) perceived

the instructor portrayed in the video as significantly more task and socially attractive

and higher in competence, character, and caring than did the control group and

the group receiving negative computer-mediated WOM (RMP ratings). Furthermore,

the negative treatment group rated the instructor significantly lower on all five

dependent variables than did the control group.

A discriminant analysis was conducted to determine whether the five variables*competence, character, caring, task attraction, and social attraction*could be used

to predict which condition student participants had been assigned to (positive,

negative, or control). Wilks’s lambda was significant, l�.68, x2(10, N�309)�117.28, p B.001, indicating that overall, the predictors differentiated among

the three conditions. The residual Wilks’s lambda was not significant, l�.98,

Table 2 Means and Standard Deviations for the Three Conditions on the Dependent

Variables

Positive Negative Control

Variable M (SD ) M (SD) M (SD )

Competence 31.13a (6.43) 22.44b (6.32) 27.53c (6.02)Character 29.87a (5.91) 22.92b (4.16) 26.24c (4.52)Caring 29.09a (5.80) 21.11b (5.96) 25.18c (6.71)Task Attraction 19.01a (3.44) 14.64b (3.73) 17.79c (3.93)Social Attraction 15.84a (4.09) 12.12b (3.30) 14.07c (3.88)

Note : Means in a row with differing subscripts are significantly different at p B.05 in the Tukeyhonestly significant difference comparison.


x2 (4, N�309)�7.75, p�.101. This test, thus, indicated that the predictors did not

differentiate significantly between the three conditions after partialling out the effects

of the first discriminant function. Therefore, we chose only to interpret the first

discriminant function.

In Table 3, we present the within-group correlations between the predictors and

the discriminant function, as well as the standardized weights. Based on these

coefficients, competence, character, and caring (the three dimensions of credibility)

demonstrate the strongest relationships with the discriminant function, whereas task

and social attraction show slightly weaker relationships. On the basis of the results,

we chose to label the discriminant function ‘‘perceived instructor desirability.’’

The means on the discriminant function are consistent with this interpretation.

The positive RMP rating condition (M�.75) had the highest mean on perceived

instructor desirability, whereas the negative RMP rating condition (M��.85) and

control condition (M�.01) had lower means. When we tried to predict condition,

we were able to correctly classify 56% of the individuals in this sample. To take into

account chance agreement, a kappa coefficient, which may range from �1 to �1,

was computed. The obtained value of .34 represents a moderate value. Finally, to

assess how well the classification procedure would predict in a new sample, we

estimated the percent of people accurately classified using the leave-one-out

technique (in which all cases are left out once and classified based on classification

functions for the N � 1 cases; Green & Salkind, 2003) and correctly classified 54% of

the cases.

Summary

The purpose of this study was to experimentally test the effects of computer-mediated

WOM on students’ perceptions of the credibility (competence, character, and caring)

and attractiveness (task and social) of instructors. Results supported hypotheses one

and two, demonstrating that students who receive positive computer-mediated WOM

about an instructor perceive the instructor as more credible and more attractive than

students who receive negative computer-mediated WOM about the instructor or

none at all.

Table 3 Standardized Coefficients and Correlations of Predictor Variables with the

Instructor Desirability Discriminant Function

PredictorsCorrelation Coefficients with

Discriminant FunctionStandardized Coefficients with

Discriminant Function

Competence .87 .33Character .87 .31Caring .85 .26Task Attraction .74 .17Social Attraction .61 .16


Study 2: Student Attitudes Toward Course Content

The findings from the first study demonstrate the importance of computer-mediated

WOM on student assessments of instructors. Given this, it is reasonable to wonder

whether computer-mediated WOM exerts an influence on student attitudes toward

learning the content of courses. Specifically, Study 2 was designed to experimentally

test the effect of computer-mediated WOM (RMP) on students’ levels of affective

learning and state motivation to learn. Brief descriptions of these variables follow.

Affective Learning

According to Kearney (1994), affective learning refers to ‘‘an increasing internaliza-

tion of positive attitudes toward the content or subject matter’’ (p. 81). Importantly,

affective learning is positively associated with student motivation to learn

(Christensen & Menzel, 1998; Christophel, 1990; Frymier, 1994; Frymier & Houser,

2000) and may serve as a precursor to cognitive learning (Rodriguez, Plax, & Kearney,

1996). Furthermore, affective learning on the part of students has been linked to

better instructor evaluations (Teven & McCroskey, 1997) and increased willingness to

enroll in future classes with the same instructor (Gorham & Christophel, 1990;

Kearney, Plax, & Wendt-Wasco, 1985; McCroskey, Fayer, Richmond, Sallinen, &

Barraclough, 1996). Previous research has demonstrated that affective learning is

positively related to a number of communication behaviors on the part of instructors,

including immediacy (Comstock, Rowell, & Bowers, 1995; Pogue & AhYun, 2006;

Witt & Schrodt, 2006), use of instructional technology (Turman & Schrodt, 2005;

Witt & Schrodt, 2006), clarity (Avtgis, 2001; Chesebro & McCroskey, 2001), and

humor (Gorham & Christophel, 1990), and is negatively related to instructor verbal

aggressiveness (Myers, 2002; Myers & Knox, 2000). In addition to being influenced by

instructor behavior, it also stands to reason that affective learning would be

influenced by information gathered through computer-mediated WOM. Therefore,

we pose the following hypothesis:

H3: Students who receive positive computer-mediated WOM (RMP ratings)about an instructor will report greater levels of affective learning thanstudents who receive no computer-mediated WOM or negative computer-mediated WOM.

Student State Motivation to Learn

Brophy (1987) characterized student motivation as the ‘‘tendency to find academic

activities meaningful and worthwhile’’ (p. 205). Whereas trait motivation refers to an

individual’s general level of motivation across situations, state motivation refers to

student behaviors or displays to complete a specific task (Frymier & Shulman, 1995).

In other words, state motivation ‘‘refers to a specific learning situation such as a

particular class, task, or content area’’ (Myers & Rocca, 2000, p. 291). Previous

research has established positive relationships between student state motivation and


student learning (Christophel, 1990) and public-speaking competence (Carrell &

Menzel, 1997). Moreover, student state motivation is related to behavior on the part

of instructors (Frymier & Shulman, 1995). Specifically, motivation is positively

associated with perceived instructor predictability (Avtgis, 2001), fairness (Chory-

Assad, 2002), and immediacy (Allen, Witt, & Wheeless, 2006; Kerssen-Griep, 2001;

Richmond, 1990) and is negatively associated with perceived instructor misbehaviors

(Wanzer & McCroskey, 1998) and verbal aggression (Myers, 2002). Computer-

mediated WOM figures as another likely source of influence on student levels of state

motivation to learn. The following hypothesis is offered:

H4: Students who receive positive computer-mediated WOM (RMP ratings)about an instructor will report greater levels of state motivation thanstudents who receive no computer-mediated WOM or negative computer-mediated WOM.

Method

Participants

Participants included 131 undergraduate students enrolled in one of two large-lecture

communication courses at the same large Midwestern university used in the first

study. Data collection for Study 2 occurred one semester after data collection for

Study 1. There were 87 females (66.40%), 43 males (32.80%), and 1 individual who

did not indicate their sex. The majority self-identified as Caucasian/White (75.60%,

n�99). Participants’ ages ranged from 18 to 40 years, with a mean of 21.59 (SD�2.74). The largest percentage of participants classified as seniors (39.70%, n�52),

followed by juniors (36.60%, n�48), sophomores (20.60%, n�27), first-years

(.80%, n�1), and ‘‘others’’ (2.30%, n�3). Student respondents received extra credit

points in return for their participation.

Procedures

Upon securing institutional review board approval, a second experiment consisting of

two treatment groups (positive and negative RMP ratings) and a control group (no

RMP ratings) was conducted to test hypotheses three and four. The groups were

drawn from two introductory communication courses. The smaller class (n�49) was

assigned to the control condition, while the larger (n�82) was assigned to the

treatment conditions (with approximately half of students receiving the positive

treatment and half receiving the negative treatment through a process of random

assignment). Following procedures identical to those described in Study 1,

participants read a consent form, received treatment according to condition (positive

RMP ratings, negative RMP ratings, or no RMP ratings) and viewed the video-taped

lecture. Upon completion of the taped presentation, participants in all three

conditions were asked to complete a four-page survey-questionnaire comprising

the following measures: (a) McCroskey’s (1994) Affective Learning Measure, (b)


Christophel’s (1990) Student Motivation Scale, and (c) a brief demographic survey.

Participants were then debriefed and thanked.

Dependent Variables

McCroskey’s (1994) Affective Learning Measure is an 8-item instrument designed to

assess students’ affect for course subject matter along a series of 7-point semantic

differential scales. The first four items address affect toward the course content (e.g.,

‘‘I feel that the class content is: valuable/worthless’’), while the last four items address

affect toward other classes in the content area (e.g., ‘‘My likelihood of taking future

courses in this content area is: improbable/probable’’). Past studies have reported

reliability coefficients exceeding .90 (McCroskey, 1994). In this study, a reliability

coefficient of .91 (M�33.69; SD�10.50) was obtained for affective learning.

Christophel’s (1990) Student Motivation Scale is a 12-item instrument designed to

assess students’ motivation to learn from a target instructor in a particular course.

Items are rated along 7-point semantic differential scales (e.g., ‘‘motivated/

unmotivated,’’ ‘‘inspired/uninspired’’). Past studies have reported reliability coeffi-

cients consistently higher than .90 (Christophel, 1990; Myers, 2002). A reliability

coefficient of .95 (M�35.69, SD�16.20) was obtained in the present study.

Results

A MANOVA was conducted to determine the effects of computer-mediated WOM

(positive, negative, or no RMP ratings) on the dependent variables of affective

learning and student state motivation to learn (which were significantly positively

correlated, r (129)�.57, p B.01). The MANOVA was significant, Wilks’s l�.803,

F(4, 254)�7.38, p B.001, n2 �.10. Table 4 contains the means and standard

deviations on the dependent variables for the three groups.

Two ANOVAs were conducted as follow-up tests to the MANOVA. Levine’s tests

indicated no violation of the assumption of homogeneity of variance: affective

learning, F(2, 128)�.33, p�.72 and state motivation to learn, F(2, 128)�2.26,

p�.11. Using the Bonferroni method (.05/2) to control for a Type I error, each of

these ANOVAs was tested at the .025 level. Both the ANOVA for affective learning

Table 4 Means and Standard Deviations for the Three Conditions on the Dependent

Variables

Positive Negative Control

Variable M (SD) M (SD) M (SD)

Affective Learning 38.46a (10.00) 31.41b (10.41) 31.32b (9.73)State Motivation 44.86a (16.25) 28.10b (12.68) 33.69b (15.03)

Note : Means in a row with differing subscripts are significantly different at p B.01 in the Tukeyhonestly significant difference comparison.


[F(2, 128)�7.25, p B.001, h2�.10] and the student state motivation to learn

[F(2, 128)�13.82, p B.001, h2�.18] achieved significance. Pairwise comparisons

using Tukey’s HSD revealed that the group receiving positive computer-mediated

WOM (RMP ratings) reported significantly higher levels of affective learning and

state motivation to learn than did the control group and the group receiving negative

computer-mediated WOM (RMP ratings). The latter two groups did not differ

significantly from one another on either variable.

To determine whether affective learning and state motivation to learn could be

used to predict the condition to which a participant had been assigned, a

discriminant analysis was performed. The predictors differentiated significantly

among the three conditions, Wilks’s l�.80, x2 (4, N�131)�28.04, p B.001. The

residual Wilks’s lambda was not significant, l�.98, x2 (1, N�131)�2.23, p�.14,

prompting an interpretation of only the first discriminant function. Based on the

coefficients for the within-group correlations between the predictors and the

discriminant function and the standardized weights (see Table 5), we chose to label

the discriminant function ‘‘attitudes toward learning course content.’’

The positive RMP rating condition (M�.64) had the highest mean on attitudes

toward learning course content, whereas the negative RMP rating condition (M��.49) and control condition (M��.17) had lower means. The procedure resulted

in the correct classification of 48.10% of the individuals in this sample, K�.23.

The leave-one-out technique resulted in an estimation of 47.30% of individuals who

would be correctly classified in a new sample.

Discussion

Studies 1 and 2 sought to experimentally test the effects of computer-mediated WOM

(RMP ratings) on students’ perceptions of instructors and their attitudes toward

learning course content. Results supported all four hypotheses, demonstrating that

students who received positive computer-mediated WOM rated the target instructor

as more credible and attractive, and reported greater levels of affective learning and

state motivation to learn when compared with students who received negative

computer-mediated WOM or none at all.5 These findings are consistent with

previous research on WOM, which has shown that communication about products or

services influences both customers’ choices and their assessment of the experience

(Harrison-Walker, 2001) and serves as an important source of consumer expectations

(Zeithaml, Berry, & Parasuraman, 1993).

Table 5 Standardized Coefficients and Correlations of Predictor Variables with the

Attitudes toward Learning Course Content Discriminant Function

PredictorsCorrelation Coefficients with

Discriminant FunctionStandardized Coefficients with

Discriminant Function

Affective Learning .68 .24State Motivation to Learn .98 .86


Importantly, these studies are the first to establish causal links between material

posted on RMP and student evaluations of instructors and courses. The results of

Study 1 demonstrate that computer-mediated WOM among students has a

significant impact on student perceptions of instructor credibility and attractiveness.

Based on the results of this study, it is reasonable to suggest that comments about

instructors posted on public websites may ultimately effect students’ perceptions

enough to influence university sanctioned student evaluations of teaching, which

often play a major role in tenure and promotion decisions (Emery, Kramer, & Tian,

2003; Marsh, 1987). Furthermore, the results of this study point to the difficulty

instructors may face in improving their evaluations. As students increasingly utilize

online rating systems to make decisions and form impressions, self-sustaining cycles

may form, in which prospective students view unfavorable ratings of an instructor,

lean on such ratings in evaluating the instructor, then pass along consistent ratings of

the instructor to future students. The newly available RMP ‘‘Tell a Friend!’’ feature, in

which student users are invited to share professor ratings via e-mail, further evidences

the ease and speed with which computer-mediated WOM travels.

Additionally, this study demonstrates that a significant proportion of variance in

student perceptions of an instructor’s credibility and attractiveness can be determined

by factors outside the instructor’s immediate realm of control. In this case, despite the

fact that instructional performance was held constant across conditions, student

computer-mediated WOM about the instructor accounted for almost 20 percent of

the variability in student evaluations of the instructor. Moreover, as evidenced by the

results of the discriminant analysis, students’ perceptions of the instructor’s level of

desirability could be used as a relatively reliable predictor of the type of WOM

(positive, negative, or none) students had been exposed to. Although the role of

student-to-student interaction is often overlooked in instructional communication

literature (Nussbaum & Friedrich, 2005), the current findings demonstrate its

consequentiality in educational encounters. Because it is clear that students take what

their peers say about instructors and courses seriously, WOM (whether traditional or

computer-mediated) provides one fruitful conceptual avenue from which to further

explore student-to-student communication in the instructional context.

The results of Study 2 demonstrate that in addition to influencing student

perceptions of instructors, computer-mediated WOM also impacts student respon-

siveness to course subject matter in terms of affective learning and state motivation to

learn. A sizeable portion of the variability in student attitudes toward the content of a

target course (10%) was accounted for by the type of WOM students were exposed to.

Interestingly, although student recipients of positive RMP ratings reported more

affective learning and motivation than the other two groups, student recipients of

negative RMP ratings and those who received none did not differ significantly from

one another on these variables. Thus, for affective learning and state motivation to

learn, it may be the case that positive computer-mediated WOM is more influential

than negative computer-mediated WOM. Previous research investigating the relative

influence of positive versus negative WOM has yielded mixed results (Ahluwalia,

2002; Fiske, 1980; Mizerski, 1982). At least one study, however, suggests that positive


WOM produces a stronger consumer response under certain conditions. Holmes and

Lett (1977) lent support to the ‘‘positivity bias’’ by demonstrating that customers

exposed to positive stimuli/events were more likely to communicate their feelings to

others than were customers exposed to negative stimuli/events. Perhaps educators can

be cautiously optimistic that while levels of affective learning and motivation are

heightened by positive RMP ratings, they are not adversely affected by negative RMP

ratings. However, an alternative interpretation might suggest that the nonsignificant

difference between the reported state motivation levels of the negative RMP group

and the control group is attributable to an issue with statistical power. With the use of

a larger sample, the state motivation level of the negative RMP group could emerge as

significantly lower than that of the control group.

Furthermore, because student participants rated the target instructor and course in

a manner that was consistent with the valence of information they received prior to

viewing the taped performance (and inconsistent with the more moderate ratings

given by the control group), findings are suggestive of an association between

exposure to information appearing on sites like RMP and the employment of the

heuristic mode of processing. Perhaps, in their effort to reduce cognitive effort while

maximizing judgmental confidence about the instructor and course, participants

applied simple decision rules to the process of evaluation (e.g., ‘‘statements of

consensus from peers are accurate’’ or ‘‘web-based reports of teaching effectiveness

can be trusted’’). Moreover, previous research on the antecedents of processing modes

indicates that the conditions of the experiments used in this study would facilitate the

use of heuristic, rather than systematic, processing. In specific, the scenario

manufactured for participants in treatment conditions was one characterized by

relatively low motivation (as participants had little at stake for producing an accurate

appraisal of the instructor/course), high self-efficacy (as most students are familiar

with the process of providing evaluations of their teachers and courses and feel

qualified to do so), and high information sufficiency (as students were provided with

a videotaped sample of instructional performance as well as quantitative and

qualitative summaries of prior teaching effectiveness from a well-known online

instructor rating site).

Limitations and Future Directions

The findings of these studies should be interpreted in light of several limitations. The

first of these pertains to the contrived nature of experimental designs. The videotape

shown to participants contained a 12-minute lecture delivered by an instructor

unknown to them. Thus, when rating the instructor’s credibility and attractiveness,

student participants were limited to early impressions, which may have caused them

to rely more heavily on the computer-mediated WOM (RMP ratings) than they

would if given the opportunity to get to know an instructor over the course of an

entire semester. Likewise, this reasoning extends to student ratings of the course

content. As such, future research should examine the relationships between these

variables in more naturalistic conditions.


The discrepancy between the experimental situation and naturally occurring

conditions also has implications for any claims regarding student processing of

material from online instructor rating systems. In reality, students use sites like RMP

to solicit information about prospective instructors and to aid in the process of

course selection (Kindred & Mohammed, 2005). Because the judgments students

form in such situations hold actual consequences for their academic success, their

motivation to be accurate in such situations is likely higher than it was for

participants in the study. This may mean that students expend greater cognitive

resources (i.e., process information more systematically) when they use sites like RMP

in their daily lives. Future research is needed to test the specific claims of the

heuristic�systematic processing model in the context of computer-mediated WOM

and instructional communication.

A second limitation concerns the operational overlap in the measures of credibility

and attractiveness used to assess student perceptions of the instructor and those of

state motivation and affective learning used to assess student perceptions of course

material. The high correlations among affect-based measures such as these speak to

the difficulty in establishing their conceptual distinctiveness. Third, caution should be

exercised in generalizing the findings from this experiment as the sample comprised

chiefly 18- to 22-year-old Caucasian sophomores and juniors enrolled in commu-

nication courses at a large public institution. Felton et al. (2005) demonstrated that

there are significant differences in the usage of web-based student evaluations of

teachers among institutions and disciplines. Therefore, the study could be replicated

in a diversity of education contexts. Future research is needed to better understand

the influence of computer-mediated WOM (especially from popular websites such as

RateMyProfessors.com) on additional classroom outcomes and facets of the student�teacher relationship. Finally, RMP continues to incorporate new on-line features.

Newly added tools, such as the option for students to upload a photograph of their

instructor and to report their grade in the course, may prove to be interesting

variables to consider in future studies.

Notes

[1] Student evaluations of instructor physical attractiveness may be indirectly related to

measures of quality teaching. Previous research has attributed this peripheral relationship

to the operation of a ‘‘halo effect’’ in student evaluations of communication instruction (cf.

Feeley, 2002).

[2] The average easiness rating of social science professors in the U.S. is 3.2 (Felton et al., 2005).

Because easiness is not factored into the overall quality rating assigned to professors by RMP,

we held the rating of 3.2 constant across conditions.

[3] Although the overall quality ratings of 1.7 and 4.9 used for the positive and negative RMP

evaluations are not equidistant from the scale midpoint of 3.0, they approximate equidistant

intervals from the average overall quality rating of social science professors in the U.S., which

is consistently several tenths higher than 3.0 (Felton et al., 2005).

[4] The fourth author/videotaped actor is an assistant professor of communication and is well

versed in the literature of instructional communication. To deliver a teaching performance of

‘‘average quality,’’ he sought to incorporate influential instructional behaviors at a moderate


level. One indicator of the ‘‘averageness’’ of the lecture is the ratings of attractiveness (3.2 on

a 1�5 scale) and credibility (4.37 on a 1�7 scale) provided by the control group, which

received no information about the previous performance of the instructor prior to viewing

the tape. Additionally, the video stimulus was subjected to a manipulation check employing

21 undergraduate students asked to rate the instructor and the lecture as ‘‘above average,’’

‘‘average,’’ or ‘‘below average.’’ The vast majority rated both the instructor and the lecture as

average.

[5] In addition to the analyses reported in the Results section, we also conducted a series of one-

sample t tests to determine whether mean scores for each of the seven dependent variables in

the positive and negative RMP treatment groups differed significantly from scale midpoints.

Generally speaking, dependent variable means for the positive RMP condition were

significantly higher than the scale midpoints, and those of the negative RMP condition

were significantly lower than the scale midpoints. Detailed results of these analyses are

available upon request from the first author.

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Received September 29, 2006

Accepted January 23, 2007


the influence of computer-mediated word-of-mouth communication on student perceptions of instructors...

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