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Study on the Influencing Factors of Mobile Users' Impulse

Purchase Behavior in a Large Online Promotion Activity

Article in Journal of Electronic Commerce in Organizations · April 2019

DOI: 10.4018/JECO.2019040108

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Journal of Electronic Commerce in Organizations
Volume 17 • Issue 2 • April-June 2019

Study on the Influencing Factors of

Mobile Users’ Impulse Purchase Behavior
in a Large Online Promotion Activity
Qihua Liu, School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China
Fuguo Zhang, School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China

ABSTRACT

A large online promotion activity provides a new shopping context for mobile users where situational
variables impact consumer behaviors simultaneously. A multitude of evidences show that mobile users
are more prone to impulsive in the large online promotion activity, yet relatively limited knowledge
is available on this phenomenon. The purpose of this article is to answer the question of what are
the important contextual drivers that lead to occurrence of mobile users’ impulse purchase behavior
in the “Double 11” promotion. The results show that promotion, impulse buying tendency, social
environment, aesthetics and interactivity of mobile platforms, and time available are key determinants
of mobile users’ urge to buy impulsively. Implications for managers and scholars are further discussed.

Keywords
Double 11 Promotion, Impulse Buying Tendency, Impulse Purchase, Mobile Commerce, Social Environment

INTRODUCTION

Shopping is already an important part of our daily lives. However, many purchases may be unplanned,
and even sudden, while they tend to be initiated on the spot and greatly related to the strong desire and
feelings of joy and excitement (Wu et al., 2016). This purchase is often referred to as impulse buying.
It has three key features, which are unplanned, the result of an exposure to a stimulus, and decided
“on-the-spot” (Piron, 1991). Impulse buying is popular in online settings. Donthu & Garcia (1999)
found that online shoppers were more impulsive than offline shoppers. A high proportion of 40% of
online consumers considers themselves as impulse shoppers (Verhagen and van Dolen, 2011; Huang
and Kuo, 2012; Liu et al., 2013). In the United States, more than 48% of consumers are estimated to
have an online impulse buying experience (GSI Commerce, 2008). Therefore, the study of impulse
buying is an important area of research in electronic commerce (e-commerce) (Huang and Kuo, 2012).
The “Double 11” promotion is a new kind of online promotion activities in China recently,
which is conducted in November 11th every year by those biggest e-commerce platforms in China.
In 2009, as the biggest B2C platform in China, Tmall.com launched the “Double 11” promotion for
the first time, which was held on November 11th. It attracted a great number of consumers and the
sales on Tmall.com reached 50 million RMB. After that, other e-commerce platforms in China such
as JD.com and Suning.com joined the “Double 11” promotion, making the “Double 11” promotion
the largest online commercial activity in China. The data released by Alibaba Group (http://www.
alibabagroup.com), shows that the sales of the “Double 11” promotion on Tmall.com was 91.2 billion

DOI: 10.4018/JECO.2019040108

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RMB in the year of 2015. The “Double 11” promotion now has become the world’s largest online
shopping festival.
With the development of mobile communication technology and intelligent terminal, more and
more people use mobile terminals for shopping on the internet in China. According to new data from
CNNIC, by December 2015, the number of mobile commerce (m-commerce) users is growing rapidly
to 340 million, an increase of 43.9% (CNNIC, 2016). The usage rate of m-commerce is increased
from 42.4% to 54.8%. In particular, more and more customers use mobile devices for shopping in
the “Double 11” promotion. In 2015, the sales of mobile commerce of Tmall.com were 62.6 billion
RMB in the “Double 11” promotion, accounting for 68% of total sales. The sales of the “Double
11”promotion on JD.com in 2015 breakthrough 10 billion RMB, in which mobile turnover of 74
billion RMB, accounting for 74%.
A large online promotion activity provides a different shopping context for mobile users where
situational variables [including promotion, social environment, mobile website characteristics, time
and money pressure] impact consumer behaviors simultaneously. Due to limitations in the mobile
terminal screen size, battery power, computing power, storage capacity, connection speed, flow rates
and other factors, mobile users are more prone to impulsive in the large online promotion activity.
According to a report from Rackspace, impulse purchases in the UK have increased by an estimated
£1.1 billion a year thanks to the online shopping convenience offered by smartphones, iPads and other
tablet computers (Rackspace, 2014). There has been extensive research on examined how different
factors related to information systems artifacts influence online impulse buying in an e-commerce
context. Nonetheless, to the best of our knowledge, scholars rarely involve empirical investigation
of mobile users’ impulse buying in a large online promotion activity, and this paper will fill this gap
in the literature.
The purpose of this study is trying to answer the question of what are the important contextual
drivers that lead to occurrence of mobile users’ impulse purchase behavior in a large online promotion
activity.

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT

Online Impulse Buying

Impulse buying refers to when a consumer experiences a sudden, often powerful and persistent,
urge to buy something immediately (Liu et al., 2013). Stern (1962) sorted impulse buying into four
distinguishing categories, they are pure, reminder, suggestive, and planned impulse buying. Pure
impulse buying refers to a completely irrational impulse buying behavior, which is triggered by the
emotional needs of consumers and the desire to purchase a product because of the novelty of this
product. After consumers see a commodity, they may think of that it is required for his home, or
recall the purchase intention when they see advertisements or other information about this product.
Then, an impulse buying behavior is produced, which is reminder impulse buying. When consumers
see a commodity for the first time, they can think of their own demand for the goods, which may
lead to suggestion impulse buying. Planned impulse buying refers to that consumer buy some goods
which are not intended to purchase in this shopping plan under the influence of business promotion
or shopping environment.
With the emergence and rapid development of e-commerce, researchers have been interested in
whether impulsivity is evident in this new shopping environment. Sultan (2002) investigated how to
improve and extend the concept of impulse purchasing and examined impulsive purchasing behavior
online. Luo (2005) found that the presence of peers increases the urge to online purchase, and the
presence of family members decreases it. Parboteeah et al. (2009) pointed out that the features of
provision on websites like high-quality, task-relevant and mood-relevant can remarkably affect the
cognitive and affective reactions of users in the website, which directly influence the probability and
extent of their online impulse buying behavior. Verhagen et al. (2011) proposed a research model and

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examined how beliefs about functional convenience and beliefs about representational delight related
to online impulse buying. Huang and Kuo (2012) explored whether impulsivity in online shopping
decisions can be impacted by consumers’ mood, and how participation can regulate it. Liu et al. (2013)
investigated how website cues impacts personality traits to urge the impulse purchase online. Gwee
and Chang (2014) proposed a theoretical model to help platforms figure out what attractive cues are
feasible in urging impulse purchases. Wu et al. (2016) proposed an integrated model investigating the
influence of technology use and trust issue on online impulse buying behavior, and added psychological
state issue as a mediator to further explain the target of online impulse buying behavior.
Analysis of impulse purchase behavior in the context of mobile commerce is in the initial stage.
Wu & Ye (2013) take the perspective of mobile media technology convergence on combination with
the impulsive personality of consumers and flow experience to understand the impulsive purchase
intent of consumers on mobile commerce platforms. Drossos et al. (2014) study the dimensionality
of the product involvement construct and its effects on consumers’ purchase intentions in mobile text
advertising via a simulated field experiment. Complementing these studies, we propose a completely
dissimilar model to empirically explore mobile users’ impulse purchase behavior in a large online
promotion activity, which mainly contains promotion, impulse buying tendency, social environment,
mobile website characteristics (Aesthetics, interactivity, resources richness) and limiting factor (time
available, money available), as shown in Figure 1.

Promotion and Mobile Users’ Impulse Purchase Behavior

In the “Double 11” promotion, businesses tend to carry out a series of promotional measures to
stimulate consumer desire to buy, for example, discounts, points, gifts and other activities. Promotion
can directly stimulate consumers in shopping process, and it is one of the most common methods
retailers prefer to take to attract consumers and luring them into unplanned consumption (Kacen et
al., 2012). Heilman et al. (2002) pointed out that when consumers faced sudden sales promotion,
they often purchased unplanned or excessive goods. Zhou and Wong (2003) found that when a
promotion or a larger price discount occurred, consumers tended to have the impulse to buy. Kacen

Figure 1. Research model

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et al. (2012) suggested that of the three retail factors, a store environment with a high–low pricing
strategy influences impulse buying the most. Consequently, we propose the following hypotheses:

H1: Promotion is positively related with mobile users’ impulse purchase intention in the “Double
11” promotion.

Impulse Buying Tendency and Mobile Users’ Impulse Purchase Behavior

After being stimulated by the same external factors, different consumers may exhibit different
emotional responses. This phenomenon is caused by the individual characteristics of each person,
which is different from others (Rook and Fisher, 1995). It is called impulse buying tendency in the
research of impulse buying, which is a response style, a tendency to react quickly without reflection,
and a kind of inherent and non-controllable factors of consumers (Beatty and Ferrell, 1998). It has
great individual differences. Highly impulsive buyers are more likely to experience spontaneous
buying stimuli; their shopping lists are more “open” and receptive to sudden, unexpected buying
ideas (Rook and Fisher, 1995; Hock and Loewenstein, 2011). Thus, we propose that:

H2: Impulse buying tendency is positively related with mobile users’ impulse purchase intention in
the “Double 11” promotion.

Social Environment and Mobile Users’ Impulse Purchase Behavior

Social environment also has significant effect on mobile users’ unplanned consumption. In the
“Double 11” promotion, a large number of consumers participate in the promotion together. They can
share promotion information, talk about their shopping plans and shopping decisions by face to face
communication, telephone, text messaging, QQ, WeChat and other social networking tools. This has
formed a social environment for the user’s shopping. Previous studies find that people shopping in
group try to balance their individual goals with the group goals and tend to purchase more products
(Lee et al., 2008; Park et al., 2013; Aouinti, 2013; Chang et al., 2013). Liu & Zhang (2014) suggest
that people usually observe or refer to others’ behavior when making decisions. That is to say people
are easily influenced by others. Consumers who have strong normative social tendency are inclined
to accept more social norms and follow the behavior of the crowd, which promotes impulsive
consumption (Yan et al., 2016). Specifically, normative social influence has a more obvious effect
on Chinese consumers (Zhang & Zhuang, 2008). Thus, we propose that:

H3: Social environment is positively related with mobile users’ impulse purchase intention in the
“Double 11” promotion.

Mobile Website Characteristics and Mobile Users’ Impulse Purchase Behavior

Mobile shopping platforms represent an important channel between customers and merchants. It
provides necessary safeguards for the communication between demand and supply from limit time
and space. From the system user standpoint, mobile user is a mobile web system user, who can search
relevant product information, complete payment and track product delivery in an mobile store. Prior
researches have examined numerous various cues in both online and offline stores that may change
consumers’ internal traits in promoting impulse purchase. In online settings, Childers et al. (2002)
put forward a term of ‘webmospherics’, which include various web design attributes such as frames,
graphics, text, pop-up windows, search engine configuration, “one-click” check-out or purchase
procedures, hypertext links, media dimensions (e.g., graphics, text, audio, color, and streaming video)
and site layout dimensions (e.g., organization and grouping of merchandise). These web design
attributes may urge online impulse purchase to varying degrees. Parboteech et al. (2009) found that

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the visual aesthetics of the mobile website positively influenced customers’ perceived enjoyment,
and directly affect online impulsive buying intention. Thus we propose that:

H4a: Aesthetics of mobile platforms is positively related with mobile users’ impulse purchase intention
in the “Double 11” promotion.

Interactivity of mobile platforms also has a significant impact on customers’ purchase intention.
Ghose & Dou (1998) found that interactivity of shopping websites positively affected customers’
evaluation of the websites. Koufaris (2002) also found that interactivity had a positive impact on
customers’ emotion and attention when purchasing online. Dawson & Kim (2009) pointed out when
customers browsed and enter the shopping website the interactivity would significantly influence
customers’ impulse purchase intention and actual purchase behavior. Based on the above discussion,
we therefore proposed that:

H4b: Interactivity of mobile platforms is positively related with mobile users’ impulse purchase
intention in the “Double 11” promotion.

Resource richness of mobile platforms refers to the richness of the types and information of
product customers can get from the platform. Among them, product information includes introduction,
comments and service information of product. Park & Han (2007) showed that persuasive and high-
quality comment information can induce consumers to generate positive emotions on online purchase
intention. Kim & Lennon (2009) found that, when stores provided plenty of product information,
consumers could know the merchandizes comprehensively and reduce their perceived risk, accordingly
produce a stronger purchase intention. Parboteech et al. (2009) found that the high-quality information
provided by shopping website positively influenced consumers’ perceived usefulness, thus prompted
impulse purchase intention and final purchase behavior. Therefore, we hypothesized that:

H4c: Resource richness of mobile platforms is positively related with mobile users’ impulse purchase
intention in the “Double 11” promotion.

Limiting Factor and Mobile Users’ Impulse Purchase Behavior

Time available have significant effects on impulse purchase intention. When in lack of shopping time,
consumers often feel anxious and reduce corresponding search activities, thus restrict consumers’
unplanned purchases (Stilley et al., 2010). In contrast, when shopping time is sufficient, consumers
would feel less stressful and have more freedom and energy to browse the information of products
outside the shopping plan (Bell et al., 2011). Moreover, when having enough shopping time, consumers
tend to be easily influenced by others. External factors such as people around consumers consume
resources that are in their self-regulation (Vohs et al., 2007) and lead to unplanned consumption.
In an empirical study, Stilley et al. (2010) confirmed that consumers tended to make unplanned
consumption when shopping time is longer. Besides that, the “Double 11” promotion lasts just 1 day
which forms a psychological pressure of opportunity loss and strengthens the influence of shopping
time on unplanned consumption (Rieskamp, 2008). Thus, we propose:

H5a: Time available is positively related with mobile users’ impulse purchase intention in the “Double
11” promotion.

Money available is viewed as another key factor of impulse purchase. When consuming, people
feel reassuring if they have more money over budget, they can adjust species of planned goods.

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Accordingly, consumers with large budget are more prone to impulse buying than those with low
budget (Bell et al., 2011). Moreover, consumers often get negative utility when they spend exceeding
their shopping budgets (Stilley et al., 2010). To avoid or reduce the negative effects, they take measures
such as making a reasonable tight budget to control unplanned consumption (Yan et al., 2016). Based
on the above discussion, we therefore proposed that:

H5b: Money available is positively related with mobile users’ impulse purchase intention in the
“Double 11” promotion.

RESEARCH METHODOLOGY

Sample and Data Collection

Our study employed a Web-based survey to empirically test the research model and hypotheses. Since
it is difficult to imitate the large online promotion activities in experiments, we select the “Double
11” promotion on Tmall.com on November 11th, 2015 as the research scenario. Moreover, a survey
methodology is used in this study to collect data.
The questionnaire was initially designed in English and then translated to Chinese. To ensure the
reliability of the Chinese translation, all original items were translated into Chinese by two authors
separately. Next, a third research whose native language was Chinese confirmed the meaning of the
English version by comparing the two Chinese versions. If there was any divergence, the researches
would discuss the translation methods until finally reaching a consensus. After developing the initial
survey, a pilot test of 30 students who have shopping experiences in the “Double 11” promotion was
conducted. The students were invited to answer the questionnaire and provided feedback. Based on
their results, the questionnaire was modified to improve the clarity and intelligibility. The revised
questionnaire released through a professional survey service website named Sojump.com and lasted
three months from December 2015 to February 2016. To recruit more users for this research, we used
a combination of communications tools such as E-mail, QQ and Wechat to send out the questionnaire.
In total, 370 responses were received and 65.0% of them have shopping experiences in the “Double
11” promotion. Each recycling questionnaire was double checked, and we eliminated responses that
were uncompleted or had the same answers to all questions. Besides, we also discarded responses of
users who had not mobile shopping experience in the “Double 11” promotion. Finally, we obtained
231 valid questionnaires.
As Table 1 shows, the majority of the respondents were between 18 and 34 years old (83.98%).
50.65% of the respondents were females and 49.35% were males. For the education background,
bachelor and master took up a major proportion, 45.45% and 39.83% respectively. 74.89% of the
respondents had shopping experience of more than 3years. Most respondents have the 2 to 4 online
shopping experiences per month, accounting for 54.98% of the total number.

Measurement Development
The questionnaire was divided into two parts. The first part contained five questions related to the
demographic information reported in Table 1 above. The second part consisted of 36 items related to
the mobile users’ impulse purchase behavior. They are used to measure the constructs of promotion (6
items), individual characteristics (6 items), social environment (4 items), Aesthetics of platforms (3
items), Interactivity of platforms (3 items), Resource richness of platforms (4 items), money available
(3 items), time available (3 items) and impulse purchase intention (4 items). Each construct items in
part two were measured on 5-point Likert scales ranging from “strongly disagree” to “strongly agree”.
The items used in this study for each construct was selected from the previously validated
measurements and slightly been modified to fit the specific context of mobile users. The scales
for promotion were derived from Heilman et al. (2002) and Kacen et al. (2012) while questions for

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Table 1. Descriptive statistics of respondents’ characteristics

Measure Category Frequency Percentage (%)

Gender Male 114 49.35
Female 117 50.65
Education Senior high and below 32 13.85
Bachelor 105 45.45
Master 92 39.83
Doctor 2 0.87
Age 18 and below 3 1.3
18~25 125 54.11
26~34 69 29.87
35~45 22 9.52
46 and above 12 5.19
Shopping experience Just started 2 0.87
Less than 1 year 9 3.9
1~3 years 47 20.35
3~5years 80 34.63
5 years and above 93 40.26
Frequency of online 0~1 time 51 22.08
shopping per month
2~4 times 127 54.98
5~7 times 31 13.42
7 times and more 22 7.23

individual characteristics were derived from Hock and Loewenstein (2011). The measure for social
environment was developed from Zhang & Zhuang (2008) and Park et al. (2013). The questions used
in the aesthetics of platforms, interactivity of platforms and resource richness of platforms came
from Childers et al. (2002), Dawson & Kim (2009), and Parboteech et al. (2009) respectively. The
measure for money available and time available was derived from Bell et al. (2011) and Stilley et al.
(2010), respectively.

RESULTS

The data were analyzed using the SPSS19.0 and Linear Structural Relations software AOMS22.0.

Testing the Validity and Reliability of Constructs

When we collected data by questionnaire survey, the quality of the questionnaire itself would impact
the survey results. Only when questionnaires are well designed can we get reliable research results,
make proper analysis and decision. In this study, reliability analysis and validity analysis were used
to measure the design quality of the questionnaire.
Reliability refers to the consistency level of a questionnaire. In the test of reliability analysis, it is
most common to test internal consistency which can be represented by Cronbach’s Alpha. A higher
value of Cronbach alpha indicates a higher internal consistency of the scale. We used SPSS 19.0

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software to test reliability of the measurement model, the Cronbach’ s alpha values of the model’s 11
variables were over the 0.7 level, suggesting that the measures have good reliability, as Table 2 shows.
Validity refers to the accuracy of a questionnaire, including two aspects, one is the purpose
of test, the other is the accuracy and authenticity the measures. We used factor analysis method in
SPSS 19.0 to test validity of the measurement model. Before carrying on a factor analysis, every
variable’s KMO index and parameter of Bartlett test of sphericity should be tested. As table 3 shows,
the KMO index value of every construct in this questionnaire is greater than 0.7. The Bartlett test
result of sphericity of every construct is also significant. Factor loadings of the constructs are all
over the thresholds of 0.5, as shown in Table 4. The results suggest unidimensionality, convergent
and discriminant validity of the measures.

Testing the Hypotheses

As shown in Table 5, all the fit indices (Chi-square/df, GFI= Goodness of Fit Index, AGFI = Adjusted
Goodness of Fit Index, RMSEA = Root Mean Squared Error of Approximation, CFI = Comparative
Fit Index, TLI = Tucker-Lewis Index, IFI = Incremental Fit Index) are within the recommended values
(“Accept”). This suggests that the model provides an appropriate fit to the data.

Table 2. Construct and Cronbach’s Alpha value

Construct N Cronbach’s α Value Reference Value

Promotion (PR) 6 0.849 Cronbach α ≥ 0.70
Impulse buying tendency (IBT) 6 0.921
Social environment (SE) 4 0.723
Aesthetics of platforms (AP) 3 0.741
Interactivity of platforms (IP) 3 0.878
Resource richness of platforms (RRP) 3 0.868
Money available (MA) 3 0.766
Time available (TA) 3 0.713
Impulse purchase intention (IPI) 4 0.847

Table 3. KMO and Bartlett test of sphericity

Construct KMO Bartlett Test of Sphericity

Approx. Chi-Square df Sig.
PR 0.834 499.848 15 .000
IBT 0.919 850.788 15 .000
SE 0.733 146.548 6 .000
AP 0.681 142.044 3 .000
IP 0.744 333.468 3 .000
RRP 0.624 441.799 3 .000
MA 0.687 168.144 3 .000
TA 0.710 238.105 3 .000
IPI 0.795 359.950 6 .000

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Table 4. Confirmatory factor analysis for research model

Construct Item Factor Loadings Variance Explanatory Power (%)

PR PR 1 0.791 61.262
PR 2 0.814
PR 3 0.785
PR 4 0.777
PR 5 0.750
PR 6 0.778
IBT IBT 1 0.823 71.867
IBT 2 0.872
IBT 3 0.856
IBT 4 0.845
IBT 5 0.854
IBT 6 0.836
SE SE 1 0.693 60.050
SE 2 0.817
SE 3 0.730
SE 4 0.741
AP AP 1 0.783 63.776
AP 2 0.825
AP 3 0.787
IP IP 1 0.889 78.439
IP 2 0.880
IP 3 0.888
RRP RRP 1 0.807 73.931
RRP 2 0.829
RRP 3 0.859
MA MA 1 0.792 68.409
MA 2 0.856
MA 3 0.832
TA TA 1. 0.736 63.717
TA 2 0.875
TA 3 0.777
IPI IPI 1 0.772 69.898
IPI 2 0.885
IPI 3 0.836
IPI 4 0.847

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Table 6 shows the standardized path coefficients with their respective significance levels and
the explanatory power of the model for dependent variables.
According to the Table 6, we found that variables except for resource richness of platforms and
money available have prominent path relationship with impulse purchase intention. This suggests
that except for H4c and H5b, all hypotheses received support.

DISCUSSION AND CONCLUSION

This paper integrates promotion, impulse buying tendency, social environment, mobile website
characteristics (Aesthetics, interactive, resources richness) and limiting factor (time available, money
available) to understand mobile users’ impulse purchase behavior in a large online promotion activity.
The following discusses main findings, practical implications, research limitation, and future research.
The results show that the promotion in “Double 11” activity has a significant impact on impulse
purchase intention, which is consistent with the study of Yan et al. (2016). Yan et al. (2016) found
that there was a positive correlation between promotion range and unplanned consumption. That
is to say, if there is a wide range of products in promotion, it is easy to meet the needs of different
consumers, even inducing consumers’ implicit needs.

Table 5. Fit indices for research model

Fit Index Recommended Value Observed Value

Accept Good
Chi-square/df <3.0 1.999
GFI [0.7, 0.9] >0.9 0.783
AGFI [0.7,0.9] >0.9 0.744
RMSEA <0.08 <0.05 0.068
CFI [0.7, 0.9] >0.9 0.832
TLI [0.7, 0.9] >0.9 0.820
IFI [0.7, 0.9] >0.9 0.834

Table 6. Model results

Path Hypothesis Hypothesis Estimate S.E. C.R. P Standardized

Confirmed? Path
Coefficients
IPI<---PR H1 Y (Yes) 0.136 0.062 2.194 0.028 0.16
IPI<--- IBT H2 Y 0.182 0.046 3.960 *** 0.30
IPI<---SE H3 Y 0.156 0.065 2.397 0.017 0.19
IPI<---AP H4a Y 0.200 0.083 2.400 0.016 0.20
IPI<---IP H4b Y 0.101 0.051 1.977 0.048 0.14
IPI<---RRP H4c N (No) -0.017 0.034 -0.501 0.617 0.03
IPI<---MA H5a Y 0.032 0.045 0.723 0.470 0.05
IPI<---TA H5b N 0.277 0.069 3.996 *** 0.33

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Our findings point out impulse buying tendency is positively related with mobile users’ unplanned
purchase intention in the “Double 11” promotion, which is consistent with previous studies (Rook
and Fisher, 1995; Hock & Loewenstein, 2011). Rook and Fisher (1995) suggested that impulsive
buyers were more likely to act on whim and to respond affirmatively and immediately to their buying
impulses. Hock & Loewenstein (2011) also found that impulsive buyers were likely to experience
buying impulses more frequently and strongly than other consumers.
Our results also show that social environment is positive to unplanned consumption in the
“Double 11” promotion, which is also consistent with previous studies (Park et al., 2013; Aouinti,
2013). Consumers can collect more valuable promotion information by taking part in the promotion
activity with others. Moreover, consumers are easily influenced by other people’s opinions and
shopping decisions, and even keep conformable with others to gain a sense of collective belonging and
decision assurance. Furthermore, when various kinds of products are promoted together, consumers
can enjoy more benefit through collaborative buying, which also leads to unplanned consumption.
The results also demonstrate that aesthetics and interactivity of mobile platforms in “Double 11”
activity have a positive impact on impulse purchase intention of mobile users. But, resources richness
of mobile platforms has no significant effect on unplanned purchase intention of mobile users. This
suggests that aesthetics and interactivity of mobile platforms are very important in the “Double 11”
promotion. In fact, some previous studies (van Noortet al. 2012; Wu et al. 2016) pointed out that a
higher level of interactivity and aesthetics in websites can cause flow experience. Wu et al. (2016)
found that flow experience has positive impact on online impulse buying.
Our findings also suggest that time available is significantly related to impulse purchase intention
of mobile users. It further illustrates the more time users spend on browsing products, the more likely
impulse purchase behavior occurs, which is also consistent with previous studies (Stilley et al., 2010;
Bell et al., 2011; Yan et al. 2016). Bell et al. (2011) pointed out that more time in the store on a trip
led to more unplanned buying. Yan et al. (2016) found that that actual shopping time in large online
promotion activities was positively related with unplanned consumption. We also find that money
available have no impact on impulse purchase intention of mobile users, which is different from the
study of Wu & Huan (2010). Wu & Huan (2010) found that the main effects of both time pressure
and economic pressure were significant in young students’ impulse buying. Several reasons might
contribute to this phenomenon. On the one hand, in the “Double 11” promotion, consumers often need
to make purchase decisions in a very short period of time, in order to grab the favorite commodity.
In such a case, they tend to ignore the impact of money. On the other hand, with the popularity of
credit cards in China, it also reduces the sensitivity of consumers to money when shopping because
of the characteristics of overdraft consumption.
Our study has provided inspiration for the practice of management. For the m-commerce
platforms, it is necessary to step up promotions and it is effective to induce unplanned consumption
when promotion activities cover various kinds of products. Second, m-commerce platforms should
try to use beautiful pictures and reasonable page layout to achieve the users’ flow experience.
Simultaneously, m-commerce platforms should also take effective measures to enforce the interactivity
between consumers and mobile stores. Third, in promotion activities, it is necessary to control over
shopping time so as to stimulate consumers’ unplanned purchasing. For the mobile Retailers, they
should try to attract consumers to participate in promotion activities in group to increase unplanned
consumption. Moreover, they should also use large data technology to analyze consumers’ impulse
buying tendency in order to develop more personalized marketing measures.
Of course, there are limitations involved in this study. First, the investigation subjects of this
paper are mainly concentrated in the four regions of Jiangxi, Beijing, Guangdong and Shanxi in
China. Moreover, the number of samples is also relatively low. Second, this paper might have omitted
some important factors that influence mobile users’ impulse purchase behavior, such as product type
and product price. It is not clear which types of products are more likely to induce impulse purchase

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behavior. We also don’t know whether unplanned purchasing is easier to occur when mobile users
purchase products of high price, compared to products of low price.

ACKNOWLEDGMENT

This paper is supported by the National Natural Science Foundation of China (No: 71363022, No:
71361012), Natural Science Foundation of Jiangxi, China (No: 20161BAB201029) and Foundation
of Jiangxi Educational Committee (No: GJJ150446).

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