Barriers to the adoption of mobile shopping channels : does product category matter?

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MASTER THESIS

Barriers to the Adoption of Mobile Shopping Channels: Does Product Category Matter?

University of Amsterdam Faculty of Economics and Business

MSc. In Business Administration Track: Marketing

Under supervision of: Jing Li

By:

Student: Qi Zheng Student Number: 11089393

Date: 22nd of June 2016

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Statement of Originality

This document is written by Student Qi Zheng who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and no sources other than those mentioned in the test and the references have been used. The faculty of Economics and Business is only responsible for the supervision of completion of this work, not for the content.

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Acknowledgement

This thesis is the final step of my master’s degree in Marketing at the University of Amsterdam. Writing this thesis has been a very interesting experience, in which I put much effort and finally gained a lot. I would like to take this opportunity to thank my supervisor Jing Li for her expertise and positive criticism, which enthused and guided me through this important process of writing the master thesis. Further, I would like to thank my family and friends for helping me out and supporting during the whole process.

I hope you enjoy reading this thesis.

Kind regards,

Qi Zheng

22nd_{of June 2016}

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Abstract

The phenomenon that consumers use multiple channels in the shopping process has become prevalent in recent years. In this multichannel environment, shopping or buying through mobile devices has become a highly discussed topic in both industry and academia. However, the number of mobile shoppers is still limited compared to the number of shoppers through traditional channels. Most existing studies investigate the factors that accelerate consumers’ adoption of mobile shopping channels, while it is not clear which factors block consumers’ adoption of mobile shopping channels. Focusing on this gap, this study explored the barriers to consumers’ adoption of mobile shopping channels, and investigated the effects of eight potential barriers (product performance risk, payment security risk, convenience loss risk, delivery costs, psychosocial risk, search effort, insufficient support of human-interactions and insufficient online shopping experience) on the intention of adopting mobile shopping channels. Furthermore, this study classified products into four categories (high-involvement and utilitarian products, low-involvement and utilitarian products, high-involvement and hedonic products, low-involvement and hedonic products) and examined the effects of product categories on consumers’ intention of adopting mobile shopping channels. Survey data from 151 Chinese consumers has been collected and processed through a preliminary analysis, regression analyses and a two-way ANOVA test. This study found that psychosocial risk and insufficient online shopping experience significantly reduced consumers’ intention of adopting mobile

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shopping channels. Further, consumers’ intention of adopting mobile shopping channels was higher for high-involvement products than low-involvement products. This study also identified different barrier effect across the four product categories. Based on the findings, this study proposed managerial implications and suggestions for further research in the end.

Key words: mobile channel adoption, barriers, consumer shopping behavior, consumer intention, product category

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List of Tables

Table 1. Summary of Items………..25

Table 2. Factor loadings………...30

Table 3. Reliability of scales………31

Table 4. Correlations………32

Table 5. Descriptive statistics of barriers……….33

Table 6. Descriptive statistics of intention………...…33

Table 7. Overall intention regression results………34

Table 8. Two-way ANOVA test results………...………35

Table 9. Product category regression results………36

Table 10. Results of hypotheses testing.………..37

List of Figure

Figure 1. The Conceptual Framework……….11

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1. Introduction

Customers increasingly use two or more channels during their shopping process in the last ten years (Neslin et al. 2006). In this multichannel environment, shopping or buying through mobile devices (mobile shopping) has become an increasingly hot topic that has drawn much attention in both industry and academia (Wang, Malthouse and Krishnamurthi, 2015). It is predicted that the total number of smartphone users by 2016 will be more than two billion, or one-quarter of the global population (Emarketer.com, 2016). Deloitte Consulting predicts that $31 billion worth of retail revenues will be transacted via mobile devices by 2016 (Brinke et al., 2012). Holmes, Byrne and Rowley (2013) also showed that the use of mobile devices in the shopping process is increasing significantly. This trend will continue with the increasing adoption of smart phones and with their increased functionality in terms of accessibility (Holmes, Byrne and Rowley, 2013).

Neslin et al. (2006) have indicated that understanding consumer behavior is one of the five major challenges for firms to manage multichannel environment. With the rapid development of mobile shopping, it is necessary for managers to understand how customers choose shopping channels and what are the impacts of the choices on their overall buying patterns (Neslin et al., 2006). Specifically, understanding consumers’ adoption of mobile shopping channels is an important part of understanding consumers’ mobile shopping behavior. Previous literature investigated numerous factors affecting consumers’ adoption of mobile shopping channels, most

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of which were positive factors that encouraged consumers’ adoption (Wu and Wang, 2005; Yang, 2010; Yang and Kim, 2012; Heitz-Spahn, 2013; Agrebi and Jallais, 2015). Very few studies investigated what factors prevented consumers from adopting mobile shopping channels (Forsythe and Shi, 2003; Lian and Yen, 2013). However, knowing the positive factors is not enough for fully understanding consumers’ mobile shopping behaviors. Thus it is necessary to research which barriers block mobile shopping channel adoption and how do these barriers affect consumers’ mobile shopping intention. Furthermore, previous study showed that product categories affected consumers during shopping process (Levin, Levin and Weller, 2005). Yadav and Varadarajan (2005) also examined the role of product category in influencing consumer behavior in traditional and electronic channels. They found product category had a moderating effect on the relationship between interactivity and value outcomes for sellers and buyers (Yadav and Varadarajan, 2005). Kushwaha and Shankar (2013) found that multichannel customers behaved differently across different product category segments (Kushwaha and Shankar, 2013). Knowing the effect of product categories can not only make the research of mobile shopping barriers more specific, but also provide practical implications for retailers selling different products. Previous researches do not show a combined analysis of the barriers to mobile shopping channel adoption and the effect of product categories. Therefore this topic remains a gap in the existing literature on the research of mobile shopping channels.

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shopping channel adoption across different product categories. The main research question is “what are the barriers to consumers’ adoption of mobile shopping channels across different product categories?” The sub-questions are as follows:

(1) Which factors prohibit consumers’ adoption of mobile shopping channels? (2) How do product categories impact consumers’ intention to adopt mobile shopping channels?

(3) How do the barriers to the adoption of mobile shopping channels differ across different product categories?

Researching these questions can result in both theoretical and managerial contributions. For theoretical implications, identifying barriers to the adoption of mobile shopping channels can enrich existing literature about mobile shopping and consumer behavior. This research identified the negative factors of innovation adoption within the mobile shopping field, which filled the research gaps of the barriers to adopting mobile shopping channels. Furthermore, by researching the effect of product categories, this study provided deeper insight into different product categories within the mobile shopping field for researchers. This research also provided meaningful managerial implications for marketers. With the knowledge of the barriers to the adoption of mobile shopping channels, marketers could avoid potential negative outcomes of certain marketing practice. Knowing why consumers do not adopt mobile channels for specific products helps marketers to design effective mobile channel strategies. For example, marketers could better leverage the advantages of mobile shopping channels, improve the functionality of mobile

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shopping channels, and develop more consumer-friendly mobile shopping activities to create values.

In order to achieve these contributions, this research is structured as follows. First, the literature about mobile shopping (adoption) and product category will be reviewed. Second, a conceptual framework will be proposed and explained, followed by several hypotheses on barriers and product category variables. Third, the sample and methodology will be introduced and the next chapter is the results of data analyses. Finally, to end this paper a careful discussion and conclusion of the results will be provided, including academic relevance, managerial implications, limitations and suggestions for future researches.

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2. Literature Review

2.1 Mobile shopping

Nowadays, consumers can use many channels (Internet, tablets, smart phones, call centers, catalogues, physical stores) when they search information and shop. If two or more of these channels are considered during the process of buying a product, the phenomenon is called multichannel shopping (Neslin et al., 2006). Among these common channels, mobile devices are becoming increasingly important and prevalent in the shopping process. Mobile shopping, as indicated by Kourouthanasis and Giaglis (2012), is Internet commerce conducted over mobile or wireless networks while using mobile devices. Smartphones and tablets are the two highly preferred mobile devices for shopping due to their conveniences of a personal computer and a handy form feature (Viswanathan, 2016). Based on the survey data from multiple countries, Groß (2014) concluded that the population of smartphone users was expected to increase worldwide and at the same time, the volume of mobile purchase was also increasing. Mobile shopping is drawing significant social attention and has much potential in the field of multichannel shopping. As a marketing tool, mobile channel also provides ample opportunities for marketers and advertisers.

The reason why mobile shopping is potential and effective for marketing lies in its characteristics. Larivière et al. (2013) has indicated that mobile devices have five main characteristics: portability, personal relationships with owners, networked, textual and visual content, and convergence of functions and services. These

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characteristics enable firms and customers create values in new ways that traditional channels cannot provide. Therefore mobile shopping channels have become popular alternative approaches for consumers to search, browse, compare and purchase products and services online from multiple retailers whenever and wherever the consumers use mobile devices (Groß, 2014). As the new generation of mobile devices, smartphones close the gap between desktop computers and traditional mobile phones and stand for a breakthrough of mobile shopping (Groß, 2014). Furthermore, mobile devices, especially smartphones, could be seen as not only functional gadgets but also cultural objects, which have become part of consumer’s daily routines and practices (Shankar et al., 2010). This phenomenon is so called “mobile lifestyle”, or “life on the screen”, in which customers’ everyday activities rely more and more on mobile channels.

2.2 Mobile channel adoption

Consumers’ use of their mobile devices in the shopping process is not restricted to purchase stage. Actually the levels of use for activities such as checking prices, comparing products, searching product information, and reading user reviews are higher than those for purchase (Econsultancy, 2016). The adoption of mobile shopping channels can be customers’ first use of mobile channels in any one of these shopping activities. Early researchers built a technology acceptance model (TAM), which was one of the influential theories for measuring and explaining the acceptance of end-user-computing technologies (Groß, 2014). TAM stated that perceived usefulness and perceived ease of use were the primary determinants among the casual

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linkage of using a technology system. Using a modified TAM, Groß (2014) indicated that in addition to traditional TAM factors, perceived enjoyment and trust in the mobile vendor affected the consumers’ intention to engage in mobile shopping.

Previous studies have identified two shopping motivations, utilitarian and hedonic, that can explain consumers’ shopping behaviors. Utilitarian shopping motivations refer to the idea that a shopping activity is a work assignment with goals while hedonic motivations stress the notion that shopping could provide entertainment and inner worth (Babin, Darden and Griffin, 1994). Motivations for mobile shopping adoption can also be analyzed from these two domains. Yang (2010) found that utilitarian and hedonic performance expectancy, social influence, and facilitating conditions were critical determinants of consumers’ intention to use mobile shopping services. Specifically, Yang and Kim (2012) further indicated that within the two motivations, idea, efficiency, adventure, and gratification shopping motivations were significant determinants of mobile shoppers, suggesting that those shopping motivations were push factors of mobile shopping. Efficiency is a utilitarian shopping motivation, while idea, adventure and gratification are hedonic shopping motivations. According to Lu and Yu-Jen Su (2009), enjoyment, usefulness, and compatibility had an impact on customers’ mobile shopping intention.

Most of the conclusions of previous studies focus on positive factors that motivate consumers to use mobile shopping channels. Rather there are also results demonstrate that anxiety, which is an affective barrier against using innovative systems, is a key negative predictor of customers’ intention to use mobile channels

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(Lu and Yu-Jen Su, 2009). But studies about what factors prevent consumers from adopting mobile channels are still limited. Previous researches have identified several barriers to online shopping, which can provide meaningful implications for studies in mobile shopping field. Wu et al. (2014) defined three costs that influenced customers’ online shopping intention: information searching cost, moral hazard cost and specific asset investment. Information searching cost was the most influential one. Survey data showed that as the consumers’ perception of the information searching cost decreased, their perception of online shopping value and repurchase intention both increased (Wu et al., 2104). Another study case indicated that value barrier and tradition barrier were the major barriers towards online shopping intention (Lian and Yen, 2013). A research in Switzerland showed that digital barrier and security barrier were the main barriers impacting existing and potential consumers’ online shopping decisions (Rudolph, Rosenbloom and Wagner, 2004). As mobile shopping is closely related to online shopping, these researches on online shopping costs and barriers provide significant reference for this study.

The barriers to the adoption of mobile shopping channels can also be researched from the channel selection determinants dimension. Channel determinants include diverse variables that influence consumers’ choice. Neslin et al. (2006) provided an extensive list of channel determinants, many of which can be seen as the barriers to a certain channel. The attributes of mobile channels can lead to barriers to consumers’ adoption. From this dimension, several barriers were proposed in this study. Although mobile shopping is becoming prevalent, there are still numerous consumers who use

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traditional channels and haven’t transferred to mobile shoppers. So it is interesting to research the reasons why these consumers do not adopt mobile channels.

2.3 Product category

Previous studies showed that product category was an important factor influencing consumers’ behavioral intention in online versus offline shopping processes. As indicated by Levin, Levin and Weller (2005), the type of product influenced consumers’ decisions and how consumers perceive the value of these products. The difference between product types can influence consumers’ preference for online versus offline purchases (Levin, Levin and Weller, 2005). Levin, Levin and Weller (2005) showed that this difference in preference for shopping online or offline was driven by the difference in importance of product attributes that were perceived to be better delivered online or offline. Besides the differences in preferences, online shoppers, including mobile shoppers, purchase a wide range of products. According to McPartlin and Dugal (2012), globally, 48% of the surveyed consumers said they shopped online in at least 10 of the categories they studied. Shoppers in China were the most committed online shoppers, with 70% of respondents stating they shopped across at least 10 categories. In the multichannel shopping environment, except for options between online and offline channels, choices for mobile devices can also be influenced by product categories. Studies showed that consumers’ use levels of mobile channels were different across different product categories. According to Holmes, Byrne and Rowley (2013), the overall use of the mobile devices was higher for the high-involvement products, such as TV and phone, middle ranking for DVD

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and footwear, and low for washing powder and bread. However, mobile channel was important for all products in pre-purchase stage, which meant that most mobile shoppers conducted activities such as checking stock, comparing alternatives and searching discount and promotion (Holmes, Byrne and Rowley, 2013).

Since there are differences in preferences for online, offline and mobile shopping for different product categories, it is interesting to further research how product categories influence consumers when they consider adopting mobile shopping channels. This issue is important because consumer behavior fundamentally varies by product categories (Kushwaha and Shankar, 2013). As an important variable, product category can be a moderator of consumers’ shopping preference or even one of the determinants of shopping channel choices when consumers conduct shopping activities.

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3. Conceptual Framework

This chapter visualizes and explains the conceptual framework developed for this research. The eight barriers to adopting mobile shopping channels will be described respectively. Two dimensions of classifying product category are defined and explained. Several hypotheses are proposed accordingly.

3.1 Conceptual framework

The figure below provides an overview of the conceptual framework for this research. The framework contains two components. The first part tests the relationships between the eight barrier variables and mobile shopping intention to identify if they are barriers for consumers to adopt mobile shopping. The second part tests how product categories influence consumers’ adoption intention of mobile shopping channel, and how do the barriers differ across different product categories. Figure 1: Conceptual Framework

Product performance risk Payment security risk Psychosocial risk Convenience

loss risk Delivery costs

Search effort Insufficient support of

human-interactions

Insufficient online shopping experience

Adoption intention of mobile shopping channels

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3.2 Barriers to mobile shopping channel adoption

The main purpose of this research is to identify the barriers to the adoption of mobile shopping channels. Similar to online shopping, mobile shopping has several unique advantages compared with traditional store-based channels, such as 24-hour availability, interactivity, powerful and inexpensive means of searching information, intangible goods distribution and customer communities (Rudolph, Rosenbloom and Wagner, 2004). But the usage of mobile shopping is still limited, suggesting that some barriers to adopting mobile shopping channels exist. In multichannel customer management area, a most heavily researched question is the antecedents of customer channel adoption and selection. The main purpose of this study focuses on consumers’ intention of adopting mobile channels, which is in line with the research of channel adoption and selection. From this start point, benefits and costs of mobile channels can be examined. In this study, eight barriers are developed based on previous studies on channel adoption and selection and other related survey data. Specifically, this research will test the following barriers to consumers’ adoption of mobile shopping channels.

3.2.1 Product performance risk

As the shopping environment of mobile channels is much different from that of traditional channels, consumers are likely to perceive risk concerning mobile shopping activities. The perceived risk represents the uncertainty and potential undesired side effects that cannot be anticipated, which can inhibit consumers’ adoption of an innovation (Ram and Sheth, 1989). Similar to online shopping, one

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characteristic of mobile shopping channels is that consumers cannot check the products in person prior to purchase. The risk arises here because consumers cannot make sure if the real products are in line with the online description and their expectation. Forsythe and Shi (2003) have defined product performance risk as the loss incurred when a brand or product does not perform as expected. Product performance risk may result from a poor product choice due to the shoppers’ inability to accurately judge the quality of products online (Forsythe and Shi, 2003). Similarly, customers who cannot overcome this perceived risk are likely to resist mobile shopping channels. The first hypothesis is:

H1: Product performance risk has a negative influence on consumers’ intention

of adopting mobile shopping channels. 3.2.2 Payment security risk

Method of payment also distinguishes mobile shopping channels from other channels. Online payment security concerns consumers substantially, especially with credit card and personal information (Rudolph, Rosenbloom and Wagner, 2004). Perceived risk of mobile shopping arises here since the mobile channel is even less secure than online channels (laptop\desktop). Although mobile payment technologies have brought much convenience, there is always a security and risk element associated with their use. Many consumers believe that it is too easy to have a credit card stolen online (Ecommercetimes.com, 2016). Rudol, Rosenbloom and Wagner (2004) also showed that security barrier was the main obstacle to buying on the

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Internet. The second hypothesis is:

H2: Payment security risk has a negative influence on consumers’ intention of

adopting mobile shopping channels. 3.2.3 Convenience loss risk

Convenience is a type of benefit that consumers seek for when they conduct shopping activities, which includes the convenience of search and purchase. Search convenience is defined as the perceived ease and speed at which consumers can gather information on products in the specific channel. Similarly, purchase convenience refers to the efficiency, ease and speed at which products can be purchased (Verhoef, Neslin and Vroomen, 2007). Convenience is an important aspect of shopping experience that influences consumers’ shopping satisfaction (Burke, 2002). Consumers have different convenience perceptions of different shopping channels. Due to consumers’ previous shopping experiences and shopping habits, mobile shopping channels maybe inconvenient for traditional shoppers because the significant differences of mobile channels. From this perspective, the convenience loss risk barrier was proposed to discover whether the convenience factor is negative to consumers’ intention of adopting mobile shopping channels. The third hypothesis is as follows:

H3: Convenience loss risk has a negative influence on consumers’ intention of

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3.2.4 Delivery costs

One of the attributes of mobile shopping is the delivery part of a purchase. Most online transactions involve physical product delivery, and both online retailers and consumers perceive the efficient delivery a real burden (Gupta, Su and Walter, 2004). Delivery can generate costs due to the delivery waiting time, delivery fee and delivery accuracy. Long delivery time reduces the convenience of mobile shopping channels and concerns consumers increasingly after orders (Gupta, Su and Walter, 2004). Previous research mentioned that delivery charge was the most possible reason why many consumers were still reluctant to use online shopping (Huang and Oppewal, 2006). Delivery accuracy generally refers to the possibility of the right products being delivered to the right addresses. Low delivery accuracy usually causes problems during shopping processes. According to previous studies and experiences in real life, negative performances of these three aspects of delivery can induce consumers’ time and financial costs, thus reduce consumers’ shopping intention and satisfaction. The forth hypothesis is proposed as follows:

H4: Delivery costs have a negative influence on consumers’ intention of adopting

mobile shopping channels. 3.2.5 Search effort

Search effort is defined as the perceived required time (time costs) and perceived difficulty for consumers to gather information on products and services (Verhoef, Neslin and Vroomen, 2007). The reduction of search effort has a positive impact on

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the intention to a certain shopping channel (Gupta, Su and Watler, 2004). Studies have found that consumers who purchase through online channels perceive significantly lower search effort online than offline (Gupta, Su and Walter 2004). Similar to online channels, mobile channels provide consumers with widespread availability of information (Gupta, Su and Walter 2004). This attribute can help consumers to save time, but also can lead to information overlap problems. To evaluate whether search effort of mobile shopping is a barrier to consumers’ intention, the fifth hypothesis is proposed:

H5: Search effort has a negative influence on consumers’ intention of adopting

mobile shopping channels. 3.2.6 Psychosocial risk

Psychosocial benefits, which involve consumers’ feelings related to using a certain channel, can influence consumers’ channel choices (Frambach, Roest and Krishnan, 2007). Psychosocial benefits can be categorized as either positive (for example, confidence) or negative (for example, regret) (Frambach, Roest and Krishnan, 2007). Negative psychosocial benefits can be seen as psychosocial risks. In another study, it is said that psychosocial risk arises from the likelihood that the purchase will fail to reflect consumer’s personality or self-image (Gupta, Su and Walter, 2004). Consumers who perceive high psychosocial risk may feel negatively before or after shopping activities. Frambach, Roest and Krishnan (2007) have concluded that positive psychosocial benefits were important drivers of channel

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choice. So it is interesting to further examine whether psychosocial risk is an obstacle to mobile shopping adoption. The sixth hypothesis is:

H6: Psychosocial risk has a negative influence on consumers’ intention of

adopting mobile shopping channels.

3.2.7 Insufficient support of human-interactions

Different from traditional channels, mobile channels do not provide in-person communication between consumers and sales personnel. Most mobile channels do not provide sales personnel’ help or only provide consulting services through online chat software. The human interaction during shopping process is an important aspect of service quality that physical store retailers provide. Service quality is defined as the perception on the delivered service in the channel during the purchase (Verhoef, Neslin and Vroomen 2007). Service quality is a type of benefit that consumers can achieve during shopping activities. In the multichannel context, it is expected that higher perceived service quality of one channel over another will lead to channel preference and that higher service quality of all channels will lead to higher overall satisfaction (Gupta, Su and Walter, 2004). To some extent, the insufficient support of human-interactions reduces the service quality that mobile channels provide. The seventh hypothesis is proposed to examine whether consumers resist mobile shopping channels because of this insufficient human-interactions factor:

H7: Insufficient support of human-interactions has a negative influence on

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3.2.8 Insufficient online shopping experience

Except for its own attributes such as personal nature and mobility, mobile shopping channels have many similarities with online shopping channels (desktop/laptop). Online channels (desktop/laptop) allowed customers to buy goods or services directly from a seller over the Internet, which was a breakthrough of traditional shopping patterns. As the functionality of mobile phones, especially smartphones, was increasingly and intensively developed, online shopping has extended to mobile shopping. Survey showed that most online retailers had a mobile strategy in place or in development (Bang et al., 2013). For online shoppers, the mobile shopping channel can be a new alternative. It is expected that consumers who get used to online shopping will be more likely to adopt mobile shopping channels. The eighth hypothesis is to test if insufficient online shopping experience will reduce consumers’ intention to adopt mobile shopping channels:

H8: Insufficient online shopping experience has a negative influence on

consumers’ intention of adopting mobile shopping channels. 3.3 Define product categories

To further examine the influence of product category, this research focuses on two dimensions that are broadly used in consumer shopping behavior studies: high-involvement products versus low-involvement products and hedonic products versus utilitarian products. These two classifications are used because they constitute fundamental bases for consumer purchase and consumption (Kushwaha and Shankar,

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2013). It is expected that different product categories influence consumers’ intention of using mobile shopping channels. Further, product categories may moderate or intensify the effect of each barrier to mobile shopping adoption. For example, some barriers may be significant for high-involvement product purchase, and moderate or non-significant for low-involvement product purchase.

3.3.1 High-involvement and low-involvement products

When consumers make decisions in shopping phases, they display different levels of involvement. This decision process depends on consumers’ experience and knowledge. The level of involvement reflects how personally important or interested consumers are in consuming a product and how much information consumers need to make decisions (Tanner and Raymond, 2016). The level of involvement determines the depth, complexity and extensiveness of cognitive and behavioral process during the consumer decision-making process (Bian and Moutinho, 2009). According to Bian and Moutinho (2009), product involvement is a central framework that is very important to understand consumer decision-making behavior. The level of involvement is mostly decided by the perceived risk level of a product, which refers to consumers’ overall perceptions of uncertainty and adverse consequences of buying a product (Kushwaha and Shankar, 2013). In most cases, the higher the perceived risk is, the higher the level of involvement is. Therefore the two categories can also be called as high-risk products and low-risk products.

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status and justifying lifestyle; for example, a car, a home theatre and an insurance policy (Chand, 2015). When purchasing high-involvement products, consumers usually engage in extensive thoughts and careful considerations. By contrast, low-involvement products are those that reflect routine purchase decisions; for example, bread, ice cream and toilet paper (Chand, 2015). Low-involvement products require limited considerations and consumers usually engage in routine response behaviors (Tanner and Raymond, 2012). The level of involvement influences consumers’ shopping intention and behavior. Holmes, Byrne and Rowley (2013) have indicated that the usage of mobile devices in shopping process was different in terms of products with different levels of involvement. In other words, the level of involvement influences consumers’ intention of adopting mobile shopping channels. Since high-involvement products usually generate more financial risk and psychological risk during the shopping process, consumers’ shopping intention may reduce more intensively compared to low-involvement products. From this dimension, this research categorizes products into high-involvement products and low-involvement products to investigate the influence of the level of involvement on the consumers’ intention of adopting mobile shopping channels. The following hypothesis is proposed:

H9: Consumers’ intention of adopting mobile shopping channels is lower for the

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3.3.2 Hedonic and utilitarian products

Hedonic and utilitarian are two broad dimensions to differentiate the drivers of consumer shopping behaviors. Although the consumption of many products involves both drivers, there is little doubt that consumers characterize some products as primarily hedonic and others as primarily utilitarian (Dhar and Wertenbroch, 2000). Hedonic products are defined as ones whose consumption is primarily characterized by an affective and sensory experience of aesthetic or sensual pleasure, fantasy, and fun (Hirschman and Holbrook, 1982). For example, movie tickets are hedonic products since consumers buy movie tickets to enjoy and relax. Utilitarian products are ones whose consumption is more cognitively driven, instrumental, and goal oriented and accomplishes a functional or practical task (Strahilevitz and Myers 1998). For example, washing powder is a utilitarian product because consumers purchase it for practical cleaning goals.

The differences between hedonic products and utilitarian products influence consumer online shopping behavior. Previous studies showed that the relative importance of online information was higher for utilitarian products than for hedonic products (Cheema and Papatla, 2010). Further, the positive relationship between the preference for multichannel and monetary value was stronger for hedonic product categories than for utilitarian categories (Kushwaha and Shankar, 2013). This implies that companies should use different marketing tools based on the product characteristics. Therefore in mobile shopping field, it is predicted that such differences between hedonic and utilitarian products are very likely to influence

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consumers’ shopping intention. Hedonic products are likely to evoke impulse purchase and variety-seeking behaviors, and in a multichannel environment, mobile shopping channels provide greater opportunity to engage in those behaviors (Kushwaha and Shankar, 2013). Thus to see if these two categories affect consumers’ intention of adopting mobile shopping channels, the following hypothesis is proposed: H10: Consumers’ intention of adopting mobile shopping channels is higher for

hedonic products (and lower for utilitarian products).

4. Methodology

To investigate the existence of the proposed barriers to mobile shopping adoption and the different effects across different product categories, quantitative data has been collected and analyzed to test the proposed hypotheses. This chapter provides descriptions of the used sample, research design and procedure, the analysis method, measures and scales used to identify the barriers and the effects of product categories.

4.1 Sample Description

The population for this study consists of the Chinese consumers who have Internet access, which is approximately 668 million people (CNNIC, 2016). Because the population is large and no sampling frame could be retrieved for it, non-probability sampling technique was used. The aim of the data collection for this study is to achieve a sample as large as possible to increase the chance of having a

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representative and heterogeneous sample. The respondents of the survey have been selected using a snowball sampling technique, in order to include a large size of respondents. A convenience sampling technique was also used since the consumers could be reached are limited and I started the survey by inviting the respondents who are accessible to me.

On the 10th of April 2016, 156 questionnaires have been filled in. Subsequently 5 of them have been deleted due to insufficient or missing data. The remaining 151 questionnaires were used for analysis. The demographic features of the valid respondents are as follows. 70 respondents were male (46%) and 81 respondents were female (54%). The mean age of the respondents was 35. The oldest respondent was 60 years old; the youngest was 18 years old. A majority of the respondents’ highest achieved education level was bachelor degree, consisting 64.9% of the total. The average annual income of the respondents was approximately 21000 euro.

4.2 Research Design

To identify and explain the barriers to the adoption of mobile shopping channels and research the effects of different product categories, a quantitative research through a survey was used, which is the most common and appropriate design in this field. Specifically, this strategy included a cross-sectional, self-administered online questionnaire, which enabled the collection of structured data and the statistical analysis based on large amount of respondents (Saunders and Lewis, 2012). After preliminary analysis, data collected through the survey was analyzed to test the hypotheses statistically and then to generalize findings and conclusions.

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4.2.1 Measures

Questions about the barriers to mobile shopping adoption and questions about consumers’ intention of adopting mobile shopping channels across different product categories were answered on a scale of 1 to 7 at interval level. All questions are shown in appendix 1 Questionnaire.

The questionnaire consisted of three parts: part one consisted of eight barrier variables, which were product performance risk, payment security risk, convenience loss risk, delivery costs, search effort, psychosocial risk, insufficient support of human-interactions, and insufficient online shopping experience. Several items were used to represent each barrier and respondents were asked to rank their degree of agreement to the items. To be able to test the influence of different product categories on consumers’ intention of adopting mobile shopping channels, the second part of the questionnaire consisted of consumers’ intention of buying different products through mobile shopping channels and their overall intention of adopting mobile shopping. The third part consisted of several demographic questions about the survey respondents.

The eight barrier variables were the test variables. Barriers to mobile shopping adoption are defined as “the factors that resist consumers from adopting mobile shopping channels”. The proposed factors were assessed with original or modified items from previous studies. For example, the search effort factor is represented by the three items from Verhoef, Neslin and Vroomen (2007). Full items and references are shown in the table 1. Totally 27 items were used to assess the eight barrier

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variables. Participants were asked to rate their degree of agreement on the statements about different possible barriers to mobile shopping. Items were rated on a scale from 1 (totally disagree) to 7 (totally agree). The following question was asked: “Please indicate on a scale from 1 to 7 that to extent do you agree or disagree the following statements (1=totally disagree, 7=totally agree).”

Table 1: Summary of Items

Barriers Items Reference

Product

performance risk

It’s difficult to judge quality of a product or service through mobile shopping.

There is a large probability that I do not get the right product through mobile shopping.

I perceive that it is very likely that something will go wrong with the product performance through mobile shopping.

Forsythe and Shi, 2003 Verhoef et al., 2007 Gupta et al., 2004

Payment security risk

I do not trust that my credit card number will be secure through mobile payment.

I perceive that it is very likely that something may go wrong with my credit card information through mobile shopping.

The probability on wrong payments for buying products through mobile shopping is large.

Forsythe and Shi, 2003 Gupta et al., 2004

Verhoef et al., 2007 Convenience loss

risk

I find that the process of mobile shopping is usually complicated.

I find that the process of mobile shopping is usually not user-friendly.

It’s faster and easier to shop in-store compared to mobile shopping.

Frambach et al., 2007 Frambach et al., 2007 Forsythe and Shi, 2003 Delivery costs I perceive waiting for the delivery of the product a

big problem for me.

I worry if the products would be delivered on time through mobile shopping.

I worry if the right products would be delivered through mobile shopping.

I worry about the delivery charge through mobile shopping.

Gupta et al., 2004 Huang and Oppewal, 2006

Huang and Oppewal, 2006

Search effort It costs a lot of time to search for information on products through mobile shopping.

It costs a lot of effort to search for information on products through mobile shopping.

Verhoef et al., 2007 Verhoef et al., 2007

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It is difficult to collect information on products through mobile shopping.

Verhoef et al., 2007 Psychosocial risk Using mobile shopping brings me negative states

prior to purchase (worried, fearful, tense, uncomfortable, uneasy).

Using mobile shopping brings me negative states after purchase (angry, sad, regret, disappointed, frustrated).

Buying products through online shopping doesn’t fit well with how I view myself.

Frambach et al., 2007 Frambach et al., 2007 Gupta et al., 2004 Insufficient support of human-interactions

I cannot get helpful assistance due to the lack of interaction with sales staff during mobile shopping. I cannot get excellent help when buying products through mobile shopping.

I cannot get convenient service through mobile shopping.

I cannot get reliable service through mobile shopping. Montoya-Weiss et al., 2003 Verhoef et al., 2007 Montoya-Weiss et al., 2003 Montoya-Weiss et al., 2003 Insufficient online shopping experience

I have used or been exposed to online shopping (desktop/laptop).

I frequently inform myself on the possibilities of online shopping (desktop/laptop).

I have little experience with online shopping (desktop/laptop).

I am not very confident in using online shopping (desktop/laptop).

Frambach et al., 2007 Frambach et al., 2007 Frambach et al., 2007 Frambach et al., 2007

The next test variable is consumers’ intention of adopting mobile shopping channels. To test the influence of different product categories on mobile shopping intention, two classification dimensions were used to indicate different product categories. First, products were classified into two categories: high-involvement products and low-involvement products. Second, products were classified into another two categories: hedonic products and utilitarian products. Practical examples of each product category were provided for respondents. Respondents were asked to indicate their intentions of using mobile channels in term of these four categories. Respondents were also asked about their overall intention of adopting mobile

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shopping channels. Consumers’ intention of adopting mobile shopping channels was rated on a scale from 1 (no intention) to 7 (full intention). The following question was asked: “Please indicate on a scale of 1 to 7 that to what extent do you intend to buy products through mobile shopping channels (1=no intention, 7=full intention).” 4.3 Procedure

An online questionnaire was created and hosted on www.sojump.com in Chinese to collect data. Firstly a pilot study was done to evaluate the study design to ensure the survey quality. Then the main study has been done.

4.3.1 Pilot Study

The pilot study has been executed from the 1st of April until the 3rd of April. Within the pilot study, three Chinese students from the University of Amsterdam, two working professionals and two people in households were asked about their critics and improvement suggestion with respect to the questionnaire. According their feedback, some controversial questions and scales were changed into more clear and precise versions before administration of the actual study.

4.3.2 Main Study

The final questionnaire has been administered on the 4th of April within the personal network of the researcher through social media (WeChat, Facebook) and email. To reach more respondents, the snowball sampling technique has been used. Several friends and relatives of the researcher have distributed the questionnaire

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through their personal networks via social media, which helped to increase the reliability of the survey. By spreading the questionnaire online, it was guaranteed to reach respondents that use the Internet. On the 13rd_{of April, all questionnaires that}

have been completed were returned. In total the questionnaire had been active for 10 days. Participation was totally voluntary and responses were confidential. The number of respondents who reached the questionnaire is unclear, but in total 156 questionnaires were returned and 151 of them were used for analysis.

4.4 Analytical methods

This research used SPSS to analyze the collected data. Preliminary analyses included cleaning data, recoding, validity and reliability tests and correlation test. Then the overall scores (means and standard deviations) of variables were calculated. To test the effect of eight barriers on consumers’ intention of adopting mobile channels, a multiple regression analysis was conducted. Next, a two-way ANOVA test was run to test the influence of product categories on consumers’ intention of adopting mobile shopping channels. Finally, another four regression analyses were done to further explore the differences among barriers across the four product categories. Specific data analysis process and results are provided in the next chapter.

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5. Data Analysis and Results

In this chapter, preliminary data analysis steps were taken in SPSS to test the validity and reliability of data for further data study. Then the proposed hypotheses were tested and analyzed in SPSS.

5.1 Preliminary Analysis

The first step was to clean data from inadequate results. Online survey collected 156 questionnaires in 10 days. 5 of them were discarded because of a low completeness less than 60%. The final database consisted of 151 respondents, N=151. The remaining questionnaires were all complete without any missing data. The items “I have used or exposed to online shopping (desktop/laptop)” and “I frequently inform myself on the possibilities of online shopping (desktop/laptop)” were recoded, because these items were counter-indicative.

5.1.1 Validity and Reliability

First, an exploratory factor analysis and a reliability analysis have been conducted to check the validity and reliability of variables. Factor analysis with Varimax rotation was used to check the discriminant and convergent validity. Eigenvalue is larger than 1. Eight factors (eight barriers) were derived, and were labeled as follows: product performance risk (PPR), payment security risk (PSR), convenience loss risk (CLR), delivery costs (DC), search effort (SE), psychosocial risk (PR), insufficient support of human-interactions (ISH), and insufficient online

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shopping experience (ISE). After analysis, one item of ISE factor (ISE2) showed a low loading score (<0.5), so this item was deleted. Another factor analysis was done and the result of the remaining three ISE items was nice. Table 2 summarizes the final factor loadings, which shows the relative importance of each item for each factor. The results proved that the construct for this research had a sufficient validity.

Table 2: Factor loadings

Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7 Factor8

PPR1 0.814 PPR2 0.839 PPR3 0.804 PSR1 0.894 PSR2 0.885 PSR3 0.684 CLR1 0.829 CLR2 0.856 CLR3 0.749 DC1 0.772 DC2 0.854 DC3 0.818 DC4 0.657 SE1 0.935 SE2 0.935 SE3 0.709 PR1 0.843 PR2 0.855 PR3 0.790 ISH1 0.830 ISH2 0.888 ISH3 0.843 ISH4 0.683 ISE1 0.791 ISE3 0.838 ISE4 0.624

Reliability test is necessary for multi-item scales analysis in order to achieve a meaningful result. To verify if all the items in one scale measure the same, or if some questions should not be used for analysis, the Cronbach’s Alphas of the eight barrier

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variables were tested. In this research, a Cronbach’s Alpha of > 0.7 was considered acceptable. Results showed that Cronbach’s Alphas of all the variables were above the limit, except for insufficient online shopping experience (ISE). This variable under the limit is discussed below. Table 3 showed the final reliability scores of all scales.

The reliability of insufficient online shopping experience was 0.605, which was not high enough to ensure the internal consistency of the scale. After deleting the item ISE4, Cronbach’s Alpha was improved to 0.659, which was still under the limit 0.70. Since this factor is an important dimension of mobile shopping research and the reliability is slightly under the limit, this research still included this factor in the further analysis. So the item ISE4 was deleted and the ISE factor of two items ISE1 and ISE3 was used in the further analysis.

Table 3: Reliability of scales

Construct N of items Cronbach’s Alpha*

Product performance risk 3 0.754

Payment security risk 3 0.764

Convenience loss risk 3 0.733

Delivery costs 4 0.779

Search effort 3 0.833

Psychosocial risk 3 0.774

Insufficient support of human-interactions 4 0.826

Insufficient online shopping experience 2 0.659

*Cronbach’s Alpha should > 0.7 5.1.2 Correlation test

Before the correlation test and further analysis, scale means were calculated in order to achieve a meaningful result. Eight new variables were computed as a function of existing variables. Each new variable was the mean of the scale items that

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were used to measure each barrier factor. The eight new variables were used for the following correlation test and the model test.

Next, Pearson’s correlation coefficients were calculated in order to check the relations between variables. The threshold is 0.5. Table 4 shows that the majority of correlations are below 0.5, with one exception (0.509) that is still considered to be acceptable. Table 4: Correlations Mean SD 1 2 3 4 5 6 7 8 1 PPR 4.13 1.37 1 2 PSR 4.11 1.50 0.368** 1 3 CLR 3.21 1.36 0.357** 0.412** 1 4 DC 4.01 1.43 0.304** 0.449** 0.308** 1 5 SE 4.19 1.57 0.275** 0.196* 0.299** 0.374** 1 6 PR 3.26 1.39 0.479** 0.394** 0.509** 0.419** 0.202* 1 7 ISH 4.18 1.43 0.461** 0.275** 0.422** 0.273** 0.165* 0.470** 1 8 ISE 1.90 1.36 0.134 0.233 0.343 0.072 0.056 0.279 0.098 1 *p<0.05, **p<0.01 (two-tailed) 5.2 Model Results 5.2.1 Overall score

Table 5 and table 6 summarize the descriptive statistics (means and SDs) of the proposed variables used in this research. Each variable is measured on a scale from 1 to 7. In other words, the higher the scores are, the higher the degree of barrier effects and intention among respondents is. Table 5 showed that there are some differences among the effects of different barriers in respondents’ perceptions. Table 6 indicated that respondents showed obviously higher intention of buying low-involvement products through mobile shopping channels than high-involvement products, which suggests that the level of product involvement may influence consumers’ intention of

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adopting mobile shopping channels. This result was further examined in the following analysis.

Table 5: Descriptive statistics of Barriers

a _{The value of each barrier is ranged from 1 to 7.}

b _{The higher the value is, on behalf the higher barriers that faced by the consumer are.} Table 6: Descriptive statistics of Intentions

Intention Overall intentiona,b

High-involvement and utilitariana,b

Low-involvement and utilitariana,b

High-involvement and hedonica,b

Low-involvement and hedonica,b

Mean (SD) 4.91(1.37) 2.90(1.89) 4.89(2.14) 2.10(1.58) 5.23(1.87)

a _{The value of each intention is ranged from 1 to 7.}

b_{The higher the value is, on behalf the stronger intention that consumers have.} 5.2.2 Barriers to the adoption of mobile shopping channels

This research first used multiple regressions to test the hypotheses. For hypotheses 1 to 8, the independent variables were eight barriers and the dependent variable was the consumers’ overall intention of adopting mobile shopping channels. Gender, age and education level were control variables. Table 7 lists the results of significance testing of the variables used in this research.

This regression model yielded a significant p-value (p<0.001) and Adj R2 explained approximately 30% of the variance in understanding consumers’ overall intention to using mobile shopping. As a control variable, age had a significant negative effect on consumer’s overall intention to mobile shopping (p<0.05). The regression results suggested that psychosocial risk (p<0.05) and insufficient online shopping experience (p<0.05) both yielded negative coefficients with a significant

Barrier Product performance riska,b Payment security riska,b Convenience loss riska,b

Delivery costsa,b Search efforta,b Psychosocial riska.b Insufficient support of human-interactionsa,b Insufficient online shopping experiencea,b Mean SD 4.13 1.37 4.11 1.50 3.21 1.36 4.01 1.43 4.19 1.57 3.26 1.39 4.18 1.43 1.90 1.36

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p-value. Based on the results, hypotheses 6 and 8 were supported; hypotheses 1, 2, 3, 4, 5 and 7 were rejected.

Table 7: Overall intention regression results

Standard error Coefficient P value

Gender 0.197 -0.040 0.584

Age 0.233 -0.236 0.006

Education 0.192 -0.084 0.310

Product performance risk 0.086 0.037 0.671 Payment security risk 0.077 -0.110 0.194 Convenience loss risk 0.090 -0.079 0.379

Delivery costs 0.083 0.111 0.201

Search effort 0.067 0.133 0.088

Psychosocial risk 0.092 -0.228 0.016

Insufficient support of human-interactions 0.083 -0.105 0.230 Insufficient online shopping experience 0.080 -0.283 0.001 5.2.3 Effect of product categories on mobile adoption intention

To investigate the influence of product categories on consumers’ intention of adopting mobile shopping channels, a two-way ANOVA analysis was done. This analysis is suitable to study the effect of two independent categorical variables and the interaction between independent variables. In this study, the two independent variables were category A (high-involvement products = 1, low-involvement products= 0) and category B (hedonic products= 1, utilitarian products= 0). The dependent variable was consumers’ intention, which is ranked from 1 to 7. Table 8 shows the two-way ANOVA test result. The product category A (high or low involvement) had a significant effect on consumers’ mobile shopping intention (p<0.05). In other words, there were significant differences between consumers’ intention to high-involvement products and low-involvement products. There was a non-significant effect of category B (hedonic or utilitarian) on consumers’ mobile

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shopping intention (p=0.08). However, there was a significant interaction effect between category A and B on consumers’ mobile shopping intention (p<0.05). Combined with the overall scores of consumers’ intention of each product category, according to this analysis, hypothesis 9 was supported and hypothesis 10 was rejected.

Table 8: Two-way ANOVA test results

Sum of Squares df Mean Square F Sig. Category A 906.623 1 906.623 244.931 0.000 Category B 11.132 1 11.132 3.008 0.083 Category A*B 51.285 1 51.285 13.855 0.000 Error 2220.927 600 3.702

Total 3189.967 604

5.2.4 Effect of product categories on mobile adoption barriers

To explore the differences among barriers across the four product categories, another four regression analyses were done. The independent variables were eight barriers and the dependent variables were consumers’ intention to buying high-involvement and utilitarian products through mobile shopping channels, intention to buying low-involvement and utilitarian products through mobile shopping channels, intention to buying high-involvement and hedonic products through mobile shopping channels and intention to buying low-involvement and hedonic products through mobile shopping channels. Gender, age and education level were control variables. Four multiple regressions were done separately and table 9 showed the results of the four regression analyses. Some different outcomes were found and discussed below.

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For high-involvement and utilitarian products, psychosocial risk and insufficient support of human-interactions have a significant negative effect on consumers’ shopping intention (p<0.05). For both of the low-involvement product categories, only insufficient online shopping experience had a negative effect on consumers’ shopping intention. Interestingly, for high-involvement and hedonic products, insufficient online shopping experience yielded a positive coefficient (p<0.05), which indicated that insufficient online shopping experience had a positive influence on consumers’ buying intention. These results were different compared to the overall intention result, which proved that barriers to consumers’ intention of adopting mobile shopping channels differ across different product categories.

Table 9: Product category regression results High-involvement and utilitarian Low-involvement and utilitarian High-involvement and hedonic Low-involvement and hedonic

Coefficient P value Coefficient P value Coefficient P value Coefficient P value Product performance risk 0.115 0.242 0.017 0.863 -0.086 0.402 0.012 0.900 Payment security risk -0.177 0.064 0.086 0.381 -0.114 0.253 -0.092 0.322 Convenience loss risk 0.168 0.097 -0.021 0.842 0.152 0.154 -0.023 0.819 Delivery costs 0.076 0.439 -0.146 0.150 0.018 0.861 0.113 0.239 Search effort 0.054 0.538 0.058 0.523 0.053 0.564 0.150 0.081 Psychosocial risk -0.224 0.035 0.181 0.097 -0.011 0.922 -0.070 0.497 Insufficient support of human-interactions -0.272 0.006 -0.124 0.223 -0.116 0.265 0.040 0.676 Insufficient online shopping experience 0.025 0.782 -0.231 0.013 0.197 0.039 -0.308 0.001 Gender -0.198 0.016 0.105 0.212 0.001 0.992 0.038 0.628 Age 0.000 0.999 -0.050 0.617 0.000 0.998 -0.178 0.058 Education 0.075 0.421 0.094 0.326 0.009 0.924 -0.027 0.761

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5.3 Summary of results

In summary, the regression analysis results proved that psychosocial risk and insufficient online shopping experience prohibit consumers’ overall intention to adopt mobile shopping channels. The two-way ANOVA test showed that consumers’ intention of adopting mobile shopping channels is lower for high-involvement product categories and higher for low-involvement product categories. No significant differences were found between consumers’ intention of buying utilitarian and hedonic products. Additionally, through data study it was found that the effect of barriers also differed across different product categories. For high-involvement and utilitarian products, psychosocial risk and insufficient support of human-interactions were significant. For both of the low-involvement product categories, the insufficient online shopping experience barrier was defined. For high-involvement and hedonic products, insufficient online shopping experience was found positively related to consumers’ adoption intention. Table 10 concluded the test results of proposed hypotheses. Findings of data analysis will be carefully discussed in next chapter. Table 10: Results of hypotheses testing

Hypotheses Test result

H1: Product performance risk has a negative influence on consumers’ intention of adopting mobile shopping channels.

Rejected H2: Payment security risk has a negative influence on consumers’

intention of adopting mobile shopping channels.

Rejected H3: Convenience loss risk has a negative influence on consumers’

intention of adopting mobile shopping channels.

Rejected H4: Delivery costs have a negative influence on consumers’ intention of

adopting mobile shopping channels.

Rejected H5: Search effort has a negative influence on consumers’ intention of

adopting mobile shopping channels.

Rejected H6: Psychosocial risk has a negative influence on consumers’ intention

of adopting mobile shopping channels.

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H7: Insufficient support of human-interactions has a negative influence on consumers’ intention of adopting mobile shopping channels.

Rejected H8: Insufficient online shopping experience has a negative influence on

consumers’ intention of adopting mobile shopping channels.

Supported H9: Consumers’ intention of adopting mobile shopping channels is

lower for the products with higher level of involvement.

Supported H10: Consumers’ intention of adopting mobile shopping channels is

higher for hedonic products (and lower for utilitarian products).

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6. Discussion

6.1 Discussion of the results

In a multichannel environment, many studies have investigated the factors that influence consumer channel preference (Frambach, Roest and Krishnan, 2007), channel-switching intentions (Gupta, Su and Walter, 2004) and determinants of online channel use (Montoya-Weiss, Voss and Grewal, 2003). Among these studies, positive factors and negative factors of channel selection both existed. In line with previous studies, this study identified several barriers (negative factors) to the adoption of mobile shopping channels. Furthermore, this study found that consumers’ intention to adopt mobile shopping channels and the effect of adoption barriers vary across different product categories.

6.1.1 Barriers to consumer adoption of mobile shopping channels

Eight barriers were proposed and two of them were found to be significant: psychosocial risk and insufficient online shopping experience. This study therefore suggested that the consumers who perceive higher psychosocial risk are less likely to adopt mobile shopping channels. Consumers perceive different levels of risk when shopping through a certain channel. Prior literature suggested that the amount of perceived risk is a major factor in determining a consumer’s decision on whether to shop via a certain channel (Gupta, Su and Walter, 2004). Wu and Wang (2005) showed that perceived risk has a significant direct impact on behavioral intention to use mobile commerce. Ram and Sheth’s (1989) framework of consumer’s resistance