Master Thesis
Determinants of the adoption of mobile shopping applications in China
University of Amsterdam Faculty of Economics and Business
MSc. in Business Administration Track: Marketing
Student Name: Siqi Wang Student Number: 11129077
Supervisor: Dr. Jing Li Date: 24.06.2016
Statement of Originality
This document is written by Student Siqi Wang who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Acknowledgement
To complete the thesis was my final step to attain my Master’s degree in Business Administration at the University of Amsterdam. Writing a thesis for this topic has been an interesting learning experience for me. When I made the decision to choose the subject, I was pretty sure that the adoption mobile application was an interesting subject that I wanted to study. After completing my work, I had a better understanding of the subject and even more interested in the topic.
I would like to take this opportunity to thank my supervisor Dr. Jing Li firstly. Thanks to all of her support and guidance, I could complete writing my thesis according to time schedule. Her constant positive criticism and professional suggestion helped me significantly through the whole writing process. Furthermore, I truly appreciate my family and friends who have been trusted me since the beginning and have never given up supporting me during the whole process.
I sincerely hope you enjoy reading this thesis and having an interest in this topic area. Best regards,
Siqi Wang
24th of June 2016
Abstract
Mobile shopping channel has become increasingly popular among customers in the past decade. As we know, customers could enjoy mobile shopping by mobile devices such as smartphones or tablet PCs. Moreover, mobile applications could provide services for customers to enjoy mobile shopping regardless time and place. Thus, mobile applications have developed dramatically and played a significant role for mobile shopping. But little researches have analyzed the adoption of mobile applications. Hence, this research focuses on the adoption of mobile apps for shopping. By analyzing the determinants of mobile applications’ adoption, it’s beneficial to promote the mobile applications and enhance the adoption of customers.
The adoption consists of attitude towards mobile applications and intention to adopt. In general, the main research question is, what factors might affect the adoption of mobile applications for shopping? The sub-research questions include 1) which factor affects customers’ attitudes and intentions to adopt mobile apps for shopping 2) whether attitude mediates the impacts of predictors on intention 3) which factor moderates variables’ impact on attitude. Hence, the objectives of this research are 1) to investigate the effects of perceived ease of use, subjective norm, time pressure, mobile knowledge and online shopping experience on customers’ attitudes and intention to adopt mobile shopping applications 2) to test whether attitude mediates the relationship between the examined factors and intention to adopt mobile shopping applications 3) to explore the moderators in the research model.
multiple regression analyses, moderation test and mediation test, several findings have been presented. The main findings indicate that the antecedents of attitudes are differing from the antecedents of intention. These antecedents of attitudes consist of perceived ease of use, subjective norm, time pressure, mobile knowledge and mobile knowledge. Furthermore, customers’ attitudes toward mobile adoption mediate the effects of subjective norm, mobile knowledge and perceived ease of use’s impact on intention to adopt mobile shopping applications. However attitudes do not mediate the impact of online shopping experience on intention to adopt mobile shopping applications. Lastly, time pressure moderates the impact of subjective norm on attitudes toward mobile shopping applications. Hence, it’s necessary to design simplified mobile applications and improve the reputation.
Content
1. Introduction ... 9
2. Literature review ... 12
2.1 Online and mobile channel adoption in general ... 12
2.2 Mobile shopping ... 13
2.3 Mobile shopping applications ... 15
3. Conceptual framework ... 16
3.1 Framework ... 16
3.2 Technology acceptance model ... 17
3.3 Subjective norm ... 18
3.4 Mobile knowledge ... 19
3.5 Online shopping experience ... 20
3.6 Time pressure ... 21
3.7 Attitude and intention ... 22
3.8 Personal information and characteristics ... 24
4. Research design ... 24
4.1 Sample ... 25
4.2 Survey Design ... 27
4.3 Measures ... 28
4.4 Statistical methods ... 30
4.5 Procedure and collection ... 30
4.5.1 Pilot study ... 31
4.5.2 Main study ... 31
5. Result and analysis ... 31
5.1 Preliminary analysis ... 32
5.2 Validity and reliability ... 33
5.3 Correlation analysis ... 35
5.4.1 Multiple linear regression ... 35
5.4.2 Mediation test of attitude ... 37
5.4.3 Moderation test of time pressure and online shopping experience ... 39
5. 5 Summary of results ... 41
6. Discussion and conclusion ... 42
6.1 Antecedents of the attitude to mobile applications adoption ... 42
6.2 Mediation effect of the attitude to mobile applications adoption ... 44
6.3 Moderation effect of online shopping experience and time pressure ... 45
7. Implication ... 42
7.1 Theoretical implication ... 47
7.2 Managerial implication ... 48
8. Limitation and future research ... 49
Reference ... 51
Appendix ... 58
List of Figures Figure.1 New Internet users in China by 2015 ... 9
Figure.2 Conceptual framework ... 17
Figure.3 The number of Internet users in China by Dec.2015 (Unit: billion) ... 25
Figure.4 The number of mobile Internet users in China by Dec.2015 (Unit: billion) ... 25
Figure.5 Percentage of online shopping and mobile shopping customers ... 32
Figure.6 Types of mobile apps that mostly be used ... 33
List of Tables Table.1 Distribution of samples’ gender and age ... 26
Table.2 Distribution of samples’ education level ... 26
Table.3 Distribution of samples’ income level ... 27
Table.4 Rotated Component Matrix ... 33
Table.6 Pearson correlation matrix ... 35
Table.7 Coefficients between predictor variables and attitude, between predictor variables and intention ... 36
Table.8 Attitude’s mediation effect between perceived ease of use and intention ... 37
Table.9 Attitude’s mediation effect between subjective norm and intention ... 38
Table.10 Attitude’s mediation effect between online shopping experience and intention ... 38
Table.11 Attitude’s mediation effect between mobile knowledge and intention ... 38
Table.12 Moderation test of online shopping experience ... 40
Table.13 Moderation test of time pressure ... 40
Determinants of the adoption of mobile shopping applications in China
1. IntroductionWith the tremendous advantages of mobile devices, mobile commerce (m-commerce) is making big improvement in business. Recently, large numbers of m-commerce applications have been widely used for banking, ticketing, traveling and shopping. Nowadays, we are witnessing the profound changes of business under the influence of wireless technology and unprecedented opportunities for mobile shopping (m-shopping).
The study Comscore-Milliannial media retail (2011) shows that over 52% of consumers started using mobile devices in different goods categories and about 40% consumers start purchasing via mobile devices at the same time. In comScore (2012)’s report, it’s the first time that the usage of apps exceeded the usage of browsers on smartphones and tablets (54.5% versus 52.7%). Specifically, Internet World Stats (2010) reports that China is the largest Internet market in the world. Meanwhile, BCG (2010) shows that Chinese users spend nearly double daily time on the Internet than United States users. Besides, CNNIC (2015) reveals that the percentages of new mobile Internet users have outweighed new non-mobile devices users in China (See Figure.1). Apparently, Chinese potential mobile shoppers are increasing dramatically in these years.
It’s well known that customers enjoy mobile shopping by mobile browsers or by mobile apps. But for European consumers, m-shopping channel is only for searching information and comparing prices of products in some occasions. However, for Chinese consumers, using mobile apps to purchase has changed their life and shopping habits dramatically. Apart from
Taobao, other e-commerce companies such as JingDong, Suning and Guomei, also developed their apps to sell electronics and living goods. Consumers could make reservations of taxies, buy tickets, order take-out foods and purchase travel products anytime anywhere via different kinds of mobile apps.
Figure.1 New Internet users in China by 2015
Many studies have investigated the adoption of the online shopping, examining the effects of beliefs (i.e. relative advantage, risk, and enjoyment) concerning on initial and continued adoption (Liu & Forsythe, 2011; Ha & Stoel, 2009). Besides, subjective norm and perceived playfulness influence the adoption of online shopping for customers as well (Çelik, 2011). As for the degree of m-shopping adoption, male and female consumers with different culture backgrounds are different (Faqih & Jaradat, 2015). Moreover, developed countries place greater emphases on the perceptions of usefulness toward m-shopping adoption, whereas perceived ease of use is more critical for adoption in developing countries (Faqih & Jaradat, 2015; Brashear, 2009).
applications. Although mobile apps are one kind of online shopping channels, there still are enormous differences between mobile apps and online channels. Overall, it’s a research gap for further studying. This study examines the determinants of adoption of mobile applications in China. These mobile apps provide different products, including take-out foods, life commodities or transportation tickets, etc. In order to fill the former researches gap, the study focus on analyzing the relationship between different factors and attitudes. Besides, I also study the mediator and moderator in my research model.
This study addresses one main research question and three sub-research questions. The main research question is, what factors might affect the adoption of mobile shopping applications? The sub-research questions include 1) which factor affects customers’ attitudes and intentions to adopt mobile apps for shopping 2) whether attitude mediates the impacts of predictors on intention 3) which factor moderates variables’ impact on attitudes.
Therefore, the objectives of this research are 1) to investigate the effects of perceived ease of use, subjective norm, time pressure, mobile knowledge and online shopping experience on customers’ attitudes and intention to adopt mobile shopping applications 2) to test whether attitude mediates the relationship between the examined factors and intention to adopt mobile applications 3) to explore the moderators in the research model.
In terms of the theoretical implication, the research extents the technology acceptance model by analyzing the factors such as mobile knowledge, time pressure and online shopping experience. Besides, the research improves the mediation check for attitude. More specific, attitude has been presented that it does not mediate the impact of online shopping experience
on intention to adopt mobile shopping apps. In practical aspect, subjective norm plays a significant role in the adoption of mobile shopping apps. For less busy customers, they are more likely to rely on other’s opinions. In this case, it’s essential for business owners to improve services and products quality of mobile applications so as to enhance the social influence and to receive more recommendation.
The study consists of seven main parts, including literature review, conceptual framework, research design and methodology, survey results and analysis, discussion and conclusion, implication, limitation and future research. Literature review includes the review of mobile channel adoption in general, mobile shopping and mobile shopping applications. In conceptual framework, citing and extending the technology acceptance model (TAM) firstly and then putting forward other affect variables and conducting several hypotheses. After doing survey and collecting data, I analyze the data and then come up with some conclusion based on survey results. Besides, I also propose some implications from theoretical aspects and practical aspects. Finally, I state the limitation of my research and come up with some proposals for further researches.
2. Literature review
2.1 Online and mobile channel adoption in general
The Internet is not only a channel for communication, information gathering and entertainment, but also an important vehicle for commercial transactions. Apparently, the Internet is altering customers’ behavior. There are many literatures have researched online
remove the need of visiting physical stores (Pechtl, 2003). Similarly, mobile channels also provide a convenient platform for users. For instance, more and more users are willing to browse news or to purchase goods via mobile devices.
In fact, the adoption of new technology influenced positively or negatively by various factors. According to technology acceptance model (TAM), perceived usefulness and perceived ease of use affect the adoption of users. Thus, as new technology, the adoption of online and mobile channel is influenced by customers’ beliefs as well. Furthermore, a compelling online experience could promote customer engagement and experience, and then enhance the adoption of online channel eventually. By contrast, some customers think online channel is frustrating and confusing because of lacking trust (Dai, Forsythe & Kwon, 2014). In this way, previous experience of online channel might affect the beliefs and adoption of mobile channel as well.
Apart from the beliefs, personal characteristics affect the adoption of online and mobile channel as well. In Brown, Pope& Voges (2003), it states that males with higher income and less risk averse are more likely to adopt online channel. In addition, convenience-oriented and innovative customers are more willing to adopt online channel. As a result, online channel adopters might think mobile channel that is compatible with their lifestyle (Handa & Gupta, 2014). It is because online channel and mobile channel enjoy some similarities such as convenience, efficiency and innovative experience to some extent.
2.2 Mobile shopping
in daily routines and practices recently. In essence, mobile commerce (m-commerce) enables retailers to offer products and services to customers. Besides, it also opens up opportunities for customers to get new types of shopping experience (Dholakia & Dholakia, 2004)
Several literatures have defined the mobile shopping (m-shopping), introduced kinds of mobile devices and analyzed the influential factors of adoption respectively. Firstly, m-shopping broadly means transactions with monetary value by mobile or wireless network (Barnes, 2002; Mylonopoulos & Doukidis, 2003). Meanwhile, m-shopping refers to the shopping activities performed by consumers via wireless Internet on mobile device (Ko, Kim & Lee, 2009; Kourouthanassis & Giaglis, 2012). Apparently, among different kinds of mobile devices, users most prefer smartphone. Due to the conveniences and portability of them, smartphones are most suitable for m-shopping rather than other non-mobile devices. Furthermore, since m-shopping enable consumers to buy goods regardless of time and place, it is a kind of competitive advantages that has resulted to a so-called ‘mobile lifestyle’
(Shankar et al., 2010). However, the perceived risks of mobile-devices also negatively affect the adoption of m-shopping. In detail, only 29 percent of mobile shoppers use mobile phone to purchase actually. But 72 percent of them use mobile phone to compare prices and to search reviews (Shankar et al., 2010). To explain the abnormal situation, firstly, it might be challengeable for some users to operate mobile’s small touch screen. Secondly, perceived risks might reduce the trust of mobile shopping channel (Shankar et al., 2010).
mobile apps). For some customers, they prefer checking the products by mobile apps or on website rather than visiting physical stores. Considering the unprecedented popularity of mobile shopping apps in China, I refer to some related literature as follow.
2.3 Mobile shopping applications
When it comes to m-commerce, mobile shopping, mobile ticketing and mobile payment
are important parts to develop mobile commerce. Overall, these functions could be realized
by mobile applications (mobile apps) (Buellingen & Woerter, 2004). Until now, the
technology development has witnessed different kinds of mobile apps services. It is suggested that there are four types of mobile applications according to the needs of consumers: communication, information, entertainment and commerce. Besides, Dholakia & Dholakia (2004) divides the functions of mobile apps into four groups, including entertainment, productivity, convenience, and efficiency. Which means, mobile apps could enhance the productivity and efficiency for professional use. Moreover, mobile apps could provide entertainment and convenience for private use as well. Additionally, Leem (2004) classifies consumer m-apps to commerce, intermediary and information categories. Until now, mobile apps are one of three mobile channel options for retailers and consumers, along with mobile website (viewed on mobile browser) and the web app (retailer’s website).
Apparently, mobile apps are providing consumers with tremendous convenience in daily life recent years. Users can download and install new mobile apps from mobile application store (Magrath & McCormick, 2013). Apparently, mobile apps benefit consumers most. For example, mobile apps offer a convenient and user-friendly method to browse information and
to purchase products. Moreover, mobile apps could provide more enjoyable shopping experience by means of one click navigation (Lu & Su, 2009). Compared to other mobile channel, it’s effortless definitely. For some retailers, sales on mobile devices have increase 320 percent each year. Besides, there are nearly a third of mobile users engaging in mobile shopping by mobile apps. Briefly speaking, it’s undeniable that the growth of m-commerce acceptance is increasing apparently in recent years (Magrath & McCormick, 2013).
Above all, mobile apps could realize the comfort and flexibility of place, time saving, timeliness of information and decreased search costs (Buellingen & Woerter, 2004), thus it’s increasingly valuable to do the research about the acceptance of mobile apps.
3. Conceptual framework 3.1 Framework
In order to realize the potential contribution mentioned above, I conduct the research model as follow to test the determinants that influence adoption of mobile shopping applications. In my research model, the examined factors consist of perceived ease of use, subjective norm, time pressure, online shopping experience and mobile knowledge. Thus I would test their impacts on attitude and intention firstly. Besides, I assume time pressure and online shopping experience are moderators between other examined factors and attitude. And I would test their moderation effect of time pressure and online shopping experience further. Moreover, since attitude is the mediator in many prior researches, so I also assume attitude as a mediator between examined factors and intention to adopt mobile shopping applications.
And then I would further test the mediation effect of attitude. Lastly, demographic characteristics are controlled variables in my research.
Figure.2 Conceptual framework
3.2 Technology acceptance model (TAM)
The initial TAM states that beliefs about using a system influence an individual’s use of
the system. These beliefs consist of perceived usefulness and perceived ease of use (Davis,
1989). And various researchers have applied TAM to predict customer acceptance of
innovative technology (Keen, 2004). Among these studies, perceived usefulness refers to the
relative benefits own by customers. And perceived ease of use refers to the degree of effortless when customer to conduct the online shopping process. They have been proved to affect the adoption of online shopping channel consistently. Above all, TAM has provided a meaningful foundation to investigate consumer’s acceptance of online shopping (Ha & Stoel,
Additionally, in order to explain and predict technology acceptance, TAM has presented the belief–attitude–intention–behavior causal relationship among potential users (Ha & Stoel, 2009). Hence, several researches reveal that perceived ease of use and perceived usefulness enhance the attitude towards online shopping (Childers, 2001). Apparently, the useful and easy-operated technology could bring more benefits to users. Besides, users’ attitude could be encouraged by more useful and simple technology to some extent. But in order to avoid collinearity between perceived usefulness and perceived ease of use, I remove perceived usefulness out of my research and only test perceived ease of use. Hereby, the hypothesis could be proposed firstly as follow：
H1: Perceived ease of use has a positive effect on customers’ attitudes toward adoption
of mobile shopping applications. 3.3 Subjective norm
Subjective norm is a belief affected by social pressure to accept or not to accept technology (Yang, 2012). Which means, subjective norm might affect whether the reference group will obey the approval or disapproval of a recommendation (Clemes, Gan & Zhang, 2014). Subjective norm has proved to be a driving factor of the adoption of services or products in former researches. Since the socialization forces and the desire to follow referent group norms affect consumer adoption of technology (Kulviwat, 2009), thus subjective norm tends to lead consumer behavior (Kim, Shin & Kim, 2011). Besides, consumers are more likely to recommend a service when they feel satisfied. Thus, referent group’s recommendation is credible and reliable so as to affect consumer adoption.
Furthermore, since mobile shopping is conducted via personalized mobile devices, thus customers might be hesitated to adopt mobile shopping due to the technology-mediated shopping environment. In this way, it is an increasing tendency for customers to rely on others’ suggestions when making decisions to adopt new technology such as mobile apps (Yang, 2012). Subjective norm is divided into two types, namely peer influence (friends and family) and external influence (mass medium and news reports) (Clemes, Gan & Zhang, 2014). In this way, when peer group dictates a technology, the consumers might have a positive belief towards that technology as well. Thus I formulate the hypothesis as:
H2: Subjective norm positively affects the customers’ attitudes toward adoption of
mobile shopping applications.
3.4 Mobile knowledge
Customers enjoy mobile shopping on mobile shopping apps via mobile devices, so it’s necessary for mobile users to have some mobile knowledge to operate mobile shopping apps. Thus, more mobile knowledge might result to more ease of use towards m-shopping apps. A greater level of knowledge and experience with mobile shopping services may be associated with a greater degree of comfort with a service, and then reducing perceived risk associated with the service as well (Yang, 2012). Yang (2012) also suggests that customers who have more mobile knowledge are more likely to have positive attitudes toward mobile shopping. Therefore they are more likely to adopt mobile shopping applications. Hence, I posit the following hypothesis:
H3: Mobile knowledge positively affects customers’ attitudes toward adoption of mobile
3.5 Online shopping experience
Since there are lots of similarities between online shopping and mobile shopping, the prior online shopping experience may have an impact on further mobile shopping. On the one hand, past enjoyable online shopping experience would reduce perceived risks and heighten expectation of potential benefits at the same time (Huh & Kim, 2008). Furthermore, Melis (2015) argues that the less familiar online shopping environment may lead shoppers to rely more on offline shopping experiences. Moreover, since online channel shares plenty of similar features to mobile channel, thus the experience of online channel might reduce perceived risk (security, payment) of Internet in general, which is very likely to reduce the perceived risk of mobile shopping. In this way, online shopping experience might positively affect the adoption of mobile shopping apps.
On the other hand, if customers have used online shopping channel in the past frequently, their prior online shopping experience could have made them rely on the online channel heavily. Therefore, customers might be unwilling to adopt a new shopping channel, even though new shopping channel is similar with the old one. In this way, online shopping experience might negatively affect the adoption of mobile shopping apps. Above all, the two opposing hypotheses are conducted as:
H4a: Online shopping experience has a positive effect on customers’ attitudes toward
adoption of mobile shopping applications.
H4b: Online shopping experience has a negative effect on customers’ attitudes toward
3.6 Time pressure
Time pressure refers to the degree to which consumers consider themselves busy when carrying out their shopping (Hansen, 2006). Several researchers propose that online shopping can save time due to less waiting time, less transportation time and less planning time (Hansen, 2006). Similarly, Verhoef & Langerak (2001) states that the larger perceived time pressure results in larger perceived advantages of e-shopping. Besides, In Morganosky & Cube (2000), majority of 243 respondents regard time saving as primary motivation for online shopping. Thus in order to be successful, m-commerce has to cater to consumers’ lifestyle and work styles nowadays (Dholakia & Dholakia, 2004). As a result, time pressure has to be a critical driving factor of the adoption of the online shopping channel.
When shopping online, customers only need to open the browser. However, customers are likely to spend more time when adopting mobile apps to shop. For example, customers have to spend some time on downloading the apps before using them. Furthermore, customers also need to spend some time to learn the operations of the mobile shopping apps. Therefore, time pressure is likely to reduce customers’ adoption of mobile shopping applications. However, on the other hand, mobile applications are likely to reduce customers’ shopping time, as they could shop via mobile apps anytime anywhere and enjoy the efficient mobile shopping experience. Furthermore, customers who perceive time pressure will have a more a positive attitude towards online buying (Hansen, 2006). In this case, the impact of time pressure would be positive or negative, so I formulate two opposing hypotheses:
H5a: Time pressure positively affects attitudes toward the adoption of mobile shopping
application.
H5b: Time pressure negatively affects attitudes toward the adoption of mobile shopping
application.
More importantly, some former researchers have tested the moderation effects of online shopping experience and time pressure between predictor variables and dependent variables. For example, online shopping experience moderates the impacts of perceived usefulness and perceived enjoyment on attitudes toward mobile shopping (Yang, 2012). Furthermore, time consciousness moderates the impacts of cognitive efforts and perceived risks on perceived value of mobile channel for customers (Kleijnen, Ruyter & Wetzels, 2007). Hence, I would conduct some tests to explore the moderation effects of time pressure and online shopping experience in my research model.
3.7 Attitude and intention
Both TAM and the theory of reasoned action (TRA) support that attitude positively influences intention. It is because that people are more likely to perform behaviors regarding an object they evaluate positively (Shen, 2015). Besides, TAM proposes that two beliefs (perceived usefulness and perceived ease of use) about a new technology influence people’s attitude towards accepting an innovative technology. In turn, customers’ attitudes determine their intentions to adopt the new technology (Ha & Stoel, 2009). In this case, attitude has been illustrated as a significant factor affecting the intention to use products or services.
driven by essential constructs such as attitude and perceived ease of use. Hence, the
following hypothesis would be:
H6: Customers’ attitudes toward mobile applications have positive effects on intention to
adopt mobile shopping applications.
In some previous researches, attitude is regarded as a mediator to affect the relationship
between independent variables and intention to adopt new technology. For instance, attitude
mediates the impact of perceived usefulness and perceived ease of use on behavioral
intention (Davis, 1989) Besides, attitude is proved to mediate the impact of perceived ease of
on intention to e-shopping (Ha & Stoel, 2009). Moreover, attitude mediates the effects of
perceived usefulness and perceived enjoyment on intention to adopt mobile shopping (Yang,
2012). Thus in order to test the mediation effect of attitude completely, I conduct several
hypotheses to test whether attitude mediate the effects of subjective norm, online shopping
experience, mobile knowledge and perceived ease of use on intention to adopt mobile
shopping applications. Hereby, these hypotheses are:
H7a: Customers’ attitudes mediate the impact of perceived ease of use on intention to
adopt mobile shopping applications.
H7b: Customers’ attitudes mediate the impact of subjective norm on intention to adopt
mobile shopping applications.
H7c: Customers’ attitudes mediate the effect of online shopping experience on intention
to adopt mobile shopping applications.
H7d: Customers’ attitudes mediate the effect of mobile knowledge on intention to adopt
3.8 Personal information and characteristics
Generally speaking, personal characteristics include gender, age, education level and income level (Faqih & Jaradat, 2015). Former researches have regarded consumer characteristics as significant predictors in determining behaviors. As a result, we could analyze different customers’ acceptance of mobile shopping applications according to their different characteristics (Yang, 2012).
For one thing, CNNIC (2005) reports that the dominant groups of mobile Internet users are aged 20-29 and 30-39. For another thing, some prior works find that the influence of hedonic motivations in purchase intention is higher for men. Contrastingly, the influence of social factors in behavioral intention is higher for women (Pascual-Miguel, 2015). Moreover, working experience and education background influence income level. And online shopping platforms provide lower-income consumers with cheaper products and require some skills to use the applications. Consequently, personal characteristics might affect the adoption of mobile shopping apps. Therefore, I use demographic factors as controlled variables in my research model.
4. Research design
Comparing to European, Chinese customers are more willing to purchase products rather than only searching information via mobile devices. Therefore, I conducted and focused the research on Chinese customers.
4.1 Sample
CNNIC (2015) reported that the Internet users have grown to 6.88 billion in China (See Fig.2). Additionally, nearly 90.1% of them surf Internet by mobile phones, and the number of mobile Internet users has reached to 6.2 billion (See Figure.3). However, non-probability sampling was used in my research, because it was impossible to collect the data from such a large population and I could only get in touch with a limited number of respondents. Besides, in order to collect data as much as possible and to enhance the representation of my sample, I targeted to find at least 100 samples of Chinese mobile applications users.
Figure.3 The number of Internet users in China by Dec.2015 (Unit: billion)
Figure.4 The number of mobile Internet users in China by Dec.2015 (Unit: billion)
My surveys included the total sample size (N=181) that surpassed the required amounts of 30 participants per research (Saunders & Lewis, 2012). However, because the questionnaire was distributed via personal social network, it was hard to measure the respondent rates. In order to record and analyze the data more efficiently, I coded the data of
gender and education as well as income level. For example，the scale of gender was coded as 1 (female) and 2 (male). Besides, the data of income level and education were coded as well. Table.1 Distribution of samples’ gender and age
Count Percent % Gender Female 116 64.1% Male 65 35.9% Age <18 3 1.7% 18-25 113 62.4% 26-35 46 25.4% 36-45 12 6.6% 46-55 6 3.3% > = 56 1 0.6%
Among the participants, the proportions of female and male were 64.1% and 35.9% respectively. Besides, most of the responds were aged from 18 to 35. More specifically, there were 62.4% and 25.4% of the responds aged from 18-25 and 26-35. Additionally, middle-aged people, who were aged from 36-45 and 46-55, also occupied 6.6% and 3.3% in my samples (See Table.1).
As for the education level, 17.7% and 23.8% of the responds achieved the junior college degree or master degree respectively. Moreover, 48.1% of the responds graduated from university (See Table.2). In addition, 26.5% of the respondents had no income, while 27.6% of respondents be paid for 2000-4000 RMB per month. Besides, 24.3% of the participants got paid for 4001-6000 RMB, while 12.2% of participants be paid for 6001-8000 RMB every month (See Table.3).
Table.2 Distribution of samples’ education level
Education level Count Percent %
No education 1 0.6%
High school 11 6.1% Junior college 32 17.7% University bachelor degree 87 48.1% University master degree 43 23.8% University doctor degree 2 1.1%
Others 1 0.6%
Table.3 Distribution of samples’ income level Income (RMB) Count Percent %
No income 48 26.5% 2000-4000 50 27.6% 4001-6000 44 24.3% 6000-8000 22 12.2% 8001-10000 10 5.5% 10001-15000 5 2.8% 15001-20000 0 0.0% > 20000 2 1.1% 4.2 Survey Design
To explore the determinants of adoption of mobile shopping applications, this study employed a quantitative research basing on survey data. Research questionnaire contained the questions related to three factors in the TAM, including perceived ease of access, attitude and intention (e.g. Davis, 1989; Agrebi & Jallais, 2005). In my research, perceived ease of use referred to the degree of simple application of mobile shopping apps. Besides, attitude referred to the customers’ attitudes toward mobile shopping apps and intention stood for customers’ intention to adopt mobile shopping apps.
Besides, other external factors (e.g. subjective norm, time pressure, online shopping experience and mobile knowledge) were taken into account to test the determinants of consumers’ adoption for mobile apps. I used not only the statistical analysis to compare the
diversified samples of respondents, but also the quantitative research methods to test hypotheses statistically. The survey contained a well-structured questionnaire on the website for collecting data systematically. Generally speaking, since the questionnaire was disclosed online in a specific period, my study was cross-sectional.
4.3 Measures
In order to distribute the questionnaire and to collect the data in China efficiently, I uploaded the questionnaire on http://www.sojump.com/, which was one of the largest website to collect data for academic research in China. I designed the questionnaire in English and posted on website in Chinese. Finally, I analyzed the data in English.
In the questionnaire, there were some demographical questions related to gender, age, education level and income level at the first part. Firstly, gender has been asked as a single choice question with two answer categories (female/male). In terms of age, there were six groups including ‘<18’, ’18-25’, ’26-35’, ’36-45’, ‘46-55’, ‘> 55’. And educational level was asked as single choice question ranging from ‘No education’, ‘Primary school graduation’, ‘Secondary school graduation’, ‘High school graduation’, ‘Junior college Degree’, ‘University Bachelor Degree’, ‘University Master Degree’, ‘University Doctor Degree’, ‘Other type of education’. Besides, income of per month was asked as single choice question as well, which was ranging from ‘No income’, ‘2000-4000’, ‘4001-6000’, ‘6001-8000’, ‘8001-10000’, ‘10001-15000’, ‘15001-20000’, ‘> 20000’.
In the second part of questionnaire, there were some filter questions to divide participants into different groups. In order to test whether mobile applications users have online shopping experience, the question was ‘Have you ever shop online before using mobile applications’. And next question was ‘Have you ever use mobile applications to purchase goods or service’. And then, in order to collect the data of the types of mobile applications, a multiple-choice question was ‘what kinds of mobile applications do you use to shop more often’. The answer options were consisted of ‘Comprehensive shopping apps (e.g. Taobao, JD, Suning, 1 the Store)’, ‘Living goods shopping apps (e.g. Tmall supermarket)’, ‘Take-out food apps (e.g. Baidu WaiMai, Ele.me)’, ‘Taxi apps (e.g. Didi Taxi, Uber)’, ‘Travel agency apps (e.g. Ctrip, Qunar)’, ‘Books seller apps (e.g. Dangdang, Amazon)’. Participants were allowed to choose two options toward this question.
In the third part, all questions were designed to test the variables on a 7-point scale (1=strongly disagree, 7= strongly agree). Firstly, the ‘subjective norm’ was measured by three questions. Referring to Pascual-Miguel et al. (2015) and Shen et al. (2015), the example item was ‘people who are important to me think that I should use mobile applications to shop’. Secondly, the ‘online shopping experience’ was measured by three questions. One of the example item was ‘I shop online frequently in the past’ (Liu & Forsythe，2010). And then four questions were designed to test the ‘mobile knowledge’, they were ‘I have some mobile knowledge’ and ‘my mobile knowledge is up-to-date’ (Montoya-Weiss, Voss & Grewal, 2003), and so on. Besides, question 11 and 12 were used to test ‘time pressure’, by items ‘ I am always busy’ and ‘I usually find myself passed for time’ (Yang, 2012). In terms of the
‘perceived ease of use’, there were three related questions as well. One of the example items was ‘Mobile shopping is easy to do via mobile applications’ (Venkatesh, 2008; Faqih & Jaradat, 2015). Moreover, the ‘attitude’ was measured by three questions according to Yang (2012), including ‘Shopping by mobile applications is a good idea’ and ‘I am positive about the applications of mobile shopping’. Lastly, the ‘intention to adopt’ was measured by ‘I intend to download mobile applications for shopping’ and ‘I intend to shop via mobile applications in the future’ (Pascual-Miguel, 2015; Overby & Lee, 2006).
4.4 Statistical methods
In order to get reliable results and to analyze the results scientifically, I used several statistical methods to analyze my data in SPSS. Firstly, I ran validity and reliability test to make sure the consistent of items and to remove some items according to the results. Secondly, I used multiple regressions to analyze the significance of items. Besides, I conducted the mediation test to identify the mediation effect in the model. Lastly, I explored moderators by regression analyses as well.
4.5 Procedure and collection
Firstly, I conducted a pilot study to evaluate the study design prior to the main study. After modification, I organized the main study and collected data on the website as mentioned above.
4.5.1 Pilot study
Before distributing the questionnaire for main study, a pilot study was thrown firstly.
The initial sample consisted of ten students from University of Amsterdam with similar age
and educational level. I made sure that students participate voluntarily without incentives.
After they completed all questionnaires, I modified some errors according to their reviews
and completed my questionnaires in final.
4.5.2 Main study
After adjusting questionnaire, I distributed and hosted them on Sojump.com. In order to collect larger scale of data, I delivered the questionnaire on my personal network through the social media app: WeChat, which was the most popular apps for mobile social communication in China. To reach more respondents, I used the snowball technique by asking participants to distribute the questionnaire in their social network. In this way, it was guaranteed to get the responds from Internet users.
To finish the research in time, the survey lasted for about 7 days via Internet and mobile wireless. All data was collected systematically by the website and was processed via Statistical Product and Service Solutions (SPSS). Based on the collected data and results analysis, I came up with some conclusion and implication in the end of my research.
5. Result and analysis
In this part, preliminary analysis is done firstly. Then the validity and reliability of the scale items are tested. Lastly, correlation and hypotheses are checked by using SPSS to do
further analyses (Field, 2013). 5.1 Preliminary analysis
In second part of questionnaire, participants are asked whether they have ever use online channel or mobile apps to purchase products or service before. There are 93% of participants who have ever shopped online and 86% of participants who have ever shop via mobile apps before (see Figure.5).
Figure.5 Percentage of online shopping and mobile shopping customers
When it comes to the types of mobile shopping applications, 162 participants use comprehensive shopping apps most often. Comprehensive hopping apps mean that users could purchase different categories of goods via the mobile apps. Besides, 54 and 49 respondents prefer taxi and take-out food apps. Taxi apps, such as Uber, couldhelp users to take taxis via mobile apps. And by take-out food apps, users could order take-out food from different restaurants and wait for the delivery at home. Furthermore, there are 40 people choosing living goods apps instead. Living goods apps allow customers to purchase their living goods via mobile apps, such as cleaning liquid and kitchen paper. All results are shown in Figure.6.
Figure.6 Types of mobile apps that mostly be used
5.2 Validity and reliability
In order to get the reliable results of the research, the validity and reliability of the scale items are tested prior to the further analyses. Firstly, I run the factor analysis by principal component analysis (PCA) and rotation method to check all items. In rotation matrix, items could be accepted if the values are above 0.7. In this way, online shopping experience 3, mobile knowledge 4 and perceived ease of use 2 are removed out of the research model. Since value of perceived ease of use 3 is 0.690 that is very close to 0.7 in rotation matrix, so I decide to keep this item. See Rotated Component Matrix in Table.4.
Table.4 Rotated Component Matrix
Items 1 2 3 4 5
Perceived ease of use 1 0.726
Perceived ease of use 2 0.676 Perceived ease of use 3 0.690
Time pressure 1 0.815 Time pressure 2 0.904 Time pressure 3 0.881 Time pressure 4 0.837 Mobile knowledge 1 0.751 Mobile knowledge 2 0.852 Mobile knowledge 3 0.776 Mobile knowledge 4 0.588 162 40 49 54 22 14 0 20 40 60 80 100 120 140 160 180 a. Comprehensive shopping apps (e.g.
Taobao, JD, Suning, 1 the store) b. Living goods shopping apps (e.g. Tmall
supermarket)
c. Take-out food apps (e.g. Baidu WaiMai, Ele.me)
d. Taxi apps (e.g. Didi Taxi, Uber) e. Travel agency apps (e.g. Ctrip, Qunar) f. Book seller apps (e.g. Dangdang,
Subjective norms 1 0.717
Subjective norms 2 0.751
Subjective norms 3 0.753
Online shopping experience 1 0.739
Online shopping experience 2 0.853
Online shopping experience 3 0.611
*Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. *Rotation converged in 6 iterations.
Furthermore, I conduct the reliability test. The Cronbach’s alpha is the most common measure to test the scale reliability (Field, 2013). Thus, I use the Cronbach’s alpha to verify whether all items of one factor measure the same. It’s acceptable that the Cronbach’s alpha is higher than 0.70. Thus, if the Cronbach’s alpha is below the limit, then some questions should be removed so as to make sure the reliability. Fortunately, according to the report of reliability testing, Cronbach’s alpha is above 0.7 of all examined items. In detail, the Cronbach’s alpha of attitude and intention to adopt are above 0.9. Apparently, the items of them are highly consistent and measuring the same. Besides, the Cronbach’s alpha of time pressure, mobile knowledge and perceived ease of use are higher than 0.8. It is also high enough to indicate a high level of internal consistency for these scales. See Table.5 for the reliability of all the scales in my research.
Table.5 Cronbach's Alpha of variables
Construct N of items Cronbach's Alpha*
Subjective norm 3 0.770
Online shopping
experience 2 0.760
Time pressure 4 0.896
Mobile knowledge 3 0.865
Perceived ease of use 2 0.8213
*Cronbach’s alpha should > 0.70
5.3 Correlation analysis
Correlation matrix presents the correlations between a pair of variables, which should be lower than 0.5 to avoid collinearity. In my Pearson correlation matrix (Table.6), the correlations of all pairs of variables are below 0.5. In this case, there is no potential collinearity in my research. Following with the correlation check, further regression analyses would be conducted to test the hypotheses further.
Table.6 Pearson correlation matrix
Subjectiv e norm Online shopping experience Mobile knowledge Time pressure Perceived ease of use Subjective norm 1.000 Online shopping experience 0.415** 1.000 Mobile knowledge 0.145 0.281** 1.000 Time pressure 0.381** 0.477** 0.096 1.000 Perceived ease of use 0.493** 0.497** 0.124 0.461** 1.000 **. Correlation is significant at the 0.01 level (2-tailed)
5.4 Hypotheses test
5.4.1 Multiple linear regression
Hereby, in order to test the hypotheses, I use multiple linear regressions to analyze the variables further. Firstly, SPSS is used to test whether the associations in the hypotheses are significant. In my study, hypotheses are tested at the significance level of p<0.05, which means that the results for hypotheses with p-values higher than 0.05 are not be supported. In my research model, subjective norm, online shopping experience, mobile knowledge, time pressure and perceived ease of use are independent variables. And the dependent variable
was attitude.
To do the regression analyses, I control the demographics factors (gender, age, income and eductaion) firstly. Then I conduct the regression analysis between all examined factors (subjective norm, perceived ease of use, time pressure, online shopping experience, mobile knowledge) and attitude. All results are presented on Table 7 as follow.
Table.7 Coefficients between predictor variables and attitude, between predictor variables and intention
Variable Attitude Intention
Coefficients (B) P-value (Sig.) Coefficients (B) P-value (Sig.) Perceived ease of use 0.509 0.000** 0.520 0.000**
Subjective norm 0.151 0.000** 0.143 0.005**
Mobile knowledge 0.228 0.000** 0.177 0.014*
Online shopping experience 0.076 0.039* 0.127 0.004**
Time pressure 0.071 0.112 0.048 0.370
Several hypotheses are tested by regression coefficients analysis. Firstly, perceived ease of use is significantly related with attitude, since p<0.01 and B=0.509. In this case, H1 is
supported. For subjective norm (p<0.01, B=0.151), so subjective norm is positive related with attitude as well. Thus, H2 is supported as well. Moreover, mobile knowledge is also
significantly related with attitude (P<0.01, B=0.228). Hereby, H3 is supported absolutely.
Additionally, p-value of online shopping experience is below 0.01 as well (p<0.05, B=0.076). Hence H4a is supported but H4b is rejected. However, p-value of time pressure is 0.112, which
is higher than 0.05 (p>0.05, B=0.071). In this case, time pressure has no significant correlation with attitude. Thus, H5a and H5b are rejected.
Even though it seems like that most predictors are related with intentions as well. But it’s hard to make sure whether mediator mediates the impacts of predictors on intention. So I would test the mediation effect of attitude so as to check whether attitude mediates the
impacts of all predictors on intention to adopt mobile shopping apps. 5.4.2 Mediation test of attitude
In order to test the mediation effect of attitude, I run the mediation test to check here. In this research, mediator is the attitude, and independent variable (IV) is perceived ease of use, dependent variable (DV) is intention. The Baron and Kenny’s steps consist of three steps. Firstly, I run the regression analysis between perceived ease of use (IV) and intention (DV). Secondly, I run the regression analysis between perceived ease of use (IV) and attitude (mediator). Lastly, I run the regression analysis between perceived ease of use (IV), attitude (mediator) and intention (DV). See Table.8.
Table.8 Attitude’s mediation effect between perceived ease of use and intention
Model Independent variable B Sig.
1* Perceived ease of use 0.791 0.000 2* Perceived ease of use 0.778 0.000 3* Perceived ease of use 0.103 0.072
Attitude 0.884 0.000
Remark:
1. Dependent variable: intention 2. Dependent variable: attitude 3. Dependent variable: intention
In Table.8, attitude is significantly related with intention. Thus, H6 is supported
definitely. However, when I run the regression analysis between perceived ease of use, attitude and intention, perceived ease of use (p=0.072>0.05) is not significantly related with intention anymore. In this case, attitude mediates the relationship between perceived ease of use and intention. Hence, H7a is supported.
Then I check attitude’s mediation effect between other predictors and intention by Baron and Kenny’s steps as well. And due to the non-significance between attitude and time
pressure (see Table.7), I remove the time pressure in mediation test. Table.9 Attitude’s mediation effect between subjective norm and intention
Model Independent variable B Sig.
1* Subjective norm 0.461 0.000
2* Subjective norm 0.461 0.000
3* Subjective norm 0.029 0.432
Attitude 0.937 0.000
Remark:
1. Dependent variable: intention 2. Dependent variable: attitude 3. Dependent variable: intention
In Table.9, p-value of subjective norm changes from < 0.01 to 0.432. Which means, subjective norm becomes insignificantly related with intention. Thus, attitude mediates the relationship between subjective norm and intention. So H7b is supported as well.
Table.10 Attitude’s mediation effect between online shopping experience and intention
Model Independent variable B Sig.
1* Online shopping experience 0.392 0.000 2* Online shopping experience 0.358 0.000 3* Online shopping experience 0.067 0.024
Attitude 0.908 0.000
Remark:
1. Dependent variable: intention 2. Dependent variable: attitude 3. Dependent variable: intention
In Table.10, after adding attitude as a mediator in the model, p-value of online shopping experience is still below 0.05. Which means, online shopping experience is not only directly related with attitude, but also directly related with intention. Hence attitude does not mediate the relationship between online shopping experience and intention. Hence, H7c is rejected.
Table.11 Attitude’s mediation effect between mobile knowledge and intention
Model Independent variable B Sig.
1* Mobile knowledge 0.629 0.000
2* Mobile knowledge 0.648 0.000
Remark:
1. Dependent variable: intention 2. Dependent variable: attitude 3. Dependent variable: intention
In Table.11, p-value of mobile knowledge changes from <0.01 to 0.80. In this way, attitude mediates the relationship between mobile knowledge and intention. Thus, attitude is the mediator as well. Hence, H7d is supported as well.
In conclusion, attitude is the mediator between subjective norm, mobile knowledge, perceived ease of use and intention. However, attitude does not mediate the impact of online shopping experience on intention. In this case, H7a, H7b, H7d are supported, but H7c is rejected.
5.4.3 Moderation test of time pressure and online shopping experience
Since time pressure and online shopping experience have ever been mediators in previous researches, so I assume them as moderator and conduct tests to explore their moderation effects in my research.
Firstly, I test the moderation effect of online shopping experience. To test the moderating effects of online shopping experience, it’s necessary to standardize the online shopping experience (moderator) and all other variables. Then I create the product of subjective norm and online shopping experience, mobile knowledge and online shopping experience, perceived ease of use and online shopping experience.
In Table 12, I use regression analyses to test the moderation effects of online shopping experience. As a result, online shopping experience is not a moderator. Because all p-values are above 0.05, so online shopping experience does not moderate the impacts of subjective norm, mobile knowledge and perceived ease of use on customers’ attitudes to adoption of
mobile shopping apps.
Table.12 Moderation test of online shopping experience
Items Coefficient (B) P-value (Sig.)
Subjective norm 0.201 0.000
Mobile knowledge 0.243 0.000
Perceived ease of use 0.511 0.000
Subjective norm * Online shopping experience -0.058 0.295 Mobile knowledge * Online shopping experience -0.034 0.567 Perceived ease of use * Online shopping experience 0.007 0.902 Remark: all items have been standardized.
In order to check the moderation effects of time pressure, I repeat the procedure above. But the moderator is changed to time pressure in this research. As shown in Table 13, p-values of mobile knowledge * time pressure ，perceived ease of use * time pressure and online shopping experience * time pressure are above 0.05. Only p-value of subjective norm * time pressure is below 0.01. Besides, coefficient of subjective norm * time pressure is -0.138, hence time pressure negatively moderates the effect of subjective norm on customers’ attitudes toward mobile shopping apps adoption.
Table.13 Moderation test of time pressure
Items Coefficient (B) P-value (Sig.)
Subjective norm 0.260 0.154
Mobile knowledge 0.103 0.411
Perceived ease of use 0.528 0.000
Online shopping experience 0.109 0.040 Subjective norm * Time pressure -0.138 0.009 Mobile knowledge * Time pressure 0.061 0.194 Perceived ease of use * Time pressure 0.170 0.403 Online shopping experience * Time pressure -0.134 0.577 Remark: all items have been standardized.
In conclusion, according to the moderation tests, time pressure negatively moderates the effect of subjective norm on attitude. However, online shopping experience is not a
5. 5 Summary of results
According to the survey results and analyses, my main findings include three parts. Firstly, subjective norm, perceived ease of use, mobile knowledge and online shopping experience positively affect customers’ attitudes toward mobile shopping apps. Secondly, attitude mediates the impacts of subjective norm, perceived ease of use and mobile knowledge on intention to adopt mobile shopping apps. However, attitude does not mediate the relationship between online shopping experience and intention to adopt mobile shopping apps. Last but not least, time pressure negatively moderates the impact of subjective norm on attitudes toward mobile shopping apps. The results of all hypotheses are shown on Table.14. Table.14 Results of hypotheses
Hypotheses loadings Results
H1: Perceived ease of use has a positive effect on customers’ attitudes
toward adoption of mobile shopping applications. Supported H2: Subjective norm positively affects the customers’ attitudes toward
adoption of mobile shopping applications. Supported H3: Mobile knowledge positively affects customers’ attitudes toward
adoption of mobile shopping applications. Supported H4a: Online shopping experience has a positive effect on customers’
attitudes toward adoption of mobile shopping applications. Supported H4b: Online shopping experience has a negative effect on customers’
attitudes toward adoption of mobile shopping applications. Rejected H5a: Time pressure positively affects customers’ attitudes toward the
adoption of mobile shopping applications. Rejected H5b: Time pressure negatively affects customers’ attitudes toward the
adoption of mobile shopping applications. Rejected H6: Customers’ attitudes toward mobile applications have positive effects
on intention to adopt mobile shopping applications. Supported H7a: Customers’ attitudes mediate the impact of perceived ease on intention
to adopt mobile shopping applications. Supported H7b: Customers’ attitudes mediate the impact of subjective norm on
intention to adopt mobile shopping applications. Supported H7c: Customers’ attitudes mediate the effect of online shopping experience
H7d: Customers’ attitudes mediate the effect of mobile knowledge on
intention to adopt mobile shopping applications. Supported
6. Discussion and conclusion
6.1 Antecedents of the attitude to mobile applications adoption
As mentioned above, my research objectives are 1) to investigate the antecedents of the attitude to mobile applications adoption 2) to test whether attitude mediates the relationship between the examined factors and intention to adopt mobile applications 3) to identify the moderators in the research model. To start with, the antecendents of the attitude to mobile applications adoption, including perceived ease of use, subjective norm, mobile knowledge and online shopping experience. They all positively affect customers’ attitudes toward the adoption of mobile shopping apps. Moreover, these antecedents of the attitude also affect the intention to adopt the mobile shopping apps directly or indirectly. Because some of them are mediated by attitude. So I would discuss the mediation effect of the attitudes toward mobile shopping apps more specifically in next part.
In terms of antecedents of the attitude, firstly, perceived ease of use positively affects customers’ attitudes toward mobile shopping apps in my research. The result is inconsistant with prior researches. For example, Ha & Stoel (2009) reports that attitude towards e-shopping is not affected by perceived ease of use. However, Groß (2015) is consistent with my research and argues that the perceived ease of use affects the attitude apparently.
The inconsistency might result from the different channels and environments for shopping. For instance, Childers (2001) proposes that different shopping environments result
differently. Which means, the impact of perceived ease of use on attitude might be different in onling shoppping environment and in mobile shopping environment separately. Moreover, I also argue that perceived ease of use has a positive effect on intention to adopt mobile apps according to the test results. Although limited researches have analyzed the impact of percieved ease of use on intention, it is reasonable that users are more likely to adopt effortless technology (e.g.mobile apps).
Secondly, I find that subjective norm positively affects attitudes to mobile applications adoption. It is highly consistent with prior studies (Yang, 2012; Bruner & Kumar, 2005). Consumers spend much time with friends and relatives during their daily life, thus they are more likely to accept friends or relatives’ advices and take actions.
Thirdly, online shopping experience has a positive impact on customers’ attitudes toward mobile shopping apps in my research. It is favorable by Yang (2012) as well. In detail, customers are hard to make decisions to adopt new technology if they have no direct experience of using (Yang, 2012). More specifically, customers who have mobile shopping experiences enjoy mobile shoping services more than those who do not have previous mobile shopping experience (Yang, 2012). Besides, I also find that previous online shopping experience has a positive impact on consumers’ intention to adopt mobile apps. It’s because more previous online shopping experiences enhance the confidence and trusts toward e-shopping. Then customers might be more confident to adopt and to try new technology such as mobile shopping apps.
mobile apps as well. My research results are consistent with some prior studies. For instance, Yang (2012) argues that late adopters of mobile shopping might lack of knowledge for mobile shopping. Doney (1998) states that knowledge reduces uncertainty and increases trust in turn. Similarly, knowledge is associated with online shopping activities positively (Jiang, Chen & Wang, 2008). Which means, consumers with more mobile knowledge might be more favorable to mobile apps. It is because the skills and knowledge would bring more benefits when adopt new technology.
Furthermore, I find that time pressure is insignificantly related with attitude and intention to adopt mobile shopping apps. Similarly, Hansen (2006) also rejects the positive relationship between time pressure and online shopping. Last but not least, attitude towards mobile apps has a positibe effect on intention to adopt mobile shopping apps. The result is highly consistent with previous studies (e.g. Ha & Stoel,2009; Shen ,2015; Yang, 2012). In other word, favorable attitude might enhance people’s intention to adopt mobile shopping apps.
6.2 Mediation effect of the attitude to mobile applications adoption
In mediation test, I test whether attitude mediates the impacts of subjective norm, mobile knowledge, online shopping experience and perceived ease on intention to adopt mobile apps. As a result, attitude mediates the effects of subjective norm, mobile knowledge and perceived ease of use on intention, while attitude does not mediate the impact of online shopping experience on intention to adopt mobile shopping apps.
and mobile knowledge on intention to adopt mobile apps in my research. In basic research of TAM, attitude mediates the effects of various factors (e.g. perceived usefulness and perceived ease of use) on intention. My research extents the mediation effects of attitude to some extents. Although perceived ease of use, subjective norm and mobile knowledge positively affect the customers’ attitudes toward mobile shopping apps, they are not strong enough to affect the intention to adopt mobile shopping apps directly.
On the other hand, attitude does not mediate the effect of online shopping experience. In my research, online shopping experience affect attitude as well as intention to adopt mobile shopping apps directly and positively. A possible explanation is that past enjoyable online shopping experience could reduce perceived risks and increase trusts toward online channel (Huh & Kim, 2008). In detail, if customers are familiar with online shopping environment, they are more likely to trust the Internet (payment and security) in general. In this case, customers with online shopping experience might hold favorable attitudes to the adoption of mobile shopping applications. In addition, customers’ familiarization with Internet might reduce the barriers to get access to mobile apps and encourage customers to adopt mobile shopping apps directly.
6.3 Moderation effect of online shopping experience and time pressure
In moderation effect, online shopping experience is not the moderator between independent variables (subjective norm, mobile knowledge, perceived ease of use) and attitude towards mobile shopping apps. However, time pressure negatively moderates the relationship between subjective norm and attitude towards mobile apps. Which means,
customers with less time pressure are more likely to rely on others’ opinions.
For one thing, time-pressured customers has no time to engage in multichannel behavior. Instead, busy customers are more likely to focus on single-channel behavior (Konus, Verhoef & Neslin, 2008). Because of the tight schedules in daily life, busy customers might prefer the most efficient shopping channels such as online or mobile shopping channels regardless of others’ recommendations. For another thing, less busy customers are available to others’ suggestions because of the less busy life. Hence, when others promote or criticize the mobile apps, their attitudes toward mobile apps are more easily to be influenced. In this case, time pressure has moderation effect on the relationship between subjective norm and attitude towards mobile apps.
In conclusion, most predictor variables affect the attitude and intention directly or indirectly. In detail, subjective norm, mobile knowledge, online shopping experience and perceived ease of use positively affect attitude to mobile applications adoption. Otherwise, only online shopping experience affects intention to adopt mobile apps directly and positively. Besides, customers’ attitudes mediate the effects of subjective norm, mobile knowledge and perceived ease of use on customers’ intentions to accept mobile shopping apps. However, customers’ attitudes do not mediate the effect of online shopping experience on intention to adopt mobile shopping apps. Furthermore, subjective norm is moderated by time pressure to affect the attitude towards mobile shopping apps. Moreover, according to the conclusion above, I propose some implications as follow.
