The document describes a study that used the theory of planned behavior to predict flossing intentions and behaviors. College students were exposed to message interventions targeting different components of the TPB and completed surveys measuring TPB constructs and self-reported flossing. Consistent with the TPB, attitudes and subjective norms predicted intentions, and intentions predicted flossing behaviors. However, perceived behavioral control did not predict intentions or behaviors as expected. The message interventions also did not increase flossing.

1. ABSTRACT
PREDICTING FLOSSING BEHAVIORS WITH THE THEORY OF PLANNED
BEHAVIOR AND MESSAGE INTERVENTIONS
Gayle Susanne Wachowiak, M. A.
Department of Communication
Northern Illinois University, 2010
Mary Lynn Henningsen, Director
The theory of planned behavior was used to predict flossing intentions and
behavior. Participants who did not floss were exposed to a flossing advocacy that
activated the attitudinal, normative, or behavioral control component of the TPB.
In the first wave of the survey, college students were exposed to message
interventions and completed a survey that measured attitudes toward behavior,
subjective norms, perceived behavioral control, and behavioral intention. In the
second wave of data collection, participants reported how often they flossed in a
two-week period.
Consistent with the theory of planned behavior, attitudes and subjective
norms predicted behavioral intention. Behavioral intention was found to be a
statistically significant predictor of flossing behavior. Contrary to expectations,
perceived behavioral control did not predict behavioral intention or behavior. The
message interventions did not increase flossing behavior. The discussion focuses

2. on the relationship between message interventions and the theory of planned
behavior.

3. NORTHERN ILLINOIS UNIVERSITY
DE KALB, ILLINOIS
JULY 2010
PREDICTING FLOSSING BEHAVIORS WITH THE THEORY OF PLANNED
BEHAVIOR AND MESSAGE INTERVENTIONS
BY
GAYLE SUSANNE WACHOWIAK
2010 Gayle S. Wachowiak
A THESIS SUBMITTED TO THE GRADUATE SCHOOL
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE
MASTER OF ARTS
DEPARTMENT OF COMMUNICATION
Thesis Director:
Mary Lynn Henningsen

4. ACKNOWLEDGEMENTS
The author wishes to express sincere appreciation to Professors Mary Lynn
Henningsen, Kathryn Cady, and Joseph Scudder for their assistance in the
preparation of this document.

5. TABLE OF CONTENTS
Page
LIST OF TABLES ………………………………………………………….. v
LIST OF FIGURES …………………………………………………………. vi
LIST OF APPENDICES …………………………………………………….. vii
Chapter
1. REVIEW OF RELEVANT LITERATURE ………………………... 1
Health Behaviors ……………………………………………. 1
Dental Health Behaviors ……………………………………. 3
Theory of Planned Behavior ………………………………… 5
Message Interventions ………………………………………. 10
2. METHOD …………………………………………………………… 12
Participants ………………………………………………….. 12
Procedure …………………………………………………… 12
Message Intervention Design ………………………………... 13
Survey Measures …………………………………………….. 14
First-Wave Survey ………………………………….... 14
Second-Wave Survey ……………………………….... 17

6. iv
Chapter Page
3. RESULTS …………………………………………………………… 18
Manipulation Check Analysis ………………………………. 18
Hypotheses 1 and 2 …………………………………………. 20
Research Question 1 ………………………………………… 22
Research Question 2 ………………………………………… 24
4. DISCUSSION ………………………………………………………. 31
Practical Implications of Messages ………………………… 31
Theoretical Implications ……………………………………. 32
Limitations and Directions for Future Research ……………. 34
Conclusion ………………………………………………….. 35
REFERENCES ……………………………………………………………… 38
APPENDICES ……………………………………………………………… 43

7. LIST OF TABLES
Table Page
1. Correlations Between Theory Components and Message Types …... 24
2. P Values Between Message Types Across AB-BI Theory
Component …………………………………………………………. 26
3. P Values Between Message Types Across SN-BI Theory
Component …………………………………………………………. 27
4. P Values Between Message Types Across PBC-BI Theory
Component …………………………………………………………. 28
5. P Values Between Message Types Across BI-B Theory
Component …………………………………………………………. 29
6. P Values Between Message Types Across PBC-B Theory
Component …………………………………………………………. 30

8. LIST OF FIGURES
Figure Page
1. The Theory of Planned Behavior as Hypothesized ………………… 8
2. Initial Model Fit ……………………………………………………. 21
3. Revised Model Fit ………………………………………………… 22

9. LIST OF APPENDICES
Appendix Page
A. MESSAGE ONE (ATTITUDE-RELATED) ………………………. 44
B. MESSAGE TWO (SUBJECTIVE NORM-RELATED) …………… 46
C. MESSAGE THREE (PERCEIVED BEHAVIORAL
CONTROL-RELATED) …………………………………………… 48
D. MESSAGE FOUR (CONTROL MESSAGE) ……………………… 50

10. CHAPTER 1
REVIEW OF RELEVANT LITERATURE
Health Behaviors
Social scientists have researched various health behaviors to identify which
determinants of behavior generate desired outcomes (e.g., Ajzen & Timko, 1986;
Åstrøm, 2008; Bish, Sutton, & Golombok, 2000; Brenes, Strube, & Storandt, 1998;
Finlay, Trafimow, & Villarreal, 2002). Ajzen and Timko (1986) argued “the
readiness to perform health-related behavior is a function of such general
orientations as health concerns, willingness to seek medical help, perceived
vulnerability to illness, faith in doctors and medicine and feelings of control over the
disease” (p. 259). It is important, then, to investigate these predispositions in order
to formulate messages for specific behavioral outcomes and to help narrow any gap
between the intention to perform the behavior and the behavior itself. One such
framework, the theory of planned behavior (TPB; Ajzen, 1991), has been utilized in
the prediction of health behaviors such as smoking cessation (e.g., Lee, Ebesu
Hubbard, Kulp O’Riordan, & Kim, 2006), exercising (e.g., Brenes et al.,1998;
Finlay et al., 2002), condom use (e.g., Sanchez-Garcia & Batista-Foguet, 2008) and
self-health examinations (e.g., Luszczynska & Schwarzer, 2003; McCaul, Sangren,
O’Neill, & Hinsz, 1993; McClenahan, Shevlin, Adamson, Bennett, & O’Neill,

11. 2
2007). General results supported Ajzen’s (2009) recommendation stating message
interventions can be formatted to address personal attitudes about the behavior, what
others may think about the behavior, or the amount of personal control perceived
over the situation.
Social psychologists and communication scholars have attempted such
message interventions within the theory of planned behavior framework to promote
healthy behaviors (e.g., Beale & Manstead, 2006; Fishbein et al., 2001; Jemmott,
Jemmott, Fong, & McCaffree, 1999; Maddock, Silbanuz, & Reger-Nash, 2008).
Interventions such as educational messages (Beale & Manstead, 2006; Fishbein et
al., 2001), counseling sessions (e.g., Fishbein et al., 2001), videos and games (e.g.,
Jemmott et al., 1999), and television and mall advertisements (e.g., Maddock et al.,
2008) have been formulated to address either the behavioral beliefs of attitudes,
normative beliefs of subjective norms, or the control beliefs of perceived behavioral
control to influence positive behavioral changes.
In addition to general health behaviors, dental health behaviors have also
been studied by persuasion scholars. Specifically, dental hygiene behaviors (i.e.,
brushing and flossing) have been given special attention in social scientific literature
(e.g., Åstrøm, 2008; Hoogstraten, DeHaan, & Klecan, 1985; Lavin & Groarke,
2005; McCaul, O’Neill, & Glasgow, 1988; McCaul et al., 1993; Schüz, Sniehotta, &
Schwarzer, 2007; Schwarzer et al., 2007; Sniehotta, Soares, & Dombrowski, 2007;
Tedesco, Keffer, & Fleck-Kandath, 1991).

12. 3
Dental Health Behaviors
Without proper brushing and flossing behaviors, the American Dental
Association asserts bad breath, decay, gum disease, and tooth loss can occur (ADA,
2005). Daily brushing will remove plaque from the teeth, but is ineffective at
removing plaque that has accumulated interproximally. According to the Academy
of General Dentistry (AGD; 2008), flossing is the most effective way of removing
plaque from between the teeth. Although nearly all Americans believe taking care
of their mouth, teeth, and gums is very important, only 49% of people floss daily
and 10% do not floss at all (ADA, 2008). Flossing, however, is greatly beneficial to
dental health (AGD, 2008).
Dental professionals are at a loss when oral hygiene instruction fails to
develop the skills or maintenance behaviors needed to continue good oral hygiene
between visits (e.g., Ashkenazi, Cohen, & Levin, 2007; Little et al., 1997). Despite
receiving education and instruction regarding proper brushing and flossing
techniques, the National Institute of Dental and Craniofacial Research
(NIDCR; 2010) estimates over 80% of all Americans have some form of gum
disease. In order to gain patient flossing compliance, past studies on dental
behaviors included supplementing the education and instruction received from
dental professionals in the form of implementation intentions or action planning
interventions (e.g., Åstrøm, 2008; Lavin & Groarke, 2005; Schwarzer et al., 2007;
Sniehotta et al., 2007). During the experiments, participants were asked to form a

13. 4
concrete plan of where, when, and how to floss. Results indicated planning
interventions significantly affected flossing behaviors (Sniehotta et al., 2007),
mediated between intention and behavior (Schwarzer et al., 2007), and planning
interventions were an independent predictor of future flossing behavior (Åstrøm,
2008).
In contrast, Lavin and Groarke’s (2005) study found no significant
differences between those participants who made implementation intentions to floss
and those who did not, indicating implementation intentions were not an effective
way to increase flossing behavior. Therefore, Lavin and Groarke suggested future
studies on flossing behaviors should focus on message interventions of the
antecedents of intention (i.e., attitudes, subjective norms, perceived behavioral
control) to increase dental flossing intentions and behaviors.
In order to strategically form dental health behavioral messages to the target
audience, a well-replicated theory which identifies the attitudes about the behavior,
what others may think about the behavior, and the amount of personal control over
the situation should be employed. In this thesis, the TPB (Ajzen, 1991) will be
utilized to measure which constructs predict intention and desired flossing behavior.
Theoretically, intervention messages should influence one or more of the
antecedents of intention (i.e., attitude, subjective norm, or perceived behavior
control) which in turn should affect intention and behavior (Ajzen & Fishbein,

14. 5
2005). Therefore, the aim of this thesis is to replicate the TPB in regards to
previous flossing studies, and to identify which intervention messages will
strengthen the intention-behavior relationship to promote the desired dental health
behavior of daily flossing.
Theory of Planned Behavior
The theory of planned behavior (Ajzen, 1991) proposes three determinants
of intention to perform a behavior: attitude, subjective norm, and perceived
behavioral control. The attitude and subjective norm conceptualizations were
adopted from Fishbein and Ajzen’s (1975) theory of reasoned action. The third
conceptualization of perceived behavioral control was introduced by Ajzen (1991)
to form the theory of planned behavior.
The theory of reasoned action (TRA; Fishbein & Ajzen, 1975) specifies
attitudes toward performing a behavior and subjective norms predict the intention to
perform the desired behavior. In the TRA, attitudes are defined as evaluations of
the behavior which are strongly influenced by behavioral beliefs. Subjective norms
are defined as the “perceived social pressure to perform or not perform the
behavior” (Ajzen & Madden, 1986, p. 454) and are influenced strongly by
normative beliefs. According to the TRA, behavioral intention is considered to be
the only determinant of the desired behavior, and performing the behavior or not

15. 6
performing it is entirely volitional (i.e., under the person’s control; Ajzen &
Madden, 1986). Theoretically, attitude and subjective norm determine the strength
of the behavioral intention, which in turn determines whether or not the behavior is
performed (Ajzen & Madden, 1986).
The TRA (Fishbein & Ajzen, 1975) has enjoyed support in health behavioral
studies (e.g., Airhihenbuwa & Obregon, 2000; Bresnahan, Guan, Wang, & Mou,
2008; McCaul et al., 1993; Randolph et al., 2009) and in dental health behavioral
research (e.g., Hoogstraten et al., 1985; McCaul et al., 1988; McCaul et al., 1993;
Tedesco et al., 1991). Hoogstraten et al. (1985) found attitudes toward going to the
dentist were positively related to intentions, and those intentions were a predictor of
seeking dental treatment.
In another study of the TRA (Fishbein & Ajzen, 1975) and dental health,
McCaul et al. (1988) studied brushing and flossing behaviors of young adults.
Results indicated attitudes, subjective norms, and intentions were positively related
to brushing and flossing behaviors (McCaul et al., 1988).
The TRA (Fishbein & Ajzen, 1975) has proven its utility of predicting
volitional behaviors. After criticisms (e.g., Ajzen & Madden, 1986; McCaul et al.,
1993) that some behaviors are out of one’s control and therefore not as accurately
predictable, Ajzen (1991; Ajzen & Madden, 1986) suggested an extension of the
TRA: the TPB.

16. 7
The TPB (Ajzen, 1991), as presented in Figure 1, shares the two
motivational determinants of intention specified in the TRA: attitude and subjective
norm. To address the volitional control issue, a third determinant, perceived
behavioral control, was added to the TPB to help predict intention and behavior
when a person has limited control (Ajzen & Madden, 1986). Ajzen and Madden
(1986) defined perceived behavioral control as a “person’s belief as to how easy or
difficult the performance of the behavior is likely to be” (p. 457) by measuring the
available resources and opportunities at the time of intention to perform the actual
behavior. In other words, individuals are more apt to perform the behavior when
they feel the behavior is less difficult and have more opportunity and fewer
obstacles to overcome. Ajzen and Madden (1986) proposed perceived behavioral
control may influence behavior either indirectly through intention or directly as a
measure of actual control.
In an indirect test of the TPB (Ajzen, 1991), McCaul et al. (1988) tested
Bandura’s (1977) concept of self-efficacy (i.e., perceived ability, control) in the
TRA (Fishbein & Ajzen, 1975) framework. Results indicated self-efficacy was a
strong predictor of intention, and explained more of the variance in intention than
attitude and subjective norms. McCaul et al. concluded adding the concept of self-
efficacy to the TRA “would make a valuable contribution to the model” (p. 126).

17. 8
Figure 1. The Theory of Planned Behavior as Hypothesized
McCaul et al. (1993) also directly tested the TPB (Ajzen, 1991) to “consider
whether self-efficacy and perceived behavioral control should be added to the
theory of reasoned action” (p. 232) to address volitional control issues. In a study of
health and dental behaviors, attitudes, subjective norms, and perceived behavioral
control were found to predict intention (McCaul et al., 1993). In addition, McCaul
et al. reported both perceived behavioral control and intention predicted brushing
and flossing behaviors. Due to this finding, McCaul et al. supported the predictive
value of perceived behavioral control in the TPB.
Other researchers have explored the utility of the TPB (Ajzen, 1991) to
predict flossing behaviors (e.g., Åstrøm, 2008; Lavin & Groarke, 2005; Schwarzer
et al., 2007; Sniehotta et al., 2007). Lavin and Groarke (2005) found attitudes,
subjective norms, and perceived behavioral control predicted intention, which was
Attitude
Subjective
Norms
Perceived
Behavioral
Control
Behavioral
Intention
Behavior

18. 9
found to be the only predictor of flossing behavior (Lavin & Groarke, 2005).
Sniehotta et al. (2007) researched flossing behaviors and also found attitudes,
subjective norms, and perceived behavioral control significantly predicted intention
to floss, while both intention and perceived behavioral control significantly
predicted flossing behavior.
To review, the TPB (Ajzen, 1991) proposes three determinants of intention
to perform a behavior: attitude, subjective norm, and perceived behavioral control.
Theoretically, these determinants influence the strength of the behavioral intention
to perform the desired behavior. Behavior is proposed to be influenced by either
intention or perceived behavioral control directly (Ajzen, 1991; Ajzen & Madden,
1986). The theory has proved its utility in promoting healthy behaviors such as
smoking cessation (e.g., Lee et al., 2006), exercising (e.g., Brenes et al., 1998;
Finlay et al., 2002), condom use (e.g., Sanchez-Garcia & Batista-Foguet, 2008) and
self-health examinations (e.g., Luszczynska & Schwarzer, 2003; McCaul et al.,
1993; McClenahan et al., 2007). In addition, dental behaviors such as flossing
have also been predicted by the TPB (e.g., Åstrøm, 2008; Lavin & Groarke, 2005;
Schwarzer et al., 2007; Sniehotta et al., 2007). To promote healthy dental
behaviors, message interventions should target one or more of the antecedents of
intention (i.e., attitude, subjective norm, or perceived behavioral control) which in
turn should elicit flossing behaviors (Ajzen & Fishbein, 2005).

19. 10
Message Interventions
Ajzen and Fishbein (2005) argue that to effectively change intentions and
behaviors, messages should be directed at one or more of the antecedents of
intention (i.e., attitude, subjective norm, or perceived behavior control).
Specifically, “interventions target the behavioral, normative and control beliefs in an
effort to produce positive intentions” (Fishbein & Ajzen, 2005, p. 3). Unless these
underlying beliefs are affected, intention and behavior are not likely to change
(Ajzen & Manstead, 2007). For example, Hoogstraten et al. (1985) applied message
interventions within the TRA (Fishbein & Ajzen, 1975) framework to change
beliefs about seeking dental treatment. Messages were formatted to include positive
and negative consequences of seeking dental treatment. After exposure to
persuasive appeals (i.e., the messages) to sign up for dental treatment, results
indicated a strong relationship between behavioral beliefs, attitude, and intention
(Hoogstraten et al., 1985). The message targeting behavioral beliefs was the most
effective in eliciting the intention and behavior of seeking dental treatment
(Hoogstraten et al., 1985).
In another study involving message interventions, McCaul et al. (1993)
researched the relationship between dental behaviors and the TPB (Ajzen, 1991).
Experimental groups were exposed to a treatment program involving educational
messages, skills training, self monitoring, and goal-setting to encourage flossing
behavior. Although McCaul et al. did not form the message interventions from

20. 11
specific beliefs (i.e., behavioral, normative, control), results indicated exposure to
the treatment program did increase flossing behavior.
The TPB (Ajzen, 1991) has proven its utility in predicting flossing behaviors
(e.g., Åstrøm, 2008; Lavin & Groarke, 2005; McCaul et al., 1993; Schwarzer et al.,
2007; Sniehotta et al., 2007); therefore it would be logical to extend this body of
research by investigating the role of message interventions on the strength of
relationships in the model.
To assist in formulating messages to address flossing behaviors, the TPB
(Fishbein & Ajzen, 1975) will be utilized to identify the motivational factors behind
these behaviors. Therefore, the following hypotheses and research questions are
provided.
H1: Attitude, subjective norm, and perceived behavioral control
will predict intention to floss.
H2: Perceived behavioral control and behavioral intent will predict desired
flossing behavior.
RQ1: Do message interventions increase flossing behavior?
RQ2: Do message interventions (i.e., attitude, subjective norm, or
perceived behavioral control-related) strengthen the
associations among the variables in the TPB?

21. CHAPTER 2
METHOD
Participants
A total of 125 (65 male, 81 female) undergraduate students from a large
Midwestern university participated in this study. Students enrolled in a large
communication course were recruited if they did not floss their teeth. Research
credit was offered for those students who participated in the study. Those students
who actively floss their teeth were also offered research credit by recruiting a non-
flossing subject to participate on their behalf. Students who did not fit the criteria
were offered alternative research opportunities for credit.
The participants’ ages ranged from 19 to 44, M = 21.54, SD = 2.44. The
ethnicity of participants included Caucasian (71.9%), African-American (11.6%),
Hispanic (6.2%), Middle-Eastern (0.7%) and Other (4.1%).
Procedure
This study involved two waves of data collection. A total of 146 subjects
completed the first wave of the study through an online survey. Participants were
randomly assigned to one of three message interventions (e.g., attitude-related,
subjective norm-related, or perceived behavioral control-related) or a control

22. 13
message (see Appendices A-D). All messages reflected positive flossing behaviors.
Participants then answered survey questions regarding attitudes, subjective norms,
and perceived behavioral control and behavioral intentions. Manipulation check
items were included to verify the retention of a particular message. Participants
provided a valid e-mail address during the first wave of data collection. After two
weeks, 125 participants completed the second wave of the study. During the online
survey, participants were asked to answer survey questions regarding current
flossing behaviors since the first-wave survey (two weeks prior). Only data from
participants who provided a valid e-mail address for both waves of collection were
used in this study.
Message Intervention Design
Following Fishbein and Ajzen’s (2005) recommendations, three messages
were designed to target the behavior-specific beliefs of attitudes, subjective norms,
and perceived behavioral control. Formative research to identify accessible beliefs
was not conducted prior to formulation of the messages due to limitations of the
participant sample.
The attitude-related message emphasized the importance and benefits of
flossing and the consequences of poor oral hygiene (see Appendix A). The
subjective norm-related message stressed the value of flossing and how it is
important to your dentist, your family and friends, and you (see Appendix B). The

23. 14
perceived behavioral control message provided instruction and moral support for the
proper technique of how to floss one’s teeth (see Appendix C).
All three messages were of similar length, layout, and format. A fourth,
shortened, control message provided general information regarding intentions to
floss (see Appendix D).
Survey Measures
Background questions of age, sex, ethnicity, and measures of prior flossing
behavior were measured in the first wave of data collection. Also measured were
the participants’ attitudes, subjective norms, perceived behavioral control, and
intentions to floss. Actual flossing behaviors after the two-week period were
measured in the second wave of data collection.
First-Wave Survey
The first-wave contained measures of attitude, subjective norm, perceived
behavioral control, and intention as suggested by Ajzen (2002). Scales were
constructed using Likert-type and semantic differential items. All items were
constructed using a 5-point response scale. Items were scored so that higher values
indicated greater endorsement of the variable.
Components of the theory of planned behavior (e.g., attitude, subjective
norm, perceived behavioral control, and intention; Ajzen, 1991) were measured in

24. 15
the first wave after participants were exposed to the message interventions. Attitude
towards flossing behavior was measured by 14 items (i.e., both Likert-type and
semantic differential items). An example of a Likert item is “It would be good for
me to floss my teeth once a day for the next two weeks” (strongly disagree/strongly
agree). An example of a semantic differential item is “For me, to floss my teeth
once a day in the next two weeks is…” (harmful/extremely beneficial). The attitude
toward flossing scale was reliable, α = .93, M = 3.89, SD = 0.61.
Subjective norm was measured by four items using Likert-type items.
Examples include “Most people who are important to me floss once a day” and
“Most of my peers floss once a day.” The subjective norm scale was reliable, α =
.72, M = 2.75, SD = 0.71.
Perceived behavioral control was measured by eight items using Likert-type
items and semantic differential items. An example of a Likert item is “If I wanted
to, I could floss once a day in the forthcoming month” (strongly disagree/strongly
agree). An example of a semantic differential item is “How much control do you
believe you have over flossing once a day in the next two weeks?” (no control-
complete control). The perceived behavioral control scale was reliable, α = .83, M =
4.42, SD = 0.52.
Behavioral intention was measured by seven items using Likert-type items
and semantic differential items. An example of a Likert item is “I plan to floss my
teeth once a day in the next two weeks” (strongly disagree/strongly agree). An

25. 16
example of a semantic differential item is “My intention to floss my teeth once a
day in the next two weeks is…” (very weak/very strong). The behavioral intention
scale was reliable, α = .96, M = 3.10, SD = 1.07.
Participants were exposed to one of four message conditions: an attitude-
related message, subjective norm-related message, perceived behavioral control-
related message, and a control message (see Appendices A-D). In all conditions,
participants were asked to complete the first wave of the survey after they received
the appropriate message about flossing. Manipulation checks were used to assess
message recall. Manipulation checks for message interventions were measured by
two items each. An example of a manipulation check for the attitude-related
message is “80% of Americans have some form of gum disease.” The attitude-
related manipulation check was reliable, α = .79, M = 2.31, SD = 1.44. An example
of a manipulation check for the subjective norm-related message is “No one wants
the embarrassment of having bad breath in front of their friends.” The subjective
norm-related manipulation check was not very reliable, α = .46, M = 3.32, SD =
1.24. Because there were four sets of manipulation check items, the measure was
retained. An example of a manipulation check for the perceived behavioral control-
related message is “Keep floss (string, picks, etc.) in three to four different areas so
it is readily available for use.” The perceived behavioral control-related
manipulation check was reliable, α = .77, M = 2.59, SD = 1.44. An example of a
manipulation check for behavioral intentions-related message is “Please take a

26. 17
moment to think about what time of day you could floss.” The behavioral
intentions-related manipulation check was reliable, α = .72, M = 2.91, SD = 1.33.
Second-Wave Survey
The purpose of the second wave of the study was to measure flossing
behaviors from a two-week period after the first wave of data collection. Flossing
behavior was measured by six Likert-type items and two open-ended items. An
example of a Likert item is “I have flossed my teeth everyday in the last two weeks”
(strongly disagree/strongly agree). The Likert-type behavioral scale was reliable, α
= .97, M = 1.72, SD = 1.20. An example of an open-ended scale is “How many
days in the last two weeks have you flossed your teeth?” The open-ended
behavioral scale was reliable, α = .87, M = 3.77, SD = 3.89.

27. CHAPTER 3
RESULTS
Manipulation Check Analysis
The goal of the manipulation checks was to verify how much information
participants recalled from exposure to the intervention message. One-way
ANOVAs with the message type (e.g., attitude-related, subjective norm-related,
perceived behavioral control-related, or control message) as the independent
variable and the manipulation check measures as the dependent variable were
conducted to verify the recall validity of the information in the messages.
The manipulation check for the attitude-related message was successful,
F(3,139) = 56.73, p < .001; partial = .55. The mean for recollection of
information that was in the attitude-related message was much higher (M = 4.07, SD
= 1.03) for participants who read the attitude-related message than for any of the
other groups (subjective norm, M = 1.55, SD = 0.86; perceived behavioral control,
M = 1.71, SD = 0.96; control, M = 1.91, SD = 1.18).
The manipulation check for the subjective norm-related message was also
successful, F(3,139) = 29.55, p < .001; partial = .39. The mean for recollection of
information that was in the subjective norm-related message was much higher (M =
4.35, SD = 0.97) for participants who read the subjective norm-related message than

28. 19
for any of the other groups (attitude, M = 2.91, SD = 1.00; perceived behavioral
control, M = 2.91, SD = 0.70; control, M = 2.25, SD = 1.41).
The manipulation check for the perceived behavioral control-related
message was successful, F(3,139) = 45.61, p < .001; partial = .50. The mean for
recollection of information that was in the perceived behavioral control-related
message was much higher (M = 4.26, SD = 0.89) for participants who read the
perceived behavioral control-related message than for any of the other groups
perceived behavioral control-related message (attitude, M = 1.91, SD = 1.03;
subjective norm, M = 1.96, SD = 1.05; control, M = 2.22,
SD = 1.25).
The manipulation check for the control message was successful, F(3,136) =
28.65, p < .001; partial = .39. The control message mean and standard deviation
were reported as M = 4.69, SD = 0.62. The other message conditions related to
lower recall of the information in the control message (i.e., attitude, M = 2.11, SD =
0.99; subjective norm, M = 2.55, SD = 1.11; perceived behavioral control, M = 3.34,
SD = 1.17).
Generally speaking, the manipulation checks demonstrated that participants
recalled information correctly from the message they read.

29. 20
Hypotheses 1 and 2
H1 and H2 investigated the relationship between attitudes, subjective norms,
perceived behavioral control, and intentions. Specifically, H1 stated attitude,
subjective norm, and perceived behavioral control will predict intention to floss. H2
stated perceived behavioral control and behavioral intent will predict desired
flossing behavior. These predictions are described in Figure 1. Specifically,
attitude toward flossing should be a statistically significant, positive predictor of
intentions to floss. Subjective norms should be a statistically significant, positive
predictor of intentions to floss. Perceived behavioral control should be a
statistically significant, positive predictor of intentions to floss. Perceived
behavioral control and intentions to floss should predict flossing behavior over a
two-week period of time.
The hypothesized structural equation model was tested using the AMOS
16.0 computer program to perform maximum likelihood estimation. At the
recommendation of Byrne (2010), several indicators of the goodness of fit of the
model were assessed. Overall indicators of fit, baseline comparison indicators and
the RMSEA were evaluated. First, a fit model had to have a non-significant (i.e.,
p < .05) Chi-square. Second, the model needed a CFI (i.e., Comparative Fit Index)
of .95 or higher. Third, the model had to have a TLI (i.e., Tucker-Lewis Index) of
.95 or higher. Fourth, the model needed a RMSEA (i.e., Root Mean Square Error of

30. 21
Approximation) of .05 or lower. After initial fit, post hoc modification was used to
remove direct paths that were not statistically significant.
The hypothesized model did not fit the data, χ2 (5) = 62.43, p < .001, CFI = .70,
TLI = .39, RMSEA = .28. Figure 2 presents the path statistics for the initial model.
It was clear from evaluating the initial model that perceived behavioral control was
not a strong contributor to the model. The revised model shows that clearly. The
model presented in Figure 3 is an excellent fit to the data, χ2 (3) = 4.44, p = .22,
CFI = .99, TLI = .98, RMSEA = .05. All standardized path coefficients that
remained in the model were statistically significant p < .05. No other model that
was tested provided a better fit.
Figure 2. Initial Model Fit
Attitude
toward
Behavior
Subjective
Norms
Perceived
Behavioral
Control
Behavioral
Intention
Behavior
e1e1
e2.64
.27
-.07
.02
.49

31. 22
Figure 3. Revised Model Fit
Research Question 1
RQ1 asked if message interventions would increase flossing behavior. A
one-way ANOVA with the message type (i.e., attitude-related, subjective norm-
related, perceived behavioral control-related, or control message) as the independent
variable and reported behavior after two weeks as the dependent variable was
conducted to determine if exposure to a specific message increased flossing
behavior. Message type did not affect “days flossed,” F(3, 117) = 0.80, p > .05. In
the “days flossed” measure, groups were similar in their flossing behaviors: attitude-
related message, M = 3.08, SD = 3.51; subjective norm-related message, M = 4.11,
SD = 3.69; perceived behavioral control-related message, M = 4.33, SD = 4.53;
Attitude
toward
Behavior
Subjective
Norms
Behavioral
Intention
Behavior
e1e1
e2
.61
.28
.49

32. 23
control message, M = 3.17, SD = 3.74. In the behavioral measure, there were no
differences among groups, F(3, 120) = 0.08, p > .05. In the behavioral measure, the
groups were similar in their flossing behaviors; attitude-related message, M = 1.66,
SD = 1.09; subjective norm-related message, M = 1.72, SD = 1.21; perceived
behavioral control-related message, M = 1.73, SD = 1.22; control message, M =
1.85, SD = 1.47.
In addition, a t-test was also performed to address RQ1. All three messages
(e.g., attitude-related, subjective norm-related, perceived behavioral control-related)
were compared to the control message. In the “days flossed” behavioral measure,
there was no difference between message and control, t(119) = -0.69, p > .05. The
“days flossed” for participants with a message (M = 3.86, SD = 3.92) were similar to
the “days flossed” for those in the control message condition (M = 3.17, SD = 3.74).
In the behavioral measure, there also were no differences between groups t(122) =
0.15, p > .05. Participants who received a content message (M = 1.70, SD = 1.17)
were similar to those in the control message condition (M = 1.85, SD = 1.47). These
results indicate message interventions did not increase flossing behavior.
For RQ1, the ANOVA and follow-up t-tests indicated that the messages did
not directly increase flossing behaviors.

33. 24
Research Question 2
RQ2 asked if message interventions would strengthen the associations
among the variables in the TPB (Ajzen, 1991). Correlations were calculated to
assess the strength of association of the components of the TPB across each
message type (see Table 1). The correlations were then compared using a Fisher’s z
score to assess the probability that the correlations differ from each other.
Table 1. Correlations Between Theory Components and Message Types
Theory
component
Attitude-
related
Message 1
Subjective
norm-related
Message 2
Perceived
behavioral
control-
related
Message 3
Control
Message 4
AB-BI r = .63, N = 33 r = .64, N = 48 r = .81, N = 37 r = .51, N = 16
SN-BI r = .42, N = 37 r = .40, N = 47 r = .31, N = 38 r = .38, N = 17
PBC-BI r = .30, N = 35 r = .29, N = 49 r = .40, N = 38 r = .33, N = 17
BI- B r = .64, N = 31 r = .49, N = 39 r = .62, N = 33 r = .37, N = 17
PBC-B r = .14, N = 31 r = .27, N = 40 r = .27, N = 33 r = -.10, N = 15

34. 25
Exact p values for the attitude-behavioral intent component are reported in
Table 2. For the attitude-behavioral intent component, the differences between
correlations of the attitude-related message and the subjective norm-related
message, (z = -0.07, p > .05), perceived behavioral control-related message (z = -
1.54, p > .05), and the control message (z = 0.54, p > .05) were not statistically
significant. The difference between the correlation of the subjective norm-related
message and the perceived behavioral control-related message was statistically
significant (z = -1.63, p = .05). The difference between the subjective norm-related
message and the control message was not statistically significant (z = 0.62, p > .05).
Lastly, the difference between the perceived behavioral control-related message and
the control message was statistically significant (z = -1.73, p < .05). Therefore, the
attitude-behavioral intent component strength of relationship was stronger for the
perceived behavioral control message group than for the subjective norm-related or
control message groups.

35. 26
Table 2. P Values Between Message Types Across AB-BI Theory Component
Theory Component
AB-BI
Subjective norm-
related
Message 2
Perceived behavioral
control-related
Message 3
Control
Message 4
Attitude-related
Message 1 p = .47 p = .06 p = .29
Subjective norm-
related
Message 2
p = .05 p = .27
Perceived
behavioral
control-related
Message 3
p = .04
Exact p values for the subjective norm-behavioral intent component are
reported in Table 3. For the subjective norm-behavioral intent component, the
differences between correlations of the attitude-related message and the subjective
norm-related message (z = 0.11, p > .05), perceived behavioral control-related
message (z = 0.53, p > .05), and the control message (z = 0.15, p > .05) were not
statistically significant. The differences between the correlations of the subjective
norm-related message and the perceived behavioral control-related message (z =
0.46, p > .05) and the control message (z = 0.08, p > .05) were not statistically
significant. Lastly, the difference between the perceived behavioral control-related
message and the control message was not statistically significant (z = -0.25, p > .05).

36. 27
Table 3. P Values Between Message Types Across SN-BI Theory Component
Theory Component
SN-BI
Subjective
norm-related
Message 2
Perceived
behavioral
control-related
Message 3
Control
Message 4
Attitude-related
Message 1 p = .46 p = .30 p = .44
Subjective norm-related
Message 2 p = .32 p = .47
Perceived behavioral
control-related
Message 3
p = .40
Exact p values for the perceived behavioral control-behavioral intent
component are reported in Table 4. For the perceived behavioral control-behavioral
intent component, the differences between correlations of the attitude-related
message and the subjective norm-related message (z = 0.05, p > .05), perceived
behavioral control-related message (z = -0.47, p > .05), and the control message (z =
-0.10, p > .05) were not statistically significant. The differences between the
correlations of the subjective norm-related message and the perceived behavioral
control-related message (z = -0.56, p > .05) and the control message (z = 0.14, p >
.05) were not statistically significant. Lastly, the difference between the perceived
behavioral control-related message and the control message was not statistically
significant (z = 0.26, p > .05).

37. 28
Table 4 P Values Between Message Types Across PBC-BI Theory Component
Theory
Component
PBC-BI
Subjective norm-
related Message 2
Perceived
behavioral
control-related
Message 3
Control
Message 4
Attitude-related
Message 1 p = .48 p = .32 p = .46
Subjective norm-
related
Message 2
p = .29 p = .44
Perceived
behavioral
control-related
Message 3
p = .40
Exact p values for the behavioral intent-behavior component are reported in
Table 5. For the behavioral intent-behavior component, the differences between
correlations of the attitude-related message and the subjective norm-related message
(z = 0.88, p > .05), perceived behavioral control-related message (z = 0.13, p > .05),
and the control message (z = 1.13, p > .05) were not statistically significant. The
differences between the correlations of the subjective norm-related message and the
perceived behavioral control-related message (z = -0.77, p > .05) and the control
message (z = 0.47, p > .05) were not statistically significant. Lastly, the difference
between the perceived behavioral control-related message and the control message
was not statistically significant (z = 0.99, p > .05).

38. 29
Table 5. P Values Between Message Types Across BI-B Theory Component
Theory Component
BI-B
Subjective norm-
related Message 2
Perceived
behavioral
control-related
Message 3
Control
Message 4
Attitude-related
Message 1 p = .19 p = .45 p = .13
Subjective norm-related
Message 2 p = .22 p = .32
Perceived behavioral
control-related
Message 3
p = .16
Exact p values for the behavioral intent-behavior component are reported in
Table 6. For the perceived behavioral control-behavior component, the differences
between correlations of the attitude-related message and the subjective norm-related
message (z = -0.54, p > .05), perceived behavioral control-related message (z = -
0.52, p > .05), and the control message (z = 0.70, p > .05) were not statistically
significant. The differences between the correlations of the subjective norm-related
message and the perceived behavioral control-related message (z = 0.50, p > .05)
and the control message (z = 1.14, p > .05) were not statistically significant. Lastly,
the difference between the perceived behavioral control-related message and the
control message was not statistically significant (z = 1.10, p > .05).

39. 30
Table 6. P Values Between Message Types Across PBC-B Theory Component
Theory Component
PBC-B
Subjective norm-
related
Message 2
Perceived
behavioral
control-related
Message 3
Control
Message 4
Attitude-related
Message 1 p = .29 p = .30 p = .24
Subjective norm-
related
Message 2
p = .50 p = .13
Perceived behavioral
control-related
Message 3
p = .14
Overall, the results indicated that the perceived behavioral control message
group showed the strongest relationship of any message group in the attitude-
behavioral intent component of the TPB (Ajzen, 1991). For RQ2, the messages did
not strengthen the associations in the model.

40. CHAPTER 4
DISCUSSION
Practical Implications of Messages
RQ1 asked if message interventions would increase flossing behavior.
Ideally, exposure to a belief message should have increased flossing behavior. The
results indicated that exposure to messages did not influence flossing behavior. To
check for a general effect of messages on behavior, message condition means were
grouped and compared to the control message mean. No statistically significant
differences were found. Although the manipulation checks indicated that the
participants retained what they read in each respective message, the messages
themselves were not effective at changing flossing behavior. It is possible that the
underlying behavioral, normative, and control beliefs were not sufficiently activated
in the messages to encourage flossing behavior.
RQ2 asked if message interventions (i.e., attitude-related, subjective norm-
related, or perceived behavioral control-related) would strengthen the associations
among the variables in the TPB (Ajzen, 1991). Surprisingly, only two statistically
significant differences in effectiveness were found across message conditions. In
the attitude-behavioral component of the model, the perceived behavioral control-

41. 32
related message group had a stronger association than the subjective norm-related
message group and the control group. Even though manipulation checks were
performed on the messages (i.e., attitude-related, subjective norm-related, perceived
behavioral control-related, and control), only the perceived behavioral control-
related message was found to strengthen the attitude-intention association.
These results imply that additional research is needed to explore why the
perceived behavioral control-related message affected attitude toward intention
while the other messages (i.e., attitude- and subjective norm-related) did not.
Theoretical Implications
Results indicated partial support for H1. Following the TPB model (Ajzen,
1991), H1 stated attitude, subjective norm, and perceived behavioral control would
predict intention to floss. After SEM analysis, the revised fit of the model revealed
attitude toward flossing and subjective norms predicted intention to floss, and
behavioral intent was found to predict flossing behaviors. Perceived behavioral
control was not found to significantly predict intention to floss.
Results also indicated partial support for H2. Following the TPB model
(Ajzen, 1991), H2 stated perceived behavioral control and behavioral intent would
predict desired flossing behavior. The results indicated intention, but not perceived
behavioral control, predicted flossing behavior.

42. 33
This study showed an ability to replicate previous research (e.g.,
Hoogstraten et al., 1985; McCaul et al., 1988; McCaul et al., 1993; Tedesco et al.,
1991) on flossing behaviors with respect to the TRA (Fishbein & Ajzen, 1975). The
current study supported the TRA in that attitudes and subjective norms predicted
behavioral intentions, and behavioral intentions predicted flossing behavior.
Interestingly, the present study does not support previous research that has
found perceived behavioral control to be a statistically significant predictor of
intention and behavior (e.g., McCaul et al., 1988; McCaul et al., 1993). In McCaul
et al.’s (1988) study of the TRA (Fishbein & Ajzen, 1975), self-efficacy was found
to predict intention to floss. A possible reason for this finding is participants in the
study were given assessments of their dental health and hygiene skills by dental
professionals and experimenters before measurement of dental behaviors. This
personalized assessment may have influenced participants’ feelings of self-efficacy
in the form of encouragement from the assessor more so than reading similar
information in written form.
In another study of the TPB (Ajzen, 1991), McCaul et al. (1993) found
perceived behavioral control to be a stronger predictor of flossing intention than
self-efficacy. In the McCaul et al. study, participants were invited to attend a dental
health treatment program that involved teaching self-care skills to help prevent gum
disease. Again, the exposure to a treatment program may have

43. 34
influenced perceived behavioral control over flossing intentions more than exposure
to written message of the same nature.
The current study has replicated previous research of the TRA (Fishbein &
Ajzen, 1975), but not the TPB (Ajzen, 1991). Perceived behavioral control was not
found to predict intent or behavior to floss as it was in previous research (e.g.,
McCaul et al., 1988; McCaul et al., 1993). Perhaps the method of the intervention
(e.g., face-to-face) in previous research was partially responsible for perceived
behavioral control to appear as a predictor in the revised TPB model.
Limitations and Directions for Future Research
Small sample sizes per treatment condition were a limitation in this study.
Although exposure to a specific message (i.e., perceived behavioral control-related
messages in the attitude-behavioral intent component) was found to strengthen the
association between TPB variables (Ajzen, 1991), a better test of message effects
could have been performed had SEM analysis on each message group been possible.
Also, it is possible the message interventions did not affect flossing
behaviors as expected due to the message format. In this study, the messages were
constructed according to theoretical components of the TPB (Ajzen, 1991) and
manipulation checks found the messages to be retained by participants. It is
possible that the underlying behavioral, normative, and control beliefs may not have
been affected enough to increase flossing behaviors. In addition, some college

44. 35
students may not have experienced serious dental problems to be motivated by the
messages. Future research should include the use of focus groups to determine if
the beliefs of the participants will be affected by each component-specific message
intervention.
Another formatting issue may be due to the convenience sample. Because of
the college sample, written messages and a survey were used as a matter of
expediency. Future research should look to formulate messages for a college
sample in a way that is more persuasive in wording and format (e.g., face-to-face) to
elicit desired flossing behaviors in addition to the written word.
Sample age may be the reason why the perceived behavioral control
component did not stay in the revised TPB model. It is possible that college-aged
subjects may have the physical agility to maneuver floss around their teeth, as
opposed to older subjects who may encounter difficulties while attempting to do so.
Therefore, college-aged subjects may have less control and efficacy issues than
older subjects.
Conclusion
This study investigated the utility of the TPB (Ajzen, 1991) and message
interventions as predictors of flossing intention and behavior. In this thesis, one
goal was to explore the naturally occurring relationships between attitudes,
subjective norms, perceived behavioral control, intention and flossing behaviors.

45. 36
Contrary to expectations, this study did not replicate the TPB, as perceived
behavioral control was not found to predict intention or flossing behaviors.
Consistent with the TRA (Fishbein & Ajzen, 1975), attitudes and subjective norms
were found to predict intention, and intention was found to predict flossing
behaviors. These results are consistent with TRA research of flossing behaviors
(e.g., Hoogstraten et al., 1985; McCaul et al., 1988; McCaul et al., 1993; Tedesco et
al., 1991).
Another goal of this study was to examine the effects of message
interventions on flossing behavior and the TPB (Ajzen, 1991) model components.
Although message interventions have been found to influence behavior in other
studies of the TPB (e.g., Beale & Manstead, 2006; Fishbein et al., 2001; Maddock et
al., 2008) and the TRA (e.g., Hoogstraten et al., 1985), the current study was unable
to replicate previous results. It is possible that the underlying behavioral,
normative, and control beliefs necessary to affect attitude, subjective norm, and
perceived behavioral control were not sufficiently activated in the message
interventions. Future research should include the use of focus groups to determine
which belief messages would be the most effective at changing behavior.
Also, message interventions were not found to strengthen the associations
between TPB (Ajzen, 1991) components has expected. Even though manipulation
checks were performed on the messages (i.e., attitude-related, subjective norm-
related, perceived behavioral control-related, and control), only the perceived

46. 37
behavioral control-related message was found to strengthen the attitude-intention
association. These results imply that additional research is needed to explore the
manner in which message interventions would significantly affect the strength of
associations between TPB components.

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doi:10.1007/BF00845111

52. APPENDICES

53. APPENDIX A
MESSAGE ONE (ATTITUDE-RELATED)

54. 45
Message 1 (Attitude-related)
Unfortunately only 49% of Americans floss every day (AGD, 2008): a
statistic which has caused 80% of Americans to have gum disease (NIDCR, 2010).
The leading cause of tooth decay and gum disease is the failure to remove plaque;
therefore without proper brushing and flossing, bad breath, decay, gum disease, and
tooth loss can occur. According the Academy of General Dentistry (2008), flossing
is the most effective way of removing plaque from between the teeth. Brushing
alone is not enough to stop tooth decay and tooth loss. Flossing removes plaque
from in-between the teeth where a toothbrush cannot reach, and stimulates healthy
gum tissue around each tooth to stop potential bone loss. Good oral hygiene habits
including brushing and flossing your teeth daily and visiting the dentist every six to
twelve months are proven preventative measures that can save time, money, and
your teeth.

55. APPENDIX B
MESSAGE TWO (SUBJECTIVE NORM-RELATED)

56. 47
Message 2 (Subjective norm-related)
Dentists and dental hygienists know the value of flossing. At your cleaning
appointment, the dentist or hygienist will instruct you on how to floss your teeth,
and then floss your teeth for you. It is their goal to have you floss at home between
visits to help eliminate plaque which in turn eliminates the need for future dental
work. Dentists would rather see you every six months with your teeth and gums in
a healthy condition than with plaque and decay throughout your mouth.
Also, oral hygiene behaviors such as flossing once a day will lessen the bacteria
count in your mouth that leads to bad breath. Brushing alone does not reach the
plaque between the teeth that causes these bacteria. No one wants the
embarrassment of having bad breath in front of their friends.
Gum disease can not only lead to bad breath, but to infection as well.
Periodontal disease is an infection in the gums and if left untreated can spread to the
rest of the body over time. No one in your family wants you to become sick,
especially from not flossing.

57. APPENDIX C
MESSAGE THREE (PERCEIVED BEHAVIORAL CONTROL-RELATED)

58. 49
Message 3 (Perceived behavioral control-related)
Dental awareness has come a long way in the last thirty years. It is not
commonplace in the 21st century to think there is nothing you can do to stop your
teeth from becoming decayed and diseased. Good oral hygiene behaviors such as
brushing, flossing, limiting your sugar intake, and visiting the dentist every six
months all directly contribute to a healthy smile.
The ability to perform these behaviors lies with you. Set aside the same time
every day to brush and floss your teeth. Keep floss (string, picks, etc.) in three to
four different areas so it is readily available for use. If using string floss, wrap an
18- inch piece around your middle fingers and use your pointer fingers to guide the
floss gently between each tooth to remove debris. As you move from tooth to tooth,
unwind clean sections of floss as needed. It may take a few tries to get the hang of
it, and soon it will take only a few minutes to floss all your teeth.
Your dental health is in your control. Setting a time to floss daily and
mastering the proper techniques are two ways that will help you start flossing your
teeth on a daily basis.

59. APPENDIX D
MESSAGE FOUR (CONTROL MESSAGE)

60. 51
Message 4 (Control Message)
Please take a moment to think about what time of day you could floss. What
would you need to floss? How can you make it easier on yourself to floss? Please
think for a moment about the benefits of flossing.