Proceedings of Applied International Business Conference 2008
ALTERNATIVE MODELS TESTING IN PREDICTING CONSUMER HEALTHY LIFESTYLE
BEHAVIOUR: AN APPLICATION OF STRUCTURAL EQUATION MODELLING
Yap Sheau Fen, Crystal ψ
KDU College, Malaysia
Abstract
This study investigates the social cognitive factors affecting healthy lifestyle behaviour by comparing three
alternative models using structural equation modelling. Cross-sectional data was collected via selfadministered surveys from general adults (N = 512). Specific findings were: (1) TPB was found to be more
superior than TRA; (2) the path from subjective norm to attitude was significant; (3) attitude and perceived
behavioural control (PBC) significantly predicted exercise intention and attitude had the strongest direct
and total effect on exercise intention across the three competing models; (4) only PBC and intention had
direct effects on exercise behaviour. Implications of this study are discussed.
Keywords: Healthy lifestyle; Theory of planned behaviour; Theory of reasoned action; Exercise; Structural
equation modelling.
JEL Classification Codes: M31; I18; I11.
1. Introduction
There are increasing concerns about health and fitness especially among urbanite and higher social class
consumer groups. The heightened public awareness and the evolvement of consumer healthy lifestyle
certainly creates new opportunities and at the same time posing marketing challenges to marketers in the
health-related industries. A good understanding of the shifting consumer social psychological factors
influencing healthy lifestyle behaviour is integral to capitalizing on these business opportunities available.
In marketing sense, healthy lifestyle behaviours are related to a set of activities, interests, and opinions
orientated toward the consumption of various goods and services. The demand for health-related products
and services is largely being driven by market segments which are health conscious and have tendency
towards adopting and/or maintaining healthy lifestyle (Divine and Lepisto 2005). Generally, people rely on
regular exercise as a means to maintain both their physical health and psychological well-being (Plante and
Rodin 1990). Given the fact that exercise is recognized as an important aspect of healthy lifestyles and
desired health behaviour, the present paper focuses on exercise participation as one of the component of
healthy lifestyle behaviour.
A social psychology model frequently used to explain exercise behaviour and behavioural intentions is the
Theory of Planned Behaviour (TPB; Ajzen, 1985, 1991). Early TPB studies have mostly focused on
exploring the influence of its predictors on exercise intention and behaviour. Some TPB researchers have
introduced additional predictor such as past behaviour and social support to the model with an attempt to
improve the predictive ability of the TPB model. There were also efforts in integrating the TPB with other
health behavioural model such as the Transtheoretical Model (TTM) in understanding individual exercise
behaviour (Courneya and Bobick 2000). Although some of these previous studies have demonstrated that
their proposed model was adequate for explaining the hypothesized links between constructs in the
theoretical model. There may well be other models that could achieve better fit to the data. In this
circumstance, alternative models with different hypothetical structural relationships may be tested against
each other to determine which has the best overall fit to the empirical data (Byrne 2001).
Ryu, Ho, and Han (2003) and Hansen, Jensen, and Solgaard (2004) conducted a model comparison of the
TRA and TPB in predicting knowledge sharing and online grocery buying intention, respectively. It should
ψ
Corresponding author. Yap Sheau Fen, Crystal. KDU College, Kuala Lumpur, Malaysia. Email:
[email protected]
Proceedings of Applied International Business Conference 2008
be highlighted that these two studies include only behavioural intention not actual behaviour in their
competing models. The measurement of actual behaviour might produce different findings. Although there
is considerable theoretical and empirical literature indicating that intent and behaviour are highly correlated
(Ajzen 1991; Fishbein and Ajzen 1975). The fact remains, however, that it is unknown if those people who
intended to exercise actually do so eventually (Milne, Orbell and Sheeran 2002). The focus on attitude and
subjective norms in predicting intentions, not the behaviour tend to pose problem with the use of TPB or
TRA models (T. Baranowski, Cullen and J. Baranowski 1999). The abovementioned issues will be
addressed in the present study. The purpose of this study is to empirically test the ability of two theories the theory of reasoned action (TRA) (Fishbein and Ajzen 1975) and the TPB - in predicting individual
exercise behaviour. A comparison of the two theories is conducted by employing overall model fit,
explanatory power, and path significance using structural equation modelling (SEM). This paper also aims
to test whether the inclusion of a path from subjective norm to attitude (Model 3) will improve the
predictive power of the specified model.
2. Theoretical background
TRA and TPB
The TPB extended the TRA by adding the perceived behavioural control (PBC) construct because the TRA
has difficulty in explaining behaviours in which a person does not have volitional control over it (Fishbein
and Ajzen 1975). The TPB model posits that intention to perform a given behaviour is the immediate
antecedent of that behaviour (Ajzen 1991). Behavioural intention refers to the amount of effort a person
exerts to engage in behaviour. It captures the motivation factors necessary to perform a particular
behaviour. That is, the more a person intends to carry out the intended behaviour, the more likely he or she
would do so (Armitage and Conner 1999). Intention is determined by three conceptually independent
variables labelled attitude, subjective norms and PBC. Generally, the more favourable the attitude and
subjective norm, and the greater the PBC, the stronger should be the individual’s intention to perform a
particular behaviour (Ajzen 2002). Theoretically, these three social cognitive constructs are very distinct
concepts (Ajzen 1991). Numerous studies in the social and behavioural research have been conducted to
demonstrate the utility of the distinctions by showing that these differentiated constructs stand in
predictable relations to intentions and behaviour (Armitage and Conner 2001).
In terms of predictability, there are sufficient empirical evidence to indicate that the addition of PBC to the
original TRA model has yielded significant improvements in the prediction of intention and behaviour
(Ajzen 1991). In comparing the predictability of TRA and TPB model, several meta-analyses have
provided support that the TPB is a useful model for predicting behavioural intentions and behaviour in
variety of context. For instance, a review of 16 studies using TPB by Ajzen (1991) revealed a considerable
amount of explained variance in intentions can be accounted for by attitude, subjective norm, and PBC;
with an average correlation of .71. For health behavioural studies, Godin and Kok’s (1996) meta-analyses
found that PBC contributed a mean additional variance of 13% to the prediction of intentions and 12% to
the prediction of behaviour. Specifically, Hausenblas, Carron and Mack (1997) report a meta-analysis on
applications of the TRA and TPB to exercise behaviour and conclude that the TPB is more useful than the
TRA in the exercise domain. Numerous studies have provided empirical evidence that the TPB is a more
superior model compared with the TRA. Nevertheless, these studies were either meta-analyses or empirical
research comparing the superiority of TPB to TRA based merely on the predictive power (i.e. R-square
value). Therefore, there is a need to conduct a simultaneous testing of alternative theoretical model to
confirm the superiority of TPB over the TRA model.
The inclusion of a path from subjective norm to attitude
Issues related to the relationship between the attitude and subjective norm influence have been raised since
Miniard and Cohen (1979) criticized that Fishbein and Ajzen did not address the conceptual and
measurement distinction between these two constructs explicitly. Although Fishbein and Ajzen
acknowledged that attitudinal and normative influences might be related to each other, they insisted the
separation of attitudes and subjective norms as they could exert different influences on different types of
behaviours (Fishbein and Ajzen 1975). Several researchers proposed the modified version of TPB by
including an additional path from subjective norm to attitude. For instance, Choo, Chung and Pysarchik
(2004) reported subjective norm to have a positive effect on Indian consumers’ attitudes toward processed
foods. Hansen, Jensen and Solgaard (2004) concluded that the modified TPB model (with the inclusion of a
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Proceedings of Applied International Business Conference 2008
path from subjective norm to attitude) provides the best model fit to the empirical data and explains the
highest proportion of variation in online grocery buying intention. This finding is consistent across the two
surveys conducted using different sampling. Therefore, the effects of subjective norm on attitude formation
should not be overlooked based on these empirical evidences.
According to the TPB theory, subjective norm is hypothesized to have a direct effect on behavioural
intention. In the exercise domain, although subjective norm often reported to have a significant bivariate
correlation with intention (Ajzen and Driver 1992) but it has been demonstrated as a poor predictor of
exercise intention. Several scholars agreed that this normative component of the TPB is a relatively weaker
predictor of behavioural intentions (Ajzen 1991; Armitage and Conner 1999). For instance, in their metaanalysis on the TPB studies, Armitage and Conner (2001) concluded subjective norm to be a very weak
predictor of intentions. Meanwhile, past research has consistently shown a strong correlation between
attitudes and subjective norms (Ajzen 2001). Hence, it is possible that subjective norm have an indirect
influence on intention through the attitude construct (Baron and Kenny 1986). Indeed, Tarkiainen and
Sundqvist (2005) reported subjective norm to have an indirect effect on purchasing of organic food through
attitude formation. Similarly, Hansen, Jensen and Solgaard (2004) found the indirect effect of subjective
norm on online grocery buying intention through attitude was in both surveys larger than its direct effect on
intention. The mediating role of attitude between subjective norm and behavioural intention will be
addressed in the present study.
From the perspective of social psychology, the TRA and TPB have gained substantial empirical support. A
number of studies have shown subjective norm to have direct influence on attitude as discussed. The three
alternative models examined in the present study are TRA (Model 1), TPB (Model 2), and a modified TPB
model that include a path from subjective norm to attitude (Model 3). The investigated research model is
shown in Figure 1. The prediction of exercise behaviour will be included in the research design. Based on
the literature review discussed, it is hypothesized that: (1) the TPB model is a better fitted model compared
to the TRA in explaining exercise intention and behaviour; (2) the additional path from subjective norm to
attitude is significant and the inclusion of this path will improve the predictive power of exercise behaviour.
Attitude
1
6
Subjective
Norm
2
Exercise
Intention
3
Exercise
Behavior
4
5
TRA: path 1 + 2 +3
TPB: path 1 + 2 + 3 + 4 + 5
Modified TPB: 1 + 2 + 3 + 4 + 5 + 6
PBC
Figure 1: Investigated research model
3. Research method
This section provides a description of the research instrument design, sampling procedure and data
collection technique. The study was carried out using a survey approach.
Research instrument
The survey instrument was a ten-page questionnaire that consists of three sections. Items measuring attitude
and PBC components were adapted from Hagger and Chatzisarantis (2005) and Rhodes and Courneya
(2003), whereas items measuring subjective norm components were taken from Rhodes, Blanchard and
Matheson (2006). Measurements of exercise intention were derived from Hagger and Chatzisarantis (2005)
and Rhodes, Blanchard, Matheson and Coble (2006). All items for the TPB predictors were assessed
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directly on 7-point Likert-type scales range from “strongly disagree” to “strongly agree”. The Godin
Leisure Time Exercise Questionnaire (GLTEQ; Godin and Shephard 1985) was adapted to assess exercise
behaviour since it is a simple, self-administered, validated, and reliable measure. Participants were asked to
complete the GLTEQ composed of three self-report items assessing the weekly frequency of strenuous,
moderate, and mild levels of exercise during leisure time for periods of 15 minutes on an open scale. The
questions have been modified to suit local context. For instance, “cross country skiing” and “alpine skiing”
activity have been excluded whilst popular activities like “Taichi” and “Chi gong” have been included in
the mild exercise category. Background information of the respondents such as gender, age, ethnicity,
religion, marital status, level of education, income, and occupation were also included. The questionnaires
were pre-tested to ensure clarity and ease of comprehension. After an explicit discussion with the
respondents, several correction and modifications were made in terms of the wording, presentation and
structure of the questionnaire.
Sampling procedure and data collection method
Participants for the present study were general adults (18 to 65 years of age) recruited by student helpers
through their informal contact. Subjects were considered qualified if they reported performing exercise
activities at least once a week during leisure time for at least 15 minutes in duration each time for the last 3
months. To provide an adequate level of confidence in this study, a sample size of 600 respondents was
targeted. Quota sampling was employed to ensure that respondents are drawn from different demographic
backgrounds. Based on the composition of the total population of Malaysia, the study set a 50-50 quota for
gender, 50-30-20 quota for ethnic group (Malay, Chinese, and Indian). The Indian group was set to be 20%
to capture more Indian respondents. Data were collected from the subjects using personally administered
questionnaires. A verbal consent was obtained from the participant prior to distributing the questionnaire.
All participants were informed that participation in the study was on voluntary basis and that information
provided will be kept confidential. The exercise of data collection was conducted both during weekdays
and weekends. Exercise is an urban phenomenon; it is thus justifiable for the present study to be conducted
in the Klang Valley areas since it is the largest urban centre in Malaysia.
4. Measurement model results
Model specification
Exercise behaviour is measured using GLTEQ which consists of three open questions. Based on predefined
metabolic units (METs), a composite score was computed to indicate how physically active respondents
were. Although multiple indicators were more desirable (Rhodes, et al. 2006), construct with single
indicator is still allowed in the use of SEM (Hair, Black Babin, Anderson and Tatham 2006). This can be
done by fixing a priori error estimates on single indicator and place theoretical constraint within the model
(Hair, et al 2006). Following previous practices in the exercise domain, the single indicator of exercise
behaviour construct was fixed to 40% measurement error estimate based on previous research identifying
the proportion of error in self-reported exercise (Courneya, Estabrooks and Nigg 1997).
Internal consistency reliability
Internal consistency reliability to test unidimensionality was assessed by corrected item-total correlations
(CITC) and Cronbach’s alpha. One subjective norm item (“most people who are important to me do not
think I should exercise”) with CITC scores lower than 0.5 was eliminated from further analysis. The alpha
values for each construct was well above the recommended value of 0.70, which is considered satisfactory
for basic research (Nunnally 1978). However, there are several limitations associated with the use of
Cronbach’s alpha, including the fact that the alpha value is inflated as the larger number of items included
in a scale (Sekaran 2000). Additionally, satisfactory Cronbach’s alpha value does not indicate
unidimensionality of a particular scale (Gerbing and Anderson 1988). Hence, confirmatory factor analysis
is employed for the assessment of unidimensionality in the present study.
Confirmatory factor analysis (CFA)
Based on data collected from 512 respondents, the measurement model was revised and confirmed using
confirmatory factor analysis. Convergent validity and discriminant validity were performed to ensure data
validity and reliability. Maximum likelihood estimation procedure using Analysis of Moment Structures
(AMOS) version 7.0 was employed. The model fit was assessed by Chi-square and Normed χ²/df value,
coupled with other model fit indices like Goodness-of-Fit Index (GFI), Comparative Fit Index (CFI),
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Proceedings of Applied International Business Conference 2008
Tucker-Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA). The cut off value
for the goodness of fit indices was based on Hu and Bentler’s (1999) recommendation.
As it was inadequate to lend sufficient empirical support for the initial measurement model, some
modification needed to determine a model that better fit the data. After these modifications were made, the
fits indices for the final CFA improved (χ² = 636.24, χ²/df = 2.392, GFI = 0.905, TLI = 0.945, CFI = 0.951,
RMSEA = 0.052). The revised measurement model has achieved satisfactory model fit. The chi-square
value was expected to be significant due to large sample size. Instead, the Chi-square normalized by
degrees of freedom (χ²/df) was referred to. An acceptable ratio for χ²/df value was reported. The three fit
indices for GFI, TLI, and CFI were greater than .90 thresholds for acceptability. The RMSEA value also
reported to be below the cut-off value of .06 for good model fit as recommended by Hu and Bentler (1999).
It is worth noting that the model fit was improved using a conservative strategy, that is, none of the error
terms was allowed to covary in the measurement model. Further, the freeing of cross-loadings was also not
allowed since the existence of significant cross-loading indicated lack of construct validity (Hair et al.
2006).
Convergent validity. The convergent validity was assessed by checking the loading of each observed
indicators on their underlying latent construct (Anderson and Gerbing 1988). Table 1 presents the CFA
results which include standardized factor loadings, variance extracted and composite reliability. To assess
convergent validity, the standardized factor loading should be significantly linked to the latent construct
and have at least loading estimate of 0.5 and ideally exceed 0.7 (Hair et al. 2006). The findings indicated
that each factor loadings of the reflective indicators were statistically significant at 0.001 level and
exceeded the recommended level of 0.50. Additionally, two other criteria were assessed to ensure
convergent validity: (1) construct reliability should be greater than 0.7 (Nunnally 1978), and (2) variance
extracted (VE) for a construct should be larger than 0.5 to suggest adequate convergent validity (Fornell
and Larcker, 1981). As shown in Table 1, the composite reliability ranged from 0.855 to 0.918, meaning
that they all exceeded the 0.70 threshold for the social science research. The variance extracted value
exceeded the cut-off of 0.5 except for attitude construct. Nevertheless, the attitude was retained as
attitudinal is an important TPB construct. Moreover, the VE value of 0.494 for attitude is only slightly
below the cut-off and the reliability score was well above 0.80. In sum, all constructs of the measurement
model demonstrated adequate reliability and convergent validity.
Table 1: Confirmatory factor analysis for convergent validity
Construct
Attitude
Subjective Norm
PBC
Exercise Intention
No. of items
7
5
4
7
Item loading
0.501 – 0.770
0.774 – 0.915
0.624 – 0.906
0.656 – 0.814
Composite reliability
0.865
0.918
0.855
0.903
Variance Extracted (VE)
0.494
0.695
0.612
0.576
Note: VE and reliability could not be computed for exercise behavior as it is measured on single item open
scale.
Discriminant validity. To examine discriminant validity, chi-square difference between two models: the
unconstrained model and the constrained model were compared (Bagozzi and Phillips 1982). In the
unconstrained model, the covariance between particular two constructs was freely correlated. However, the
covariance of a certain two construct was fixed to 1.0 in the constrained model. Two constructs are claimed
as having well discriminant validity if the χ² difference between the two models is significant. A series of
chi-square difference tests were conducted and the results are shown in Table 2. The results indicated that
the differences in chi-square between the fixed and free solutions were significant (i.e. the minimum ∆χ² =
21.03, p < 0.05, df = 1). These χ² differences were much larger than the 3.84 threshold, indicating that each
pair of constructs was indeed distinct. The chi-square value for unconstrained measurement model was
significantly lower than any constrained models with the possible pair of constructs. In other words, the
findings revealed good discriminant validity in TPB measurement structure.
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Table 2: Measurement model fit: discriminant validity
Links
ATT – SN
ATT – PBC
ATT – INT
SN – PBC
SN – INT
PBC – INT
BEH – ATT
BEH – SN
BEH – PBC
Fixed correlation
d.f.
Chi square
54
347.93
44
303.54
77
413.7
27
157.0
54
259.35
44
206.64
21
252.52
10
27.41
6
73.09
Freely estimated correlation
Correlation
d.f.
.34
53
.59
43
.92
76
.30
26
.33
53
.64
43
.52
20
.29
9
.46
5
Chi square
221.18
208.54
341.05
36.18
110.55
99.21
163.71
6.38
10.41
Chi square
difference
126.75
95.0
72.65
120.82
148.8
107.43
88.81
21.03
62.68
Note: ATT = Attitude; SN = Subjective Norm; PBC = Perceived Behavioral Control; INT = Exercise
Intention; BEH = Exercise Behavior.
5. Structural model results
The alternative models were tested and compared using SEM. The same set of model fit indices used for
measurement model estimation was employed. Table 3 presents the model fit indices of the three
alternative models. The model fit indices for all three models exceeded their respective recommended
acceptable value, except for Model 3 that yielded GFI value of 0.90, slightly below the recommended cutoff value. The findings suggest that the three structural models achieved a good fit with the empirical data
compared against the recommended fit criteria. Table 3 also shows that the total variance in exercise
intention explained by its predictors is high (i.e. exceeded 80%) for all three models. The three alternative
models also explained a substantial amount of variance in exercise behaviour. Notably, TPB (SMCBEH =
.359) appeared to be more superior to TRA (SMCBEH = .348) and modified TPB (SMCBEH = .308) in
predicting subjects’ exercise behaviour. As expected, the comparative results in the model fit and explained
variance demonstrate that the TPB (Model 2) has relatively better model fit and explanatory ability than the
TRA (Model 1) and the modified TPB (Model 3). The path coefficients, sign and their significance levels
for each model are presented in Table 4. The structural relationships among the studied constructs in these
models will be discussed in the following section.
Table 3: Overall fit indices of the alternative models
Fit index
Absolute
measures
fit
Incremental
fit
measures
Parsimonious fit
measures
Squared multiple
correlation
χ²
d.f.
χ² / df
GFI
RMSEA
TLI
CFI
PGFI
PNFI
SMCAT a
SMCINT b
SMCBEH c
Recommended cutoff value
Near to d.f.
≤ 3.0
≥ 0.90
≤ 0.08
≥ 0.90
≥ 0.90
The higher, the better
The higher, the better
-
TRA
(Model 1)
466.497
167
2.793
.911
.059
.945
.951
.724
.814
.842
.348
TPB
(Model 2)
588.294
245
2.401
.909
.052
.948
.954
.742
.820
.856
.359
Modified TPB
(Model 3)
719.626
246
2.925
.894
.061
.928
.936
.733
.808
.121
.834
.308
Note: aSMCAT squared multiple correlation to subject’s attitude toward exercise, b SMCINT squared multiple
correlation to subject’s exercise intention; c SMCBEH squared multiple correlation to subject’s exercise
behavior
Model: the TRA
As shown in Table 4, only the path from subjective norm to intention is insignificant. Consistent with past
exercise literature, the subjective norm construct did not predict exercise intention significantly. It was
reported that attitude (β= .905) is a significant predictor of exercise intention and attitude has indirect
effects (β= .534) on behaviour through intention as hypothesized in the original TRA model. The finding
found a significant direct effect of exercise intention on exercise behaviour. Overall, exercise intention has
the highest total effects (β= .590) on exercise behaviour.
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Table 4: Path coefficients and strengths of individual paths
Path coefficient
Effect on
attitude
Effect on
intention
Direct effect
Indirect effect
Total effect
Direct effect
Indirect effect
Total effect
Effects on
behaviour
Direct effect
Indirect effect
Total effect
SN → ATT
ATT → INT
SN → INT
PBC → INT
PBC → BEH
INT → BEH
SN
SN
SN
ATT
SN
PBC
SN
ATT
SN
PBC
PBC
INT
ATT
SN
PBC
ATT
SN
PBC
INT
TRA
(Model 1)
.905***
.036
.590***
.905
.036
.905
.036
.590
.534
.021
.534
.021
.590
TPB
(Model 2)
.824***
.016
.148***
.142**
.498***
.824
.016
.148
.824
.016
.148
.142
.498
.411
.008
.074
.411
.008
.216
.498
Modified TPB
(Model 3)
.348***
.856***
.001
.238***
.145***
.490***
.348
.348
.856
.001
.238
.298
.856
.298
.238
.145
.490
.419
.146
.117
.419
.146
.262
.490
Note: Standardised path estimates are reported. ***p<.001, **p<.01.
Model 2: the TPB
Most paths in the TPB model were significant. Similar to the TRA model, the only exception is the path
from subjective norm to exercise intention. Contrary to the hypothesized links in the original TPB model,
subjective norm is neither a significant predictor of exercise intention nor has its indirect effects on exercise
behaviour through intention. It was reported that attitude (β= .824) is a significant predictor of exercise
intention and has indirect effects (β= .411) on exercise behaviour through intention. Among the TPB
predictors, attitude has the strongest total effect on exercise intention. PBC significantly predicted both
exercise intention (β= .148) and behaviour (β= .142). The direct effect of PBC on exercise behaviour is
much greater than its indirect effect. Indeed, the magnitude of 0.074 for the indirect effects of PBC on
exercise behaviour is rather too small to be meaningful (Hair et al. 2006). Lastly, exercise intention (β=
.498) has a direct effect on exercise behaviour and emerged as the strongest predictor of exercise
behaviour.
Model 3: the modified TPB
To address the research question in the present study, it was found that the additional path from subjective
norm to attitude is significant as expected. However, the inclusion of this path to the original TPB did not
contribute to the improvement of the predictive power of the specified model. Further investigation
revealed that subjective norm is not a significant predictor of exercise intention. Instead, subjective norm
influences exercise intention indirectly through attitude construct. Among the TPB predictors, only attitude
and PBC significantly predicted exercise intention and attitude has the strongest direct and total effect on
exercise intention. For the prediction of exercise behaviour, it was found that both PBC (β= .490) and
intention (β= .145) has significant effect on behaviour. Attitude was reported to have the strongest indirect
effect on exercise behaviour through intention compared to PBC and subjective norm. Lastly, exercise
intention has the strongest direct and total effects on exercise behaviour among all other TPB constructs.
6. Conclusion and discussion
The present study conducted an alternative model comparison among three competing theoretical models
for explaining individual exercise behaviour. Generally, all three models have achieved acceptable model
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fit to the data with a caveat for the modified TPB model whereby the GFI was slightly below the cut-off
value. The discussion and reflections on comparative findings are presented as follows:
• In an extensive review of the theories of exercise behaviour, Biddle and Nigg (2000) concluded that
TPB is one of the most comprehensive and validated theory for explaining and predicting exercise
behaviour. From the comparison of three competing model, the present study found TPB to be more
superior to TRA and modified TPB in predicting exercise behaviour. Although there is no general
consensus among researchers exists regarding which is the best theoretical framework for the studying
exercise behaviour (Wood 2008). Nevertheless, the TPB can be considered a promising framework
basis from which a more integrative model of exercise behaviour may be developed.
• The finding about attitude being significant predictor of exercise intention makes theoretical sense as
the more favourable the perception in one’s attitude toward participating in exercise activities, the
greater likelihood that the person will participate in exercise activities. Among the TPB predictors,
attitude was found to exert the highest direct and total effects on exercise intention and its effect on
exercise behaviour was mediated through exercise intention. This finding is consistent across the three
models tested in the present study. In many studies using Ajzen’s TPB, the attitude variable has
consistently produced the strongest effect on behavioural intention in a wide variety of context (Ajzen
1991). This finding has important implications on decision making for health care marketers and
public policy makers especially decisions related to communication strategies.
• The present study did not find subjective norm to be predictive of exercise intention and behaviour for
all three models tested. Having such poor performance for subjective norm in the prediction of
intention did not suggest that the social cognitive models are disconfirmed as there is nothing in the
TPB theory to suggest that all TPB predictors will each make a significant contribution to the
prediction of intention. The relative weight of these social cognitive constructs in determining intention
is expected to vary across behaviour, situation and population (Ryan and Bonfield 1975; Miniard and
Cohen 1979). For example, certain group of sample may be more incline to social influence and
therefore may perceive greater social pressure than other sampling groups. Similarly, certain behaviour
such as purchase behaviour of luxury products may be more susceptible to reference group influence.
Besides, cultural difference may also play an important role here (Choo, Chung and Pysarchik 2004). It
is not surprising that one or another of the TPB predictors may be found to predict intention poorly or
insignificantly in some circumstances (Ajzen 1991). The results pertaining to subjective norm may
infer that the subjects are generally not sensitive to social influence when it comes to exercise
participation.
• The normative construct contained in the TPB has been demonstrated to be the weakest predictor of
exercise intention in the past research. This has led some exercise researchers to divert their attention
to other social construct such as social provision and family supports (Saunders, Motl, Dowda,
Dishman and Pate 2004). The present finding investigated further the possibility of subjective norm
influences intention through other TPB components instead of discarding the normative construct. The
results demonstrated that normative components does contribute to the formation of attitude and
influence intention to exercise indirectly through attitude. Health care practitioners and public policy
makers could make substantial use of potential normative influence on exercise participation in the
development of health and wellness program.
• The most controversial issue regarding PBC construct is whether it influences behaviour directly or
through behavioural intention. This lies in whether the given behaviour is volitional or non-volitional
(Ajzen 1991). As originally formulated by Ajzen, when the behaviour under studied is not completely
under the volitional control of the individual, PBC can influence behaviour directly to the extent that
PBC is accurately reflects actual control and ability. It was found in the present study that the direct
effect of PBC had on exercise behaviour is greater than its indirect effect in both the TPB (Model 2)
and modified TPB (Model 3). The result makes theoretical sense because whether or not a person
exercises may not be totally under his or her volitional control. There may be some other constraints
such as time factor that restraint a person from exercising.
• TPB model was reported to be a more superior model in predicting exercise behaviour compared to
TRA. PBC was also found to be a significant predictor of exercise intention and behaviour in Model 2
and 3. These findings seem to support the inclusion of PBC construct into TRA model. Hence, it is
deemed to be necessary to examine beyond the attitude and subjective norm construct in the TRA but
to explore further the control factor that possibly influence individual’s exercise behaviour. This is
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because there might be some control factors that affect individual’s exercise participation such as
physical inability, time constraint, neighbourhood security, availability of the exercise equipment and
so on.
There were several limitations encountered during the implementation of the study which should be
considered when interpreting the results of this study. First, a cross-sectional design was employed and
hence did not adhere to the logically time interval stated in the TPB. Realistically, the cross-sectional
approach is preferred given the difficulty of having to re-contact the subjects at Time 2. Besides, exercise
can be considered a habitual activity and considerable stable behaviour (Rhodes and Courneya 2003) and it
is quite common in TPB research. Second, although subjects were assured of anonymity and
confidentiality, potential social desirability may have artificially inflated the observed relationship when
self-report measures were adopted. Nevertheless, most TPB researchers still rely largely on self-report data
as it is considered the most common and practical approach for its consistent reliability and validity (see
Godin and Shephard 1985). The last limitation concerns the methodology adopted in the present study. As
the participation in the study was voluntary, subjects may be more health conscious and tend to be better
educated and knowledgeable. Although careful consideration has been given in the quota set, the
generalisability of the findings to a wider population should be done with caution.
Future research should replicate the study using a longitudinal approach to fully test the specified theories
and analyze the causal links between constructs in the model. More objective measures such as fitness class
attendance or activity monitoring should be considered. It is also suggested that quantitative approach
should be employed in conjunction with qualitative approach to explore the antecedents of TPB constructs
so that more in-depth insights could be obtained in understanding exercise behaviour. For instance, future
research can investigate further the determinants of attitude and PBC construct. Lastly, the attempt to
investigate other consumer healthy lifestyle behaviours such as healthy eating, tobacco-free lifestyle,
substance use, health preventive practices, and weight control is needed to explore more comprehensive
aspect of healthy lifestyle.
Acknowledgement
The author gratefully acknowledges the guidance and comments granted by Professor Dr. Md. Nor Othman
on the early versions of this paper and would like to also thank the student helpers of the research project
for their assistance with data collection
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