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Predicting consumers’ intention to purchase sporting goods online: An

application of the model of goal-directed behavior

Article  in  Asia Pacific Journal of Marketing and Logistics · February 2018

DOI: 10.1108/APJML-02-2017-0028

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Weisheng Chiu Doyeon Won

The Open University of Hong Kong Liverpool John Moores University
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An application
Predicting consumers’ intention of the MGB
to purchase sporting goods online
An application of the model of
goal-directed behavior 333
Weisheng Chiu Received 15 February 2017
Lee Shau Kee School of Business and Administration, Revised 12 June 2017
19 September 2017
Open University of Hong Kong, Hong Kong Accepted 21 September 2017
Taejung Kim
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Yonsei University, Seoul, South Korea, and

Doyeon Won
Liverpool John Moores University, Liverpool, UK

Abstract
Purpose – The purpose of this paper is to apply the model of goal-directed behavior (MGB) as a research
framework to investigate consumers’ behavioral intention to purchase sporting goods online.
Design/methodology/approach – Mall intercept sampling was used to collect data from Korean
consumers who have the experience of purchasing sporting goods online. After the elimination of invalid
responses, total 314 valid questionnaires were used for further analysis.
Findings – The results revealed that attitude, subjective norm, positive and negative anticipated emotions
had significant influences on consumers’ desire to buy sporting goods online. Moreover, the frequency of past
behavior and desire played significant roles in influencing on Korean consumers’ intention. Further analysis
revealed that male consumers had higher levels of positive attitude, subjective norm, positive and negative
anticipated emotions, desire, intention, frequency of past behavior toward the online purchase of sporting
goods than female consumers did. It also found that male consumers’ desire had a significantly stronger
influence on behavioral than female consumers did.
Research limitations/implications – The study suggests the benefit and gender-based targeting
strategies in marketing sporting goods online. The primary limitation of this study was that respondents
were all Korean online consumers of buying sporting goods. Future research should apply MGB to different
countries or regions to generalize the results of this study.
Originality/value – The findings of this study provide a better understanding of Korean consumers’
intention to purchase sporting goods online and gender differences in their decision-making process.
Keywords Purchase intention, Sporting goods, Model of goal-directed behaviour, Sports apparels
Paper type Research paper

Introduction
The rapid growth of the internet users has resulted in the phenomenal increase of
e-commerce, which has significantly influenced consumers’ buying behavior. According to a
report surveyed in 25 countries, 54 percent of internet online shoppers buy products online
weekly or monthly (PwC, 2016). Also, eMarketer forecasted that the world e-commerce sales
would top $4 trillion and the double-digit growth rate of e-commerce sales will continue
through 2020 (eMarketer, 2016). Consequently, it is not surprising that e-commerce of
sporting goods has also developed in recent years. For example, the global rate of online
purchase intention in 2014 have doubled since 2011 for sporting goods (31 percent)
(Nielsen, 2014). Moreover, the Australian Sporting Goods Association (2012) reported an
Asia Pacific Journal of Marketing
increase in online sales of footwear (11.5 percent) and apparel (7.6 percent), and the sales of and Logistics
online sporting goods in 2016 account for 14.8 percent of online consumption in the USA Vol. 30 No. 2, 2018
pp. 333-351
(Statista, 2016c). Also, Statistics Korea (2016) reported relatively high percentage of online © Emerald Publishing Limited
1355-5855
sales in categories of apparel (10.6 percent) and sport and leisure appliance (3.8 percent). DOI 10.1108/APJML-02-2017-0028
 APJML In addition, over half of consumers purchased a variety of sporting goods on different
30,2 online platforms, such as online department store (e.g. amazon.com), manufacturer online
(e.g. nike.com), or specialist stores online (e.g. eastbay.com) (Statista, 2016a). It indicates that
the internet has already changed how people shop for sporting goods, and more consumers
search for and purchase sporting goods on the internet.
Whereas classical attitude theory, including the model of goal-directed behavior (MGB),
334 has been widely applied to various purchasing behavior, especially in traditional business
settings, only a handful of studies have examined the predictive power of MGB in online
purchase behavior (Chen et al., 2016). MGB posits that consumers’ behavioral intention and
overt behavior is influenced by volitional, non-volitional, motivational (desires), affective,
and habitual elements (Perugini and Bagozzi, 2001, 2004). However, research has shown that
consumers, even the same consumers, exhibit different behaviors depending on shopping
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channels (Chu et al., 2010; Muthaly and Ha, 2009). Thus, understanding what predicts online
purchase behavior among the MGB elements would be beneficial for e-marketers.
In addition, while there is an extensive body of research conducted on exploring
consumers’ online shopping behavior toward generic products (Park and Kim, 2003; Senecal
et al., 2005), far less is known about consumer behavior toward purchasing sporting goods
online. It has been argued that consumers have a unique relationship with sporting goods.
This is because that consumers frequently exercise with sporting goods they purchase, and
sporting goods are closely related to their performance on the court (Chiu and Won, 2016a).
Although studies have been conducted to explore consumers’ behaviors toward sporting
goods in brick-and-mortar stores (Chiu and Won, 2016a; Lu and Xu, 2015; Tong and Hawley,
2009; Tong and Li, 2013), not much is known about their behaviors leading up to sporting
goods purchases online even now. However, it should be noted that online sporting goods
consumers have unique characteristics as compared to general online shoppers (Chiu et al.,
2014). Online sporting good shoppers spend more time on browsing retail and auction
websites to search for and purchase sporting goods (Chiu et al., 2014).
Accordingly, the purpose of this study was to fill the knowledge gap by investigating the
factors that influence online sporting goods consumers’ purchase intention and decision.
In doing so, this study used the MGB as the base theory to investigate online consumers’
behavior toward purchasing sporting goods. The application of MGB could be especially
meaningful in investigating individuals’ behavioral intention and decision as MGB
comprehensively incorporates volitional, non-volitional, motivational, affective, and
habitual aspects, providing more precise predictions of human decisions and behavior
(Perugini and Bagozzi, 2001, 2004). From the practical standpoint, the results of this study
could provide practitioners with effective marketing strategies to promote their products
and better satisfy consumers’ needs.
For research purposes to be achieved, this paper is structured as follows. The first
section deals with the literature review and hypotheses development. After which research
method is presented, with full details of participants and procedure in the research, and of
the survey instrument and data analysis used. Results are then presented, with through
assessments of the measurement and structural model. Finally, results are discussed, and
implications are drawn.

MGB
The theory of reasoned action (TRA) and the theory of planned behavior (TPB) are
representative social-psychological theories extensively applied by researchers to predict
an individual’s intention/behavior across a wide variety of domains (Ajzen, 1991;
Zint, 2002). TRA assumed that a person’s decision to perform a particular rational
behavior is formed through a volitional/cognitive process determined by his/her intention
to perform the behavior, and this intention is functioned by his/her attitude and subjective
 norm toward the behavior (Fishbein and Ajzen, 1975). However, TRA is limited to An application
predicting behaviors that do not require special skills or resources as the theory did not of the MGB
consider an individual’s non-volitional situations, such as time, money, and opportunities
that limit actual behavior (Ajzen, 1985, 1991). To improve the usefulness of TRA, Ajzen
(1985, 1991) developed TPB by adding a non-volitional variable “perceived behavioral
control” as a predictor of intention to act the actual behavior. TPB emphasizes that
individuals’ actual behaviors are determined by not only attitude and subjective norm 335
(i.e. social pressure) but also the perception of behavioral control reflecting a person’s
confidence and ability to engage in the behavior. Therefore, TPB is widely recognized
as a more appropriate to predicting intention and behavior as compared with TRA
(Armitage and Conner, 2001; Hagger et al., 2002).
However, Perugini and Bagozzi (2001, 2004) argued that TPB did not capture people’s
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past behavior as well as motivational and affective components of human behavior. More
specifically, the variables in TPB did not consider whether individuals want to do
something, which is connected to the emotions they expect to feel if they do it (Perugini and
Bagozzi, 2001, 2004). Thus, Perugini and Bagozzi (2001) extended TPB and proposed MGB
to address the limitation of TPB. It argued that habitual, motivational, and affective aspects
must be taken into consideration to better comprehend human intentions and behaviors.
Consequently, MGB adds motivational (desire), affective (positive and negative anticipated
emotions), and habitual process (past behavior) into TPB. MGB suggested that the
individuals’ intention to perform a particular behavior is primarily motivated by how they
want to perform the behavior (i.e. desire), and their desire is determined by attitude,
subjective norms, perceived behavioral control, and positive and negative anticipated
emotions. Moreover, past behavior or habits are assumed to be a significant predictor of
desire, intention and actual behaviors (Perugini and Bagozzi, 2001). Specifically, the role of
desire mediates the influence of attitude, subjective norm, perceived behavioral control, and
anticipated emotions on behavioral intention in MGB.
MGB was reported to explain significantly greater amounts of variance in individuals’
intention and behavior as compared to TPB and TRA (Carrus et al., 2008; Esposito et al.,
2016; Taylor et al., 2005). Recently, MGB has been extensively used as the base theory to
explain individual’s behavioral intention in the various domains, such as tourist behavior
(Han and Hwang, 2014; Kim et al., 2012; Lee et al., 2012; Meng and Han, 2016; Song et al.,
2017), mobile usage behavior (Kim and Preis, 2016), exercise and health behavior
(Baranowski et al., 2013; Esposito et al., 2016; Hingle et al., 2012), airport-shopping behavior
(Han et al., 2014), and restaurant re-patronage (Han and Ryu, 2012). Although TPB has been
applied to investigate online shopping behavior (Cheng and Huang, 2013; Choi and
Geistfeld, 2004; Hansen et al., 2004), it is surprising that MGB, a more advanced theory than
TPB, has not been used as a base theory to comprehend online shopping behavior so far.
However, it should be noted that numerous studies applied the technology acceptance
model and the unified technology of acceptance and use of technology to understand
consumers’ online shopping behavior (Ha and Stoel, 2009; Lian and Yen, 2014; Smith et al.,
2013). These models focus on consumers’ perception toward technology system,
i.e. perceived ease of use or perceived usefulness, rather than individual perception
toward performing a certain behavior. MGB could be an appropriate framework for
exploring online shopping behavior as it is a goal-directed consumption behavior (Bagozzi
and Dholakia, 1999). After the online purchase, consumers’ emotional responses are
generated to whether the goal is achieved or not. Consumers’ emotions play a significant
role in MGB to predict consumer behavior (Bagozzi and Dholakia, 1999; Phillips and
Baumgartner, 2002); however, the importance of emotion is often ignored in the literature of
online shopping (Éthier et al., 2006; Koo and Ju, 2010). Hence, MGB was applied as the theory
base to understand consumers’ behavior of purchasing sporting goods online.
 APJML Hypotheses development
30,2 Attitude, subjective norm, perceived behavior control, and desire
An attitude toward a behavior is the degree to which an individual has a favorable/
unfavorable evaluation of performing a certain behavior (Ajzen, 1985, 1991). That is, when
the outcomes a certain behavior are positively evaluated, individuals tend to have a
stronger attitude to perform this behavior (Ajzen, 1985, 1991). Subjective norm is the
336 perceived social pressure to engage or not to engage in a certain behavior (Ajzen, 1985,
1991). An individual is influenced by the opinions of other people (e.g. peers, family, and
colleagues) when performing a certain behavior. Perceived behavioral control reflects an
individual’s confidence and ability to engage in behavior. According to TPB, an
individual’s intention is predicted by attitude, subjective norm, and perceived behavioral
control (Ajzen, 1985, 1991). In MGB, attitude, subjective norm, and perceived behavioral
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control affect intention indirectly through desire toward a certain behavior (Perugini and
Bagozzi, 2001; Prestwich et al., 2008). The desire, a main motivational source to perform a
behavior, was added in MGB in order to strengthen the predictive power in explaining
intention (Malle, 1999; Perugini and Bagozzi, 2004). Also, desire serves as a primary
predictor of intention and mediates the effects of attitude, subjective norm, perceived
behavioral control, and anticipated emotions on behavioral intention, representing the
most important feature of MGB.
In the recent years, studies applying MGB across various domains has revealed that
attitude, subjective norm and perceived behavioral control were significant factors in the
formation of desire (Esposito et al., 2016; Han and Ryu, 2012; Kim et al., 2012; Meng and Han,
2016). For example, Esposito et al. (2016) applied MGB to understand individuals’ physical
activity intention and found that attitude, subjective norm, and perceived behavioral control
are the most significant factors influencing the desire to perform physical activities.
Moreover, Han and Ryu’s (2012) extended MGB in the context of restaurant services and
revealed that attitude, subjective norms and perceived behavior control played a critical role
in the formation of re-patronage intention through desire. Hence, based on the literature
review, this study proposed the following hypotheses in the context of online purchasing
behaviors of sporting goods:
H1. Attitude will have a positive influence on desire.
H2. Subjective norms will have a positive influence on their desire.
H3. Perceived behavior control will have a positive influence on desire.

Relationships between anticipated emotions and desire

Individuals usually consider the emotional consequence in advance of performing or not
performing a certain behavior (Bagozzi et al., 1998). It is defined as “predictions of outcome’s
emotional consequences or belief about one’s own emotional responses to future outcomes
(Bagozzi et al., 2016, p. 630).” For instance, if individuals have a higher level of the expected
psychological benefits experienced by performing a specific behavior, he/she tends to have
positive emotions, whereas if individuals have a higher level of the expected psychological
damages derived from not performing the behavior, he/she tends to have negative emotions.
Research has found that emotional expectations have influences on individuals’
decision-making process (Bagozzi et al., 1998; Phillips and Baumgartner, 2002). Leone
et al. (2004) stated that anticipated emotions play the role of the hedonic motive in promoting
a positive outcome of affairs and avoiding a negative outcome of affairs. Hence, there are
two types of emotions, positive and negative anticipated emotions, considered to be the
critical predictors of desire and intention (Bagozzi et al., 1998, 2016; Leone et al., 2004;
Perugini and Bagozzi, 2001).
 Empirical studies revealed that two anticipated emotions have important roles in An application
forming an individual’s desire to perform a behavior (Bagozzi and Dholakia, 2006; of the MGB
Perugini and Bagozzi, 2001). A recent study by Meng and Choi (2016) found that both
positive and negative anticipated emotions have significant influences on travelers’ desire
for slow tourism. Also, Song et al. (2017) found that Chinese tourists’ positive and negative
anticipated emotions significantly affect their desire to visit South Korea. Accordingly, two
anticipated emotions for a target behavior are hypothesized to significantly influence the 337
individuals’ desire-related behavior of purchasing sporting goods online:
H4. Positive anticipated emotion will have a positive influence on desire.
H5. Negative anticipated emotion will have a negative influence on desire.
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Frequency of past behavior, desire, and intention

The frequency of past behavior has been known as a critical factor that can influence an
individual’s decision-making process. Also, the frequency of past behavior is usually
considered to be a proxy of habit. If an individual frequently and habitually performs a certain
behavior, it will enhance his/her desire and behavioral intentions (Hutchinson, 1983; Perugini
and Bagozzi, 2001). Individuals with a higher frequency of past behavior might have a higher
level of familiarity than those who have no or limited past behavior (Marks and Olson, 1981).
Additionally, desires are thought to be very important in the first step of human actions and
argued to lead to intentions to perform a behavior (Perugini and Bagozzi, 2001, 2004).
According to Perugini and Bagozzi (2004), desires are distinct from intentions and serve as
personal motivation which is the proximal element leading to intention formation.
The relationships between these three variables can be found in many studies using
MGB (e.g. Kim and Preis, 2016; Meng and Choi, 2016; Meng and Han, 2016). For instance,
found that seniors’ past behavior of using mobile devices has a significant impact on their
desires and intentions to use mobile devices for tourism-related purposes. Moreover, a study
by Meng and Han (2016) showed that past experience of bike-traveling was a powerful
predictor of bicycle travelers’ desire and behavioral intention. As such, this study posited
the following hypotheses:
H6. Frequency of past behavior will have a positive influence on desire.
H7. Frequency of past behavior will have a positive influence on intention.
H8. Desire will have a positive influence on intention.

Gender differences
Gender differences in decision making and online purchasing behavior have been addressed
for decades. Although the gender gap in internet usage is decreasing, men and women display
different perspectives, motives, rationales, and patterns of online shopping (Pascual-Miguel
et al., 2015; Rodgers and Harris, 2003). A review of literature revealed that males are more
likely to purchase products through online shopping and have more positive perceptions and
attitudes toward online shopping than females have (Bae and Lee, 2011; Fan and Miao, 2012;
Hasan, 2010; Lian and Yen, 2014; Pascual-Miguel et al., 2015; Wu, 2003). Moreover, Garbarino
and Strahilevitz (2004) noted that females have higher levels of perceived risk regarding online
shopping than men have. Males, therefore, feel more relaxed, effective, efficient and less time
consuming when buying products online. Also, males’ attitude is much more goal-focused and
target-driven as they perceived benefits online shopping (Hasan, 2010). In addition, it should
be noted that Kim et al.’s (2012) study applying MGB found gender differences in overseas
tourists’ behavior. Their results demonstrated that female tourists had significantly higher
 APJML influences of attitude on desire, anticipated positive emotions on behavioral intention, and
30,2 perceived behavioral control on behavioral intention than male tourists did. Male tourists also
had significantly greater influences of subjective norms on behavioral desire, anticipated
positive emotions on desire, and desire on behavioral intention than female tourists did.
Accordingly, it is assumed that males and females will differ in their purchase intention of
purchasing sporting goods online. Specifically, paths in the MGB will differ between males
338 and females. Hence, the following hypothesis was established (Figure 1):
H9. Relationships between the variables in the MGB will be different between male and
female consumers.

Method
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Research context
The research context for this study is the Republic of Korea (hereafter referred to as Korea).
Korea is home to the third largest Asia-Pacific retail e-commerce market, behind China and
Japan (PFSweb, 2016). According to Statistics Korea (2016), e-commerce accounted for
almost 10 percent of Korea’s retail spending in 2016. Most of the online shoppers (84 percent)
in Korea prefer to search and purchase products online (Statista, 2015). Moreover, the online
shopping transaction value of sporting goods ranks highly among different product
categories (Statista, 2016b). Specifically, the transaction value of sport-related products in
2016 increased 32.5 percent as compared to 2014, indicating more and more Korean
consumers purchase sporting goods online (Statistics Korea, 2016). Given the situation,
Korea was selected as the research context for this study.

Participants and procedure

Participants who have the experience of purchasing sporting goods online were recruited
for this study. Convenience sampling was conducted in Korea. Specifically, data were
collected around two largest metropolitan areas in Korea, Seoul, and Busan where have
higher rates of sports participation than other areas in Korea. Mall intercept personal
interview was conducted by two survey administrators in the shopping mall and public

Attitude
Frequency of
H1 Past Behavior

Subjective H2 H6 H7
Norm

Perceived H3 H8
Behavioral Behavioral
Desire
Control Intention

H4
Positive
Anticipated
Emotion
H5

Negative
Figure 1. Anticipated
Research model Emotion
 space with crowds. To maintain the robustness of the study, before filling out the An application
questionnaire, survey administrators explained the research purpose and the definition of of the MGB
sporting good to respondents. In this study, sporting goods include footwear, apparel,
accessories, and equipment used for the purpose of participating in different sports
activities. Moreover, only the respondents who have the experience of purchasing sporting
goods online in the last 12 months were included in this study. After the elimination of
invalid responses, a total of 314 valid questionnaires were used for further analysis. Of the 339
respondents, 58.6 percent (n ¼ 184) were male and 41.4 percent (n ¼ 130) were female.
Most of the respondents were aged between 20 and 29 years old. The more detailed
information on the study participants was reported in Table I.

Measures
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The survey instrument consists of eight measures which were modified to fit the context of
purchasing sporting goods online. All measures were adopted from the previous studies
concerning MGB (Han and Hwang, 2014; Meng and Han, 2016; Perugini and Bagozzi, 2001;

Characteristics n %

Gender
Male 184 58.6
Female 130 41.4
Age (years old)
Less than 20 9 2.9
21-30 212 65.5
31-40 87 27.7
More than 41 6 2
Education
High school 18 5.7
College/College degree 254 80.9
Graduate school 42 13.4
Internet usage per day
Below 2 hours 85 27.1
2-4 hours 135 43
Over 4 hours 94 29.9
Frequency of purchasing sporting goods online in the past 1 year (times)
1 74 23.6
2 80 25.5
3 66 21.0
4 17 5.4
5 26 8.3
6 7 2.2
7 7 2.2
8 5 1.6
9 1 0.3
10 23 7.3
12 1 0.3
13 1 0.3
14 1 0.3
15 1 0.3 Table I.
20 4 1.3 Demographics of
Note: n ¼ 314 respondents
 APJML Song et al., 2017): attitude (four items), subjective norm (four items), perceived behavioral
30,2 control (four items), anticipated positive emotion (four items), negative anticipated emotion
(three items), desire (three items), and behavioral intention (four items). In particular, the
frequency of past behavior was assessed with a single item (i.e. “How many times have you
purchased sporting goods online in the last 12 months?”).
These items were originally written in English and translated into Korean using the
340 approach of back translation (Brislin, 1970). The Korean version of the survey instrument was
carefully reviewed by sports management experts in Korea. Moreover, a small group of
Korean graduate students participated in the pilot survey to ensure its face validity. All items
except the frequency of past behavior were assessed on a five-point Likert scale, ranging from
strongly disagree (1) to strongly agree (5). Reasons for using a five-point Likert scale rather
than seven-point Likert scale is that five-point scale appears to be less confusing and to
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increase response rate (Dawes, 2008; Devlin et al., 1993). Moreover, evidence showed that both
scales are comparable and no significant difference between them (Colman et al., 1997). Finally,
the frequency of past behavior was coded as a continuous variable.

Data analysis
A preliminary test was conducted to scrutinize the normality assumption of the data using
descriptive statistics. Then, a two-stage data analysis recommended by Anderson and
Gerbing (1988) was carried out using AMOS 20.0. First, reliability and validity of the measures
were examined by using a confirmatory factor analysis (CFA). In the second stage,
a structural equation modeling (SEM) analysis was employed to test the proposed hypotheses.
With the structural model analysis, this study utilized the standard factor loading and t-value
of the path coefficient to determine the path strengths and significance levels of the latent
variables. In addition, a multi-group SEM was conducted to investigate the gender differences
regarding the paths in MGB. The sample size of this study (n ¼ 314) is considered to be
appropriate for SEM analysis to provide precise estimations (Hair et al., 2010).

Results
Preliminary test
The initial descriptive analysis revealed no missing values, outliers, or invalid value.
Skewness and Kurtosis statistics were used to examine the normality of the data whether it
violates the assumption required in SEM. Skewness values of survey items ranged within
the ±1.00 cut-off suggested by Kline (2010). Kurtosis statistics of survey items were smaller
than the criterion of 3 proposed by Byrne (2010). It indicates that Skewness and Kurtosis
statistics of all survey items supported the normality for SEM analysis.

Reliability and validity of measures

As a next step, CFA was employed to examine the the reliability and validity of measures.
The results revealed that the measurement model fitted the data well: χ2 (278) ¼ 735.40,
χ2/df ¼ 2.65, CFI ¼ 0.93, TLI ¼ 0.92, RMSEA ¼ 0.07 (Hair et al., 2010; Hu and Bentler, 1999).
Moreover, the reliability of measures was evaluated by calculating Cronbach’s α coefficients
and composite reliability (CR). As reported in Table II, the results showed that the measures
possessed good reliability as the Cronbach’s α coefficients of all constructs were acceptably
high, surpassing the 0.70 threshold (Nunnally and Bernstein, 1994); and the values of CR
ranged exceeded the criterion (0.60) suggested by Bagozzi and Yi (1988). The construct
validity of measures was examined calculating convergent and discriminant validity.
Convergent validity was supported as all factor loading of the measures were highly
significant (po 0.01), ranging from 0.67 to 0.98, and the AVE values were all greater than
0.50, fulfilling the criterion suggested by Hair et al. (2010). In addition, discriminant validity
 Constructs/Items Factor loading
An application
of the MGB
Attitude (AVE ¼ 0.76, CR ¼ 0.93, α ¼ 0.92)
I think that purchasing sporting goods online is good 0.84
I think that purchasing sporting goods online is wise 0.93
I think that purchasing sporting goods online is worthy 0.87
I think that purchasing sporting goods online is beneficial 0.82
341
Subjective norm (AVE ¼ 0.66, CR ¼ 0.88, α ¼ 0.88)
Most people who are important to me agree with that I purchase sporting goods online 0.83
Most people who are important to me support that I purchase sporting goods online 0.87
Most people who are important to me understand that I purchase sporting goods online 0.67
Most people who are important to me recommend that I purchase sporting goods online 0.86
Perceived behavioral control (AVE ¼ 0.61, CR ¼ 0.86, α ¼ 0.86)
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Whether or not I purchase sporting goods online is completely up to me 0.77

If I want, I can purchase sporting goods online 0.75
I am capable of purchasing sporting goods online 0.77
I have enough resource (money or time) to purchase sporting goods online 0.85
Positive anticipated emotion (AVE ¼ 0.76, CR ¼ 0.93, α ¼ 0.92)
If I can purchase sporting goods online, I will be excited 0.82
If I can purchase sporting goods online, I will be glad 0.92
If I can purchase sporting goods online, I will be happy 0.93
If I can purchase sporting goods online, I will be satisfied 0.81
Negative anticipated emotion (AVE ¼ 0.86, CR ¼ 0.95, α ¼ 0.95)
If I cannot purchase sporting goods online, I will be worried 0.90
If I cannot purchase sporting goods online, I will be disappointed 0.93
If I cannot purchase sporting goods online, I will be sad 0.95
Desire (AVE ¼ 0.80, CR ¼ 0.92, α ¼ 0.91)
I want to purchase sporting goods online in the future 0.93
I desire to purchase sporting goods online in the future 0.98
I hope to purchase sporting goods online in the future 0.76
Behavioral intention (AVE ¼ 0.70, CR ¼ 0.90, α ¼ 0.89)
I am planning to purchase sporting goods online in the future 0.68 Table II.
I prefer to purchase sporting goods online next time 0.83 The results of
I will make an effort to purchase sporting goods online in the future 0.92 confirmatory factor
I will try to purchase sporting goods online next time 0.90 analysis

is established when the AVE square roots are greater than inter-construct correlations
(Fornell and Larcker, 1981). As reported in Table III, the inter-correlation coefficients
(from −0.11 to 0.65) were much less than the AVE square roots for individual variables
(ranging from 0.78 to 0.93), supporting the discriminant validity. Overall, the survey
instrument exhibited good psychometric properties.

Hypothesis testing
As reported in Table IV, the proposed research model fitted data well: χ2 (308) ¼ 842.30,
χ2/df ¼ 2.74, CFI ¼ 0.92, TLI ¼ 0.91, RMSEA ¼ 0.07 (Hair et al., 2010; Hu and Bentler, 1999).
It revealed that attitude, subjective norm, positive anticipated emotion, and negative
anticipated emotion have significant influences on desire (βATT→DE ¼ 0.19, t-value ¼ 3.13,
p o0.01; βSBN→DE ¼ 0.17, t-value ¼ 1.97, p o0.01; βPAE→DE ¼ 0.44, t-value ¼ 5.87, po 0.001;
βNAE→DE ¼ −0.20, t-value ¼ 5.03, p o0.001), supporting H1, H2, H4, and H5. Moreover,
desire, and frequency of past behavior have significant influences on behavioral intention
 APJML Constructs 1 2 3 4 5 6 7
30,2
ATT 0.87
SBN 0.61*** 0.81
PCB 0.40*** 0.45*** 0.78
PAE 0.53*** 0.54*** 0.42*** 0.87
NAE 0.08 0.24*** −0.07 0.07 0.93
342 DES 0.53*** 0.50*** 0.40*** 0.64*** −0.11** 0.89
INT 0.60*** 0.56*** 0.30*** 0.58*** 0.06 0.65*** 0.84
Notes: ATT, attitude; SBN, subjective norm; PCB, perceived behavioral control; PAE, positive anticipated
Table III. emotion; NAE, negative anticipated emotion; DES, desire; INT, behavioral intention. Italic diagonal elements
Discriminant validity are square root of AVE and off-diagonal elements are inter-construct correlations. **p o0.01; ***p o0.001
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Hypothesis Path Standardized coefficient (β) t-value

H1 ATT → DES 0.19 3.13**

H2 SBN → DES 0.17 2.59**
H3 PBC → DES 0.04 0.73
H4 PAE → DES 0.44 7.34***
H5 NAE → DES −0.20 −4.27***
H6 FRE → DES 0.08 1.85
H7 FRE → INT 0.17 3.60***
H8 DES → INT 0.63 9.97***
Table IV. Notes: ATT, attitude; SBN, subjective norm; PCB, perceived behavioral control; PAE, positive anticipated
Summary results of emotion; NAE, negative anticipated emotion; DES: desire; FRE, frequency of past behavior; INT, behavioral
hypothesized model intention. Model fit: χ2 (308) ¼ 842.30, χ2/df ¼ 2.74, CFI ¼ 0.92, TLI ¼ 0.91, RMSEA ¼ 0.07. *p o0.05;
testing **p o 0.01; ***po 0.001

( βDE→INT ¼ 0.63, t-value ¼ 9.97, p o0.001; βFPB→INT ¼ 0.17, t-value ¼ 3.60, p o0.001),
supporting H7 and H8. However, the paths from perceived behavioral control and the
frequency of past behavior to desire were statistically insignificant ( βPBC→DE ¼ 0.04,
t-value ¼ 0.729, p ¼ 0.466; βFPB→DE ¼ 0.08, t-value ¼ 1.85, p ¼ 0.064), and thus, the H3 and
H6 were not supported.

Test of gender differences

Independent t-tests were employed to investigate differences between male (n ¼ 184) and
female (n ¼ 130) respondents. As reported in Table V, there were significant differences in
the mean scores of attitude, subjective norm, positive and negative anticipated emotions,

Mean (SD)
Construct Total (N ¼ 314) Male (n ¼ 184) Female (n ¼ 130) t-value

ATT 3.49 (0.79) 3.57 (0.87 ) 3.37 (0.66 ) 2.34*

SBN 3.59 (0.75) 3.70 (0.73) 3.44 (0.76 ) 3.04**
PBC 4.09 (0.76) 4.11 (0.79) 4.06 (0.73) 0.61
PAE 3.52 (0.79) 3.62 (0.80) 3.37 (0.74) 2.89**
NAE 3.90 (0.97) 4.08 (0.83) 3.64 (0.99) 3.86***
DES 3.22 (0.84) 3.33 (0.85) 3.06 (0.79) 2.84**
Table V. INT 3.17 (0.89) 3.35 (0.90) 2.90 (0.81) 4.53***
Results of FRE 3.61 (3.32) 4.34 (3.88) 2.58 (1.88) 5.35***
independent t-tests Notes: *po 0.05; **po0.01; ***po0.001
 desire, intention, and frequency of past behavior between male and female consumers. An application
Compared to female consumers, male consumers reported significantly higher attitude of the MGB
(Mmale ¼ 3.57, Mfemale ¼ 3.37; t ¼ 2.34, p o 0.05), subjective norm (Mmale ¼ 3.70,
Mfemale ¼ 3.44; t ¼ 3.04, p o 0.01), positive anticipated emotion (Mmale ¼ 3.62,
Mfemale ¼ 3.37; t ¼ 2.89, p o 0.01), negative anticipated emotion (Mmale ¼ 4.08,
Mfemale ¼ 3.64; t ¼ 3.86, p o0.01), desire (Mmale ¼ 3.33, Mfemale ¼ 3.06; t ¼ 2.84, p o0.01),
intention (Mmale ¼ 3.35, Mfemale ¼ 2.90; t ¼ 4.53, p o0.001), and frequency of past behavior 343
(Mmale ¼ 4.34, Mfemale ¼ 2.58; t ¼ 5.35, p o0.001) toward purchasing sporting goods online.
Moreover, a multi-group analysis was conducted to compare whether male and female
consumers differ significantly on any path in the proposed model. First, two models were
established, the first model assumes that all parameters were fixed to be equal across
groups (fully constrained model); the second model allows these parameters to vary across
groups (unconstrained model). Then, the two models were compared using χ2 difference test.
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The result showed that the two models are significantly different, Δχ2 (8) ¼ 42.08, po 0.001,
indicating the two groups differed at the model level.
Furthermore, the gender difference on each path in the proposed model was examined. In
order to test the gender difference on each path, models that are different only on each path in
the proposed model were compared. More specifically, the constrained model was made by
constraining certain parameters to be equal across groups at a time and compares with the
unstrained model using χ2 difference test. As these are nested models with the restricted
model having one degree of freedom higher than the unconstrained model (Δdf ¼ 1),
the χ2 value will always be higher for the restricted model than for the unconstrained model. If
the value of χ2 increases significantly when adding the restricted to the path, the gender
difference is found on the path. As reported in Figure 2, the results revealed that the path from
desire to behavioral intention was significantly different between male and female consumers
(Δχ2 (1) ¼ 6.87, po0.01), supporting the difference between male and female consumers (H9).

Discussion
Little literature has paid attention to online consumers’ behavior and decision-making
process of purchasing sporting goods. To address the gap in the literature, the main purpose

Attitude
0.23** Frequency of
Past Behavior
0.16*

Subjective 0.15* 0.02 0.00 0.12** 0.16**

Norm
0.22**

Perceived 0.05 0.81***

Desire Behavioral
Behavioral
0.03 0.35*** Intention
Control
0.42***
Positive
Anticipated 0.44***
Emotion
–0.25***
–0.16* Figure 2.
Negative Results of multi-group
Anticipated analysis for male
Emotion (bold) and female
(italics) consumers
Notes: *p< 0.05; **p<0.01; ***p< 0.001
 APJML of this study was to investigate consumers’ intention to buy sporting goods online by
30,2 applying the MGB, a theoretically more advanced model than TRA and TPB. Although this
study was conducted in Korean, one of the most developed countries for online retail among
Asian countries, the findings of this study might be generalized to other Asian countries or
regions where e-commerce is well developed (e.g. Japan, China, Taiwan, or Hong Kong).
The results of this study found that attitude, subjective norm, anticipated positive emotion,
344 negative anticipated emotion have significant influences on consumers’ desire to purchase
sporting goods online. Moreover, it revealed that desire and frequency of past behavior
significantly influence on consumers’ intention to purchase sporting goods. Also, a gender
difference was also found in consumers’ perceptions toward purchasing sporting goods
online and the relationship between variables in the research model. The findings of this
study provide theoretical and practical implications in several ways. The details and
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implications of these findings are discussed below.

Theoretical implications
First, MGB accounts for a significant variance in consumers’ intention to purchase sporting
goods online (R2 ¼ 0.44), supporting that MGB provides an effective means for predicting
consumers’ intention to buy sporting goods. Moreover, it is consistent with most of the
previous studies of MGB that desire acts as a significant impetus for intention formation
(e.g. Han and Hwang, 2014; Meng and Han, 2016; Perugini and Bagozzi, 2001; Song et al.,
2017). Furthermore, the most important determinants to desire were the emotional factors,
including positive and negative anticipated emotions; while the other determinants such as
the subjective norm and attitude were less important predictors to desire. These results
stress the vital role of emotion in consumers’ decision-making process of purchasing
sporting goods online (Bagozzi and Dholakia, 1999; Phillips and Baumgartner, 2002). This
finding indicates that consumers’ desire to buy online sporting goods is mainly due to
emotional factors (i.e. positive and negative anticipated emotions) rather than cognitive
factors (i.e. attitude, subjective norm, and perceived behavior control). Because the process
of online shopping involves consumers’ emotion toward purchasing products (Éthier et al.,
2006; Koo and Ju, 2010), consumers are more likely to be motivated by a high expectation of
online shopping experience. As such, this study using MGB provides an appropriate way to
predict the intricate consumers’ intention and behavior of purchasing sporting goods online.
Second, it is noteworthy that perceived behavioral control had no significant influence on
consumers’ desire to purchase sporting goods online. Similar insignificant associations can
be found in previous studies (e.g. Han et al.’s 2014) findings in airport-shopping behaviors,
Bagozzi and Dholakia’s (2006) findings in the participation of small-group-brand
communities, and Lee et al.’s (2012) results in international tourist behaviors). These
studies argued that easily achievable goals and decision for a certain behavior
(e.g. shopping in the airport, joining communities, or traveling internationally) are not
mainly associated with perceived difficulty. Buying things online has become a common
way for shopping, and many consumers are capable of purchasing products online without
much trouble. Thus, in this study, online consumers’ perceived behavioral control of
purchasing sporting goods online is less predictive of their desires to engage in the behavior.
Third, it also found that frequency of past behavior has a significant influence on
behavioral intention but not desire. In this study, the frequency of past behavior reflects the
consumers’ experience of online shopping. The respondents of this study reported the
relatively high frequency of purchasing sporting goods online (M ¼ 3.61; SD ¼ 3.32).
According to Li et al. (1999), frequent online buyers are more convenience oriented and have
more knowledge how to purchase products online. Frequent online buyers’ knowledge and
convenience of online shopping serve as motivational factors which directly influence their
decision and intention. The roles of knowledge and convenience of online shopping are similar
 with desire which considered a state of mind that serves as a personal motivation less An application
connected to action (Perugini and Bagozzi, 2004). Therefore, the frequency of past behavior of the MGB
and desire act as two parallel factors predicting behavior intention. Moreover, the link
between frequency of past behavior and behavioral intention indicates that the frequency of
past behavior is a predictor more connected to action (Perugini and Bagozzi, 2004).
Fourth, this study uncovers some hidden patterns between genders (male and females)
that would not have been discovered if they were lumped together as an overall group. 345
According to the results of the independent t-test, except for perceived behavioral control,
male consumers have significantly greater values of attitude, subjective norm, positive and
negative anticipated emotions, desire, the frequency of purchasing sporting goods online,
and behavioral intention toward purchasing sporting goods online. This is consistent with
previous studies (Bae and Lee, 2011; Fan and Miao, 2012; Hasan, 2010; Lian and Yen, 2014;
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Pascual-Miguel et al., 2015; Wu, 2003) that males have more positive perceptions and
attitudes toward online shopping. Moss et al. (2006) confirmed that male and female
consumers have different perceptions toward male- and female-produced websites.
Specifically, male-produced websites are more attractive for male consumers; whereas
female-designed websites are preferred by female consumers. They argued that the male
domination of the internet technology profession could be a barrier to the effective mirroring
of female online websites preferences (Moss et al., 2006). As such, males generally have
higher perceptions and attitudes toward online shopping websites. Another possible
explanation could be the unique category of sporting goods. Consumers’ purpose of buying
sporting goods is not only symbolic but also utilitarian. That is, consumers would perceive
benefits after using sporting goods. Male consumers are more sensitive to benefits perceived
from the purchasing process (Hasan, 2010). As compared to females, males are more focused
on the process of purchasing sporting goods, and therefore, have more positive perceptions
and attitudes.
Moreover, although both male and female consumers’ desires positively predicted
behavioral intention, it found that male consumers’ desire had a significantly stronger
influence on behavioral than female consumer did. This finding is consistent with the results
of Kim et al. (2012) that desire has a more crucial impact on the behavioral intention for male
consumers tan for female consumers. This may be attributed to women consumers’ higher
levels of perceived risk regarding online shopping. As such, they show greater concern for
making a purchase even though they have the desire to do that.

Practical implications
The findings of this study also provide several practical implications for sporting goods
retailers to promote their products and better satisfy consumers’ needs. First, it found that
emotional factors (i.e. positive and negative anticipated emotions) are the most influential
predictors to desire toward purchasing sporting goods online. It suggests that creating a
hedonic online shopping experience is necessary for sporting goods consumers. This can be
achieved by providing a better online shopping environment for consumers. Consumers are
more likely to feel positive emotion when browsing and shopping on a well-designed
website with the high quality of security and information (Éthier et al., 2006). Moreover, the
high quality of a website may stimulate consumers’ intention to revisit repeatedly (Chiu and
Won, 2016b). Also, online retailers should incorporate the concepts of game mechanics
(i.e. gamification) and exclusivity (e.g. web-exclusive items or deals) to foster hedonic
shopping experience (Insley and Nunan, 2014).
Second, the use of reference group (e.g. referrals, word-of-mouth, and viral marketing)
and consumer experience management should be encouraged by the online retailers based
on the results concerning attitude and subjective norm toward purchasing sporting goods
online (Rose et al., 2012).
 APJML Third, the results regarding the influence of past behavior indicate that online sport
30,2 retailers have not been successful in customer retention. Given the importance of customer
loyalty and habitual purchasing, e-retailers should consider developing better loyalty
programs and the effective use of retargeting and remarketing (Bleier and Eisenbeiss, 2015).
Lastly, the findings identified the gender difference in the online consumers’ decision-
making process of purchasing sporting goods. It found that the link between desire and
346 intention was weaker for female consumers. Online retailers need to come up with some
marketing strategies to lower female consumers’ levels of perceived risk of purchasing
sporting goods online and persuade them to make a purchase rather than just think to do.
For example, sports retailers can plan sales promotion of women’s sporting goods exclusive
for female consumers to raise their awareness. Also, the design of websites should meet
females’ needs and satisfaction (Moss et al., 2006). This can trigger and strengthen their
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desire to purchase sporting goods and further stimulate their behavioral intention.

Limitations and future research

Although this study provides several valuable implications, there are not without
limitations. First, respondents of this study were all Korean online consumers of buying
sporting goods. Future study should apply MGB to other more developed countries in Asia,
such as Taiwan, Hong Kong, or Japan, to generalize the results of this study. Second, most
respondents of this study were younger consumers (i.e. the 20 s and 30 s). Although it has
been known that online shoppers skew younger ages (Patricia et al., 2005), the older
generations (e.g. Generation X and baby boomers) who are relatively not familiar with
online shopping should be taken into future consideration. Third, although MGB
significantly provides predictive power for consumers’ intention of purchasing sporting
goods, future studies may incorporate additional variables in MGB, such as prior
knowledge, trust, or perceived value to understand more comprehensively online
consumers’ decision-making process. Some scholars have suggested the necessity for a
revision or extension of the existing socio-psychological theories (e.g. MGB) by adding
additional constructs in specific contexts (Ajzen, 1991; Conner and Armitage, 1998; Perugini
and Bagozzi, 2001). By doing so, it broadens and deepens a theory, which can improve the
predictive power of human behavior in specific contexts. Finally, what online consumers
purchase this study includes a broad spectrum of sporting goods (e.g. footwear, apparel,
accessories, equipment, etc.). Due to the different characteristics of sporting goods, it is
necessary to classify sporting goods into various categories in the future study.

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Corresponding author
Doyeon Won can be contacted at: d.won@ljmu.ac.uk
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