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10.1177/1090198105277393 Tolma ARTICLE et al. / Theory of Planned Behavior Health Education & BehaviorApril (April 2006)

Examining the Theory of Planned Behavior and the Construct of Self-Efficacy to Predict Mammography Intention Eleni L. Tolma, MPH, PhD Belinda M. Reininger, DrPH Alexandra Evans, MPH, PhD John Ureda, DrPH

This article examines the applicability of the Theory of Planned Behavior (TPB) with the addition of the selfefficacy construct in the understanding of the motivation to obtain an initial screening mammogram among Cypriot women. The study sample consisted of 293 women aged 40 to 65 years, asymptomatic of breast cancer, and with no previous mammography experience. The study took place at the General Hospital of Nicosia in Cyprus. The results of the study provided support of the TPB with the addition of self-efficacy in an international setting. Self-efficacy was the strongest predictor of intention. Other predictors of intention included educational level, time of last clinical breast examination, and age. The study also provided some empirical support of the distinction between self-efficacy and perceived behavioral control. Researchers may want to include self-efficacy in addition to the TPB and other demographic characteristics in future applications to more fully explain behavioral outcomes. Keywords: mammography; theory of planned behavior; self-efficacy

Despite the efforts to curtail the problem of breast cancer, it continues to be an important public health problem not only in the United States (National Cancer Institute, 2001) but also in Europe (European Union, 1997). In Cyprus, the 5-year prevalence of breast cancer was the highest of all forms of cancer among women in Cyprus during the period 1995-2000 (International Research for Research on Cancer, 2001). The Cypriot government follows the European Union’s recommendations for mammography, which includes one screening mammogram every 2 to 3 years for women aged 50 to 69 who are Eleni L. Tolma, Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City. Belinda M. Reininger, School of Public Health, University of Texas–Houston, Brownsville. Alexandra Evans and John Ureda, Department of Health Promotion, Education and Behavior, University of South Carolina, Columbia. Address reprint requests to Eleni L. Tolma, Department of Health Promotion Sciences, College of Public Health, 801 NE 13th Street, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73190; phone: (405) 271-2017, ext. 46757; fax: (405) 271-2099; e-mail: [email protected]. The authors would like to thank all the participants for their cooperation in this study, as well as the Radiology Department of the General Hospital of Nicosia, the Movement Against Breast Cancer, and the Ministry of Health in Cyprus for their assistance. We also thank Andrew Gordon, Robert McKeown, and Ruth Saunders for their contribution as scientific advisers. Health Education & Behavior, Vol. 33 (2): 233-251 (April 2006) DOI: 10.1177/1090198105277393 © 2006 by SOPHE

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asymptomatic of breast cancer (Lynge, Patnick, Tornberg, Faivre, & Schroder, 2000). Despite the efforts by governmental and nongovernmental organizations to promote screening mammography, the percentage of eligible women who get screened was less than 20% in 1998 (Department of Statistics and Research, 1999a, 1999b; Tolma, Reininger, Ureda, & Evans, 2003). Several studies have examined factors influencing mammography screening behaviors and have consistently concluded that women who are better educated and more affluent are more likely to receive a mammogram (Blackman, Bennet, & Miller, 1999; Swan, Breen, Coates, Rimer, & Lee, 2003). Moreover, women aged 50 to 64 years old are more likely to have had a recent mammogram (within the last 2 years) than women who are younger or older (Blackman et al., 1999; Swan et al., 2003). In addition to the above factors, women who are least likely to have had a recent mammogram are those with no usual source of care; with no health insurance; recent immigrants; and those who belong to an ethnic minority group, such as Hispanic, Asian, or American Indian/Alaska Native (Swan et al., 2003). The motivating factors for women in Cyprus have not previously been reported in the literature and may or may not reflect above-mentioned findings. Interest in increasing mammogram screening behavior has intensified in the last 30 years. Based on a recent review, less than one third (28%) of the intervention studies reviewed and that took place between 1960 and 1997 based their research design or interpretation of the results on a sound theoretical framework (Meissner et al., 1998). Another review with diverse populations found that only 68% of the intervention studies reviewed between 1984 and 1997 used a theoretical framework (Legler et al., 2002). However, because of its stringent criteria (i.e., controlled, experimental, and quasi-experimental interventions), this review included only 38 studies. The most predominant theory used was the Health Belief Model, which has been extensively applied in the United States (Champion & Husler, 1995; Duan, Fox, Pitkin, & Carson, 2000). Other theories that were used were the Social Cognitive Theory (Slater et al., 1998, the Transtheoretical Model (Crane et al., 1998), the Triandis theory (Lauver, Nabholz, Scott, and Youngran, 1997), and the Precaution Adoption Process Model (Clemow et al., 2000). Several studies integrated constructs from different theories (Allen, Sorensen, Stoddard, Golditz, & Peterson, 1998; Skinner, Arfken, & Waterman, 2000). In this study, the Theory of Planned Behavior (TPB), an expectancy value theory, was used as the conceptual model for the development of the assessment survey. The TPB is an extension of the Theory of Reasoned Action (Ajzen, 1988; Fishbein & Ajzen, 1975). According to the Theory of Reasoned Action, intention is the immediate antecedent of the behavior, and it is assumed to capture the motivation to behave in a particular way (Ajzen, 1988). In turn, intention is determined by two factors; attitude toward the behavior and subjective norms. Attitudes are formed by salient beliefs about the expected outcomes derived from the performance of a behavior and the subsequent evaluation of realization of those outcomes. Subjective norms consist of the person’s perception of social pressure to perform or not to perform the behavior under consideration, based on significant individuals’, known as referents, approval or disapproval of performing the behavior (Fishbein & Ajzen, 1975). The Theory of Reasoned Action is built on the assumption that most of the behaviors are under volitional control, an assumption that is not true for all behaviors (Ajzen, 1988). To overcome the weakness of the model, Ajzen (1988) introduced to the model the construct of perceived behavioral control. Personal behavioral control beliefs can be divided into two categories: (a) internal factors such as acquisition of information, skills, and abil-

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ities, as well as emotions and compulsions, and (b) external factors, that is, situational and environmental factors external to the individual. The more resources and opportunities individuals think they possess and the fewer obstacles or impediments they anticipate, the greater their perceived behavioral control is over their behavior. Perceived behavioral control can predict behavior directly, only when it reflects actual control with some degree of accuracy (Ajzen, 1988). The TPB is a useful model in predicting intention and behavior. According to a review by Godin and Kok (1996) regarding the application of the TPB to health behaviors, the TPB components explained on average 41% of the variance in intention and 31% of the variance in behavior in prospective studies. This theoretical framework is appropriate to study breast cancer screening for two reasons. First, the TPB allows for an understanding of the cultural perspectives affecting the behavior (Poss, 2001) because it provides a methodology for the elicitation of the salient cultural beliefs of the population under investigation. Second, mammography screening behavior is not fully under volitional control (Godin et al., 2001) because it is influenced by environmental factors; thus, perceived behavioral control becomes a valuable theoretical construct. In mammography screening, a few studies have used the Theory of Reasoned Action as the basic theoretical framework. In fact, these studies used an expanded Theory of Reasoned Action, which included other variables in addition to the original ones, such as habit (Michels, Taplin, Carter, & Kugler, 1995), facilitating conditions and constraints (Montano & Taplin, 1991; Montano, Thompson, Taylor, & Mahloch, 1997), and affect (Montano et al., 1997). The results from these studies were mixed. In one study (Montano et al., 1997), subjective norms were the most significant predictor of intention, followed by facilitating conditions, attitudes, and affect. In another study (Montano & Taplin, 1991), facilitating conditions were the strongest predictors of participation, followed by attitude, affect, and subjective norms. In the final study (Michels et al., 1995), habit and perceived risk of breast cancer were the strongest predictors of intention, followed by subjective norms and attitude. Other studies have used the TPB as the theoretical framework. In one study (Godin et al., 2001), subjective norms and perceived behavioral control were the most important contributors to the intention to get a mammogram, followed by attitude. In another study (Rutter, 2000), attitude was the leading predictor of intention to get an initial mammogram, followed by perceived behavioral control and subjective norms. In a third study (Drossaert, Boer, & Seydel, 2003), attitude was the main predictor of intention to get an initial mammogram, followed by perceived control and expected difficulties. Some earlier studies that applied a variation of the TPB model found subjective norms, attitude, and self-efficacy to be the main predictors of mammography attendance (Allen et al., 1998; Lechner, de Vries, & Offermans, 1997; Vaile, Calnan, Rutter, & Wall, 1993). In this study, the construct of self-efficacy is added to the TPB framework. Self-efficacy refers to the confidence one feels about performing a particular behavior, including confidence in overcoming the barriers to achieve that behavior (Bandura, 1986). Self-efficacy has been referred to in the literature as a similar or identical construct to perceived behavioral control (Ajzen, 2002; Lechner et al., 1997; Terry & O’Leary, 1995), which has created a dialogue regarding the distinction between these two constructs. A number of studies successfully paired self-efficacy with the TPB in various behavioral settings, such as the prediction of alcohol use (Armitage, Conner, Loach, & Willets, 1999), food choices (Povey, Conner, Sparks, James, & Sheperd, 2000), and academic achievement (Manstead & van Eekelen, 1998). In fact, in the above studies, self-efficacy

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had the greatest impact on intention (controlling for the other constructs of the TPB). Moreover, the strong link between self-efficacy and intention has been empirically established (Parcel et al., 1995; Terry & O’Leary, 1995; White, Terry, & Hogg, 1994). The present study was conducted to (a) test the TPB conceptual framework in the understanding of screening mammography among Cypriot women; (b) examine whether the addition of self-efficacy enhances the predictability of the model; (c) examine the relationship between self-efficacy and perceived behavioral control; and (d) examine whether additional constructs external to the model, such as demographics, can further improve the prediction of mammography intention.

METHOD The Setting, Participants, and Data Collection The study took place at the General Hospital of Nicosia in Cyprus. The study was cross-sectional using a convenience sample of 293 women aged 40 to 65 years asymptomatic of breast cancer, with no previous mammography experience, and who visited the outpatient clinics of the General Hospital of Nicosia during the fall of 1999. Women at the lower end of the age range, aged 40-49 years, were included in the sample because at the time of the study, the Cyprus Ministry of Health (1997) was recommending a baseline mammography at the age of 40. Women at the upper range of the age group (65 years of age) were included in the sample for two reasons. Firstly, we wanted to ensure that the sample consisted of women who were fairly similar in age so that we would have more explanatory power regarding the beliefs of women in this age-group. For instance, past research has shown that women older than 65 have different intentions and beliefs regarding screening mammography than women aged 50 to 64 (American Geriatrics Society Clinical Practice Committee, 2000). Second, intention to obtain the initial mammogram was the study focus; however, because of lack of evidence indicating when women initiate mammography, we felt it was important to have a generous age range for initial mammography screening. During data collection, all women who appeared to be above 40 years old were approached about their participation in the study. A written authorization to conduct this study was obtained by the director of the Public Health Services of the Ministry of Health in Cyprus, and an oral informed consent was obtained from each participant prior to the interview. Data were collected through the administration of a structured face-to-face interview. Each interview lasted about 30 minutes. To ensure that a sufficient number of women who intended to get a mammogram would be interviewed, 2 days of the week, participants were recruited at the radiology department where women were waiting to get their first mammogram. During the rest of the week, the participants were recruited from other outpatient clinics while the women were waiting to use the health services. The data collection lasted for approximately 3 months. Questionnaire The survey was developed according to the methodology suggested by the founders of the theory (Fishbein & Ajzen, 1975). This entailed several steps, with the most important being the elicitation of individual salient beliefs through the administration of the elicita-


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tion questionnaire among women (n = 40) of the priority population. Three kinds of information were gathered during the elicitation phase: (a) positive and negative outcomes associated with screening mammography, (b) salient referents who might influence the woman’s decision to get a mammogram, and (c) environmental conditions that might influence a woman’s decision to get a mammogram. A more detailed description of the above methodology can be found elsewhere (Tolma et al., 2003). The final draft of the instrument was tested for internal consistency and for construct validity. The variables included in the survey are described below. The dependent variable was measured as a single 5-point Likert-type item (response categories were strongly agree to strongly disagree) assessing degree of agreement with the following statement: “I intend to get my first screening mammogram at the General Hospital of Nicosia within the next 6 months.” The independent cognitive constructs included attitude, subjective norms, perceived behavioral control, and self-efficacy. Each construct was measured by an individual scale with the Likert-type scaling method. Construct validity was assessed for each construct by using the common factor analysis with oblique (promax) rotation. The reliability or internal consistency of each scale was assessed using Cronbach’s alpha values as the reliability estimates and ranged from .67 to .86. A Cronbach’s alpha of .7 is generally considered acceptable (Nunnally, 1978). The four independent cognitive constructs were measured by a total of 34 individual items (Table 1). The psychometric properties of the scales have been described in depth elsewhere (Tolma et al. 2003). The survey also contained external to the TPB model variables. These included demographic questions (age, family history of breast cancer, marital status, education, and location of residency), practice of other preventive health behaviors (performance of regular breast self-examination or BSE, existence of a regular physician, time of last clinical breast examination or CBE), and knowledge of breast cancer screening. Knowledge was measured with four items. Each knowledge item was coded 0 when the false answer was given and 1 when a correct answer was given. The number of correct items was totaled to obtain the knowledge score. A Social Desirability Scale (Strahan & Gerbasi, 1972) (Cronbach’s a = .99) was also included at the end of the instrument so that the response bias could be measured. The 10-item scale (M-C1[10]) contained an equal number of positively and negatively keyed items (five items were keyed true, and five were keyed false). Each item was coded 0 when the correct answer was given. Some examples of the items include “I’m always willing to admit it when I make a mistake” and “I like to gossip at times.” Statistical Methods Data analysis included descriptive statistics, followed by bivariate and multivariate analyses. Pearson correlation coefficients examined the association between intention and the continuous independent variables (cognitive variables and some external characteristics). One-way ANOVA examined the relationship between cognitive variables and categorical external variables. The presence of interactions between external characteristics and cognitive variables, as well as among the cognitive variables, was also examined. Common factor analysis with oblique (promax) rotation was conducted to assess whether self-efficacy and perceived behavioral control–related responses reflected one or two underlying factors. A series of multiple regression analyses (general linear modeling) were conducted to examine the predictability of the cognitive variables and external characteristics. First, all

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Attitude Outcomes related to prevention and early detection weighted by evaluation of outcome Other positive outcomes weighted by evaluation of outcome Subjective norms Normative belief weighted by motivation to comply Family members and other close influential sources Distant influential sources 52.9

59.25

28.83

(–7)-24 7.27

291 0.03 (–20)-20 7.69

291 2.02 (–11)-16 5.37

28.08

1.74

Variance

292 13.10

1.31

SD

(–8)-16 5.29

1-5

Range

292 10.05

293 3.45

Intention (Only one item)

(Only one item) 2933.451-51.311.74NAI intend to get my first screening Strongly agr ee-

N

Ma

.70

.75

.85

.86

NA

Cronbach’s a

Descriptive Statistics and Reliability Estimates for Cognitive Subscales

Cognitive Subscale

Table 1.

The physician who provides my health care thinks I should get my FSM at the GHN within the next 6 months. People whom I know who had or have breast cancer think I should get my FSM at the GHN within the next 6 months.

If I were to get my FSM at the GHN, it would help me live longer.

If I were to get my FSM at the GHN, it would help me in the prevention of breast cancer.

I intend to get my first screening mammogram (FSM) at the General Hospital of Nicosia (GHN) within the next 6 months.

Sample Item

Very likely-very unlikely weighted by Very strongly-not at all

Very likely-very unlikely weighted by Very good-very bad

Strongly agree-strongly disagree

Response Category (5-point Likert-type scale)

239

293 3.45

293 7.62

293 12.35

293 18.04

3-15

2-10

4-19

10-24

1.31

2.46

3.13

2.5

1.74

6.05

9.85

6.25

.67

.85

.70

.75

a. Higher mean score value represents stronger agreement with cognitive belief.

Related to patient/ physician communication

Self-efficacy Related to screening

Impediments

Perceived behavioral control Facilitators

Strongly agree-strongly disagree

I am confident that I can get my FSM at Very confident-not at all the GHN within the next 6 months. confident I am confident that I can suggest to the doctor who provides my health care when to get my FSM even if he doesn’t suggest it.

It is easy for me to get my FSM at the GHN within the next 6 months because the hospital staff treats me with respect and dignity. It is difficult for me to get my FSM at the GHN within the next 6 months because the hospital’s schedule is not convenient for me.

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the original constructs of the TPB were included in the model, followed by the addition of self-efficacy. Based on the results of the bivariate analysis, certain external characteristics were significantly associated with intention or at least with one other cognitive variable. Those characteristics were selected and introduced in a final regression model that included the TPB variables, self-efficacy, and the identified interactions. The backward elimination process was used to identify the best model. SAS (version 6) was used to conduct the analyses (SAS Institute, 1990). An alpha significant level of .05 was used to determine the statistical significance for all analyses.

RESULTS Demographic Profile of Participants The overall response rate (defined as the number of completed interviews out of the total number of completed and partial interviews and refusals) was 88%. The high response rate can be attributed to the fact that 108 eligible women (37% of the sample) were recruited at the radiology department with the help of a radiology technician. All 108 women agreed to participate in the study; consequently, the response rate of this subgroup was 100%. Among the rest of the eligible women (63% of the sample) who were recruited at the outpatient clinics, the response rate was 80%. In general, the participants indicated a positive intention to get a screening mammogram (mean score of 3.45 in a range of 1-5). The main characteristics of the women in relation to demographics and practice of other preventive health behaviors are shown in Table 2. Relationships Among Model Components The results of the Pearson correlations suggest positive linear relationships between each independent cognitive variable and intention in the following order of increasing strength: (a) perceived behavioral control (r = .33), (b) attitude (r = .33), (c) subjective norms (r = .39), and (d) self-efficacy (r = .49). All of the main independent variables were significantly intercorrelated, with correlations ranging from .14 to .49. The highest correlation was found between self-efficacy and intention (r = .49). Among the external characteristics, whereas knowledge and educational level were positively related to intention, age was negatively associated with this construct. Finally, the time of CBE was significantly associated with all cognitive variables. In fact, it was found that women who had had their last CBE in the same year as the interview had the highest intention of getting their first mammogram (p < .01). Examination of Possible Interactions A model was created of the four main independent cognitive variables and their six possible interactions. None of the interaction terms examined were significant, indicating that the effects of each cognitive variable were consistent across the other three. Examination of interaction between the cognitive variables and the external variables identified two interactions: age and self-efficacy (p = .0594) and knowledge and perceived behavioral control (p = .0154). Regarding the first interaction, for women who belonged to the 40 to 49 and 50 to 59 age-groups, there is a positive relationship between self-efficacy and intention. As their self-efficacy level increased, so did their intention. This relationship


Table 2.

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Description of Participants by External Characteristics and Social Desirability

Variable Age, years 40-49 50-59 60-65 Ethnicity Greek Cypriots Other Education, years 0-6 6-12 13-16 Knowledge 0-1 point 2-4 points Residency Urban Nicosia Rural Nicosia Other areas Marital status Married Widow Never married Divorced Husband missing in war Family history No Yes BSE status No Yesa Regularb physician No Yes Time of last CBE Never Last year 2 years ago 3 years ago More than 3 years ago This year Social desirability

Frequency

%

117 114 62

39.9 38.9 21.2

291 2

99 1

181 90 22

61.8 30.7 7.5

187 106

63 27

167 109 15

57 37.2 5.1

246 22 11 9 2

84 7.5 3.8 3.1 0.7

261 27

89.1 9.2

117 173

39.9 59

128 161

44.3 55.7

91 33 21 10 18 115 206c

31.1 11.3 7.2 3.4 6.1 39.2 0.7

M

SD

51.91

7.53

7.62

3.65

1.32

0.90

7.26

1.58

NOTE: BSE = breast self-examination; CBE = clinical breast examination. a. This indicates that a woman performs BSE at least once every 1 to 2 months. b. This is defined as visiting the physician at least once a year. c. Eighty-seven participants did not complete this scale.

was not true for the women of ages 60 to 65. Women in this age-group had the lowest intention regardless of their self-efficacy level (low or high). Regarding the second inter-

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action, for women with low levels of perceived behavioral control, there is a positive relationship between knowledge and intention. As their knowledge increased, so did their intention. However, this was not true for women with higher levels of perceived behavioral control. Women in this group had the highest intention regardless of their level of knowledge (low or high). Model Predictions of Behavioral Intention When intention to get a screening mammogram was regressed on attitude, subjective norms, and perceived behavioral control (the original TPB constructs) simultaneously, the regression model was significant in its prediction of intention, explaining 26.7% of its variability. Based on the partial F values, subjective norms (F = 31.73, p = .0001) were the strongest predictor of intention, followed by attitude (F = 23.67, p = .0001) and perceived behavioral control (F = 16.28, p = .0001). With the addition of self-efficacy to the model, the variability explained by the model increased substantially from 26.7% to 34.5%. Furthermore, with the presence of self-efficacy, the p values for attitude and perceived control slightly decreased (p = .019 and p = .003, respectively). Based on the bivariate analysis, the external characteristics that were associated with intention or at least one other cognitive variable (i.e., attitude, subjective norms, and selfefficacy) were age, education, knowledge, time of last CBE, regular physician, marital status, and BSE performance. All seven external variables along with the two interactions were included in the expanded model of the TPB. However, the interaction between knowledge and perceived behavioral control lost its significance and consequently was removed from the final model. As seen in Table 3, all cognitive variables but attitude retained their significance (R2 = .498). Among the cognitive variables, self-efficacy remained the most predictive. Among the external variables, those that retained their significance were the time of last CBE, educational level, and BSE status. Social Desirability Two hundred six participants completed the Social Desirability Scale with a mean score of 7.26 within a range of 0 to 10. A 0 at the lowest end of the range indicated no response bias, whereas a 10 indicated high response bias. The mean score of 7.26 indicated that there was some bias in the respondents’ answers toward the socially desirable response options. To assess whether social desirability was consistent across the respondents, the sample was divided into three groups (intenders, nonintenders, and neutral). The true mean of social desirability among the three groups did not differ (p = .289). Therefore, the influence of social desirability is consistent across the three groups. Common Factor Analysis for Self-Efficacy and Perceived Behavioral Control The purpose of this analysis was to determine whether responses to the items that dealt with self-efficacy and perceived behavioral control by the participants in this study reflected a single or a two- factor model. The results indicated a two-factor model by reaching simple structure with all loadings on each factor greater than .35. The first factor concerned the personal control belief in which the behavioral outcome can be influenced by internal reasons (e.g., feeling trust toward staff) or external reasons (e.g., inconve-


Table 3.

243

Multivariate Analysis of Prediction of Intention to Obtain the First Screening Mammogram by the Cognitive Variables and External Factors Using Backward Elimination Procedure, Final Model

Independent Variable Attitude Self-efficacy Subjective norms Perceived control Time of last CBE Never 1 year ago 2 years ago 3 years go More than 3 years ago This year Age, years 40-49 50-59 60-65 Education Regular BSE No Yes Self-Efficacy ´ Age 40-49 years 50-59 years 60-65 years

Parameter Estimate

SE

F

p Value

Model R2

Intercept

.006 .005 .017 .033

.005 .029 .005 .011

1.13 12.40 9.67 8.98 7.73

.288 .0005 .0021 .003 < .0001

.498

1.26

–.837 –.542 –.89 –.909 –.906 .000

.156 .195 .233 .333 .262 — 2.36

.0963

–.777 –1.388 .000 .046

.735 .642 — .018

6.68 6.55

.0103 .0111

3.96

— .0202

.308 .000 .063 .097 .000

.12 — .037 .034 —

NOTE: Model F = 17.51, p < .0001, df = 15, 264. BSE = breast self-examination; CBE = clinical breast examination.

nience of mammography schedule). At the same time, the first factor concerned the personal control belief in which the behavioral outcome can be influenced by facilitating conditions (e.g., quick delivery of mammography results) or impediments (feeling frustrated while visiting the hospital). Both interpretations of the first factor are similar to the construct of perceived behavioral control as described by Ajzen (1988). The second factor concerned the confidence a woman has in her ability to achieve the target behavior, that is, to get a mammogram or to communicate with her physician, and is similar to the construct of self-efficacy as described by Bandura (1986).

DISCUSSION The results associated with the study objectives are discussed below. Testing the TPB in an International Setting The findings of this study provide empirical support of the TPB for mammography screening because all the variables contributed significantly to the prediction of intention

244


to get an initial screening mammogram. The proportion of variance in intention explained by the three variables (attitude, subjective norms, perceived behavioral control) was 27%, which is consistent with the results of two similar studies by Rutter (2000) and Drossaert et al. (2003), which explained 29% and 48.6% of the variance, respectively. The low percentage of variance explained in this study can be attributed to the fact that breast cancer screening is a complex behavior, which entails personal, social, and environmental factors in its explanation. It has been argued that mammography participation is more difficult to predict using a cognitively based model than other less emotional behaviors because of the difficulty of measuring underlying fears and emotions related to mammography (Montano & Taplin, 1991). The contribution of the subjective norms construct was considerable and supported by the literature (Allen et al., 1998; Godin et al., 2001; Lechner et al., 1997; Rutter, 2000; Vaile et al., 1993). Included in the measurement of subjective norms was the motivation to comply with the physician’s recommendation. Consequently, the considerable contribution of subjective norms was an expected finding because physician recommendations have been established as a main motivating factor in screening mammography (Rimer, 1997). We expected a low contribution of attitude to the prediction of screening mammography in this study. The qualitative phase of the study indicated that Cypriot women hold a positive attitude toward mammography (Tolma, 2001). This meant that the variability of attitude was not great, and therefore, its explanatory power was weak. Two studies (Godin et al., 2001; Michels et al., 1995) support the above finding; however, three other studies (Drossaert et al., 2003; Rutter, 2000; Vaile et al., 1993) found that attitude was the best predictor of intention to obtain a screening mammogram. In the current study, perceived behavioral control was the weakest predictor of intention to obtain the first mammogram, a finding supported by the literature (Drossaert et al., 2003; Rutter, 2000). This suggests three things. First, the fact that perceived behavioral control directly predicted intention indicates that screening mammography is not fully under volitional control as supported by the literature (Godin et al., 2001), which further supports the justification of using the TPB as a model in examining this particular behavior. Second, the low contribution of perceived behavioral control suggests that the degree of volition among the participants is high. This is not surprising because the participants had access to the health care system and were already using its services. Therefore, they had the opportunity, if they so decided, to arrange a mammogram. Third, because none of the participants had a previous mammogram, it can be assumed that consequently, they had little direct experience about this particular behavior. This may be another reason the strength of this variable is so low. Thus, it can be argued that if the participants had experience with a mammogram, the contribution of perceived behavioral control might have been higher. In this study, the theoretical construct of subjective norms contributed the most in the explanation of intention, followed by attitude, and with perceived behavioral control last. There is inconsistency in the literature regarding the magnitude of each component in the explanation of intention to get a mammogram as it has been operationalized through the TPB (Allen et al., 1998; Godin et al., 2001; Lechner et al., 1997; Michels et al., 1995; Montano & Taplin, 1991; Rutter, 2000). The varying influence of each construct is not surprising because the participants in the above studies represent various nationalities including American (Michels et al., 1995), British (Rutter, 2000), Canadian (Godin et al., 1996), and Dutch (Drossaert et al., 2003; Lechner et al., 1997). According to Godin et al. (2001), salient beliefs may vary from one priority population to another. Moreover, the


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developers of the theory recognized the importance of eliciting relevant information, such as beliefs and referents from the priority population because the conceptualization of behavioral outcomes varies across populations for the same behavior (Montano, Kasprzyk, & Taplin, 1997). Another reason for this discrepancy in the results can be attributed to differences in the methodology used to develop the survey. For instance, only three (Godin et al., 2001; Michels et al., 1995; Montano & Taplin, 1991) of the above studies have followed the methodology suggested by Fishbein and Ajzen (1975), which includes the use of pilot work to elicit salient beliefs. Testing the Expanded Form of the TPB The addition of self-efficacy enhanced the predictability of the model because the variance explained increased from 26.7% to 34.5%. In fact, in the final model, self-efficacy was the strongest predictor. This finding is partially supported by the literature because similar results were obtained at different behavioral settings such as eating five portions of fruit and vegetables (Povey et al., 2000), alcohol use (Armitage et al., 1999), and exercising (Terry & O’Leary, 1995). On the other hand, one study on physical activity (1996) found that self-efficacy did not add to the amount of variance of intention explained by the model. Some of this discrepancy in the results can be attributed to differences in the operationalization of the construct, differences in the types of behaviors involved (alcohol use, exercising), and priority populations (blue-collar workers, university students). The importance of self-efficacy in the explanation of intention to get a mammogram is supported by the literature (Allen et al., 1998; Rutter, 2000; Lechner et al., 1997). These results highlight the importance of increasing women’s self-efficacy by teaching them personal skills to overcome psychological and physical barriers to getting a screening mammogram. Increased self-efficacy may enhance their motivation to get a mammogram. An interesting observation relates to the way self-efficacy has been operationalized. In one study (Rutter, 2000), self-efficacy was operationalized with items such as “I would have a mammogram even if I were afraid of breast damage.” In a study by Lechner et al. (1997), self-efficacy was measured with items such as “Do you think you are able to participate in the screening if the screening costs money?” In a study by Allen et al. (1998), the measurement of self-efficacy was centered on the ability of the woman to discuss mammography screening with her health care provider and how confident she was that she could have a mammogram regularly. This last study’s approach to measurement is similar to the way self-efficacy was operationalized in this study. Self-efficacy has been defined in the past as the perceived difficulty of a behavior (Povey et al., 2000), as the confidence one has in his or her ability to perform a behavior (Armitage & Conner, 1999a), or as the ease or difficulty in performing a behavior (Terry & O’Leary, 1995). Clearly, as the research community reaches a consensus on defining and measuring this construct, the understanding of its contribution to the explanation of behavior will become more transparent. The addition of self-efficacy caused the construct of attitude to lose some of its significance. This finding suggests that attitude may be partially redundant with self-efficacy. According to Bandura (1986), self-efficacy and expected outcomes, although they are two different constructs, are also related. He argues that “the types of outcomes people anticipate depend largely on their judgments of how well they will be able to perform in a given situation” (Bandura, 1986, p. 392). This is true for behaviors in which outcomes are either inherent to the actions or highly linked by social codes. We inferred that there is an inherent linkage between the outcomes of the action (e.g., prevention of breast cancer)

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and the action (getting a mammogram). Therefore, women who were fully confident in their ability to get a mammogram anticipated that their action would result in the prevention of breast cancer. This suggests that motivating women to get a mammogram is likely to be more successful by influencing their self-efficacy levels rather than their attitudinal beliefs. Relationship Between Self-Efficacy and Perceived Behavioral Control Recently, there has been a lot of discussion within the research community regarding the relationship between self-efficacy and perceived behavioral control (Ajzen, 2002; Armitage, 1999a, 1999b; Manstead & van Eekelen, 1998; Povey et al., 2000; Terry & O’Leary, 1995). Consequently, we felt it was important to examine this issue in this study as well. The weakening of the importance of perceived behavioral control in the prediction of intention on the addition of self-efficacy to the model and the low association (r = .33) between the two constructs suggest that the two constructs share some influence on intention, although they are two distinct constructs. However, the fact that the responses of the corresponding beliefs were grouped in two underlying factors indicates that the constructs are also different. One explanation for this relationship may have to do with the way the two constructs have been operationalized. Perceived behavioral control was measured as the perceived ease or difficulty of getting a screening mammogram focusing primarily on external factors (e.g., overall hospital environment and mammography procedures), whereas self-efficacy was measured as the perceived confidence in the ability to get a screening mammogram and to communicate with the physician, focusing primarily on internal factors. Self-efficacy is concerned not with the skills themselves but with the judgments about what one can do with those skills. There is less focus on the achievement of the goal or the actual control of the behavior (Bandura, 1991). On the other hand, perceived behavioral control is concerned with how easy or difficult the behavior will be based on the presence of the skills. This indicates more focus on the actual control of the behavior (Ajzen, 1988). Another explanation for this relationship is that both constructs also reflect beliefs about the presence of internal factors. For example, an internal perceived behavioral control internal factor deals with the feeling of trust a woman has toward the hospital staff or the frustration she experiences during her visit at the hospital. An internal self-efficacy internal factor deals with the confidence a woman has to get a mammogram or to know what questions to ask her physician regarding mammography. We believe that the study provided some empirical support for the distinction between self-efficacy and perceived behavioral control (Armitage & Conner, 1999a, 1999b; Manstead & van Eekelen, 1998). Our study also provided empirical support that selfefficacy is a stronger predictor of intention than perceived behavioral control. Other studies (Povey et al., 2000; Terry & O’Leary, 1995) have reached similar conclusions; however, their operationalization of both self-efficacy and perceived behavioral control in the above studies differs and therefore makes it difficult to compare results. Our results also suggest that enhancement of personal skills (self-efficacy) is more likely to influence motivation to get a screening mammogram than influencing environmental factors (perceived behavioral control). Finally, it can be suggested that self-efficacy may be an additional cognitive construct of the TPB. Nevertheless, more research is needed to test the relationship of the two constructs in diverse behavioral settings and to differentiate the two constructs not only con-


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ceptually but also operationally. In fact, in a recent review, Ajzen (2002) introduced a new concept regarding the relationship between self-efficacy and perceived behavioral control. He argued that “the overarching concept of perceived behavioral control . . . is comprised of two components: self-efficacy (dealing largely with the ease or difficulty of performing a behavior) and controllability (the extent to which performance is up to the actor).” Relationship of External Characteristics and the Theory of Planned Behavior Education and age (as part of the age/self-efficacy interaction) contributed significantly to the prediction of intention after controlling for all the cognitive variables. In addition, the time of last CBE and BSE status (practice of other health behaviors) variables also predicted intention while controlling for all the cognitive variables. These findings contrast what the founders of the precursor Theory of Reasoned Action suggest, that is, “External factors, such as demographic or personality characteristics of the actor, the nature of the particular behavior under investigation, or situational variables can affect intentions only if they influence the attitudinal or normative components or their relative weights” (Fishbein & Ajzen, 1975, pp. 315-316). The role of external characteristics in the prediction of intention has not been extensively examined through the application of the Theory of Planned Behavior. First, the TPB is primarily a cognitive-based theory (Montano & Taplin, 1991) and therefore, cognitions are the focus of the research using this theory. Second, it could be argued that the construct of perceived behavioral control, which was added later to the theory, reflects the external characteristics (nature of particular behavior under investigation, and situational variables), and thus, the inclusion of external characteristics as a separate construct may be redundant. However, Ajzen (1988) in his original definition of control factors includes (a) internal factors (information, skills, and abilities, emotions and compulsions) and (b) external factors such as situational or environmental factors that facilitate or interfere with the performance of the behavior. One can observe that demographic factors are not included in this definition. This suggests that their effect may not be mediated through the effect of perceived behavioral control or other cognitive constructs, and thus it is worth examining the effect of demographic characteristics separately in future applications of the model. Implications for Practitioners In the planning of tailored health communication interventions toward screening mammography, the TPB with the construct of self-efficacy is a useful framework that can help to elicit salient beliefs of the priority population. Practitioners can use the identified beliefs to develop the right messages (e.g., mammography can detect breast cancer early) delivered from the appropriate sources (e.g., the physician). The contribution of perceived behavioral control also suggests that the establishment of a helpful clinical environment (promoting trust toward the hospital staff, convenient schedule for the patients) will enhance women’s perception of control over their screening mammography behavior. In fact, it can be suggested that having women obtain a CBE can be a key factor in motivating them to obtain a screening mammogram. Finally, attention should be placed on the enhancement of self-efficacy.

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According to Bandura (1986), there are four ways to increase self-efficacy. These are mastery of the behavior through the successful performance of successive steps, vicarious observation, verbal persuasion and reinforcement, and management of emotional arousal. When applied to mammography screening behavior, for instance, women could be taught the steps involved in making a mammogram appointment, how to effectively communicate with their physician or mammography technicians, or how to alleviate their tension just before the appointment. In addition, women can be asked to watch a video related to a mammography procedure and benefits of mammography prior to their mammography appointment (Montano et al., 1997). Moreover, verbal persuasion by the physician can further emphasize the messages in the video and contributes to self-efficacy toward obtaining a mammogram. Limitations This study was cross-sectional, and therefore any causal relationships between its components cannot be implied. Future prospective studies should be conducted to examine the causal ordering of the components of the theory. In addition, this study examined intention to obtain a mammogram and not actual behavior. Because of time constraints and lack of resources, it was impossible to assess whether the participants actually obtained a mammogram within the 6 months following the interview. It would have been extremely interesting to determine to what extent the model predicted actual behavior by identifying how many women actually got a mammogram as documented by the hospital records. Nonetheless, self-reported intention as an outcome variable is a common practice in screening mammography (Clemow et al., 2000). Furthermore, one study that used the TPB in breast cancer screening indicated successful prediction of behavior within 3 years (Rutter, 2000). Moreover, based on a review study (Randall & Wolf, 1994), several studies that applied the TPB in other behavioral settings have indicated a correlation (average level .40) between intention and behavior, where the interval time was more than 1 year and even up to 15 years (Davis, 1985). A second limitation concerns the fact that the study used convenience sampling. This means that the results of this study should not be generalized beyond the segment of the female population who uses the outpatient clinics of the General Hospital of Nicosia and those sharing similar characteristics with the women who participated in the study. A third limitation refers to the fact that our dependent variable was very specific in terms of the location. However, according to the founders of the Theory of Reasoned Action (Fishbein & Ajzen, 1975), the theory predicts a person’s intention to perform a specific behavior in terms of action, target, context, and time. For example, it can be anticipated that a different setting (e.g., a private clinic) could have a different effect on a woman’s intention to get a mammogram than the setting of the general hospital. Moreover, it is critical that during the operationalization of the TPB constructs there is a high correspondence between the measures of intention and behavior (Fishbein & Ajzen, 1975). Consequently, the results of the study (as stated previously) refer only to women who have access to the health care services of the General Hospital of Nicosia. Nevertheless, the results of the study are still useful, because the majority (more than 75%) of the female population eligible for screening mammography in Cyprus prefer to use the services offered by the public sector because they are free (Tolma, 2001). A final limitation concerns the data collection. The face-to-face administration of the interviews in a public setting might have influenced participants to answer with socially desirable responses. However, the response bias is consistent across the sample, and


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therefore its effect on the results is evenly distributed and does not undermine the usefulness of the insight provided by this study. The Social Desirability Scale that was used in this study was tested primarily among college students in the United States and thus, further research should be conducted to test the Social Desirability Scale’s applicability among other populations and especially in international settings.

CONCLUSION The results of this study indicate that the TPB is a sound and useful model in the explanation of intention to obtain a screening mammogram. Moreover, the study provided support to the literature suggesting that additional constructs, especially self-efficacy, may improve the predictive ability of the model. Furthermore, the findings provided empirical support for the distinction between the constructs of self-efficacy and perceived behavioral control. Despite the success of this research in confirming the relevance of an expanded TPB in an international setting, further research is needed to examine the role of self-efficacy or other constructs in the enhancement of the predictive ability of the model in various behavioral settings and among different populations.

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