What drives people to purchase clothing and apparel by using mobile shopping applications?

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Master Thesis

What drives people to purchase clothing and apparel by using

mobile shopping applications?

University of Amsterdam

Faculty of Economics and Business

MSc. in Business Administration -- Marketing track

Under supervision of: Dr. Umut Konus

By: Tingran Shen (10704841)

Date: June 29, 2015

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Statement of Originality

This document is written by Student Tingran Shen 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.

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Abstract

The rapid growth of mobile commerce has changed the way people purchase clothing and apparel. In order to capture more market share and generate more revenues, fashion retailers have developed mobile shopping applications to interact with their customers. This research studies the drivers that encourage people to use mobile shopping applications to buy clothing and apparel, and whether and how these drivers might be different from those in other industry. Benefit drivers (ease of access, ease of use and richness of product information), psychological drivers (innovativeness, time pressure and hedonism) and demographic drivers (age and gender) were explained in this study. 178 data was collected, regression analysis and independent t-test were conducted to examine the proposed hypotheses. Results show that ease of access and ease of use were important drivers of mobile apparel shopping and the personal innovativeness also contribute to the actual use of mobile shopping applications. Females and younger consumers were suggested to be the two big groups that use mobile shopping applications to purchase clothing and apparel. The results of this research will be useful for clothing and apparel

companies to properly segment the markets, optimize their marketing strategies, and illuminate them how to promote the usage of their mobile shopping applications.

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1. Introduction

In the last decade, the dramatic growth in the number of mobile phone users accelerates the development in the area of mobile commerce (m-commerce), and this development will undoubtedly continue in the near future(Forge, Blackman, & Bohlin, 2006). Mobile commerce has been claimed to be the new service frontier (Kleijnen, De Ruyter, & Wetzels, 2007) and mobile channel is suggested to be an ideal supplementary channel for online and offline shopping channels (Shankar, Venkatesh, Hofacker, & Naik, 2010).

With the advantage of mobile device portability and the innovation of wireless Internet technology, mobile services (m-services) has been adopted by companies in nearly every aspect of people’s lives (Varnali & Toker, 2010). The retail industry is a pioneer that has started to interact with customers in a new way after the introduction of mobile technology, which is known as mobile shopping (m-shopping). Generally, m-shopping is often considered as an advanced m-service that enables customers to search information or purchase products and services from retailers via mobile devices at any time without geographic restrictions (Yang & Kim, 2012). However, it is believed that m-shopping has a far more comprehensive definition. For instance, Wong et al. (2012) defined m-shopping as “any monetary transactions related to purchases of goods or services through internet-enabled mobile phones or over the wireless telecommunication network” (p.25). Another research conducted by Lai, Debbarma, & Ulhas (2012) stated that m-shopping “empowers shoppers with the ability to gather information on the spot from multiple sources, check on product availability, special offers and alter their selection at any point along the path to purchase” (p. 387). Therefore, m-shopping is often regarded to be a new and critical part of mobile marketing (Lamarre, Galarneau, & Boeck, 2012).

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A research by Juniper (2012) stated that there were 393 million of mobile shoppers (m-shoppers) in 2012 worldwide, and this number increased by nearly 50 per cent to approximately 580 million by the end of 2014. At the same time, Forrester Research (2012) concluded that m-commerce genetrated €1.7 billion revenues in 2012 in Europe and this number is expected to rise to €19.2 billion by 2017, of which €11.1 billion will be generated by retail. Thus we can see due to the unavoidable trend towards m-commerce, many retailers have implemented m-shopping to capture more market share. Currently with this trend, Deloitte Consulting (2012) reported the revenues generated from m-shopping in the USA will reach $31 billion and the mobile influenced retail sales are estimated to be $689 billion in 2016, and it is just the beginning according to the analysis.

While m-shopping is continuously gaining popularity, it is necessary to obtain a further understanding of consumer’s m-shopping behaviour. Current literature is limited on m-shopping behaviours with respect to a specific product category, such as clothing and apparel. Peterson, Balasubramanian, & Bronnenberg (1997) described clothing and apparel as an experience product that may differ greatly according to specific attributes such as price, quality, body fit, and the like. As a highly visible product, consumers like to be able to see, touch and handle the clothes before buying it and will often perceive great variations in quality for such differentiated product. Thus shopping online for clothing and apparel is often perceived as more risky than offline purchasing (Grewal, Gopalkrishnan, & Levy, 2004). However it comes as a surprise that statistics (Statistics Denmark, 2007) show that clothing and apparel is one of the most common product categories purchased online. This finding together with the evidence that the number of m-shoppers is on the rise makes it interesting to investigate whether consumers also purchase clothing and apparel on mobile devices and why they choose to do so for this particular product.

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This research is going to study what drives people to purchase clothing and apparel by using mobile shopping applications. Although scholars have extensively studied drivers of online shopping behaviours, a few studies have examined the possible reasons for adopting mobile shopping applications for purchasing clothing and apparel. In addition, clothing and apparel in nature is different from other products, so we shall see whether the drivers of adopting mobile shopping applications for clothing and apparel are really different. This gap in literature leaves space for the investigation of factors that motivate consumers to purchase clothing and apparel by using mobile shopping applications, and whether and how they might be different from those in other industry.

This research is theoretical relevant because it adds value to the existing marketing literature by examining the factors that may influence the use of a particular type of mobile shopping applications (i.e. clothing and apparel m-shopping apps). This research is also practical relevant, because it is important for companies to know what drives consumers to purchase clothing and apparel by using mobile shopping applications and whether and how they might be different. In light of this, clothing and apparel retailers can utilize the m-shopping application as a critical shopping channel and as a personal shopping assistant for their customers. Moreover, clothing and apparel retailers may use the results of this study to adjust the environment of their mobile shopping applications according to consumers’ preferences and needs.

This paper is structured as follows: the second chapter will present the relevant literature, followed with the third chapter of the conceptual framework and proposed hypotheses. The forth chapter will describe the methodology used in this research and the fifth chapter will interpret the research results. After that, a discussion chapter is offered, following the chapters of managerial implications and limitations.

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2. Literature Review

This chapter provides a detailed theoretical background of mobile shopping. Firstly, it provides an understanding of mobile shopping in general. Next, it explains the possible benefit, psychological and demographic drivers of mobile shopping. After that, the explanation of the online and mobile shopping in clothing and apparel industry will be given.

2.1 Mobile Shopping

Although the essence of marketing still remains to design and create impressive customer experience, the overwhelming m-commerce over conventional retailing has rearranged the layout of marketing. According to Morgan Stanley Research (2010), the usage of desktop Internet will be overturned by the use of mobile Internet by 2014. Goldberg (2010) reported that the statistics revealed nearly 82 percent of American smartphone users have conducted purchasing behaviours or at least searched shopping related information through their phones. Therefore, in order to acquire and maintain sustainability in the market, establishment of mobile marketing must be prioritized. According to Shankar & Balasubramanian (2009), mobile marketing refers to a mutually communicative interaction between a corporation and its target audiences with the help of any mobile devices, media, or techniques. Similarly, mobile shopping is defined as e-commerce (Internet e-commerce) conducted on mobile devices while using mobile or wireless networks (Kourouthanassis & Giaglis, 2012). As indicated by Viswanathan (2014), there are two highly preferred mobile devices for shopping, which are smartphones and tablets. The mobility of these mobile devices arms customers with a real-time access to useful tools without spacious limitations and also enables customers to gather product and service information, directly purchase and get after-sales service at anywhere anytime (Cunha, Peres, Morais, Bessa, & Cabral

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Reis, 2010). According to Adobe Digital Publishing (2013), 27 percent of consumers who did not shop on a mobile device in 2012 will plan to make a purchase on mobile in 2013. Therefore consumers has adapted to a mobile lifestyle which they use mobile channels for multiple activities and shopping indeed has become more and more of interest.

The mobile shopping process can be divided into three stages which are information search, purchase, and after sales service. Detlor, Sproule, & Gupta (2003) suggested that search for product information is the most important task for consumers in the pre-purchase phase. Search oriented m-shopping consumers like to compare brands, check retail price, and read reviews for products. Therefore, instead of processing actual purchase, consumers are provided with guidance in this pre-purchase procedure (Ozer & Gultekin, 2015). The second stage in m-shopping is purchase which consumers actually buy from the retailer, for example, you simply add your product to the shopping cart and directly go to the payment page where you input the bank card information and place the order. After sales service is the last stage in m-shopping process in which companies provide follow-up contact and effectively deal with anything that is related to the product purchase, which help them build a better relationships with customers (Gaiardelli, Saccani, & Songini, 2007). In this research, we mainly focus on the purchase phase to understand conusmers’ mobile shopping behaviours.

Saylo (2012) stated in his book that there are two forms of commerce which are m-internet, shopping via the open-ended source (e.g. browser) and m-apps, shopping within mobile applications which are closed-ended source. According to Adobe Digital Publishing (2013), m-shopping applications usage is on the rise and they are rapidly catching up to mobile browsers as a viable shopping channel. In the case of paticular use of m-shopping applications, Adobe Digital Publishing (2013) found that 49 percent of shoppers are interested in using apps instead of the

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browser to make a mobile purchase in 2013. However, on the other hand, the research showed that only 38 percent of smartphones shoppers and 46 percent of tablet shoppers used an app to make a purchase in the past three months (Adobe Digital Publishing, 2013). This finding was also proved by Shankar, Venkatesh, Hofacker, & Naik (2010) that they found only 29 percent of retail shoppers in possession of a smartphones actually used their device as part of the shopping experience, most shoppers (72 percent) used smartphones mainly for pre-purchase purposes. Figure 1 presents the different use of mobile shopping and Figure 2 presents the different stages of shopping process.

Figure 1 Different use of mobile shopping

Figure 2 Different stages of shopping process

So to date, m-commerce regarding the pattern of purchase phase is still in its infancy, relatively few have developed studies on mobile shopping regarding a specific product, such as clothing and apparel (Ko, Kim & Lee, 2009). What scholars found were drivers and barriers that indicate why consumers accept or reject mobile channel (Shankar et al., 2010; Yang and Kim

Devices

• Smartphones

• Tablets

Forms

• Open-ended source

(e.g. browsers)

• Close-ended source

(e.g. m-apps)

Information

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2012), and they suggested that the TAM model (technology acceptance model), financial barriers, trust and risk factors could be the major reasons. The unique advantages of mobile shopping devices will have a positive impact on consumers shopping behaviours because consumers are no longer restrained by geographical locations, and can connect to the mobile internet whenever they want. Therefore, previously unmet needs are all satisfied now by mobile shopping device, which in turn will result in a more favorable attitude towards mobile shopping channels (Chong, 2013).

Given by the truth that the number of mobile shoppers is on the rise and the m-shopping apps are widely been used, it is interesting to study why consumers started to use mobile applications to purchase certain products. The next section will analyze the possible benefit, psychological and demographic drivers of adopting mobile applications for shopping.

2.2 Drivers of mobile shopping

At first, we briefly present a figure to show the possible drivers of m-shopping for clothing and apparel. After that we will further explain each driver.

Possible drivers of m-shopping for clothing and

apparel

Expected

Benefits Psychological Traits

Demographic Traits Ease of use Ease of access Richness of product information Time pressire

Innovativeness Hedonism Age Gender

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2.2.1 Expected Benefits

Ease of access. By offering more convenience and access at any time and any place, M-commerce is distinguished from the Internet in terms of delivering value (Clarke, 2001). Instant connectivity of the mobile device allows consumers to place and cancel orders at anytime and anywhere, which saves the time and effort they have to contribute. With these benefits, ease of access has been suggested by Lee & Park (2006) as the primary determinant of consumers’decision to buy at home, and the key strength of in-home retailing. It is also supported by Kim, Chan, & Gupta (2007) that ease of access is a convenience cost in the mobile Internet environment, the speed and time efficiency are often regarded as the main benefits consumers can achieve through the use of technology. From a mobile shopping point of view, the delivery of convenience to consumers is powered by the communication facilities within the m-commerce (Chen, Li, Chen, & Xu., 2011). Due to the time and effort savings the shopping channels will provide, consumers usually select a ideal channel which will maximize their benefits. Therefore, it is interesting to investigate if ease of access actually motivate people to purchase clothing and apparel on mobile shopping channels through m-shopping apps.

Ease of use. Previous TAM theory (Davis, 1989) suggested that perceived ease of use is an

important determinant that may influence system use. The perceived ease of use is defined as “the degree to which a person believes that using a particular system would be free of effort” (Davis 1989, p320). For example, if potential m-shopping app users find the application is too hard to use and the effort they contribute to use the app outweigh the performance benefits, they may refuse to use it (Venkatesh, 2000) . Thus, the actual use of a mobile application is theorized to be influenced by perceived ease of use. According to Gefen & Straub (2000), perceived ease of use measures user assessments of ease of use and ease of learning and thus user motivation is

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based on the assessment of the intrinsic aspect of the system such as the interface and the process involved in using it. In previous literature, there are inconsistent explanations on how ease of use influence attitude, and this influence is found to vary from different types of product or context (Kulviwat, Bruner, Kumar, Nasco, & Clark, 2007). Some literatures found that ease of use is not significantly related to attitudes toward adoption (Kulviwat et al., 2007; Nysveen, Pedersen, & Thorbjønsen, 2005; Wu & Wang, 2006). It is, however, explained by value-based adoption theory that, ease of use appears to have a direct impact on the perceived value of the technology which in turn motivates actual use (Ko et al., 2009). Based on these contradictory findings, it will be worthwhile to investigate if ease of use encouage the use of mobile shopping application when people want to purchase clothing and apparel.

Richness of product information. Restrained by the size and architectural design, the physical store is able to display a limited number of products (Hsiao, 2009). Latest collections of products are the prior choice for display while even if a certain demand of previous products still exists, therefore, the physical stores are unable to meet customers search needs (Ha & Stoel, 2012). On the contrary, the attribute of online and mobile shopping eliminates the problem. One prominent characteristic of virtual world is its capacity of storage (Cho & Workman, 2011). The massive storage enables retailers to publish as many products as they can. Besides, unlike retail store being forced to hide excessive products in the warehouse, Internet technology offers retailers endless online space to display each product in a delicate way (Yoon & Jeong, 2013). High - resolution pictures, videos and texts are combined together to demonstrate the product in details, especially products like clothing, which is often carefully scanned by customers (Park, Kim, Funches, & Foxx, 2012). Consumers have been able to reach out to their favourite brands and receive instant access to sales information such as discounts, offers, and coupons on mobile

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shopping channel. A recent study by Juniper research predicts that worldwide consumers will redeem more than $43 billion worth of mobile coupons by 2016, compared to an estimated $5.4B in 2011 (Juniper, 2012). Based on these findings, it is critical to study if the richness of product information is a key driver that encourages consumers to purchase clothing and apparel via m-shopping apps.

2.2.2 Psychological traits

Innovativeness. Innovativeness is defined as “a personality construct that reflects whether individuals are willing to adopt products or ideas that are new in the context of their individual experience” (Aldás-Manzano, Ruiz-Mafe, & Sanz-Blas, 2009, p. 740). Similar definition can also be found in the research by Hirunyawipada & Paswan (2006), they stated if an individual is innovative, he or she is more willing to embrace changes, try new things and buy new products more often than others. It is believed that innovativeness is possessed by all individuals at a different level (Citrin, Sprott, Silverman, Steven, & Stern, 2000). According to Goldsmith & Flynn (2004), online apparel buyers were more innovative toward using the Internet than non-buyers were. Therefore, it is more likely for an innovative clothing buyer to have a positive attitude towards adopting a new technology (Yang K. , 2005). Many scholars have studied consumer innovativeness, for example, Eastlick & Lotz (1999) showed that innovators are heavy users of interactive electronic shopping media. The study by Limayern, Khalif, & Frini (2000) found innovativeness has a both direct and indirect impact on internet shopping behaviour which are mediated by consumers’attitude and intentions. Moreover, Goldsmith (2000) also stated that innovativeness is able to predict the frequency of online shopping and future online shopping intention. This conslusion is also supported by Citrin et al.(2000) which they found consumers’attitude towards internet shopping adoption is positively infulenced by

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innovativeness and internet usage. According to previous literatures we are able to draw a conclusion that innovativeness positively influence consumers’engagement in online shopping channel. By looking a step further to the mobile shopping channel, this conclusion allows this paper to hypothesize that innovativeness may also play a important role in consumers’mobile shopping behaviours.

Time pressure. Time pressure is defined as “the degree to which a consumer feels that there is not enough time available for performing a specific task” (Bruner, 2009, p. 941) . It is indicated by Lee, Paswan, & Xavier (2009) that some consumers do not like spend too much time on shopping, they prefer to choose products and service very quickly in order to save time. A positive relationship between total household working hours and online buying was found by Bellman, Lohse, & Johnson (1999) that people who work more hours have a high tendency to buy online. Similarly findings can be found that, high time pressured consumers value the convenience of online shopping so that they are more likely to have a positive attitude towards online shopping (Xu-Priour, Cliquet, & Fu, 2012). Therefore, from these literatures we can conclude that people who are restrained by time are more likely to shop online so that time-pressure positively influences the decision to shop online. There are limited literature examines time pressure in a mobile shopping domain which leaves space for this study. The advantages (e.g. portability, instant connectivity etc.) of mobile devices enable consumers to use the Internet anywhere at any time (Yang & Kim, 2012), so we can suggest that mobile shopping would be favorable for time pressured consumers.

Hedonism. Hedonism is proved to be an important antecedent for adopting a particular technology such as Internet and mobile Internet (Sánchez-Franco & Roldán, 2005; Nysveen et al., 2005). It is defined as “an overall assessment (i.e. judgment) of experiential benefits and

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sacrifices, such as entertainment and escapism” (Overby & Lee, 2006, p. 1162). Kaul (2007) explained that from a consumption background, hedonism refers to the sense of pleasure associated with shopping. According to Kulviwat et al. (2007), hedonism shows a stronger effect on determine the adoption of a technology. This finding was also supported by Kim et al. (2007), suggesting that hedonism as an intrinsic benefit increased perceived value which in turn motivates the actual use of a technology. In the online shopping context, scholars found that most of experiential online shoppers prefer to buy online because they feel fun and a sense of freedom during the buying process (Wolfinbarger & Gilly, 2001). Moreover, the curiosity and enjoyment online shopping offers encourages hedonic conusmers to frequently conducted online shopping behaviours (Scarpi, Pizzi, & Visentin, 2014). Considering these findings, it is interesting to study whether and how hedonism influences consumers’ mobile shopping behaviours.

2.2.3 Demographics of m-shoppers

Although the use of mobile shopping is on the rise, our knowledge about who is using mobile shopping is limited. Breitenbach & Van Doren (1998) reported that the mobile users are well-educated, younger male consumers in the USA. However, Shim & Drake (1990) reported the opposite observation that older female consumers tend to be the major mobile shopping users. This is because older people have a stronger buying power and easier access to credit cards whereas young people lack of these benefits so they only search information on mobile devices (Douthu & Garcia, 1999). Although male shoppers are early adopters of internet shopping, the number of female shoppers is increasing dramatically as well (Asch, 2001). Scholars have reported that more than half of the online shoppers are females in the USA, and that number is even bigger in the UK, about 61.5 percent of females who have children are shopping online

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(McIntosh, 2001). Particularly, online retailers have seen this trend and shifted their focus to sell more female-oriented products such as cosmetics, apparel, jewelry and gifts (Allen, 2001; Chiger, 2001; Elkin, 2001).

Previous literature suggested younger males have the most positive attitude towards new technology adoption (Modahl 2000; Rogers 2003; Schiffman and Kanuk 2003). It will be interesting to examine whether this conclusion also applied to m-shoppers. Bigne, Ruiz, & Sanz (2005) demonstrated that age and gender moderate online shopping behavior, hence, in mobile shopping domain, age and gender may also influence consumer behavior. Research by Kumar & Lim (2008) indicated that different generations have different perceptions and expectations for mobile services. Also, Bigne et al. (2005) found that age and gender have an impact on consumers’ decision-making at different stages. However, Goldsmith & Goldsmith (2002) found that online apparel buyers could not be distinguished by their demographics. Therefore we could say that current literatures leave space for this research to further explore the relationship between demographic traits and the mobile shopping behaviours for clothing and apparel.

2.3 Online and Mobile Shopping in Clothing and Apparel Industry

Consumers used to shop on the Internet for products which they already had sufficient information, such as books, electronics, travel, health and beauty products (Schaeffer, 2000). However, due to the improvement of technology, items previously thought to be saleable only in a touch-and-feel environment (e.g. apparel) are enjoying increased sales in the online and mobile environment (Kim et al. 2011). By giving consumers access to interactive try-on sessions such as the “virtual dressing room”, and “online fit prediction” (Abend, 2001), online and mobile apperal retailer have experienced profitablity increase (Kang & Johnson, 2013). Today, most revenuses in the clothing and apparel industry are generated by multi-channel concepts, therefore, apparel

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retailers can merge different retail channels (eg. in-store, online, mobile) to expand their market share and reach customers internationally (Welling, 2000).

Currently, mobile commerce has much potential to grow in many different industries, including the clothing industry (Kim, Ma, & Park, 2009). However, previous researches mostly focused on online shopping for clothing, few research findings are available for consumers clothing m-shopping behaviours. Thus, it is necessary to gain knowledge for clothing and apparel industry in the m-shopping domain. Clothing in nature is different from other product category, for example, Then and Delong (1999) suggested that consumers are willing to buy more clothing online if they discover the system is secure and easy to use, and the displayed product information are sufficient. On the other hand, Shim, Eastlick, Lotz, & Warrington (2000) reported that “for the sensory experiential products (e.g. apparel and accessories), consumers are less likely to be influenced by functional attributes such as fast transaction service and speedy shopping than they are for cognitive products (e.g. books, computer software, music and videos)” (P.887). Based on these contradictory findings, and the trend towards mobile shopping in clothing and apparel industry, it is urgent to investigate why consumers adopt mobile shopping to buy clothing and apparel.

2.4 Gaps and Contributions

Most of previous literature focused on online shopping behaviours and thus provided limited information about mobile shopping behaviours especially for usage of m-shopping applications. Moreover, as a frequently bought product online, clothing and apparel has never been studied in a mobile shopping context. In addition, clothing and apparel in nature is different from other products, so we shall see whether the drivers of adopting mobile shopping applications for clothing and apparel are really different. This gap in literature leaves space for

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answering the research question of this study that is “what drives people to purchase clothing

and apparel by using mobile shopping applications, and whether and how these drivers might be different?” By answering this research question, this study will not only add value to the existing

marketing literature by examining the factors that may influence the use of a particular type of mobile shopping applications (i.e. clothing and apparel m-shopping apps) but also enlighten companies to utilize the m-shopping application as a critical shopping channel and as a personal shopping assistant for their customers.

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3. Conceptual Framework

Based on the analysis of previous literatures, we present the conceptual framework of this research in Figure 4. Ease of use Ease of access Richness of product information Inovativeness Time pressure

Use of mobile shopping application to purchase clothing

Gender Age Hedonism

Figure 4 Conceptual Framework

Expected

Benefits

Psychological

Traits

Demographic

Traits

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3.1 Hypotheses

Based on the analysis of expected benefits, psychological attributes and demographic attributes, the following hypotheses are proposed in this chapter.

Ease of access. Ease of access has been suggested by Lee & Park (2006) as the primary determinant of consumers’decision to buy at home, and the key strength of in-home retailing. It is also supported by Kim et al. (2007) that ease of access is a convenience cost in the mobile Internet environment. Schloars also indicated that easy access provides consumers with speed and time efficiency which is often regarded as the main benefits can be achieved through the use of technology in a retail setting (Clarke, 2001; Kleijnen et al., 2007; Lee & Park, 2006). Therefore, considering these findings, the following hypothesis will be tested in this study:

H1a. Ease of access is positively associated with the use of mobile shopping applications to

purchase clothing and apparel.

Ease of use. Previous TAM theory (Davis, 1989) suggested that perceived ease of use is an important determinant that may influence system use, thus, the actual use of a mobile application is theorized to be influenced by perceived ease of use. In previous literature, there are inconsistent explanations on how ease of use influence attitude, and this influence may varies from different types of product or context (Kulviwat et al., 2007). Some literatures found that ease of use is not significantly related to attitudes toward adoption (Kulviwat et al., 2007; Nysveen et al., 2005; Wu & Wang, 2006). It is, however, explained by value-based adoption theory that, ease of use appears to have a direct impact on the perceived value of the technology which in turn motivates actual use (Ko et al., 2009). On the basis of these arguments, this study suggests the following hypothesis:

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H1b. Ease of use is positively associated with the use of mobile shopping applications to

Richness of product information. According to Hsiao (2009), the physical store is able to display a limited number of products due to the limited size and architectural design. Therefore the physical stores are unable to meet customers search needs (Ha & Stoel, 2012). On the contrary, the attribute of online and mobile shopping eliminates the problem. One prominent characteristic of virtual world is its capacity of storage (Cho & Workman, 2011). Internet technology offers retailers endless online space to display each product in a delicate way which benefits most of consumers (Yoon & Jeong, 2013). A research by Juniper (2012) suggested that consumers have been able to reach out to their favourite brands and receive instant access to sales information such as discounts, offers, and coupons on mobile shopping channel. Based on the above findings, this research proposed the following hypothesis:

H1c. Richness of product information is positively associated with the use of mobile shopping

applications to purchase clothing and apparel.

Innovativeness. Considering innovativeness, Kumar & Mukherjee (2013) found an positive relationship between new technology adoption and personal innovativeness. Similarly, Yang K. (2005) proved that an innovative consumer is more likely to have a positive attitude towards adopting a new technology

.

Eastlick & Lotz (1999) showed that innovative consumers have a positive attitude towards electronic shopping media. Moreover, Limayern et al. (2000) found innovativeness has a direct effect on internet shopping behaviour. Citrin et al. (2000) indicated in their research that consumers’attitude towards internet shopping adoption is positively infulenced by innovativeness. Therefore, this study proposes the following hypothesis:

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H2a. Innovativeness is positively associated with the use of mobile shopping applications to

Time pressure. Time pressured consumers do not like spend too much time on shopping, they prefer to choose products and service very quickly in order to save time (Lee et al., 2009). A positive relationship between total household working hours and online buying was found by Bellman et al. (1999) that people who work more hours have a high tendency to buy online. Similarly findings can be found in Xu-Priour et al.’s (2012) study that high time pressured consumers value the convenience of online shopping so that they are more likely to have a positive attitude towards shopping online. Yang & Kim (2012) suggested that the unique advantages of mobile Internet attract more time pressured consumers to use the channel. According to the above literature, the following hypothesis is tested in this study:

H2b. Time pressure is positively associated with the use of mobile shopping applications to

Hedonism. Hedonism was found to have a positive relationship with product purchase and shopping values (Deli-Gray, Gillpatrick, Marusic, Pantelic, & Kuruvilla, 2011). In the context of online shopping, hedonic consumers tend to purchase more frequently and heavily on the Internet (Scarpi, 2012). Moreover, Sweeny & Soutar, (2001) explained that emotional value was the most important factor which predicts consumer intention to purchase products or service in the retail setting. Considering these findings, hedonism as an emotional value is able to predict the use of mobile shopping applications to purchase clothing and apparel. Therefore the following hypothesis is tested in this study:

H2c. Hedonism is positively associated with the use of mobile shopping application to purchase

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Age. According to (Bigne et al., 2005), age is one of the best predictors of mobile shopping behaviours. Scholars suggested that younger consumers are more used to technology changes because they have been using new technologies such as mobile phone since a very early age (Schiffman & Kanuk, 2003). Young consumers have both hedonistic and utilitarian motives for using Internet and mobiles, considering these media as a source of information, entertainment and an alternative shopping channel (Bordeau, Chebat, & Couturier, 2002). Therefore, it is believed that younger consumers are more frequently engaged in online and mobile channels than older counterparts (Chong, 2013). Based on these findings, the following hypothesis is proposed in this study:

H3a. Younger consumers are positively associated with the use of mobile shopping applications

to purchase clothing and apparel.

Gender. Regarding gender, Farag, Schwanen, Dijst, & Faber, (2007) suggested in their research that male consumers tend to engage more in online shopping than female consumers. However, Bhatnagar & Ghose, (2004) found that in online shopping domain, the number of female consumers exceed the number of male counterparts in all product categories. From a mobile shopping point of view, Chong (2013) indicated there is no significant difference in mobile shopping activities between male and female. In contrast, for some specific products, there indeed is difference that female are found to be more likely to buy producs such as apparel, jewelry and gifts (Allen, 2001; Chiger, 2001; Elkin, 2001). Based on these contradictory findings, the following hypothesis is developed:

H3b. Female consumers are positively associated with the use of mobile shopping applications

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4. Methodology

In order to test the proposed hypotheses, quantitative data need to be collected. This chapter will describe respectively the sample, research design and procedure, measures and scales used to identify the variables.

The sample. The population of this sample consists of the shopping consumers especially the online shoppers in the Netherlands, which approximately consists of 11.8 million people (CBS, 2014). Because no sampling frame could be retrieved from this enormous research population, a non-probability sampling technique will be used via social media which aims at respondents from age 18 to 29, the other channel will be used is email which targets at respondents from age 30 to 50. In order to generalize conclusions over population, the main goal for data collection is to achieve a sample as large as possible to increase the chance of having a representative sample including both younger and elder people. According to past researchers, average response rate is 40%-50% (Konus, Verhoef, & Neslin, 2008). In order to get a minimum sample size of one hundred shopping consumers (both youngsters and elderly) in the Netherlands, at least 200-250 potential participants should be reached.

Research Design. To be able to test what drives consumers to purchase on mobile application for clothing and apparel, a quantitative research through a survey was most appropriate because survey enables researchers to collect a large amount of respondents (Saunders & Lewis, 2012) and can provide formal outcomes to use in quantitative research to test hypotheses statistically. This study is cross-sectional because a Web-based English questionnaire will be delivered via social media and email, and respondents will fill out the questionnaire at one moment.

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Data Collection. A web-based English survey questionnaire was created and hosted on Qualtrics to test the hypotheses of this study. On April 23, 2015, the survey was distributed via the social media website Facebook and by e-mailing acquaintance. During a period of 12 days, 232 responses were collected, 54 responses were deleted because the respondents did not fully complete the questionnaire, and this yields a final sample of 178 respondents.

Piolet study. A pilot study was conducted on the 22nd of April 2015, using six master students from the University of Amsterdam. These students participated voluntary, no incentives were offered. All six questionnaires were completed in less than 10 minutes and the identified errors were corrected and incorporated in the final version of the questionnaire.

Independent variables. The independent variables of this study are expected benefits, psychological traits, and demographical traits from the use of mobile shopping applications to purchase clothing and apparel. The benefits variables ease of use and ease of access were measured with 2 items, each by Davis (1989) and Venkatesh, Morris, Davis, & Davis (2003). Another benefit variable richness of product information was measured by 1 item from Hsiao (2009). As for psychological variables, 2 items from the Consumer Novelty Seeking (CNS) scale were adapted to measure consumer innovativeness (Manning, Bearden, & Madden, 1995). Measurements for time pressure were examined by 2 items derived from Konus et al. (2008). Moreover, 2 items derived from Kim & Han (2011) were used for hedonism. Lastly, demographical variables are measured by age and gender.

Dependent variables. Both the actual use and use appropriateness of mobile shopping applications to purchase clothing and apparel were served as dependent variables, though we applied the actual use to conduct the main regression analysis to test the hypotheses and the analysis for use appropriateness was served as a validation check. Regarding measurement, the

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actual usage was measured by the frequency of use of mobile shopping applications, so respondents indicated how many times in the past six months they have used mobile shopping applications to purchase clothing and apparel. Furthermore, the use appropriateness was measured by asking “How appropriate you find the mobile channels for purchasing clothing?” and answers are scoring on a 5-point Likert scale ranging from “totally inappropriate” to “totally appropriate”.

5. Results

In this chapter, the reliability of scales and hypotheses were test by doing data analyses in statistical software SPSS 20. For a period of two weeks, 232 responses were collected but 54 respondents did not complete the survey, so the final database consisted of 178 responses.

5.1 Missing value and recoding

All the data need to be checked for missing values before analysis. Statistically, missing values occur when no data is recorded for the variable in an observation. The frequency test was applied for all variables to check whether there were missing data and the missing value is not detected in the data set. Besides, recoding can be applied to indicate if there are counter-indicative items, which are items that are the opposite of what you want to measure in a scale. In this research, no items are counter-indicative so there is no need to recode.

5.2 Descriptives

Among the 178 respondents, 73 are male respondents which represent 41% of the total group. The rest of 59% respondents are female (105) and we can see that more female consumers

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participate in this study. In terms of age, 4 respondents did not indicate their ages so it yields 174 valid values. Respondents aged between 18 and 25 represented 73% of the group, so more younger consumers filled out the survey than older consumers. Most respondents are very well-educated that 84 of them have a bachelor degree (47.19%) and 80 of them have a master degree (44.94%). All the information is listed in table 1.

Table 1 Descriptive statistics of respondent profile

Measure

Items

Frequency

Percentage

Gender Male 73 41 Female 105 59 Age 18-25 127 73 26-33 33 19 34-41 8 4.49 42-49 3 1.68 >49 3 1.68

Education High School and below

8 4.49

Bachelor degree 84 47.19

Master degree 80 44.94

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5.3 Preliminary Test

Reliability Test. Reliability is examined by Cronbach’s Alpha which measures the internal consistency level. For multiple Likert questions, Alpha can test if the scale is reliable. A Cronbach’s Alpha above 0.7 is considered to be an acceptable reliability coefficient (Nunnaly, 1978). For benefit variables, i.e. ease of access (α=0.703) ease of use (α=0.740) richness of information (α=0.732), the coefficient is all above 0.7, thus the scale is reliable. Among psychological variables, the Cronbach Alpha coefficient was found significant for time pressure (α=0.723) and hedonism (α=0.715). However, for innovativeness the Alpha is below 0.7, and by investigating the item-total correlations, one item regarding innovativeness was found very low (0.043). This item was deleted because after checking square multiple correlations, the item still receive a very low value. Afterwards, Cronbach Alpha for innovativeness was improved to 0.716. All results are showed in table 2.

Table 2 Reliability Results

Variables Cronbach’s Alpha

Ease of access 0.703 Ease of use 0.740 Richness of product information 0.732 Innovativeness 0.716* Time pressure 0.723 Hedonism 0.715 *After correction

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Correlation check. A correlation matrix which listed the Pearson’s correlation coefficients of all the independent variables is presented in table 3. Pearson’s correlation coefficient was calculated in order to determine whether the variables are related and if there was multicollinearity problem. We checked the correlation matrix and found that all independent variables are not highly correlated, so there is no multicollinearity issues (Duzan & Shariff, 2015) and we can move on to the regression analysis.

Table 3 Means, Standard Deviations and Correlations

**Correlation significant at the 0.01 level (2-tailed)

*Correlation significant at the 0.05 level (1-tailed)

5.4 Hypotheses testing

In order to test a causal model, a standard multiple regressions was chosen as the most appropriate method that allows the examination of how much variance in a dependent variable can be explained by a group of independent variables (Duzan & Shariff, 2015). In this study, the standard multiple regressions indicated the contribution of each independent variable in the use of mobile shopping applications to purchase clothing and apparel. The regression allowed the investigation of the causality suggested by hypotheses H1a-H2c. The expected benefits (ease of

Means, Standard Deviations, Correlations

Variables M SD 1 2 3 4 5 6 7 8 1. Ease of access 3.56 0.9 1 2. Ease of use 3.58 0.91 0.61** 1 3. Richness of product information 3.49 1.2 0.52** 0.46** 1 4. Innovativeness 3.24 0.83 0.43** 0.37** 0.37** 1 5. Time pressure 3.33 0.82 0.22** 0.15* 0.24** 0.40** 1 6. Hedonism 3.46 0.95 0.32** 0.24** 0.27** 0.33** 0.26** 1 7. Age 25.29 7.01 -0.19** 0.02 0.06 -0.09 0.05 -0.14 1 8. Gender 1.59 0.49 0.17* 0.13 0.09 0.07 0.04 0.41** 0.01 1

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access, ease of use and richness of product information) and psychological traits (innovativeness, time pressure and hedonism) were the independent variables. The actual use of mobile shopping applications was the dependent variable of the model.

Firstly, we found that all tolerance values were greater than 0.1and VIF values less than 10 thus confirming there is indeed no multicollinearity issue in this model. R square of the model was 0.212 which indicates that 21.2 % of the variance in usage of mobile shopping applications to purchase clothing was explained by the benefit and psychological variables (Cameron & Windmeijer, 1997). Ease of use made the strongest contribution to explaining the model (beta= 0.52, t= 3.54, p=0.01), while ease of access (beta= 0.33, t=2.64, p=0.02) and innovativeness (beta=0.29, t=2.23, p=0.04) followed, these three variables were all significant on the 95% level. Richness of product information, time pressure and hedonism did not contribute significantly, because their p-values were statistically too high to reject null hypothesis. The results are shown in table 4.

Table 4 Multiple regression analysis (actual use)

Model Independent variable B Std.

Error

Std. Beta

t Sig. Tolerance VIF

1 Constant -6.24 3.82 -1.63 0.12 Ease of access 0.09 0.01 0.33 2.64 0.02 0.51 1.92 Ease of use 0.14 0.02 0.52 3.54 0.01 0.67 1.48 Richness of product information 0.02 0.10 0.16 0.83 0.24 0.58 1.70 Innovativeness 0.08 0.05 0.29 2.23 0.04 0.67 1.47 Time pressure 0.03 0.09 0.18 1.58 0.21 0.81 1.22 Hedonism 0.02 0.08 0.13 0.44 0.32 0.82 1.20

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A validation analysis is necessary to check the results of the main regression. In this model, we apply the use appropriateness as the dependent variables and the independent variables stays same as the expected benefits (ease of access, ease of use and richness of product information) and psychological traits (innovativeness, time pressure and hedonism).

R square of this model is 0.254 which indicates that 25.4 % of the variance in use appropriateness of mobile shopping applications to purchase clothing and apparel was explained by the benefit and psychological variables (Cameron & Windmeijer, 1997). Similarly, ease of access, ease of use and innovativeness was still positive predictors to explain the use appropriateness model. Interestingly we found that time pressure (beta=0.16, t=2.06, p=0.04) and richness of product of information (beta=0.16, t= 1.82, p=0.07) which were not strong predictors in the actual use model were significantly predicting the use appropriateness at 95% level and 90% level respectively. The results are shown in table 5.

Table 5 Multiple regression analysis (use appropriateness)

Model Independent variable B Std.

Error

Std. Beta

t Sig. Tolerance VIF

1 Constant 1.77 0.43 4.08 0.00 Ease of access 0.22 0.11 0.19 2.07 0.04 0.59 1.70 Ease of use 0.08 0.11 0.15 1.75 0.05 0.52 1.93 Richness of product information 0.14 0.07 0.16 1.82 0.07 0.67 1.49 Innovativeness 0.03 0.11 0.14 1.71 0.06 0.68 1.48 Time pressure 0.20 0.10 0.16 2.06 0.04 0.82 1.23 Hedonism 0.12 0.09 0.11 1.27 0.23 0.83 1.20

For hypotheses H3a, H3b which address whether demographic variables (age and gender) are associate with the use of mobile shopping applications to purchase clothing and apparel, the most appropriate method is to use the independent t-tests (Pallent, 2005). Because age and

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gender verse use of mobile shopping applications are two unrelated groups of categorical variables, t-test is used to investigate if there is a statistically significant association between the means in the two groups.

In order to test whether younger consumers are positively associated with the use mobile of shopping applications to purchase clothing and apparel, t-test was conducted to check the relationship. Because more than half of the respondents participated in this study were younger than 25, so we will define younger consumers as age less than 25. According to table 6, we find that there was a significant difference (at the 95% level) between consumers aged under 25 and above 25, (t=2.43, p= 0.04).

Table 6 Independent t-test analysis for age

A t-test was also conducted to investigate hypothesis 3b. As we can see in table 7, a significant difference (at the 95% level) was found between males (M=1.64, SD=0.48) and females (M=1.76, SD=0.42) who use mobile shopping applications to purchase clothing and apparel (t= 2.312, p=0.035). Therefore, female consumers indeed exhibit a slightly higher effect on shopping clothing on mobile shopping applications.

Age under 25 Age over 25

Item M SD M SD t p

Use of mobile shopping application to purchase clothing and apparel

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Considering the results from hypotheses testing in total, we can present the following conclusions:

Table 8 Hypotheses results

Hypotheses Results

H1a.Ease of access is positively associated with the use of mobile shopping applications to purchase clothing and apparel.

Supported

H1b. Ease of use is positively associated with the use of mobile shopping applications to purchase clothing and apparel.

Supported

H1c.Richness of product information is positively associated with the use of mobile shopping applications to purchase clothing apparel.

Rejected

H2a. Innovativeness is positively associated with the use of mobile shopping applications to purchase clothing apparel.

Supported

H2b. Time pressure is positively associated with the use of mobile shopping applications to purchase clothing apparel.

Rejected

Male Female

Item M SD M SD t p

Use of mobile shopping application to purchase clothing and apparel

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H2c. Hedonism is positively associated with the use of mobile shopping applications to purchase clothing and apparel.

Rejected

H3a. Younger consumers are positively associated with the use of mobile shopping applications to purchase clothing and apparel.

Supported

H3b. Female consumers are positively associated with the use of mobile shopping applications to purchase clothing and apparel.

Supported

6. Discussion

This research studies the drivers that encourage people to use mobile applications to purchase clothing and apparel. The possible drivers fall into three categories which are expected benefits, psychological traits and demographic traits.

Ease of access is supported to have a positive relationship with the actual use of m-shopping applications to purchase clothing and apparel thus supporting hypothesis 1a. This finding is consistent with the researches by Lee & Park (2006) and Kim et al. (2007), which suggests that the main goal for system provider is to reduce the effort involved in connecting with the mobile internet (Lu & Yu-Jen Su, 2009). For example, in addition to develop large network technologies, providers should also “eliminate the effort involved in switching between systems and coping with variations in communication quality while traveling between metropolises and suburbs” (Lu & Yu-Jen Su, 2009, p. 454). Similarly, as another cognitive trait,

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ease of use is also a positive driver in using m-shopping applications, therefore hypothesis 1b is supported. This indicates that consumers regard m-shopping apps as a utilitarian service and they pay attention to the effort they have contribute to use the device. It is suggested, the less effort they have to contribute the more frequently they will use the technology. This finding is in line with the studies by Venkatesh (2000) and Ko et al. (2007) that ease of use appears to have a direct impact on the perceived value of the technology which in turn motivates actual use.

According to previous literatures, physical stores are consider to be a traditional but a less appropriate shopping channel for clothing and apparel because of the restricted display space (Hsiao, 2009; Ha & Stoel 2012). M-shopping apps on the contrary can display plenty of products and enable consumers to have sufficient product information, therefore, we propose that due to the rich information m-shopping apps can offer, it will be a better channel for purchasing clothing and apparel. However, the result of this study did not support that richness of product information is a driver to determine the actual use of m-shopping apps to purchase clothing and apparel, so the hypothesis 1c is rejetecd. One possible explaination could be that only 1 item was applied to measure the variable, thus the information is insufficient to prove a relevant association. Interestingly, in the second regression analysis model, richness of product information do have a impact on the use appropriateness of m-shopping apps, indicating that people think m-shopping apps are appropriate to purchase clothing and apparel because it provides much information about clothes which including price, promotions, and reviews etc, and these information is more important for clothes than other categories (Grewal et al. 2004). This finding suggests that richness of product information might become a important driver to determine the use of m-shopping apps in clothing and apparel industry, and it is a different driver from the use of m-shopping apps in other industries.

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Regarding psychological attributes, hypothesis 2a is proved that innovativeness has a positive relationship with the use of m-shopping apps to purchase clothing and apparel. Consistent with the researchs by Eastlick & Lotz (1999) and Citrin et al.(2000), who suggested innovativeness has a positive influence in predicting online shopping, this finding extends the claim to the mobile shoppping domain. We could say that innovators will be heavy users of m-shopping apps in buying clothing because they like to try new things. Moreover, Goldsmith & Flynn (2004) explained that online apparel buying is motivated more by Internet innovativeness than by clothing innovativeness. This finding will illuminate the system provider to develop a unique application to attract innovative consumers, and enlighten markerters to establish a creative marketing plan for those innovator who are heavy users of m-shopping apps.

Although this study did not support hypothesis 2b that time pressure is positively associated with the actual use of m-shopping apps for buying clothing and apparel, a positive relationship between time pressure and use appropriateness was detected. A possible explanation could be that apparel shopping is different from book shopping or flight tickets shopping, because detailed information such as price, colour and fit is critical for clothes (Peterson et al. 1997). Time pressured consumers usually don’t have much time to study product information for clothes, namely, comparing prices, reading product reviews, and checking prices etc. It is also explained by Pousttchi (2003) that complex payment process inhibit time pressured consuemrs to buy online. As a whole, time pressured consumers do not often purchase clothing and might ask their friends and families to buy for them. However, they still consider using m-shopping apps is appropriate to purchase clothing which indicates someday they might conduct m-shopping behaviours. Moreover, unlike other studies which have shown that hedonism is positively associated with technology use, this study did not find a relationship between hedonism and

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usage of apparel m-shopping apps, therefore hypothesis 2c is rejected. One possible explanation could be that the two items used to measure hedonism were insufficient to test the causal relationship. Another possible reason is that respodent self-report their answers to the variables that may cause measurement errors in the data set.

In line with the expectations, female and younger consumers are positively associated with the use of m-shopping apps to purchase clothing and apparel, thus both hypothesis 3a and 3b are supported. This finding is consistent with previous studies that Statistics Denmark (2007) showed “clothing is one of very few categories with more women than men purchasing online” (p. 1155). It could be explained that men’s clothing may often be purchased by their partners instead of being purchased by themselves (Hansen& Møller Jensen, 2009). As a result, female consuemrs do shop more on m-shopping apps for clothing and apparel. In addition, the result is not surprising that younger consumers will use m-shopping apps more frequently than their older counterparts as Chong (2013) has already suggested younger users engaged more in m-commerce. Moreover, clothing and apparel appares to be more important for young people because they pay much more attention to appearance than older people (Piacentini & Mailer, 2004). Therefore, it is indeed understandable that younger consumers will purchase clothing and apparel on m-shopping apps more heavily than older people.

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7. Managerial Implications

This study provides a deeper understanding for consumers’ clothing mobile shopping behaviours. The findings of ease of access and ease of use implicate that the main goal for system provider is to reduce the effort involved in connecting with the mobile internet and the effort to use a particular system. The faster connectivity and the less effort contributions will generate a higher adoption rate from potential buyers. In addition, the richness of product information is a potential important factor that motivates people to buy clothing and apparel on mobile, thus, companies should pay attention to the product information displayed on the system. For example, it is recommended to give consumers access to interactive try-on sessions such as the “virtual dressing room”, and “on mobile fit prediction”. Moreover, the popular QR code can also be developed to link consumers to user-generated-content website where other consumers comment and write reviews for products. In this way, the potential buyers can have a clear image of the product which helps them to make a better decision.

Another implication for marketers is that they should pay more attention to time

pressured consumers. The finding in this research shows time pressured consumers have a trend to purchase clothing and apparel through m-shopping apps as long as the usage of system is fast and easy. System providers should emphasize the simplicity of check out process, for example, to make the payment as credit card and PayPal use friendly in order to save time for customers. The easier and clearer the system is, the more likely that time pressured consumers will purchase on mobile apps.

Concerning the demographic of mobile shoppers, younger and female consumers show a heavier usage of mobile shopping applications to purchase clothing and apparel. Hence, fashion retailers should develop strategies that attract these two groups of buyers. For instance, younger

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consumers are interested in an application with a lot of fun, and females prefer a delicate layout of mobile applications. Therefore, based on this sort of perceptions, companies are able to develop efficient and effective applications to attract new customers and retain the current ones.

8. Limitations

This research is not without limitations. First, it is limited by the sample and measures used. Only 178 people took part in the research study, and most of them are young people from the Dutch populations. Moreover, the measures used in this study are limited due to the

conciseness of the survey questionnaire. Future studies should also include other populations of consumers and use more measures to confirm and expand the results. Second, the self-report nature of the benefit, psychological variables might cause measurement error into the data. Future studies could incorporate a two dyads data set to increase the reliability and validity of the research. A longitudinal study might be helpful in the future to assess how benefit, psychological and demographic attributes change over time and what are some other variables may predict the actual use of mobile shopping applications in clothing and apparel industry. Lastly, this research takes the clothing and apparel industry as the only industry to investigate consumers’ mobile shopping behaviours, future scholars will obtain a more comprehensive results by examining the usage of mobile shopping applications across more industries. Although this research has several limitations, the findings do contribute to our understanding of clothing and apparel shopping in the mobile commerce background, and future studies can build on these results to complete this picture.

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Appendix A. Survey Questionnaire

Dear respondent,

Thank you for participating in my research about mobile shopping behaviours for my master thesis at the Amsterdam Business School of University of Amsterdam. This survey will take around 8 minutes and the answers given will be confidential and anonymous.

If you have any questions, feel free to contact me by sending an email to tingran.shen@student.uva.nl.

Please keep in mind that:

I.It is important that all the questions are answered

II.Read the descriptions carefully and answer the questions according to your own opinion III.There are no wrong answers

Thank you in advance for your participation.

Kind Regards,

Tingran Shen

University of Amsterdam

The first set of questions will be about your shopping behaviours.

Consider the time(s) when you intended to buy or bought clothing.

Q1. Which of the channels stated below have you ever used to search information* on clothing? (could be more than one)

* Search information in this context means search for new and/or specific items, exploring new collection trends and checking price or discount information.

-Stores

-Regular Internet Websites from your PC/Laptop -Smartphone/Tablet applications

Q2. Which of the channels stated below have you ever used to purchase clothing? (could be more than one)

-Stores

Q3. Which of the channel stated below you use most often to search information on clothing?

-Stores

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-Smartphone/Tablet applications

Q4

.

Which of the channel stated below you use most often to purchase clothing?

-Stores

Q5

. How many times approximately in the last 6 months you use the channels stated below

to search information on clothing? (please give numbers in the blanks, also indicate 0). -Stores

Q6. How many times approximately in the last 6 months you use the channels stated below to purchase clothing? (please give numbers in the blanks, also indicate 0).

-Stores

Q7. How appropriate you find the channels stated below for searching information on clothing? Totally inappropriate Inappropriate Neither Appropriate nor Inappropriate Appropriate Totally Appropriate Stores Regular Internet Websites from your PC/Laptop Smartphone/Tablet applications

What drives people to purchase clothing and apparel by using mobile shopping applications?

Master Thesis