Modification Paper

BRIEF REPORT

An Experimental Examination of Students’ Attitudes Toward Classroom Cell Phone Policies Alexander L. Lancaster & Alan K. Goodboy

This study examined the manipulation of instructors’ persuasive messages to introduce

classroom cell phone policies to college students. Guided by Chaiken’s (1980, 1987)

heuristic-systematic model, we predicted significant differences in students’ systematic mess-

age processing and favorable attitudes held toward a cell phone policy based on the number of

arguments presented and involvement (i.e., motivation). Using a 2 (number of arguments:

high; low)�2 (involvement: high; low) experimental design, 101 undergraduate students participated by watching a video-recorded persuasive message about a hypothetical cell phone

policy. Results indicated that participants did not differ significantly in systematic or heuristic

message processing based on the assigned condition but held more favorable attitudes toward

the cell phone policy when assigned to the lower number of arguments condition.

Keywords: Attitudes; Cell Phone Policies; Heuristic-Systematic Model; Message

Processing; Persuasion

Recent research (Finn & Ledbetter, 2013; Johnson, 2013; Ledbetter & Finn, 2013) has

examined the effect of instructor characteristics and technology policies on instructor

credibility and learner empowerment. Finn and Ledbetter (2013) identified three types

of instructor technology policies: encouraging, discouraging, and laissez-faire policies.

An instructor’s policy on student cell phone use in the classroom is important to

Alexander L. Lancaster (MA, California State University, Long Beach, 2012) is a doctoral candidate in

the Department of Communication Studies at West Virginia University. Alan K. Goodboy (PhD, West Virginia

University, 2007) is an associate professor in the Department of Communication Studies at West Virginia University.

Correspondence: Alexander L. Lancaster, Department of Communication Studies, West Virginia University, P.O. Box

6293, Morgantown, WV 26506; E-mail: allancaster@mix.wvu.edu

Communication Research Reports

Vol. 32, No. 1, January–March 2015, pp. 107–111

ISSN 0882-4096 (print)/ISSN 1746-4099 (online) # 2015 Eastern Communication Association

DOI: 10.1080/08824096.2014.989977

consider because students who use their mobile devices in class take less-detailed notes,

recall less lecture information, and receive lower scores on exams (Kuznekoff &

Titsworth, 2013). Additionally, Johnson (2013) found that students’ use of cell phones

to engage in computer-mediated communication (CMC) during class time (i.e., to

text) was a threat to student engagement and called for instructors to find ways to abate

such behavior. It is important to examine how instructors communicate these policies

in classrooms, as students may respond differently to a policy depending on how it is

presented to them. Therefore, this study explores students’ perceptions of cell phone

policies that instructors present with persuasive messages.

Chaiken’s (1980, 1987) heuristic-systematic model of information processing

(HSM) is a dual-process model of persuasion that posits that individuals can process

a message in one of two ways: systematically or heuristically. Systematic processing

occurs when a receiver is motivated to process all argument-relevant pieces of

information presented in a message. Heuristic processing relies on a few informa-

tional cues to come to a judgment on the message (Todorov, Chaiken, & Henderson,

2002). Consistent with the HSM, two hypotheses are proposed:

H1: Participants in the (a) fewer-arguments condition will be more likely to process a cell phone policy heuristically than participants in the more-arguments con- dition, and participants in the (b) more-arguments condition will be more likely to process a cell phone policy systematically than participants in the fewer-arguments condition.

H2: Participants in the more-arguments condition will hold more favorable attitudes toward a cell phone policy afterwards than participants in the fewer-arguments condition.

The HSM also posits that individuals who are motivated to think about a message

tend to process persuasive appeals systematically, whereas people who are not as

highly motivated engage in heuristic processing (Chaiken, 1980). Therefore, the

following hypothesis is proposed:

H3: Participants in the (a) high-involvement condition will engage in greater systematic processing than participants in the low-involvement condition, and participants in the (b) low-involvement condition will engage in greater heuristic processing than participants in the high-involvement condition.

Method

Undergraduates (N¼118; 47 men and 71 women) participated during class time at a large, mid-Atlantic university. Ages ranged from 18 to 31 (M¼19.97), and 89% were White.

The study used a 2 (number of arguments: high; low)�2 (involvement: high; low) experimental design. Participants responded to half of the items, viewed one of two

video clips (more or fewer arguments), then finished the survey. A pilot test for the

manipulation of more or fewer arguments with two groups of participants (n¼138) who did not participate in the main study revealed a significant difference between the

more-arguments (M¼2.21, SD¼ .89) and fewer-arguments (M¼1.70, SD¼ .74)

108 A. L. Lancaster & A. K. Goodboy

pilot groups, t(133)¼3.58, p < .001. A manipulation check of involvement was successful, with all but 17 participants, who were excluded from further analyses,

responding correctly to the single-item check.

Cell phone use was measured using four forced dichotomy (i.e., yes=no) items that were developed for this study. Participants used their cell phones to send or receive

text messages (n¼114), surf the Internet (n¼99), make calls (n¼9), and watch media (n¼13).

Campbell’s (2006) Attitudes toward Mobile Phones scale was used to assess cell

phone policy attitudes. Measurements were conducted for attitudes preexposure

(M¼4.08, SD¼1.35, a¼ .76) and postexposure (M¼3.79, SD¼1.35, a¼ .72). Griffin, Neuwirth, Giese, and Dunwoody’s (2002) systematic and heuristic risk

information processing items were modified to address cell phone policies. For systematic

processing, measurements were conducted for premessage exposure (M¼3.63, SD¼1.17, a¼ .59) and postmessage exposure (M¼4.50, SD¼1.29, a¼ .61). For heu- ristic processing, measurements were conducted for premessage exposure (M¼4.78, SD¼1.05, a¼ .53) and postmessage exposure (M¼4.08, SD¼1.26, a¼ .58).

Participants rated message quality using three 7-point semantic differential items

modified from the 9-point semantic differential items used in Petty, Cacioppo,

and Schumann’s (1983) study. Measurements were conducted for the

more-arguments (M¼3.00, SD¼1.41, a¼ .88) and fewer-arguments (M¼3.91, SD¼1.45, a¼ .86) conditions.

Four 7-point Likert-type items (1¼completely unlikely; 7¼completely likely) were developed for this study to measure propensity to comply with cell phone policies.

Measurements were conducted for the more-arguments (M¼2.96, SD¼1.43, a¼ .83) and the fewer-arguments (M¼2.37, SD¼1.12, a¼ .77) conditions.

Results

Before testing the hypotheses, all data were included in a manipulation check for the

number of arguments conditions. The manipulation was successful, t(113)¼2.098, p < .05. Participants in the more-arguments group (M¼1.92) reported that the speaker used more arguments than participants in the fewer-arguments group

(M¼1.63). For H1, results of t-tests revealed no significant differences in systematic,

t(99)¼ .114, p > .05, or heuristic, t(99)¼ .447, p > .05, processing based on the condition to which participants’ were assigned. Thus, H1a and H1b were not sup-

ported. For H2, results of a t-test revealed that a significant difference between the

groups existed, t(99)¼�2.941, p < .01, but in the direction opposite the prediction. Participants in the fewer-arguments condition (M¼4.21) had more favorable attitudes toward the cell phone policy than individuals in the more-arguments con-

dition (M¼3.45). Hypothesis 2 was not supported. For H3, results of two t-tests revealed no significant differences between participants in different conditions for

either systematic processing, t(99)¼1.813, p > .05, or heuristic processing, t(99)¼�1.868, p > .05. Thus, Hypotheses 3a and 3b were not supported.

Communication Research Reports 109

Given the unexpected findings contrary to the HSM, two post hoc tests were con-

ducted. The first revealed a positive relationship between participants’ attitudes

toward cell phone policies and their attitudes toward the cell phone policy in the

experiment (r¼ .72, p < .001). Participants who had unfavorable attitudes toward cell phone policies in general also held negative attitudes toward the cell phone policy in

this study. See Table 1 for all correlations.

The second post hoc test revealed a negative relationship between participants’

attitudes toward the cell phone policy and participants’ likelihood to use their cell

phones during class time (r¼�.28, p < .01). Thus, participants who had less-favorable attitudes toward a cell phone policy also were likely to use their cell

phones in classes that have such policies in place.

Discussion

The results of this study suggest that the number of arguments does not significantly

affect systematic or heuristic message processing and that a greater number of sup-

porting arguments in the persuasive message led to participants holding

less-favorable attitudes toward the message. Furthermore, the results suggest that

there is no significant difference in systematic or heuristic message processing based

on where the cell phone policy will be implemented. This finding should be inter-

preted with caution, considering the low reliability of the HSM measure.

Two explanations for these results are discussed. First, from a social judgment

(Sherif, Sherif, & Nebergall, 1965) and cognitive miser (Fiske & Taylor, 1991) per-

spective, individuals tend to maintain consistent attitudes and will not change their

opinion without sufficient reason to do so. Thus, because participants held negative

attitudes toward cell phone policies in general, it is logical that they would hold nega-

tive attitudes toward the cell phone policies in this study as well. Second, in this

study, nearly all participants reported using their cell phones to send text messages

during class time, which likely influenced their attitudes toward cell phone policies

that ban the use of these devices during class time. Thus, because participants were

already behaving in a manner that was counter to the advocated policy, it follows that

they also would be likely to hold negative attitudes toward the policy.

Table 1 Pearson Correlations Among Variables

Variables 1 2 3 4 5 6

1. Policy attitudes in general – – – – – –

2. Advocated policy attitudes .72�� – – – – –

3. Argument quality perceptions .48� .60�� – – – –

4. Cell phone use likelihood �.22� �.28� �.48�� – – – 5. Heuristic processing .02 �.03 �.06 �.03 – – 6. Systematic processing .16 .07 .28� �.20� �.28�� – �p < .05; ��p < .01 (two-tailed).

110 A. L. Lancaster & A. K. Goodboy

This study complements previous research on instructor technology policies (Finn

& Ledbetter, 2013; Ledbetter & Finn, 2013) by looking at students’ attitudinal

reactions to a type of technology policy in the wake of an instructor’s attempt to

implement the policy in a classroom. On a practical level, instructors should avoid

the use of threats because students appear to be unlikely to respond favorably to this

type of communication. According to psychological reactance theory (Brehm, 1966),

individuals may perform the very behavior that a persuasive message attempts to

induce them to stop. Thus, classroom technology policies may be more successful

when they include an encouraging aspect, as well as discouraging aspect (Finn &

Ledbetter, 2013; Ledbetter & Finn, 2013). For instructors, who are charged with

maintaining an orderly, productive classroom environment, the challenge remains

to find a classroom policy that discourages the nonacademic use of cell phones in

class, and one that students will follow.

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