Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 1

Modal Salient Belief and Social Cognitive Variables of Anti-Doping Behaviors in Sport:

Examining an Extended Model of the Theory of Planned Behavior

Derwin King Chung Chan1, Sarah Hardcastle2, James A. Dimmock3, Vanessa Lentillon-

Kaestner4, Robert J. Donovan1, Matthew Burgin5, 6, & Martin S. Hagger1

Curtin University, Australia1

University of Brighton, UK2

University of Western Australia, Australia3

University of Teacher Education, Switzerland4

Murdoch University, Australia5

Western Australia Institute of Sport (WAIS)6

Correspondence  concerning  this  article  should  be  addressed  to  Derwin  K.-­‐C.  Chan,  

School  of  Psychology  and  Speech  Pathology,  Curtin  University,  Perth,  Australia.  Email:  

derwin.chan@curtin.ac.uk  or  kc.derwin@gmail.com  

 

Acknowledgement  

This  research  was  supported  by  a  grant  from  the  Australian  Government  Anti-­‐Doping  

Research  Programme  (#01-­‐CURTIN-­‐2011-­‐12)  awarded  to  Prof.  Martin  S.  Hagger.  

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 2

Abstract

Objectives: This study examined the modal salient behavioral, normative, and control beliefs

within the theory of planned behavior (TPB) in the context of anti-doping in sport. We tested

the efficacy of four hypothesized expectancy-value models as predictors of the directly-

measured social-cognitive components of the TPB toward doping avoidance: attitude,

subjective norm, perceived behavioral control (PBC), and intention.

Methods: After developing the belief-expectancy and belief-value of modal salient beliefs

items based on a pilot belief-elicitation study of young  elite  athletes  (N  =  57,  mean  age  =  

18.02),  410  young athletes (mean age = 17.70) completed questionnaire items of the modal

salient beliefs and direct measures of the social-cognitive components of doping avoidance.

Variance-based structural equation modeling was used to examine the four proposed

expectancy-value models.

Results: Belief-expectancies, belief-values, and the expectancy-belief multiplicative

composites formed positive associations with their corresponding social cognitive variables.

The model in which belief-expectancies were the sole predictors of the social cognitive

provided the most parsimonious and reliable model to explain the relationship between modal

salient beliefs and directly-measured social-cognitive variables for doping avoidance in sport.

Conclusion: Belief-expectancies including behavioral belief strength (e.g., “doping

avoidance is likely to ease the worry of being caught doping”), normative belief strength (“my

coach thinks that I should avoid doping”) and control belief strength (“I expect I have power

to ‘say no’ to doping”) are the belief-based components that underpin direct measures of the

social-cognitive variables from the TPB with respect to doping avoidance.

Keywords: Doping avoidance; Expectancy-value muddle; Normative belief strength;

Outcome evaluation; Motivation to comply; Control belief power.

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 3

Modal Salient Belief and Social Cognitive Variables of Anti-Doping Behaviors in Sport:

Examining an Extended Model of the Theory of Planned Behavior

Use of banned performance-enhancing substances and methods, collectively known as

‘doping’, is regarded as one of the most critical problems in sport (World Anti-Doping

Agency, 2009). For decades, sport governing bodies, sport scientists, and medical

professionals have invested substantial amount of effort and resources to restrict the

accessibility of banned substances and improve the effectiveness of doping control process.

There is, however, no discernible reduction in the incidence of doping in sport (World Anti-

Doping Agency, 2012). A growing amount of research has placed the attention on the

psychological factors that influence athletes’ use of prohibited substances or methods to

enhance their sport performance (Barkoukis, Lazuras, Tsorbatzoudis, & Rodafinos, 2011;

Ehrnborg & Rosén, 2009; Gucciardi, Jalleh, & Donovan, 2011; Ntoumanis, Ng, Barkoukis, &

Backhouse, 2013).

Researchers have typically adopted social-cognitive and motivational models from

social psychology to guide their investigations into identifying the influential factors on

doping intentions and behavior. The theory of planned behavior (TPB; Ajzen, 1985, 1991) is

one of the most frequently used frameworks applied to explaining athletes’ doping intention

and behavior (Goulet, Valois, Buist, & Cote, 2010; Lucidi et al., 2008; Ntoumanis, et al.,

2013; Wiefferink, Detmar, Coumans, Vogels, & Paulussen, 2008; Zelli, Mallia, & Lucidi,

2010). The theory proposes that intention (i.e., the extent to which one plans to engage in the

behavior in the future), the most proximal predictor of behavior, is a function of an

individuals’ attitude (i.e., the subjective evaluation towards the behavior), subjective norm

(i.e., the perceived social appropriateness of the behavior), and perceived behavioral control

(PBC; the perceived controllability of the behavior) with respect to the behavior. A number of

studies have examined TPB in relation to athletes’ doping intentions and behavior and have

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 4

provided evidence to support the hypothesized pathways of the model (Goulet, et al., 2010;

Lucidi, et al., 2008; Ntoumanis, et al., 2013; Wiefferink, et al., 2008; Zelli, et al., 2010).

There is, however, a relative dearth of research applying the TPB regarding the belief-

based components considered to underpin the attitude, subjective norm, and PBC constructs

within the theory (Hagger, Anderson, Kyriakaki, & Darkings, 2007; Hagger, Chatzisarantis,

& Biddle, 2001). According to the TPB, the attitude, subjective norm, and PBC constructs,

typically measured using global or direct measures that summarize sets of personal, social,

and volitional beliefs, refer to behavioral, normative, and control beliefs, respectively (Ajzen,

1985, 1991; Fishbein & Ajzen, 1975). These sets of specific beliefs are proposed to predict

the direct measures and provide an indirect estimation of attitude, subjective norm, and PBC

respectively. Ajzen (1985, 1991) proposed an expectancy-value model to specify the process

by which the sets of beliefs impact on intentions and behavior. He proposed that individuals

used memory, past experience, and situational information to develop representations or

expectancies of the salient outcomes, and referent, barriers or facilitating factors with respect

to the behavior (Ajzen, 1985, 1991; Fishbein & Ajzen, 1975). The extent to which these

belief-expectancies impacted formation of future intentions to act was determined by the

value placed on each belief. The value component acted as a weighting in its determinant of

intentions. The effects of the beliefs on intentions, therefore, could be operationalized as the

interactive or multiplicative of the belief-expectancy and belief-value attached to it. Ajzen

(1985, 1991) proposed the following expectancy-value formulation with respect to the three

sets of beliefs.

Behavioral belief = behavioral belief-strength (i.e., the perceived probability of

behavioral outcomes) x outcome evaluation (i.e., the subjective evaluation on the

expected outcomes).

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 5

Normative belief = normative belief-strength (i.e., the perceived social approval/

disapproval of a particular social agent) x motivation to comply (i.e., one’s willingness

to comply to the social agent)

Control belief = control belief-strength (i.e., the likelihood of behavioral facilitation/

inhibition) x control belief-power (i.e., the perceived power of the behavioral

facilitation/ inhibition).

The expectancy-value model of beliefs within the TPB outlined how the beliefs led to

intention formation and predicted actual future behavioral engagement via the mediation of

intention (Ajzen, 1985, 1991). The beliefs are usually identified through open-ended

elicitation questionnaires in which people from the target population are asked to provide an

exhaustive list of salient factors likely to affect their behavior. Then the list is subjected to a

content analysis to identify those that are more salient or frequently occurring. However,

empirical tests of the model have typically avoided the time-consuming process of belief

elicitation, and focused on using more direct measures of the three factors considered

antecedent to intentions and behavior: attitudes, subjective norms, and perceived behavioral

control (Hagger, et al., 2007; Hagger, et al., 2001). These direct measures serve as summary

statements of the behavioral, normative, and control-related beliefs, usually tapped through

self-reported questionnaire (e.g., Chan, Fung, Xing, & Hagger, 2013; Chan & Hagger, 2012a;

Lucidi, et al., 2008; Zelli, et al., 2010). The direct measures have been proposed to be

sufficient in capturing the salient beliefs and, therefore, the effect of expectancy-value models

of beliefs are expected to be fully mediated by the direct measures, ostensibly negating the

need for belief elicitation (Hagger & Chatzisarantis, 2005; Rhodes & Courneya, 2003).

However, as the elicited beliefs provide specific information regarding which beliefs are most

closely linked to intentions to perform future behaviors, they have been identified as the key

targets for interventions focused on changing intentions (Hardeman et al., 2002). This means

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 6

that capturing the beliefs and examining which are most strongly associated with intention is

important to gather evidence that will inform future intervention content (McEachan, Conner,

Taylor, & Lawton, 2011).

The expectancy-value model within the TPB has been tested in a number of health

contexts, such as patients’ self-medication (Ried & Christensen, 1988), drug abuse (Armitage,

Conner, Loach, & Willetts, 1999), healthy dieting (Armitage & Conner, 1999), physical

activity (Hagger, et al., 2001) and hand hygiene (Clayton & Griffith, 2008). However, the

multiplicative approach of the modal salient beliefs recommended by Ajzen has often led to

statistically uninterpretable results due to inconsistencies in the proposed item scaling

procedures, computational processes, and notable measurement errors. These problems have

led researchers to speculate that belief-expectancy and belief-values alone, without any

interactive or multiplicative approach, might resolve some of these analytic and

computational problems and may account for similar or even more proportion of the variance

in their corresponding directly-measured social cognitive variables than the multiplicative

composites (French & Hankins, 2003; Sullivan, McGee, & Keegan, 2008).

French and Hankins (2003) identified four possible solutions to the use of belief-

expectancies and belief-outcomes in the TPB, a problem they refer to the ‘expectancy value

muddle’: (1) using belief-expectancies only, (2) using both belief-expectancies and belief-

values, (3) using the multiplicative composites of belief-expectancies and belief values only,

and (4) using the all the belief-expectancies, belief-values, and the multiplicative composites.

Their research suggested that the use of multiplicative composites alone or in conjunction

with the belief-expectancies and belief-values offer no substantial advantage when it comes to

behavioral prediction above the use of belief-expectancies alone. Their research suggests that

the belief-value and the proposed interactive model did not provide a more effective

prediction of intentions, possibly because respondents intuitively assign high value when

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 7

making self-report ratings of belief-expectancies negating the need for a separate value

variable. However, this may not exclusively be the case and there are examples in the

literature that demonstrate the importance of multiplicative composites of belief-expectancies

and belief-values on behavior (e.g., Armitage & Conner, 1999; Armitage, et al., 1999;

Clayton & Griffith, 2008).

The Present Study

In the present study, we aim to test the four models proposed by French and Hankins

(2003) to examine the effects of modal salient beliefs within the theory in the context of

doping avoidance. In particular, the primary purpose of the present study was to identify the

model that is most effective in interpreting the role of key modal salient beliefs.

Our research will extend previous applications of TPB in the explanation of doping

intention (Goulet, et al., 2010; Lucidi, et al., 2008; Wiefferink, et al., 2008; Zelli, et al., 2010)

by applying the TPB to the prediction of doping avoidance. It is because an important

perspective for achieving anti-doping objectives is the avoidance of doping, in which athletes

exhibit personal effort (e.g., to “say no” to anyone who offer banned-performance enhancing

drugs, to increase personal knowledge of the prohibited list of banned substances/methods)

and self-awareness (e.g., being aware of the ingredients/chemicals in unknown food, drinks,

supplements, and medications; avoiding the social situations where unintentional intake of

banned substances may occur) in order to avoid doping (Lamont-Mills & Christensen, 2008;

Petroczi, Aidman, & Nepusz, 2008). However, to our knowledge, there has not been any

study using the TPB as the framework to understand athletes’ anti-doping intentions and

behavior (see Ntoumanis, et al., 2013). This preventive behavioral context is highly relevant

for professional athletes and consistent with goals of most anti-doping organizations (World

Anti-Doping Agency, 2009). Accordingly, the aim of the present study was to test the role of

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 8

modal salient beliefs in the prediction of the directly-measured social-cognitive variables and

the intention of young athletes’ doping avoidance in sport.

Based on previous research adopting the TPB in an anti-doping contexts (Goulet, et

al., 2010; Lucidi, et al., 2008; Ntoumanis, et al., 2013; Wiefferink, et al., 2008; Zelli, et al.,

2010) and the literatures on modal salient beliefs (Armitage & Conner, 1999; Armitage, et al.,

1999; Clayton & Griffith, 2008; Sutton et al., 2003), we hypothesized that direct measures of

attitude, subjective norm, and PBC, would not only form significant and positive associations

with intention to avoid doping, but they would also be predicted by behavioral belief,

normative belief, and control beliefs respectively. Through testing the four possible models

proposed by French and Hankins (2003), we expected that belief expectancies alone would

lead to the most effective and parsimonious way of estimating the modal salient beliefs

regarding doping avoidance in sport.  

Method Participants

After the Research Ethics Committee of the first author’s institution approved the

study protocol, we recruited 410 young athletes (mean age = 17.70, SD = 3.92; male =

55.4%) to participate in the study. Participants comprised elite (35.7% of the sample were

national level, 10.3% international level, 1.8% world-class) and sub-elite (22.9% regional

level, 29.4% state level,) and were involved in a number of different sports including six

individual sports (i.e., athletics track, athletics field, badminton, gymnastic, swimming, and

triathlon), and six team sports (i.e., soccer, field hockey, water polo, basketball, rugby, and

cricket). On average, they trained for 12.43 hours per week (SD = 5.63 hours) and had

competed in their sport for 9.05 years (SD = 3.15). Prior to data collection, participants and

their parent/legal guardians signed a consent form to confirm that they understood the aims of

the study, voluntary nature of participation, confidentiality and anonymity, and their rights to

withdraw from the study and have their data deleted at any time without giving a reason. The

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 9

research adopted a correlational design with self-report measures of psychological variables

presented to participants in paper format.

Measures

Indirect Belief-Based Measures. Prior to the data collection, we constructed indirect

measures of the TPB constructs using the modal salient beliefs with respect to doping in sport

and according to Ajzen’s recommendations (Ajzen, 2002). To identify the salient beliefs, we

conducted a brief pilot study in a separate sample using an open-ended belief-elicitation

procedure. Pilot study data were collected at the beginning of a separate qualitative study

using focus group interviews regarding doping in Western Australia (Chan et al., in press).

The pilot study sample comprised 57 elite and sub-elite athletes (mean age = 18.02, SD

=2.72) from seven different individual (athletics-track, athletics-field, and swimming) and

team (basketball, hockey, netball, and water polo). Participants completed a brief survey

about their modal salient beliefs of doping in sport, both engaging in and avoiding doping. A

prologue to the survey outlined the purpose of the research to the athletes and that it was their

confidential views and opinions being sought. The survey comprised six open-ended

questions focused on eliciting beliefs corresponding to the behavioral belief (i.e., “Can you

think of the (1) advantages or (2) disadvantages of taking banned performance-enhancing

substances?”), normative beliefs (i.e., “Are there any individuals or groups who would (3)

approve or (4) disapprove of you taking banned performance-enhancing substances in the

future?”), and control belief (i.e., “What factors or circumstances would make it (5) easy or

(6) difficult for you to take banned performance-enhancing substances in the future?”)

constructs from the TPB. Participants were also asked to report any other issues that came to

their minds in relation to doping.

Although the survey questions referred to the pros and cons of doping in sport, a

notable proportion of the responses referred to the reasons for which athletes would want

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 10

avoid doping (e.g., disadvantages, social disapproval, and controllability of doping avoidance).

In the present study we focused on the avoidance of doping in sport and anti-doping behaviors,

and we organized responses to the open-ended survey questions according to the three

proposed themes of the modal salient beliefs (i.e., behavioral, normative, and control beliefs).

For the behavioral beliefs construct, the responses were associated with natural ability,

morality, avoidance of health side-effect, worries of getting caught or punished, addiction of

drugs, and the integrity of sport achievements. For the normative beliefs construct, the

majority of the responses were linked to how the coach, teammates, close friends, family,

supporters (e.g., fans, spectators), and the media would disapprove or react negatively to any

use of prohibited substances for performance enhancement in sport. For the control beliefs

construct, many responses related to the perceived challenges that young athletes would

expect they had to overcome in order to avoid taking banned performance enhancing

substances, such as pressure to conform, desperation for winning, accessibility to drugs, or

lack of information, awareness, and support for doping avoidance.

On the basis of the responses from the open-ended belief-elicitation study and the

literature on the construction of modal salient belief items (Ajzen, 2002; Sutton, et al., 2003),

we constructed seven pairs of items for behavioral belief strength and the corresponding

outcome evaluation, five pairs of items for normative belief strength and motivation to

comply, and eight pairs of items for control belief strength and control belief power (see

Appendix A for the items and scale anchors). These items reflected the core content of each

expectancy-value components of the TPB revealed in the belief-elicitation pilot study. Three

researchers with a track record of research with the TPB and doping reviewed and revised the

items until the coverage, face validity and clarity of the items were viewed as satisfactory.

Seven-point Likert scales were used for participants to give responses to the behavioral belief-

strength (1 “extremely unlikely” to 7 “extremely unlikely”), outcome evaluation (1 “bad to 7

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 11

“good”), normative belief strength (1 “I should not” to 7 “I should”), motivation to comply (1

“not at all” to 7 “very much”), control belief-strength (1 “strongly disagree” to 7 “strongly

agree”), and control belief-power (1 “difficult” to 7 “easy”) constructs. The unipolar scale

ranging from 1 to 7 was used because the bipolar scales typically used for these measures

ranging from, -3 to +3 may lead to an underestimation of factor correlations for negatively-

framed items and a biased estimation of the multiplicative scores due to the problem of

‘double negatives’ (French & Hankins, 2003; Sullivan, et al., 2008; Trafimow & Finlay,

2002).

Direct Measures of TPB Constructs. Items for attitude, subjective norm, and PBC of

doping avoidance were developed according to Ajzen’s (2002) guidelines. All direct measures

were preceded by the common stem: “For me, avoiding using banned performance-enhancing

substances/methods in sport in the forthcoming month is (something)…” For the attitude

measure, participants’ responded on six seven-point semantic differential scales to the

following bi-polar adjectives: “valuable - worthless”, “beneficial - harmful”, “pleasant -

unpleasant”, “enjoyable- unenjoyable”, “good - bad”, and “virtuous - not virtuous”. Measures

of subjective norm (three items; e.g., “…most people who are important to me think that I

should do”), PBC (five items; e.g., “…possible for me to do”), and intention (three items; e.g.,

“…I intend to do”) were rated on seven-point Likert-type scales ranging from 1 (“strongly

disagree”) to 7 (“strongly agree”).

Data Analysis

Data were analyzed by variance-based structural equation modeling (VB-SEM) using

the WarpPLS 3.0 statistical software (Kock, 2012). This analytical method simultaneously

estimates the measurement and structural level of the hypothesized model using a partial

least-squares algorithm. As partial-least squares algorithm does not make any assumptions

regarding the normal distribution of variables, the model estimation of VB-SEM is an

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 12

ostensibly distribution-free method. VB-SEM enables a robust construction of error-free

latent factors regardless of the complexity of the model, small sample size, or non-normality

of the data (Reinartz, Haenlein, & Henseler, 2009). The complexity of our model would

require a much larger sample size for achieving adequate statistical power if we were to used

typical covariance-based structural equation modeling (Chan, 2009; MacCallum, Browne, &

Sugawara, 1996), leading us to choose VB-SEM for the analysis of our data.

In VB-SEM, we evaluated the measurement model using a number of indices

regarding the convergent and discriminant validity of the hypothesized factors: factor

loadings, cross-loadings, average variance extracted (AVE), composite score reliability, and

Cronbach’s alpha (Barclay, Thompson, & Higgins, 1995; Chin, 1998; Henseler, Ringle, &

Sinkovics, 2009). The measurement model was typically considered acceptable when factor

loading exceeded .70, all cross-loadings smaller than factor loadings, AVE exceeding .50,

composite score reliability and Cronbach’s alpha greater than .70, and a square-root of AVE

greater than the mean factor-to-factor correlation (Barclay, et al., 1995; Chin, 1998; Henseler,

et al., 2009). Goodness of fit (GoF) index, averaged R-squared (ARS), averaged variance

inflation factor (AVIF), and averaged path coefficient (APC) were computed to reveal the

global fit of the model. The fit of the proposed model with the data was considered

satisfactory if the GoF (for medium effect size) exceeded .25 (Tenenhaus, Vinzi, Chatelin, &

Lauro, 2005; Wetzels, Odekerken-Schroder, & van Oppen, 2009), the p-values for ARS and

APC were less than .05, and the AVIF was less than 5 (Kock, 2011). These indices were used

to examine and compare the adequacy of the four hypothesized models (see Figure 1)

proposed by French and Hankins (2003):

Model 1 (Expectancy model) comprised latent exogenous factors of the three belief-

expectancies, in which behavioral belief-strength, normative belief-strength, and

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 13

control belief-strength were set to predict attitude, subjective norm, and PBC

respectively.

Model 2 (Expectancy and Value Model) included the latent exogenous factors of

Model 1 together with the exogenous latent factors of the three belief-values (i.e.,

outcome expectancy, motivation to comply, and control belief-power). Attitude

(predictors = behavioral belief-strength and outcome evaluation), subjective norm

(predictors = normative belief-strength and motivation to comply), and PBC

(predictors = control belief-strength and belief-power) were predicted by their

corresponding factors of modal salient beliefs.

Model 3 (Expectancy-Value Multiplicative Model) consisted of the three multiplicative

composites for the latent exogenous factors of the three modal salient beliefs, in which

the multiplicative composites of behavioral belief, normative belief, and control belief

were inserted as predictors of attitude, subjective norm, and PBC respectively.

Model 4 (Full Model) was the complete model that included all the latent exogenous

factors and paths of both Model 2 and Model 3.

The items representing the belief-expectancy and belief-value constructs served as

independent indicators of their corresponding latent factors. Each indicator for the

multiplicative composite of the behavioral belief (i.e., behavioral belief-strength X outcome

evaluation), normative belief (i.e., normative belief-strength X motivation to comply), and

control belief (i.e., control belief-strength X control belief-power) variables was the cross-

product of each pair of belief-expectancy item and belief-value item. We used raw scores

rather than the standardized scores for computing the cross-products to avoid the double-

negatives problem where the negative z-scores of belief-expectancy and belief-values could

mistakenly produce false positive cross-products of modal salient beliefs (French & Hankins,

2003; Sullivan, et al., 2008; Trafimow & Finlay, 2002).

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 14

In Model 1 to Model 4, intentions to avoid doping served as a latent endogenous

factor predicted directly by the three social cognitive factors from the TPB (i.e., attitude,

subjective norm, and PBC), and indirectly by their respective latent exogenous factors of the

model. Mediation analysis was conducted to test whether the direct measures of the TPB

social cognitive factors mediated the relationship between the exogenous factors and

intention. Mediation was confirmed by significant direct and total indirect effects of the latent

exogenous factor on the latent endogenous factor, and a significant effect of the mediators on

the latent exogenous factor (Zhao, Lynch, & Chen, 2010). The type of mediation was

determined by the combined effects (when the effects of the mediators were controlled) of the

latent exogenous factor on the latent endogenous factor. Non-significant combined effects

represented full mediation while a significant indirect and direct effect reflected partial

mediation (Zhao, et al., 2010). To verify the stability of model, a bootstrapping resampling

technique with 999 replications was employed to produce the averaged path estimates, direct

and indirect effects, and associated significance levels.

Results

Measurement Model

Results of the measurement level of VB-SEM fully supported the convergent and

discriminant validity of the latent variables representing the indirect measures of the TPB

social cognitive constructs, namely, the modal salient beliefs, and the direct measures across

all four hypothesized models. The Cronbach’s alpha (range = .70 to .96), composite score

reliability (range = .73 to .97), AVE (range = .53 to .82), and factor loadings (range = .63 to

.90) met the criteria put forth by the literatures (Barclay, et al., 1995; Chin, 1998; Henseler, et

al., 2009) for acceptable convergent validity of the latent factors. Results also revealed

acceptable levels for tests of discriminant validity. The factor loadings were higher than the

cross-loadings by a mean difference of .63 (range = .23 to .76), and the squared-root of the

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 15

AVE was higher than the mean factor-to-factor correlation of any of the latent factors by

mean difference of .34 (range = .19 to .63). Table 1 displays the latent-factor correlation

matrix, descriptive statistics, and details of the validity indices for each factor.

Structural Equation Model

At the structural level, Models 1 to 4 adequately fitted with the data based on the

global goodness-of-fit indicators. The values of GoF (range = .31 to .36), ARS (range = .16 to

.20; all p < .05), AVIF (range = 1.30 to 4.03), and APC (range = .18 to .29; all p < .05) also

satisfied published criteria for a well-fitting model for VB-SEM (Pauwels, Patterson, De

Ruyter, & Wetzels, 2009). Furthermore, the path estimates in the model were generally in

agreement with our hypotheses. The fit indices, bootstrapped estimates, and effect

decompositions of the models are presented in Table 2.

In brief, behavioral belief-strength, normative belief-strength, and control belief-

strength were significant positive predictors of attitude, subjective norm, and PBC

respectively in the expectancy model (Model 1), as well as in the expectancy and value model

(Model 2). The three forms of belief-values also established positive associations with their

corresponding directly-measured social cognitive variables in Model 2. The proportion of

explained variance in the attitude (R2model 1 = .19, R2

model 2 = .21), subjective norm (R2model 1 =

.09, R2model 2 = .11), and PBC (R2

model 1 = .08, R2model 2 = .19) variables with the inclusion of

belief-values in Model 2 deemed higher than that in Model 3. In the expectancy-value

multiplicative model (Model 3), the multiplicative composites of behavioral, normative, and

control beliefs were positive predictors of attitude, subjective norm, and PBC respectively,

but the proportion of explained variance among each directly measured variable was

comparable to that of Model 1, and was smaller than that of Model 2. In the full model

(Model 4), all the belief-expectancies, belief-values, and multiplicative composites

established positive relationships with their corresponding direct measures, apart from

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 16

motivation to comply, and the multiplicative composite of behavioral belief and control belief,

which were not significantly related to their proposed direct measures. Indeed, the full model

with the inclusion of all the hypothesized exogenous latent factors did not appear to explain a

higher proportion of variance of the social cognitive variables than the other models,

particularly Model 2. Finally, consistent across all models, intention was positively predicted

by subjective norm and PBC, but its proposed positive relationship with attitude was not

significant.

In relation to mediation analyses, we only observed a few significant mediation effects

among the variables in our models due to the non-significant association between directly

measured attitude and intention. In particular, subjective norm and PBC fully mediated the

effects of their corresponding belief-based indirect measures on intentions in Models 1 and 3.

PBC fully mediated the relationships between control belief-power and intention in Model 4.

Although attitude was not shown to be a significant mediator in any model, behavioral belief-

strength and outcome evaluation formed positive direct effects on intention in all models.

Discussion

Based on the theory of planned behavior, the present study explored the relationships

between the indirect belief-based measures derived from modal salient beliefs and the

directly-measured of the social cognitive variables from the theory in the context of doping

avoidance in sport. Based on data from the pilot belief-elicitation study and the input of

experts, we developed a set of items that measure the belief-expectancies (behavioral,

normative, and control belief-strength) and belief-values (outcome evaluation, motivation to

comply, and control power) according to Ajzen’s expectancy-value model for formulating the

modal salient beliefs that would theoretically be regarded as indirect antecedents of the social

cognitive factors outlined in the TPB. We examined four possible models for the impact of

the indirect measures of belief-expectancies and belief-values on the directly-measured

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 17

attitude, subjective norm, and perceived behavioral control variables with respect to doping

avoidance according to French and Hankins’ (2003) recommendations.

Results of a series of variance-based structural equation models revealed that the

measurement component of the indirect measures exhibited strong convergent, discriminant,

and predictive validity. Results from the structural component of the models generally

showed that the expectancy-value multiplicative composites, in which the indicators of

indirect measures of the TPB variables were the cross-products of belief-expectancy and

belief-value components, had only marginally better predictive power as the belief-

expectancy components alone in explaining variance in the direct measures of the social

cognitive factors for doping avoidance. Therefore, using the strength (i.e., belief-expectancy)

of behavioral, normative, and control beliefs as predictors of attitude, subjective norm, PBC,

and intention of doping avoidance appeared to be the most parsimonious model that explains

the relationships between indirect and direct measures of the social cognitive variables for

doping avoidance in sport.

The Expectancy-Value ‘Muddle’

The model in which the expectancy-value multiplicative components were predictors

of the direct measures (Model 3) only yielded slightly better path estimates and goodness of

fit as the model in which the predictors of the direct measures were the belief-expectancy

variables alone (Model 1). The pattern of results did not reflect heightened measurement error

in assessing the modal salient beliefs identified in previous research (French & Hankins, 2003;

Sullivan, et al., 2008). This could be due to the robust psychometric properties of the

measures of the modal salient beliefs in the anti-doping context used in the current study, and

the use of unipolar scaling in the assessment. It is important to note that assessment and

statistical modeling for the expectancy-value multiplicative composites was far more

demanding and complex than that of the belief-expectancy model (French & Hankins, 2003;

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 18

Sullivan, et al., 2008). Therefore, we concur with the perspective of French and Hankins

(2003) that the expectancy-value multiplicative approach should only be used when the study

concerns the expectancy-value hypothesis of TPB or when the individual belief-expectancy or

belief-value components are manipulated in an experimental factorial design. However, when

the study is merely interested in the relative contribution of the indirect belief-based measures

on the direct attitude, subjective norm, and PBC measures, omitting the multiplicative

composites is preferable as it exhibits the greatest parsimony, requires fewer assumptions at

the measurement level, and potentially places less response demand on participants (French &

Hankins, 2003).

However, we should point out that the model including both expectancy and value

components (Model 2), referred to as the conjoint model by French and Hankins (2003), and

the full model containing both sets of beliefs and the multiplicative composites (Model 4)

brought forth higher averaged explained variance in the direct measures than the two other

models, but the differences of the explained variances were marginal and should be

considered unsubstantial. Possible reasons for the differences was the high degree of

covariances among the belief-expectancy, belief-value, and expectancy-value multiplicative

composite components, thus the inclusion of the additional latent exogenous factors did not

lead to a substantive increase in the total variance explained in the directly-measured social

cognitive factors. This phenomenon could also be explained by the plausible relationships

between the belief-expectancy and belief-value components in the context of doping

avoidance. In terms of behavioral beliefs, young athletes might tend to form better

impressions regarding the doping avoidance behaviors when the behavioral outcomes are

expected to be positive. In terms of normative beliefs, a majority of young athletes could be

more likely to comply with the significant others (e.g., coaches, family; Chan, Lonsdale, &

Fung, 2012) who supported their endorsement of doping avoidant behaviors. For control

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 19

beliefs, it was highly possible that young athletes who perceived more challenges for doping

avoidance were likely to rate the execution of the behaviors in the future as more difficult.

These possible causal mechanisms not only reflect the redundancy problem of the inclusion of

the value component when tapping modal salient beliefs, but it also provides a plausible

explanation as to why belief-expectances alone were adequate in predicting the direct

measures in the current context of doping avoidance.

Modal Salient Beliefs

In addition to examining the expectancy-value model of beliefs within the TPB in the

context of anti-doping, this study also identified the modal salient beliefs with respect to

doping in elite and sub-elite athletes using an open-ended elicitation study as recommended

by Ajzen (1985, 2002). It is important because the indirect effects of the beliefs on intention

via the direct measures of the social cognitive variables indicate the extent to which the

variance in the beliefs about doping that is shared by the intentions is subsumed by the global

measures of attitudes, subjective norms, and perceived behavioral control. This is important

theoretically as many studies adopting the theory to study doping behaviors tend to adopt

global measures only (e.g., Chan & Hagger, 2012a, 2012b; Chan, et al., 2012; Hagger,

Chatzisarantis, & Biddle, 2002; Hardeman, et al., 2002). If these measures do not account for

the effects of the beliefs on intentions, then research adopting direct measures alone may not

be capturing the full gamete of factors likely to account for variance in the target behavior.

This may provide impetus for researchers to focus on the beliefs rather than the direct

measures alone.

By basing our measure of beliefs on the most frequently identified in the elicitation

study we were able to provide a close approximation of young athletes’ feelings and

perceptions toward doping avoidance in sport. This set of salient belief measures included in

our questionnaire enriches previous research that adopted generalized direct measures of the

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 20

social cognitive components of the TPB using a purely top-down process for scale

development (Sullivan, et al., 2008). This is important because it provides greater precision in

the specific beliefs that underpin direct measures of TPB constructs and makes it more

relevant to applied settings because these are likely to be the beliefs that are targeted in

interventions designed to change attitudes, subjective norms and PBC and, through this,

fostering positive anti-doping intentions and behaviors.

In terms of the specific content of the belief-expectancies in the current study, the

behavioral belief items covered a number of aspects with respect to doping such as fairness,

performance enhancement, health side-effects, and punishment for doping, frequently

regarded as the core components that comprise athletes’ attitudes towards doping (Goulet, et

al., 2010; Lucidi, et al., 2008; Ntoumanis, et al., 2013; Wiefferink, et al., 2008; Zelli, et al.,

2010). The normative belief items reflected perceived influence of coaches, teammates,

friends, family, supporters, and all other people in the social environment of young athletes

(Chan, et al., 2012), that are in line with the significant social agents often considered

influential on athletes’ anti-doping intentions and behavior (Connor, Woolf, & Mazanov,

2013; Lentillon-Kaestner & Carstairs, 2010; Lentillon-Kaestner, Hagger, & Hardcastle, 2012;

Strelan & Boeckmann, 2003). The control belief items included a number of coping strategies,

such as refusal of prohibited substances or methods and maintaining knowledge of prohibited

substance lists, and perceived challenges, such as being in the situations that unintentional

doping is likely, that young athletes’ perceptions would facilitate or inhibit their anti-doping

behaviors (Chan, et al., in press; Donovan, Egger, Kapernick, & Mendoza, 2002; Gucciardi,

et al., 2011). The content of these modal salient beliefs and their corresponding assessment

tool developed alongside this study, convey useful information and resources for practitioners

and educators interested in promoting anti-doping. Such materials may aid the development

of intervention strategies using either print or oral communication that target and reinforce the

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 21

salient behavioral, normative, and control beliefs, in particular, as they have been shown

empirically to impact, directly or indirectly, on intentions of anti-doping behavior in the

current study.

Social Cognitive Processes in the Theory of Planned Behavior

In relation to prediction of intention by the three fundamental directly measured

social-cognitive variables from the TPB, the attitude, subjective norm, and PBC variables

were positively correlated with intention as we expected, so the findings were congruent with

previous literature adopting the TPB in doping in sport (Connor, et al., 2013; Ehrnborg &

Rosén, 2009; Lamont-Mills & Christensen, 2008; Lentillon-Kaestner & Carstairs, 2010).

Nevertheless, when included in an omnibus test of the theory, the effect of attitude on

intention was non-significant. This finding is in stark contrast to the majority of studies

examining the role of attitudes toward doping intentions and behavior using the TPB (Goulet,

et al., 2010; Lucidi, et al., 2008; Ntoumanis, et al., 2013; Wiefferink, et al., 2008; Zelli, et al.,

2010), but not exclusively and there have been occasions where this effect has been rejected

(for a discussion see Sniehotta, Presseau, & Araújo-Soares, 2014). One possible reason for

this result might be that that attitudes only predict intentions in the context of doping behavior

(Ehrnborg & Rosén, 2009; Petroczi, et al., 2008; Strelan & Boeckmann, 2003). However,

when it comes to doping avoidance, attitudes may be less important while subjective norms

and PBC, which were significant predictors of anti-doping intentions in the current study,

may play a more substantive role. This pattern of effects could imply that facilitating young

athletes’ intentions to avoid doping could be most effectively emphasized by focusing on

beliefs related to social influence and behavioral control towards the doping avoidance

behavior. However, the zero-order correlation matrix showed that attitude was significantly

related to intention and the relationship was only slightly smaller than the correlation of

between subjective norm and PBC. The non-significance of attitude in the VB-SEM could be

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 22

due to the notable amount of shared variance between attitude and subjective norm or PBC.

Therefore, the present pattern of results suggests that while attitude was related to doping

avoidance, its effect may have been attenuated by the inclusion of subjective norms and PBC,

which seem to be more salient predictors in this context. Further replication of these findings

is necessary and, importantly, conducting a systematic experimental or intervention

manipulation of each of the three belief-based psychological factors from the TPB that impact

on anti-doping behavior, similar to Sniehotta’s (2009) 2 x 2 x 2 design.

On a different note, the indirect measures of attitude, namely, behavioral belief

strength, outcome evaluation, and the behavioral belief expectancy-value multiplicative

component, formed slightly stronger correlations with intention than the direct measures of

attitude, so it appeared that the indirect measures of attitude that differentiated between

different specific beliefs with respect to anti-doping could be more indicative of the factors

that underpin young athletes’ intentions to avoid doping. It might be that the modal salient

belief items captured the essence of the attitude factor more effectively and were more closely

linked to individuals’ subjective feelings and attributions regarding doping avoidance. These

specific beliefs could be more accessible when young athletes responded to questions about

their subjective evaluations, and thus they were more relevant to the formation of intentions to

engage in the target actions. Our findings stress the importance of indirectly measuring

individuals’ modal salient beliefs in conjunction with direct measures of the three social

cognitive components from the TPB when it is used as a core framework to investigate the

psychological factors linked to doping avoidance in sport.

Limitations and Further Directions

A few limitations regarding the methodology of the current study should be pointed

out. The cross-sectional design meant we were unable to draw any casual inference regarding

our hypothesized effects, so future studies should employ a cross-lagged panel design or

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 23

latent growth curve model using longitudinally-assessed data to better infer the direction of

the relationships between the TPB indirect measures using modal salient beliefs, the direct

measures of social cognitive components, and intentions toward doping avoidance (Sniehotta,

2009). Self-report measures could also make responses more vulnerable to social desirability,

particularly for questions related to doping (Hagger & Chatzisarantis, 2009). The present

study variables primarily focused on the avoidance of doping, which is a socially-acceptable

behavior among athletes, so participants might feel more inclined towards providing their true

ratings for these items than they would for items related to doping. However, our measure of

modal salient beliefs was constructed based on an open-ended belief-elicitation survey in

which we asked questions about athletes’ beliefs regarding use of banned performance-

enhancing substances (Ajzen, 2002). The responses we received were predominantly focused

on anti-doping behaviors with very few beliefs expressed regarding engaging in doping. The

heavy focus on anti-doping in participants’ responses could have been due to social

desirability, regardless of the assurances provided of the confidentiality and anonymity of the

survey. Also, we could not assume that the measures built upon these responses could

adequately capture the full compliment of salient beliefs of doping avoidance. The

involvement of expert review could improve the coverage of the items, but it is important for

future studies to evaluate and refine the items using the pilot belief-elicitation study data with

an exclusive emphasis of doping avoidance with an expert panel to evaluate the face validity

and clarity of the items (Aiken, 1985). In addition, the quantitative analysis using VB-SEM

only allowed us to test the significance of a single model at global level. Further development

of statistical methods for contrasting alternative models in partial least squares path modeling

is warranted. Such techniques will enable the generation of stronger evidence as to whether

the target model is statistically superior relative to other plausible models. Finally, the

generalizability of the results could be limited because the participants were adolescent

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 24

athletes who were generally competitive in several major sport events in Australia, so

replications of the study among athletes in other age-groups, cultures, and sports (e.g.,

weightlifting, body-building) is desirable.

In terms of the theoretical boundaries, the modal salient beliefs only represent the

accessible beliefs commonly shared among individuals (Aiken, 1985). Despite adopting an

open-ended elicitation study and a qualitative design to construct items for belief-expectancy

and belief-value constructs, our use of modal (most frequent) beliefs means that we may not

have captured all of the salient beliefs for each individual. There could be individually-

elicited salient beliefs unique to certain athletes. This could be one of the reasons why modal

salient beliefs did not form perfect associations with the directly-measured social-cognitive

components of the TPB. An alternative solution would be to assess individually-elicited

salient beliefs by asking respondents to list and rate the belief-expectancies and belief-values

of their personally valued beliefs (French & Hankins, 2003; Newton, Ewing, Burney, & Hay,

2012). Although this method might potentially create some problems and difficulty in terms

of data-collection and analysis (e.g., inconsistency in the measurement model), this approach

is worthy to be explored in order to bridge the gap between indirectly-measured salient beliefs

and direct measures of the TPB variables.

Finally, future studies should investigate the possibility of implementing the

intervention strategies by systematically mapping the modal salient beliefs that were

empirically shown to be important to young athletes’ intentions and behavior with respect to

doping avoidance. However, the efficacy of the intervention should be tested using a fully-

factorial and randomized-controlled designs, in which the impact of behavioral strategies on

modifying attitudes, subjective norms, and PBC could be examined independently.

Conclusions

The present study extended the application of the TPB to important aspect of anti-

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 25

doping behaviors, namely, the avoidance of doping. Findings offered support for the

effectiveness of subjective norm and PBC in predicting intentions toward doping avoidance.

Moreover, this preliminary investigation of the expectancy-value model of the TPB revealed

that the directly-measured social cognitive components are closely associated with the belief-

expectancy, belief-values, and the expectancy-values multiplicative components that are

purported to underpin them in Ajzen’s (1985, 1991) original conceptualization of the model.

Our analysis is unique in that it also examined multiple models of the potential effects of

these belief-based components on direct measures of the TPB constructs and intention

(French & Hankins, 2003; Newton, et al., 2012; Sullivan, et al., 2008), an endeavor that has

received very little attention in the literature. This is important as it enables researchers not

only to identify the relative contribution of beliefs-expectancies and belief-values, and their

interaction, on intentions and behavior with respect to doping avoidance, but also provides

possible targets for interventions to promote anti-doping behaviors. In our study, a model

which included belief-expectancies alone was robust and the most parsimonious model that

exerted comparable predictive power in the prediction of the directly-measured social

cognitive factors and intention. Hence, the predictions of belief-expectancies toward attitude,

subjective norm, and PBC imply that young athletes are more likely to intend to be aware of

and manage the risk of doping when they perceive the behavior to be personally beneficial,

important, achievable, and be supported by people that they value.

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 26

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Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 30

Table 1 Correlation matrix and convergent validity indices. Correlations

1 2 3 4 5 6 7 8 9 10 11 12 13 1. BbeliefS 1 2. NbeliefS .47** 1 3. CbeliefS .07 .15** 1 4. OutcomE .47** .40** .19** 1 5. Mcomply .17** .07 .07 .13* 1 6. CbeliefP .41** .39** .27** .36** .03 1 7. BehavM .83** .47** .16** .81** .17** .42** 1 8. NormM .27** .28** .10* .20** .97** .12* .26** 1 9. ContrM .31** .32** .61** .31** .03 .86** .34** .10* 1 10. Attitude .43** .48** .17** .34** .02 .40** .42** .13* .35** 1 11. Norm .29** .28** .14** .32** .09* .35** .33** .15** .29** .44** 1 12. PBC .25** .27** .16** .26** .13** .37** .28** .19** .33** .39** .43** 1 13. Intention .29** .25** .11* .27** .13** .23** .30** .18** .19** .26** .41** .43** 1

Mean 5.34 5.67 6.65 4.98 3.54 2.22 32.73 33.18 8.54 6.35 6.29 5.93 6.18 SD .94 1.09 .73 2.00 1.69 1.35 8.93 14.37 7.61 1.09 1.37 1.53 1.60 α .72 .85 .83 .81 .96 .84 .81 .95 .84 .82 .70 .86 .89

CR .73 .89 .89 .86 .97 .89 .85 .96 .89 .87 .81 .90 .93 AVE .54 .53 .62 .61 .80 .61 .61 .77 .62 .53 .54 .64 .82

F-Loading .65 .73 .77 .63 .89 .78 .61 .88 .78 .73 .71 .80 .90 C-Loading .22 .17 -.11 .20 .13 -.21 .25 .17 .08 .16 .16 .14 .14

Note. BbeliefS = behavioral belief-strength; NbeliefS = normative belief-strength; CbeliefS = control belief-strength; OutcomE = outcome evaluation; Mcomply = motivation to comply; CbeliefP = control belief-power; BehavM = behavioral belief multiplicative composite; NormM = normative belief multiplicative composite; ContrM = control belief multiplicative composite; Norm = subjective norm; PBC = perceived behavioral control; Intention = intention of doping avoidance; CR = composite reliability; AVE = average variance extracted; F-loading = mean factor loadings; C-Loading = mean cross-loadings. **p < .01 at 2-tailed, *p < .05 at 2-tailed.

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 31

Table 2 Results of main effects, fit indices, and mediation analysis.

Model IVs Mediators Effect IVs -->

Mediators R2

Effect Mediators -->

Intention R2 GoF ARS AVIF APC Direct

Effect Combined

Effect Indirect Effect

Type of Mediation

1 Expectancy Model

BbeliefS Attitude .43** .19 .07 .29 .31 .16** 1.44 .27**

.21** .10* .02 None NbeliefS Norm .30** .09 .19* .13* .02 .05* Full CbeliefS PBC .28** .08 .38** .10* .01 .10** Full

2 Expectancy and Value Model

BbeliefS Attitude

.34** .21 .07

.29 .35 .20** 1.3 .23**

.10* .08 -.01 None OutcomE .19** .13** .03 .04 None NbeliefS

Norm .24**

.11 .19* .05 .02 -.05 None

Mcomply .16** .16** .07 -.01 None CbeliefS PBC .13* .19 .38** .08 0 .03 None CbeliefP .38* .05 .07 .14** None

3

Expectancy-Value Multiplicative Model

BehavM Attitude .43** .18 .07

.29 .34 .18** 1.44 .29**

.18** .08 -.02 None NormM Norm .30** .09 .19* .20** .09 .05* Full

ContrM PBC .38** .15 .38** .10* .06 .14** Full

4 Full Model

BbeliefS Attitude

.38** .19 .07

.29 .36 .20** 4.03 .18**

.14* .10 .03 None OutcomE .23* .16** .05 -.01 None BehavM .07 .07 .02 .00 None NbeliefS

Norm .20**

.12 .19* .03 .00 .03 None

Mcomply -.01 .07 .00 .00 None NormM .19** .11 .09 .03 None CbeliefS

PBC .12**

.20 .38** .10** .03 .05 None

CbeliefP .37** .08 .04 .14** Full ContrM .00 .05 .06 .00 None

Note. BbeliefS = behavioral belief-strength; NbeliefS = normative belief-strength; CbeliefS = control belief-strength; OutcomE = outcome evaluation; Mcomply = motivation to comply; CbeliefP = control belief-power; BehavM = behavioral belief multiplicative composite; NormM = normative belief multiplicative composite; ContrM = control belief multiplicative composite; Norm = subjective norm; PBC = perceived behavioral control; Intention = intention of doping avoidance; GoF = goodness of fit index; ARS = averaged R-squared; AVIF = averaged variance inflation factor; APC = averaged path coefficient. **p < .01 at 2-tailed, *p < .05 at 2-tailed.

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 32

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 33

Figure 1. Proposed variance-based structural equation models 1 to 4 depicting the latent factors, indicators, and path estimates in each. The indicators of each latent factor are the scores or composite scores of the items of the Expectancy-Value Model. The corresponding items or computation processes of each indicator are shown. AV = behavioral belief-strength, AS = outcome evaluation, Att = attitude, NV = normative belief-strength, NS = control belief-power, Norm = subjective norm, CV = control belief-strength, CS = control belief-power, PBC = perceived behavioral control.

Running head: MODAL SALIENT BELIEFS OF DOPING AVOIDANCE 34

Appendix A Modal Salient Belief Items

Dimension Items Anchors Behavioral

Belief Belief-strength

Stem: Avoiding using banned performance-enhancing substances/ methods in the forthcoming month will…

1. … Help fully utilise my own ability in sport 2. ... Allow me to compete fairly against other players 3. … Protect me against the potential negative side effects of drugs on my health 4. … Ease my worries about being caught doping 5. … Prevent me from the punishments and lengthy ban for doping 6. … Keep me away from drugs that might lead to addiction 7. … Lead to poorer results/victories/wins in competitions 8. … Give me disadvantages against other competitors 9. … Weaken my physical ability to train/ recover in sport

1 = extremely unlikely, 7 = extremely likely

Outcome Evaluation

1. Fully utilising my own ability in sport is … 2. Competing fairly against other players is … 3. Not having any potential negative side effects of drugs on my health is … 4. Not having any worries about being caught for doping is … 5. Not receiving any punishments and lengthy ban for doping is … 6. Being away from drugs that might lead to addiction is … 7. Poorer results/victories/wins in competitions are … 8. Having disadvantages to other players in competitions is … 9. Having poorer physical ability to train/ recover in sport is …

1 = bad, 7 = good

Normative Belief

Belief-strength

1. My coach thinks that I should/ shouldn’t … 2. My teammates think that I should/ shouldn’t … 3. My close friends think that I should/ shouldn’t … 4. My family think that I should/ shouldn’t … 5. My supporters (e.g., fans, spectators) thinks that I should/ shouldn’t … 6. The media think that I should/ shouldn’t … 7. People in my community think that I should/ shouldn’t … Suffix: …avoid using banned performance-enhancing substances/ methods in the forthcoming month

1 = I should not, 7 = I should

Motivation to comply

How much I want to do what this person thinks I should do? 1. My coach 2. My teammates 3. My close friends 4. My family 5. My supporters (e.g., fans, spectators) 6. The media 7. People in my community

1 = not at all, 7 = very much

Control Belief

Belief-strength

1. I expect that I will have limited power to “say no” to banned performance-enhancing substances/ methods in sport in the forthcoming month

2. I expect that I have limited knowledge to ensure the supplements or medication I take do not contain banned performance-enhancing substances/ methods in sport in the forthcoming month

3. I expect that there will be a lot of challenges to avoid being in the situations where I might unintentionally take banned performance-enhancing substances/ methods in sport in the forthcoming month

4. I expect that I will have limited opportunities to seek information or advice on anti-doping in sport in the forthcoming month

5. I expect the doping tests will reveal whether or not I have managed to avoid using banned performance-enhancing substances/ methods in sport in the forthcoming month

1 = strongly disagree, 7 = strongly agree

Belief-power

1. My ability to “say no” to the banned performance-enhancing substances/ methods in sport would make it …

2. My knowledge to ensure the supplements or medications I take do not contain banned performance-enhancing substances/ methods in sport would make it …

3. The challenge of having to avoid situations where I might unintentionally take the banned performance-enhancing substances/ methods in sport would make it …

4. Having opportunities to seek information or advices on anti-doping in sport would make it …

5. Taking doping tests would make it … Suffix: …for me to avoid using banned performance-enhancing substances/ methods in sport in the forthcoming month

1 = difficult, 7 = easy

Note. Reverse-scored items are presented in italic format.