Drivers of consumers’ adoption of M-shopping
applications in food and non-food grocery shopping
Master thesis
Author: Keyu Shi
Student number: 11088427
University of Amsterdam
Faculty of Economics and Business
MSc. Business Administration- Marketing Track
Supervisor: Dr. Umut Konus
Date of submission: June 24, 2016
Statement of Originality
This document is written by Student Keyu Shi 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.
Content
Abstract ... 3
1. Introduction ... 4
2. Literature Review ... 8
2.1 Mobile shopping ... 8
2.2 Mobile shopping applications ... 10
2.3 Mobile grocery shopping ... 11
2.4 Drivers of general mobile shopping ... 13
2.5 Potential drivers of mobile grocery shopping ... 15
2.5.1 Demographics ... 15
2.5.2 Psychological and lifestyle factors ... 16
2.5.3 Perceived benefits and risks ... 18
3. Research gaps and research questions ... 21
4. Conceptual framework and hypothesis ... 22
4.1 Conceptual framework ... 22 4.2 Hypotheses ... 23 5. Methodology ... 28 5.1 Research sample ... 28 5.2 Research design ... 28 5.3 Variable measurement ... 29 6. Data analysis ... 32 6.1 Data collection ... 32 6.2 Descriptive statistics ... 32 6.3 Reliability analysis ... 33 6.4 Hypotheses testing ... 34
6.5 Summary of results ... 47
7. Discussion and conclusion ... 50
7.1 Findings ... 50
7.2 Managerial implications ... 52
7.3 Limitations and further research ... 53
Reference ... 55
Appendix A ... 60
Abstract
Mobile commerce has big impact on consumers’ shopping behavior. However, for grocery shopping, mobile channel still has low transaction share when compared with other categories. Besides, there is a research gap on motivations of usage of mobile shopping in grocery products. Therefore, this study will investigate the main factors that influence consumers’ adoption of mobile shopping in food and non-food grocery and whether and how these factors influence the usage incidence, purchase frequency, order size and intention to use the mobile shopping for food grocery and non-food grocery separately. This study conducts online survey to test potential nine drivers of mobile grocery usage from three dimensions: demographics, psychological and lifestyle factors, and perceived benefits and risks. The results indicate that different drivers have different impacts on mobile food and non-food groceries and different drivers have influences on different aspects of the usage. The findings of this study will be useful for managers to improve their mobile grocery shopping systems by accordingly focusing on different aspects of the usage.
1. Introduction
Mobile shopping has become a popular purchasing channel in recent years. According to a report conducted by Cretio (2015), mobile transaction takes 29% of e-commerce transaction in US and the percentage is 34% in global. Additionally, the increase of mobile shoppers is also dramatically from 210 millions in 2012 to 686 millions in 2015 (Statistics, 2016). Mobile shopping has become an inevitable trend for consumer’s purchasing behavior.
However, when comes to grocery shopping, one of the most frequent retail shopping behavior, things become different. According to eMarketer (2014), grocery is the category that consumers prefer to purchase in-store, around 40% consumers prefer to buy groceries in store and only 31% consumers would likely to purchase grocery in digital ways. This number is quite different in consumer electronics and computers industry, only 17% consumers prefer buy electronics and computers in stores and more than 74% consumers prefer digital purchase on electronics (eMarketer, 2014). A report from EY (2014) indicates that grocery products is the category that most people prefer to purchase in stores, and around 74% consumers prefer purchase food and beverage through offline channel. Although the increasing mobile usage has huge impact on consumers’ shopping behavior in general industries, mobile shopping has small effect on grocery shopping. In 2013, the impact of digital influence factor influence highest in electronics (58%), the following is home furnishings (56%), but for grocery, the influence factor is only 29% (Deloitte, 2014). More specifically, the mobile influence factor for grocery stores is only 16% when compared with electronics which is 31%, sporting goods which is 27%, and clothing/footwear which is 24%
The phenomenon of low usage of mobile grocery shopping is due to the specific characteristics of the groceries. Grocery shopping is ingrained and consumer behavior change is slow (Peck Fellowship, 2013). According to the report of Deloitte (2014), although the influence of mobile has increased in grocery stores, compared with other stores (like electronic stores, furniture stores, clothing stores) the mobile influence is significant lower in grocery shopping (Deloitte, 2014). The possible concerns of failure of non-store grocery shopping are sensory stimulation (Hansen, 2005), spoilage (Neilson, 2015), privacy issues (Kumar & Mukherjee, 2013), and delivery costs (UPS, 2014).
In addition, grocery shopping is one of the most frequent retail shopping habits for consumers and there is big potential for mobile grocery shopping. According to a research conducted by Ninth Decimal (2014), 65% consumers who used online grocery shopping or delivery service make purchase through mobile devices. The impact of mobile influence factor on grocery stores has increased from only 6% in 2012 to 16% in 2013, which shows the dramatic increase of mobile influence factor on grocery stores (Deloitte, 2014). Therefore, it is interesting to focus on the motivations of why consumers use or not willing to use mobile shopping applications in grocery shopping.
It is important for marketers to understand the key factors that drive consumers to use mobile grocery shopping. First of all, many retailers have failed to launch digital platforms (Deloitte, 2014) and it is necessary to study why consumers refuse to use the new channel for grocery purchase. Grocery goods has its own characteristics that different from other goods, thus general mobile shopping research may not suitable for this specific domain. For example, sensory imitation is important for consumers because they prefer to touch, smell to check out
whether the grocery goods are fresh or not, which is different from other dry goods. In addition, understanding the main motivations of consumer’s mobile grocery shopping can help retailers improve the mobile grocery shopping experience. If marketers know why adoption of mobile grocery shopping is less and what are specific drivers, they can focus on these motivations to steer customers to mobile channel. Furthermore, marketers should also notice the differences of food and non-food mobile grocery shopping. Different drivers have different impact on food and non-food groceries and each driver has different influence on different aspects of mobile shopping usage. Thus, managers can learn from the results to set diverse strategies on food grocery and non-food grocery to achieve the desire expectation on improving the specific usage of mobile grocery shopping.
Most prior studies of drivers of consumers’ mobile shopping focus on general industries without specific perspective on different kinds of goods. As for grocery shopping, one of the most frequent shopping habits in retailing, the studies is lacked and no study has researched drivers of mobile grocery shopping. Therefore, this study will fill this research gap by focusing on key drivers why consumers do grocery shopping through mobile shopping applications and whether and how these drivers impact consumers’ mobile grocery shopping on frequency, order size and intention to use. Based on previous literature, this study will select relevant drivers and divided them into demographics, lifestyle and psychological factors, and perceived benefits and risks three main aspects and establish hypothesis to examine the relationships with the usage of mobile grocery shopping.
potential drivers of mobile grocery shopping in the second section. In the third section, research gap and research questions will be illustrated. The conceptual framework and hypotheses of this study will be specifically illustrated in the fourth section. Subsequently, the fifth section will explain the research methodology. The data analysis and results will be presented in the sixth section. Finally, in the seventh section, discussion and conclusions will be illustrated and ended with the managerial implications and limitations of this study.
2. Literature Review
2.1 Mobile shopping
According to MMA, mobile commerce refers to one way or two-way value exchange via mobile device (Kumar & Mukherjee, 2013). In retailing market, mobile shopping is a new purchasing channel that is a form of e-commerce services conducted over mobile or wireless networks (Kourouthanasis & Giaglis, 2012; Kumar & Mukherjee, 2013).
As the development of the advanced mobile technology, mobile device has become an important part of people’s daily life that enables to influence consumers’ purchasing behavior. Mobile shopping has become an inevitable trend world widely. According to a report from Statista (2016), the global number of m-commerce buyers increased dramatically from 210 millions in 2012 to 686 millions in 2015 and there will be more than 1 billion people purchase retail goods by 2018 (See figure 2.1). Furthermore, the average spending per buyer via mobile shopping also increased significantly. In 2012, m-commerce spending per global mobile buyer is $290, and in 2015 it increased to $435, until 2018, in average people will spend $575 on m-commerce (see figure 2.2). There is a big potential for mobile commerce and it is important to have a clear and specific research on mobile shopping.
Figure 2.1:Global number of mobile retail commerce buyers from 2012 to 2018 Source: Statista, 2016.
Figure 2.2: Worldwide mobile commerce spending per buyer from 2012 to 2018 Source: Statista, 2016.
The key distinctiveness of mobile shopping is that it breakouts the spatial and temporal barriers (Zhou, 2013). Consumers can use mobile devices to browse or search products or
services across multiple retailers at anytime and anywhere (Yang & Kim, 2012). Additionally, mobile technology also enhances customer shopping experiences and engagement. Kumar & Mukherjee (2013) point out that although there is wide usage of consumers using mobile devices to enhance shopping experiences like searching information, comparing prices and getting coupons, the actual purchase via mobile devices is limited. There is gap between the consumers’ usage of mobile devices for shopping related experiences and actually purchase via mobile devices. Therefore, it is necessary for retailers to have a well understanding of the key motivations that drive consumer to purchase via mobile devices.
2.2 Mobile shopping applications
A mobile app is a computer program designed to run on mobile devices such as smartphones and tablet computers (Mobile Marketing Association, 2010). Gupta (2013) divides mobile applications into five categories: games and entertainment, social networks, utilities, discovery and brands. According to Peck Fellowship (2013), grocery shopping apps can be divided into single function (e.g. constructing shopping list, coupons linkages, or providing nutrition information) and multi-function (sync and share with multiple devices and users). Wang et al. (2015) indicate that providing products via mobile applications is an approach that retailers can increase storefront accessibility and sales. In addition, according to Kumar & Mukherjee (2013), although there are external and internal obstacles of mobile shopping adoption in retail industry, mobile devices have strong potential to become popular retail shopping channel.
2.3 Mobile grocery shopping
Compared with general mobile shopping, mobile grocery shopping has its unique characteristics due to the specific features of grocery shopping. Grocery shopping is one of most frequent and ingrained retail shopping habits for consumers (Peck Fellowship, 2014). The pattern of review, price comparison, coupon, and familiarity with products, brands, retailers have impacts on grocery consumption (Hartman, 2012). As the development of smartphone technology and commercial combined with social media networks, there is a trend for consumers using mobile apps in food shopping (Peck Fellowship, 2013). Many retailers consider store traffic, conversion, order size and loyalty as key performance of digital investments (Deloitte, 2014).
However, the proportion of mobile shopping in grocery goods still very small when compared with other industries like electronics, fashion, and books (Peck Fellowship, 2013). Fashion and luxury retailers have the highest share of mobile transactions and in home category has the low mobile transaction share (Cretio, 2015). The low usage of mobile grocery shopping maybe refers a little to the low e-commerce market share for grocery. The grocery has lowest market e-commerce market share (less than 5%) when compared with entertainment (around 35%), clothing and footwear (around 15%) and electronics (around 15%) (Oliver Wyman, 2015). Besides, McKinsey & Company (2013) points out the potential motivations for low adoption of online groceries are quality, assortment and price. According to Deloitte (2014), there are many retailers failed to launch digital platforms to fulfill consumers’ expectations. The possible concerns of failure of non-store grocery shopping are
sensory stimulation (Hansen, 2005), spoilage (Neilson, 2015), privacy issues (Kumar & Mukherjee, 2013), and delivery costs (UPS, 2014).
According to the report from Cretio (2015), lack of impulse purchase in grocery shopping and the mobile conversion rate is driven by quality of mobile site experience. Furthermore, price, quality, warranty and user ratings are the most important factors that influence food and beverages selection process according to a report from EY (2014). Mohammad et al. (2013) point out the difficulties of non-store grocery shopping are the large amount of frequent customers, many items in shopping basket and critical requirement of delivery. Besides, it is hard for consumers to see, touch, and smell the fresh goods to ensure they are at good quality through online or mobile channel (Mortmier et al., 2016).
Thus, a clear understanding of the motivations of mobile grocery shopping for consumers especially understanding the consumers’ preference for grocery app enable better mobile grocery shopping development and promotion (Peck Fellowship, 2013).
The prior studies have researched the determinants of consumers’ usage of online purchase in grocery shopping context, but there is no study of drivers of mobile grocery shopping. Similarly to mobile shopping, some studies dived motivations of online grocery shopping into hedonic and functional facts (Yim et al., 2014; Jayasankaraprasad & Kathyayani, 2014). However, there are some factors that are more specific toward the grocery shopping. Verhoef & Langerak (2001) believe physical effort, time pressure and shopping enjoyment are determinants of for consumer’s usage of e-grocery shopping. Life style and attitude toward online grocery shopping are also the key factors that motivate
2015). Furthermore, the time pressure (Vermeir & Kenhove, 2005;Verhoef & Langerak, 2001), price conscious (EY, 2014; Nilsson et al., 2015; Mortmier et al., 2016) and impulsive purchase (Cretio, 2015; Yim et al., 2014; Arora, 2015;Ramus & Nielsen, 2005) are most distinctive factors compared with drivers of general mobile shopping.
2.4 Drivers of general mobile shopping
At present, most researches about motivations of mobile shopping are focus on general study without specific insights on different kinds of products. Yang & Kim (2012) classify mobile shopping motivations as utilitarian motivation (efficiency and achievement shopping), hedonic motivation (adventure, social, gratification, idea, role and value shopping), and mobile shopping experience. Similarly, Jayasankaraprasad & Kathyayani (2014) point out shopping motives of grocery shopping contain utilitarian factors (such as price, convenience), hedonic factors (shopping enjoyment, recreational, arousal), and social and local shopping factors. Zhou (2013) considers both extrinsic and intrinsic factors affecting mobile purchase and examines that trust, flow (perceived enjoyment, concentration, and control) and perceived usefulness are determinants of mobile purchase intention. Unlike most researches that mainly focus on user characteristics to predict mobile shopping, Kumar & Mukherjee (2013) combine TAM and TRI to analysis the interdependencies among consumer personality (optimism, innovativeness, insecurity, discomfort), perception (usefulness, ease of use, enjoyment, security and trust), and attitude toward mobile shopping.
In recent research, Mortimer et al. (2016) add consumer online purchasing frequency into consideration and examine that the perceived risk is the mediator between the trust and
online grocery purchase intention. In addition, consumers usually buy habitual products that they have purchasing experiences through mobile and mobile shopping can influence consumers’ order size and order rate especially for light buyers (Wang et al., 2015).
The table 2.1 below displays the summary of general drivers of mobile shopping of prior studies.
Table 2.1: General drivers of mobile shopping
Drivers Source Description
Demographics Serenko et al. (2006) Kushwaha & Shankar (2013)
Bigne et al. (2007)
The demographic factors of a customer may significantly influence his or her choice of channel.
Utilitarian Yang & Kim (2012) Prasad and Aryasri (2011)
Teo (2001)
Utilitarian shopping motivation reflects the convenience, time saving, efficiency and achievement.
Hedonic Yang & Kim (2012) Cardoso & Pinto (2010) Jayasankaraprasad & Kathyyayani (2014)
In hedonic motivation, different types of emotional feelings, which are both physiological and
psychological, play major roles.
Trust Zhou (2013)
Toufaily et al. (2013) Mortimer (2016)
Online trust refers to the conviction that allows consumers be exposed to retailers willingly after considering characteristics of retailers.
Usefulness Zhou (2013)
Kumar & Mukherjee (2013)
Perceived usefulness has been found to be a variable predicting initial adoption that positively affects purchase intention.
Innovativeness
Hurt et al. (1977)
Kumar & Mukherjee (2013)
Lu (2014)
Innovativeness is the degree that a consumer prefers change, tries new and different products, and seeks new experiences
Compatibility Lu & Su (2009) Hand et al. (2009)
Compatibility is the degree of congruence between an innovation and adopter’s values, past experiences, and needs.
Attitudes Sheppard et al. (1988) Bigne et al. (2007)
Attitude towards activity refers to an interest in a certain behavior or occasion.
Easy to use Kumar & Mukherjee (2013)
Aldaz et al. (2009) Lu (2014)
Ease to use refers to the degree that people believe that using a system would be free of effort
Enjoyment Kumar & Mukherjee (2013)
Zhou (2013)
Shopping enjoyment is defined as consumer’s personality trait that finds shopping trips to be associated with great pleasure and enjoyable qualities. Security Kumar & Mukherjee
(2013)
Mohanty et al. (2007) Chong & Chan (2012)
Security refers to issues that are dealing with the payment of transactions, online credit card security, and alternative payment options
2.5 Potential drivers of mobile grocery shopping
In order to study the drivers of mobile shopping in specific grocery purchasing, this study bases on the previous researches on motivations of general mobile shopping and also combines the features of grocery shopping into consideration. This study will analyze the potential mobile grocery shopping drivers from three main aspects: demographics, psychological and lifestyle factors, and perceived benefits and risks.
2.5.1 Demographics
Age. Some researches find out that young people are more likely to accept the mobile
channel than middle-aged people (Burkolter & Kluge, 2011; Cowart & Goldsmith, 2007). In contrast, according to a report conducted by Columbia Business School, M-shoppers under
30 years old occupied 26% of total M-shoppers, people between 30-49 occupied 48%, the perception of people between 50-64 years old is 20%, and people above 65 years old only occupied 6% (Aimia, 2013). Additionally, Tjan (2014) shows that middle-aged consumers are more likely to use mobile channel than young consumers.
Gender. Gender usually as a key factor on consumer segmentation and has impact on
options of purchasing channels. Aimia (2013) figures out that there is no big difference between the overall male and female mobile shopper, but more female younger M-shoppers less than 30 years old and more male M-shoppers above 30 years old. A report from eMarketer (2014) points out that women are more often using mobile grocery shopping than men.
2.5.2 Psychological and lifestyle factors
Innovativeness. Innovativeness is the degree that a consumer prefers change, tries new
and different products, and seeks new experiences (Hurt et al., 1977, in Hand et al., 2009). Kumar & Mukherjee (2013) based on TRI define innovativeness as the tendency to use a new technology firstly. Hand et al. (2009) classify online grocery shopping as a discontinuous innovation because it requires big change in consumption behavior. Different from conventional in-store shopping which consumers can have rich sensory attributes, consumers select items through a list from web page (Nielson, 2015; Hand et al. 2009). Lu (2014) indicates that mobile shopping is persistent innovation and upgrade of existing mobile devices and applications, which has positive influence on continuance intention toward mobile shopping.
Time conscious. Time pressure refers to the degree that consumers feel busy (Srinivasan
&Ratchford, 1991, in Verhoef & Langerak, 2001). Kleijnen et al. (2007) define time consciousness as individual preference to regard time as a scarce resource, use it carefully, and interact with determinants of mobile value creation. Many studies regard time conscious as a focal factor to segment grocery shoppers including convenience shoppers, low-price shoppers, recreational shoppers, social shoppers (Jayasankaraprasad & Kathyayani, 2014). In shopping environment, consumers need to consider the costs of time and energy spending in information searching (Vermeir & Kenhove, 2005). Some studies already proposed that time saving is a motivation that consumer use online grocery shopping (Yang & Kim, 2012). Kleijnen et al. (2007) show that perceived time consciousness is related to the perceived value of mobile channel.
Shopping enjoyment. Shopping enjoyment refers to an emotional utility derived from
purchasing across retail alternatives (Jayasankaraprasad & Kathyayani, 2014). According to Zhou (2013), perceived enjoyment reflects a user’s pleasure and enjoyment. Verhoef & Langerak (2001) believe shopping enjoyment is a disadvantage of electronic grocery shopping because it lack of sensory stimulation, physical activity and learning, social communication, and pleasure of bargaining. On the contrary, other researches indicate that perceived enjoyment would lead positive attitude toward mobile shopping (Kumar & Mukherjee, 2013; Zhou, 2013).
Impulsiveness. According to Verhagen & Dolen (2011), impulsive buying happens when
people experience an urge to buy a product without conscious consideration the reason why one needs the products. There are limited studies on online impulse shopping. Verhagen &
Dolen (2011) show how beliefs of convenience and representational delight influence the online impulse shopping. Park et al. (2011) improve how web browsing and sensory effects e-impulse buying. Further study shows that information quality and usage of interaction features have effect on online impulse purchase (Lin &Chuan, 2013).
2.5.3 Perceived benefits and risks
Price conscious. Price has a significant influence on consumers’ purchase decision,
according to Vermier & Kenhoeve (2005), price and promotional information can provide easy to use heuristics for consumers to help them make quick and confident options. A survey conducted by EY (2014) shows that price is one of the most important factors impact consumer’s selection processes in food and beverage. In addition, Yang & Kim (2012) indicate that comparison of prices in mobile sites can increase consumers’ shopping efficiency by offering prices information across multiple retailers. Besides, Jayasankaraprasad & Kathyayani (2014) prove that price conscious is the main factor impacting consumers’ cross-format purchasing behavior.
Convenience. Rohm & Swaminathan (2004) identify convenience as a distinct motive
for store choice in the offline setting. Convenience in online grocery shopping is the forefront of consumers’ minds when mention the new method to purchase daily groceries (Ramus & Neilsen, 2005). Perceived convenience emerged as a potentially decisive factor in determining consumers’ perceived relative advantage and compatibility of electronic grocery shopping (Neilson, 2015). Jayasankaraprasad & Kathyayani (2014) point out that convenience is one of utilitarian motives that influence consumers’ store patronage behavior.
Security & privacy. In the context of online shopping, security refers to issues that are
dealing with the payment of transactions, online credit card security, and alternative payment options (Mohanty et al., 2007; Miyazaki and Fernandez, 2000). Many consumers may still avoid Internet shopping due to anxiety about how to return or exchange unsatisfying products (Hand et al., 2009). In mobile commerce, the security and privacy risks are high for consumers due to the wireless data transaction environment (Nilashi et al., 2015). Privacy is the right to be along (Warren & Brandies, 1890, in Peltier et al, 2009), right to determine when, how and to what extent information about ourselves is communicated to others (Westin, 1967, in Peltier et al., 2009). Information privacy is control over information disclosure and unwanted intrusions into consumer’s environment (Goodwin, 1991, in Peltier, 2009). In mobile shopping there are some privacy and security issues like poor Internet connection, fear of compromising personal information, and losing of mobile devices (Kumar & Mukherjee, 2013). Besides, perceived risk of receiving perishable food through online is one barrier for online grocery shopping (Mortimer et al., 2016).
Table 2.2 Summary of potential drivers of mobile grocery shopping
Drivers Description Source
Demographics:
Age Consumers’ age has impact on option of shopping channels, middle aged people occupied most percentage of m-shopping.
Serenko et al. (2006), Nilsson et al. (2015), Aimia (2013), eMarketer (2014) Gender Gender has impact on consumers’ purchase channel
choice and there are more female m-shoppers.
Kushwaha & Shankar (2013), eMarketer (2014), Hartman Group (2014)
Psychological and lifestyle factors:
Innovativeness Mobile grocery shopping can be considered as an innovation that changes consumers’ normal grocery shopping habits.
Kumar & Mukherjee (2013), Lu (2014), Hand at el. (2009)
Time conscious Time conscious is one important factor to segment grocery shoppers.
Kim & Yang (2012), Jayasankaraprasad & Kathyayani (2014) Shopping
enjoyment
Shopping enjoyment refers to emotional utility, which
is important in grocery shopping. Jayasankaraprasad & Kathyayani (2014), Mortimer et al. (2016) Impulsiveness Impulsive buying happens when people experience an
urge to buy a product without conscious consideration.
Verhagen & Dole (2011)
Perceived benefits & risks:
Price conscious Price is important factor that influences consumers’ purchase decision.
Neilson (2015), Jayasankaraprasad & Kathyayani (2014) Convenience Mobile shopping allows consumers purchase
breakthrough the limitation of spatial and temporal.
Ramus & Nielson (2005), Nielson (2015) Security &
privacy
Mobile transaction security and personal information protection are perceived risks.
Mohanty et al. (2007), Kumar & Mukherjee (2013)
3. Research gaps and research questions
Previous researches have studied the main determinants that drive consumer to use mobile shopping in general and drivers of online grocery shopping. However, there still have research gaps for mobile grocery shopping. First of all, as the development of mobile technology, m-shopping has become an inevitable trend in retail markets but currently the studies still focus on motivations for general mobile shopping instead of focusing on specific products. In addition, most studies about motivations for general mobile shopping focus on whether and how these factors influence consumers’ purchase intention (Binge et al., 2007; Zhou, 2013; Kumar & Mukherjee, 2013; Lu, 2014; Lai & Lai, 2014) and few study research how these factors affect consumers’ order size and purchase frequency (Wang et al., 2015). Secondly, the prior studies about grocery shopping still on online level and lack of the researches on mobile grocery shopping. Furthermore, there is no research analyzing food and non-food grocery shopping separately and there might have different drivers that managers should understand and manage accordingly. Therefore, this study will fill the research gap of drivers on mobile food and non-food grocery shopping by formulating the following research questions:
1. What are the drivers that consumers do food and non-food grocery shopping through mobile shopping applications?
2. And whether and how these drivers impact mobile food and non-food grocery shopping on usage incidence, purchase frequency, order size, and intention to use?
4. Conceptual framework and hypothesis
4.1 Conceptual framework
The goal of this paper is to test the influences of consumers’ demographics, lifestyle and psychological factors, and perceived benefits & risks on consumers’ usage of mobile grocery shopping. Based on prior studies, this study indicates the following conceptual framework (see figure 4.1) to answer the research questions.
Figure 4.1: Conceptual framework
The framework is build on previous literature, this study select the main factors that from motivations for mobile shopping and combined the determinants for grocery shopping
factors, and perceived benefits and risks. In addition, in the framework there are nine hypotheses formulated to answer the research questions including age, gender, innovativeness, time conscious, shopping enjoyment, impulsiveness, price conscious, convenience, and security and privacy. Furthermore, for the usage of mobile grocery shopping, this study will not only focus on consumers’ behavior like usage incidence, purchase frequency and order size but also consider the perceived intention to use mobile grocery shopping in the future.
4.2 Hypotheses
Age
Nilsson et al. (2015) indicate that age has impact on grocery shopping frequency and order size. Age is also one of factors that impact consumer’s choice of shopping channels (Bigne et al., 2007). A report from Aimia (2013) shows that middle aged people occupied the most (48%) of M-shoppers and the young people only took 26%. This is also happened in grocery shopping, according to a report from eMarketer (2014), in US households with children are more likely to use App for grocery shopping. Therefore, this study assumes that:
H1: Middle aged consumers are more likely to use mobile grocery shopping than young consumers.
Gender
Based on Kushwaha & Shankar (2013)’s view, gender as one demographic factor may significantly influence consumers’ choice of purchase channel. For specific grocery shopping, female households are the main customers, according to a report conducted by Hartman
Group (2014), 57% female are primary food shoppers in US. Besides, eMarketer (2014) done a research in us showed that more female (41%) would likely to use app for grocery shopping than male (39%). Therefore, this study proposes the following hypothesis:
H2: Female consumers are more likely to do grocery shopping through mobile shopping applications than male consumers.
Innovativeness
Innovativeness is a personality reflecting the willingness of adoption about products or ideas that are new in their personal experiences (Aldaz et al., 2009). Kumar & Mukherjee (2013) find out that people who consider themselves to be innovative about the new technology are often the early adopters of the technology. Additionally, consumers' perception of characteristics of innovation can influence the rate of adoption (Hand et al., 2009). Mobile grocery shopping can be considered as an innovation because it requires big change of consumers’ traditional grocery shopping behavior such as select products without touch the goods (Neilson, 2015). Therefore, this study suggests the hypothesis below:
H3: Consumer innovativeness is positively associated with the usage of mobile grocery shopping.
Time conscious
In grocery shopping context, according to Vermeir & Kenhove (2005), perceived time pressure is important determinant of searching effort for price or promotion. Similarly, Verhoef & Langerak (2001) find that time pressure has positive impact on both perceived compatibility and perceived relative advantage in electronic grocery shopping. Moreover,
behavior in grocery products purchase (Jayasankaraprasad & Kathyayani, 2014). Therefore, this paper assumes that:
H4: Consumer time conscious is positively associated with the usage of mobile grocery shopping.
Shopping enjoyment
According to Jayasankaraprasad & Kathyayani (2014), shopping enjoyment is an emotional utility that substantially motivate consumers’ across retail shopping alternatives. Another study tests that the high degree of perceived enjoyment will lead to positive affective attitude towards mobile shopping (Kumar & Mukherjee, 2013). However, for the specific grocery shopping, Mortimer et al. (2016) point out that shopping enjoyment is very nature of general online shopping but not for the mundane, routine task online grocery shopping. Similarly, Verhoef & Langerak (2001) believe that shopping enjoyment is one disadvantage of online grocery shopping because it lacks social contact, sensory stimulation and bargaining pleasure. Therefore, consider the unique characteristics of grocery shopping, this study suggests the following hypothesis:
H5: Shopping enjoyment is negatively associated with the usage of mobile grocery shopping.
Impulsiveness
Easy access to products, easy to buy, lack of pressures and absence of delivery effort are triggers of impulsive online expenditure (Verhagen & Dolen, 2011). Yim et al (2014) point out that impulsiveness is one of the main psychological routes of grocery hedonic shopping and has positive effect on consumer purchase in store shopping. In addition, in grocery
shopping, impulse buying is important element for consumers, but this part is sadly missing in online grocery shopping because online grocery shopping seems to be more planned and organized instead of occasional activities (Ramus & Nielsen, 2005). Moreover, the mobile grocery shopping lacks of sensory stimulates which is the main driver of spontaneous purchase. Therefore, this study proposes the following hypothesis:
H6: Impulsiveness is negatively associated with the usage of mobile grocery shopping.
Price conscious
As mentioned before, price and promotions is becoming dominant in affecting consumers’ cross format shopping behavior (Jayasankaraprasad & Kathyayani, 2014). Price is the most important grocery store switching driver, according to Neilson (2015), 68% consumers in global say that prices drive them to switch grocery stores and the percentage in Europe (70%) is higher. According to Ninth Decimal (2014), 69% of consumers redeemed coupons through their mobile devices in 2014 and the highest mobile coupon redemption by CPG category is food and beverage (71%). Thus, this study provides the following hypothesis:
H7: Consumer price conscious is positively associated with the usage of mobile grocery shopping.
Convenience
Convenience motive provides opportunity for consumers to save time and effort on store choice and purchase decisions (Jayasankaraprasad & Kathyayani, 2014). Ramus & Nielsen (2005) indicate that convenience is one advantage for Internet grocery shopping when
purchase their desired products at anytime and at anywhere without spatial and temporal limitation（Nielson, 2015). Therefore, this study points out the hypothesis below:
H8: Convenience is positively associated with the usage of mobile grocery shopping.
Security & privacy
According to Kumar & Mukherjee (2013), mobile shoppers will concern the revealing of their personal or financial information through mobile devices and security and privacy protection play an important role in attitude towards mobile shopping. Some researches improve that perceived risk has negative impact on consumers’ repurchase intention on online grocery shopping (Mortimer et al., 2016; Hansen, 2005). Mobile grocery shopping has some similar security issues with online grocery shopping such as protection of personal information, transaction risks, and payment risks (Kumar & Mukherjee, 2013). Therefore this paper will provide the following hypothesis:
H9: Perceived security and privacy is negatively associated with the usage of mobile grocery shopping.
5. Methodology
This research will conduct a quantitative research because it will focus on the relationships between the different drivers and the usage of mobile food and non-food grocery shopping. This research will take online survey as a method to test the hypotheses because it is an ease and common way in this domain.
5.1 Research sample
The main target group of this study is consumers who buy grocery goods via mobile devices. Because the population has large range and the sampling frame is not certain, this research will conduct a non-probability convenience sampling (Fan & Yan, 2010). The research will take online questionnaire via e-mail and social media like Facebook to catch as many respondents as possible. To ensure that the data is adequate and analyzable, there will be at least 200 respondents participant the survey research.
5.2 Research design
In order to investigate the main drivers of mobile grocery shopping and how these factors influence the usage incidence, frequency and order size, and usage intention of mobile food and non-food grocery shopping, survey could be a reasonable method. Not only because online survey can consider variety issues, but also it can easily reach as many respondents as possible (Bryman & Bell, 2011).
online and mobile before. Because we think that people who shop via mobile would have higher probability to use mobile grocery shopping than those who never shopping via mobile, we have filter questions to select the target group. Then we want to know what categories that consumer purchase through mobile devices and the frequency and order size of their mobile shopping. In the second part we want to measure consumers’ psychological and lifestyle of shopping and investigate the perceived benefits and risks. Thus, this part considers seven potential motivations of why consumer use mobile shopping and it uses five point-Likert scales to measure the agreement level for each statement. In the last part of the survey, the respondents will answer some demographic questions like gender, age and nationality.
This survey will enable to analyze whether and how each potential motivation affects the incidence, frequency, order size and intention to use mobile food and non-food grocery shopping separately. This study will use SPSS to analyze the collected data to test the hypotheses including t-test to analysis the impacts of age and gender, Binary logistic regression to test the usage incidence due to the categorical measure, Poisson regression to test frequency and order size because of the count measurement, and use linear regression for usage intention science the scale measurement (Field, 2013).
5.3 Variable measurement
In this study some variables are measured by multiple items. The dependent variable is the usage of mobile grocery shopping, and we will test it in four aspects: usage incidence (yes/no), frequency (how often), order size (how much), and intention to use (how likely).
The independent variables are demographics, lifestyle and psychological factors, and perceived benefits & risks. For demographics, age and gender are selected. For lifestyle and psychological factors four drivers (innovativeness, time conscious, shopping enjoyment and impulsiveness) are selected and use 5 point Likert scale to measure each variable. To measure consumer innovativeness, an adapted version of 3 items of Lu (2014) will be used. Time conscious is measured by 3 items seleted from Jayasankaraprasad & Kathyayani (2014)’s scale. For shopping enjoyment, Verhoef & Langerak (2001)’ 3 items scale can be adopted. Verhagen & Dolen (2011)’s 5-item measures can be used to measure consumer impulsiveness (see the appendix A for whole questionnaire).
Moreover, for the perceived benefits & risks, price conscious, convenience and perceive security & privacy are potential drivers. To measure price conscious, we can base on Jayasankaraprasad & Kathyayani (2014)’s 4 items scale. Convenience can be measured by selected 3 items from Jiang, Yang and Jun (2012)’s measure. Lastly, an adopted version of Lallmahamood (2007)’s 4-item measure can be used to measure perceived security and privacy.
Table 5.1 Summary of variables measurement
Hypotheses Usage of mobile grocery shopping
Usage incidence
Frequency Order size Intention
to use
Food Non-food Food Non-food Food Non-food
H1 Age H2 Gender H3 Innovativeness H4 Time conscious H5 Shopping enjoyment H6 Impulsiveness H7 Price conscious H8 Convenience H9 Security & privacy
6. Data analysis
6.1 Data collection
In this study, an English online questionnaire is conducted through university webmail and social media (Facebook and Wechat). There are 223 respondents in total and 32 uncompleted responses are deleted. Because the target group of this survey is someone who purchased online and mobile before, thus the final valid responses are 124.
6.2 Descriptive statistics
During this survey, there is unequal distribution that female respondents (68.5%) are more than male respondents (31.5%). Most respondents (73.4%) age between 18 and 24. In terms of nationalities, most respondents come from Asia which accounts for 58,9% and European occupies 37.9%.
Table 6.1 Demographic profile of respondents (n=124)
Measure Items Frequency Percentage (%) Gender Male 39 31.5 Female 85 68.5 Age Under 18 1 0.8 18-24 91 73.4 25-34 31 25.0 35-44 1 0.8 Country Europe 47 37.9 Asia 73 58.9 America/Latin America 2 1.6
Respondents are also asked which categories they purchase through mobile channel and which kind of mobile device they use when purchasing groceries. The table 6.2 shows that clothing and footwear is the most frequent category that people usually purchase through mobile (25.1%), followed by tickets and books/music/video games which occupied 24.2% and 14.1% separately. Smart phone is the main mobile devices that people use when doing mobile food grocery shopping (69.6%) and mobile non-food grocery shopping (70.0%).
Table 6.2 Profile of respondents’ mobile channel usage
Measure Items Frequency Percentage (%) Mobile shopping categories Electronics 40 12.0
Books/music/video games 47 14.1 Grocery (food) 37 11.1 Grocery (non-food) 24 7.2 Clothing and footwear 84 25.1 Tickets 81 24.2 Others 21 6.3
Mobile devices for food grocery purchase (among people who do mobile food grocery shopping)
Smart phone 32 69.6
Tablet PC 14 30.4
Mobile devices for non-food grocery purchase (among people who do mobile non-food grocery shopping)
Smart phone 21 70.0
Tablet PC 9 30.0
6.3 Reliability analysis
According to Field (2013) reliability refers to measure whether an instrument yield consistency across different situations. In order to test the internal consistency of variables,
Cronbach’s alpha can be used to measure the reliability. From the table 6.3, innovativeness (α=.624), time conscious (α=.672) and convenience (α=.683) yield Cronbach’s alpha scores lower than.7 but they are still acceptable because all the variables’ Cronbach’s alpha above 0.6. Time conscious (α=.722), shopping enjoyment (α=.844), impulsiveness (α=.725), and security & privacy (α=.705) have higher Cronbach alpha coefficient. Therefore, all the variables are maintained.
Table 6.3 Reliability analysis
Variable Number of items Cronbach’s Alpha
Innovativeness 3 .624 Time conscious 3 .722 Shopping enjoyment 3 .844 Impulsiveness 3 .725 Time conscious 3 .672 Convenience 3 .683
Security & privacy 2 .705
6.4 Hypotheses testing
Hypotheses H1 and H2 address whether age and gender associated with usage of mobile grocery shopping. Independent samples t-test is used to measure whether there is significant difference of mobile grocery shopping usage between male and female, people age under 24 and above 24. The table 6.4a shows that there is no significance between two groups of age under 24 (M=3.72, SD=3.089) and age above 24 (M=2.08, SD=1.240) for the frequency of mobile food grocery shopping (t(122)=1.758, P=0.087). Age under 24 and above 24 have no
significant differences on order size (t(122)=1.213, p=0.233) and usage intention (t(122)=0.005, p=0.996) of mobile food grocery shopping. Similarly, table 6.4b shows there is no significant difference between age under 24 and age above 24 on frequency, order size, and usage intention of usage mobile non-food grocery shopping.
Table 6.4a Independent samples t-test: age on usage mobile food grocery shopping
Food grocery Under 24 Above 24
Item M SD M SD t Sig. (two
tailed)
Frequency 3.72 3.089 2.08 1.240 Equal variances assumed 1.758 .087 Equal variances not
assumed 2.292 .028 Order size 29.84 27.013 27.013 5.403 Equal variances assumed 1.213 .233
Equal variances not
assumed 1.680 .103 Usage
intention 3.53 1.346 3.53 1.436 Equal variances assumed .005 .996 Equal variances not
assumed .005 .996
Table 6.4b Independent samples t-test: age on usage mobile non-food grocery shopping
Non-food
grocery Under 24 Above 24
Item M SD M SD t Sig. (two
tailed)
Frequency 5.12 2.781 6.14 6.939 Equal variances assumed -.527 .603 Equal variances not
assumed
-.379 .717 Order size 42.00 58.744 36.71 37.624 Equal variances assumed .219 .829
Equal variances not
assumed .263 .796 Usage intention 3.53 1.346 3.53 1.436 Equal variances assumed .005 .996
Equal variances not assumed
Hypothesis H2 tests whether gender associate with the usage of mobile grocery shopping. We still use independent samples t-test to measure how male and female act different on frequency, order size and usage intention of mobile grocery shopping. Table 6.5a indicates that there is no significant difference between male and female on usage frequency (t(122)=-0.729, p= 0.471), order size (t(122)=1.255, p=0.218), and usage intention (t(122)=0.600, p=0.550) of mobile food grocery shopping. For mobile non-food grocery shopping (see table 6.5b), there is also no significant difference between male and female on usage frequency (t(122)=-0.489, p=0.629), order size (t(122)=1.255, p=0.218), and usage intention (t(122)=0.600, p=0.550).
Table 6.5a Independent samples t-test: gender on usage mobile food grocery shopping
Food grocery Male Female
Item M SD M SD t Sig. (two
tailed)
Frequency 2.81 1.834 3.48 3.265 Equal variances assumed -.729 .471 Equal variances not
assumed -.783 .439 Order size 32.06 32.700 22.62 9.866 Equal variances assumed 1.255 .218
Equal variances not
assumed 1.117 .279 Usage
intention 3.64 1.308 3.48 1.394 Equal variances assumed .600 .550 Equal variances not
Table 6.5b Independent samples t-test: gender on usage mobile non-food grocery shopping
Non-food grocery
Male Female
Item M SD M SD t Sig. (two
tailed)
Frequency 4.67 2.875 5.67 4.678 Equal variances assumed -.489 .629 Equal variances not
assumed -.621 .544 Order size 31.33 24.427 43.50 59.506 Equal variances assumed -.482 .635
Equal variances not
assumed -.707 .487 Usage intention 3.64 1.308 3.48 1.394 Equal variances assumed .600 .550
Equal variances not
assumed .614 .541
Regression. In this study, there are two types of grocery products (food and non-food)
and we use usage incidence, frequency, order size and usage intention to measure the dependent variable. Because we need to examine the relationships among multiple independent variables and each aspect of dependent variable, thus we use regression to test the hypotheses.
Usage incidence
This study will test whether the potential drivers has influence on usage incidence of mobile grocery shopping. Binary logistic regression is conducted because the usage incidence is categorical (yes/no) (Field, 2010). Table 6.6a shows the result of regression analysis on usage incidence of mobile food grocery shopping. The binary logistic regression is statistically significant (p<0.001) and explains 44.3% (Negelkerke R2=0.443) of the variance in usage incidence of mobile food grocery shopping. This model classified 79.8% of cases. Clothing and footwear (Exp(B)=5.599, p=0.008), books/music/video games (Exp(B)=4.521,
p=0.010), and electronics(Exp(B)=0.268, p=0.035) have significant contribution to the usage incidence of mobile food grocery shopping. Male usage incidence is 11.290 times of female usage incidence and the incidence of European is 4.9% that of non-European.
Table 6.6a Binary regression on mobile food grocery shopping usage incidence
B S.E. Wald df Sig. Exp(B)
Step 1a Age(1) -.847 .630 1.810 1 .179 .429 Gender(1) 2.424 .678 12.776 1 .000 11.290 Country(1) -3.014 .767 15.459 1 .000 .049 Innovativeness .353 .333 1.126 1 .289 1.424 Time conscious .490 .318 2.382 1 .123 1.633 Enjoyment .306 .419 .533 1 .465 1.358 Impulsiveness .368 .381 .935 1 .333 1.445 Price conscious -.007 .347 .000 1 .983 .993 Convenience .574 .407 1.985 1 .159 1.775 Security & privacy -.088 .324 .073 1 .787 .916 Electronics -1.316 .624 4.444 1 .035 .268 Books/music 1.509 .583 6.692 1 .010 4.521 Non-food grocery .343 .649 .280 1 .597 1.409 Clothing 1.723 .652 6.988 1 .008 5.599 Tickets .761 .587 1.683 1 .195 2.141 Others -.507 .744 .465 1 .496 .602 Constant -9.081 3.053 8.845 1 .003 .000 a. Variable(s) entered on step 1: Age, Gender, Country, Innovativeness, Time conscious, Enjoyment,
Impulsiveness, Price conscious, Convenience, Security, Electronics, Books/music /video games, Non-food grocery, Clothing and footwear, Tickets, Other categories.
For usage incidence of mobile non-food grocery shopping, the model explains 35.8% of variance in mobile non-food grocery shopping usage incidence and classifies 85.5% of cases correctly. Security & privacy (Exp(B)=2.871, p=0.008) and shopping enjoyment
(Exp(B)=2.996, p=0.20) positively associate with the usage incidence. However, increasing impulsiveness (Exp(B)=0.384, p=0.031) is associated with a reduction of usage incidence of mobile non-food grocery shopping (see table 6.6b).
Table 6.6b Binary regression on mobile non-food grocery shopping usage incidence
B S.E. Wald df Sig. Exp(B)
Step 1a Age(1) -1.199 .702 2.917 1 .088 .302 Gender(1) -.158 .705 .050 1 .823 .854 Country(1) -1.823 .856 4.535 1 .033 .162 Innovativeness .091 .393 .054 1 .817 1.095 Time conscious -.488 .348 1.968 1 .161 .614 Enjoyment 1.097 .473 5.392 1 .020 2.996 Impulsiveness -.958 .444 4.659 1 .031 .384 Price conscious -.082 .375 .048 1 .826 .921 Convenience -.199 .477 .174 1 .676 .819 Security & privacy 1.055 .395 7.138 1 .008 2.871 Electronics .125 .653 .037 1 .848 1.134 Books/music .394 .643 .374 1 .541 1.482 Food grocery .225 .644 .122 1 .727 1.252 Clothing .097 .663 .021 1 .884 1.102 Tickets .536 .656 .668 1 .414 1.710 Others .936 .792 1.397 1 .237 2.551 Constant -3.787 2.757 1.887 1 .170 .023 a. Variable(s) entered on step 1: Age, Gender, Country, Innovativeness, Time conscious, Enjoyment,
Impulsiveness, Price conscious, Convenience, Security, Electronics, Books/music /video games, Food grocery, Clothing and footwear, Tickets, Other categories.
Usage frequency & Order size
First of all, we will test the relationships among potential drivers and frequency of mobile grocery shopping. Poisson regression is used because the frequency (how many) is a count (Field, 2013).
The model indicates statistically significant (p=0.000). From the table 6.7a, the increasing time conscious (Exp(B)=0.652, p=0.013) and price conscious (Exp(B)=0.589, p=0.004) are statistically significant to the reduction of frequency of mobile food grocery usage.
Table 6.7b shows the result of poisson regression on mobile usage frequency of non-food grocery products. Time conscious (Exp(B)=0.554, p=0.001) and impulsiveness (Exp(B)=0.593, p=0.031) are statistically significant to the non-food grocery mobile usage frequency and the association is negative.
Table 6.7a Poisson regression on mobile food grocery usage frequency Parameter B Std. Error Hypothesis Test Exp(B) Wald Chi-Square df Sig. (Intercept) 3.617 1.4211 6.476 1 .011 37.207 Age above24 -.412 .3496 1.392 1 .238 .662 Age under24 0a . . . . 1 Non-Europe .899 .5124 3.080 1 .079 2.458 Europe 0a . . . . 1 Female -.258 .3599 .514 1 .473 .773 Male 0a . . . . 1 Not electronics .725 .3884 3.482 1 .062 2.064 Electronics 0a . . . . 1 Not books/music -.022 .3018 .005 1 .942 .978 Books/music 0a . . . . 1
Not non-food grocery -.058 .3580 .026 1 .872 .944
Non-food grocery 0a . . . . 1 Not clothing -.092 .3016 .094 1 .759 .912 Clothing 0a . . . . 1 Not tickets -.245 .2953 .688 1 .407 .783 Tickets 0a . . . . 1 Not others .305 .4160 .537 1 .464 1.356 Others 0a . . . . 1 Innovativeness -.106 .1781 .353 1 .552 .900 Time conscious -.428 .1727 6.140 1 .013 .652 Enjoyment -.119 .1898 .392 1 .531 .888 Impulsiveness -.224 .1483 2.272 1 .132 .800 Price conscious -.529 .1833 8.322 1 .004 .589 Convenience .216 .2288 .887 1 .346 1.241 Security & privacy .049 .2206 .049 1 .825 1.050
(Scale) 1b
Dependent Variable: food grocery order size
Model: (Intercept), Age, Gender, Country, Electronics, Books/music/video games, Non-food grocery, Clothing and footwear, Tickets, Other categories.
a. Set to zero because this parameter is redundant. b. Fixed at the displayed value.
Table 6.7b Poisson regression on mobile non-food grocery usage frequency Parameter B Std. Error Hypothesis Test Exp(B) Wald Chi-Square df Sig. (Intercept) 1.998 1.9639 1.035 1 .309 7.371 Age above24 -.058 .4398 .017 1 .895 .944 Age under24 0a . . . . 1 Non-Europe .774 .7118 1.183 1 .277 2.169 Europe 0a . . . . 1 Female .057 .6238 .008 1 .927 1.059 Male 0a . . . . 1 Not electronics -.215 .3280 .429 1 .513 .807 Electronics 0a . . . . 1 Not books/music -.426 .4004 1.132 1 .287 .653 Books/music 0a . . . . 1
Not food grocery .334 .4579 .531 1 .466 1.396
Food grocery 0a . . . . 1 Not clothing -.168 .4572 .135 1 .713 .845 Clothing 0a . . . . 1 Not tickets .914 .4468 4.186 1 .041 2.495 Tickets 0a . . . . 1 Not others -.367 .4805 .583 1 .445 .693 Others 0a . . . . 1 Innovativeness .538 .3319 2.628 1 .105 1.713 Time conscious -.591 .1707 11.987 1 .001 .554 Enjoyment .162 .2781 .339 1 .560 1.176 Impulsiveness -.523 .2432 4.628 1 .031 .593 Price conscious -.049 .2177 .050 1 .822 .952 Convenience .054 .2634 .042 1 .837 1.056 Security & privacy -.131 .2916 .202 1 .653 .877
(Scale) 1b
Dependent Variable: food grocery order size
Model: (Intercept), Age, Gender, Country, Electronics, Books/music/video games, Food grocery, Clothing and footwear, Tickets, Other categories.
a. Set to zero because this parameter is redundant. b. Fixed at the displayed value.
Secondly, this study will test how the potential drivers affect the order size of mobile grocery shopping. Poisson regression model is selected because the answer of order size is a count (Field, 2013). Table 6.8a shows that impulsiveness is negatively associated with food grocery order size (Exp(B)=0.770, p=0.000). Innovativeness is statistically significant to the order size of food grocery (Exp(B)=1.119, p=0.050). Besides, people come from Europe have lower order size than people come from non-Europe (Exp(B)=0.472, p=0.000). Moverover, mobile non-food grocery (Exp(B)=1.630, p=0.000) is significant to the order size of food grocery and electronics (Exp(B)=0.602, p=0.000) contribute to the reduction of mobile food grocery order size.
Table 6.8b illustrates the result of poisson regression analysis on mobile non-food grocery order size. People age under 24 are more significant to the order size of non-food grocery products than people age over 24 (Exp(B)=0.499, p=0.000). Female (Exp(B)=0.295, p=0.000) is less significant to order size when compared with male. Shopping enjoyment (Exp(B)=1.834, p=0.000), impulsiveness (Exp(B)=1.326, p=0.001), convenience (Wxp(B)=1.352, p=0.006), and security & privacy (Exp(B)=1.641, p=0.000) are statistically significant to the model. Innovativeness (Exp(B)=0.547, p=0.000) and time conscious (Exp(B)=0.675, p=0.000) are negatively associated with the order size.
Table 6.8a Poisson regression on mobile food grocery order size Parameter B Std. Error Hypothesis Test Exp(B) Wald Chi-Square df Sig. (Intercept) 3.887 .4336 80.343 1 .000 48.741 Age above24 -.198 .1051 3.542 1 .060 .820 Age under24 0a . . . . 1 Non-Europe -.751 .1462 26.360 1 .000 .472 Europe 0a . . . . 1 Female .269 .1364 3.892 1 .049 1.309 Male 0a . . . . 1 Not electronics -.508 .1246 16.593 1 .000 .602 Electronics 0a . . . . 1 Not books/music -.119 .1009 1.388 1 .239 .888 Books/music 0a . . . . 1
Not non-food grocery .489 .1308 13.968 1 .000 1.630
Non-food grocery 0a . . . . 1 Not clothing .053 .0895 .345 1 .557 1.054 Clothing 0a . . . . 1 Not tickets -.165 .1000 2.738 1 .098 .847 Tickets 0a . . . . 1 Not others -.251 .1418 3.143 1 .076 .778 Others 0a . . . . 1 Innovativeness .113 .0574 3.855 1 .050 1.119 Time conscious .035 .0630 .304 1 .582 1.035 Enjoyment .104 .0614 2.854 1 .091 1.109 Impulsiveness -.261 .0454 33.033 1 .000 .770 Price conscious .056 .0539 1.072 1 .300 1.057 Convenience -.097 .0650 2.239 1 .135 .907 Security & privacy .078 .0705 1.222 1 .269 1.081
(Scale) 1b
Dependent Variable: food grocery order size
Model: (Intercept), Age, Gender, Country, Electronics, Books/music/video games, Non-food grocery, Clothing and footwear, Tickets, Other categories.
a. Set to zero because this parameter is redundant. b. Fixed at the displayed value.
Table 6.8b Poisson regression on mobile non-food grocery order size Parameter B Std. Error Hypothesis Test Exp(B) Wald Chi-Square df Sig. (Intercept) .356 .8629 .170 1 .680 1.428 Age above24 -.695 .1934 12.900 1 .000 .499 Age under24 0a . . . . 1 Non-Europe -.464 .2754 2.841 1 .092 .629 Europe 0a . . . . 1 Female -1.221 .3078 15.745 1 .000 .295 Male 0a . . . . 1 Not electronics -.954 .1587 36.148 1 .000 .385 Electronics 0a . . . . 1 Not books/music .157 .1784 .770 1 .380 1.169 Books/music 0a . . . . 1 Food grocery .590 .1654 12.722 1 .000 1.804 Food grocery 0a . . . . 1 Not clothing -.065 .1840 .125 1 .723 .937 Clothing 0a . . . . 1 Not tickets .781 .1519 26.432 1 .000 2.184 Tickets 0a . . . . 1 Not others 1.481 .2247 43.433 1 .000 4.396 Others 0a . . . . 1 Innovativeness -.604 .1012 35.583 1 .000 .547 Time conscious -.392 .0839 21.878 1 .000 .675 Enjoyment .606 .1326 20.904 1 .000 1.834 Impulsiveness .282 .0843 11.192 1 .001 1.326 Price conscious -.017 .1115 .023 1 .880 .983 Convenience .302 .1096 7.583 1 .006 1.352 Security & privacy .496 .1137 18.982 1 .000 1.641
(Scale) 1b
Dependent Variable: food grocery order size
Model: (Intercept), Age, Gender, Country, Electronics, Books/music/video games, Food grocery, Clothing and footwear, Tickets, Other categories.
a. Set to zero because this parameter is redundant. b. Fixed at the displayed value.
Usage intention
The usage intention of mobile grocery shopping will be tested by linear regression. The model is statistically significant (F(15,108)=2.479, p=0.004) and explains 25.6% of variance in usage intention. From the table 6.9, the innovativeness is statistically significant (β=0.217, p<0.05). People from non-European countries are positively associated with the usage intention (β=0.292, p<0.01).
Table 6.9 Linear regression on usage intention of grocery shopping
Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .627 1.150 .545 .587 Innovativeness .338 .151 .217 2.240 .027 Time conscious .023 .134 .016 .169 .866 Enjoyment .319 .167 .203 1.913 .058 Impulsiveness -.292 .159 -.184 -1.839 .069 Price conscious -.186 .158 -.111 -1.173 .243 Convenience .257 .186 .140 1.380 .170 Security .142 .136 .100 1.043 .299 Electronics -.244 .277 -.084 -.878 .382 Books/music -.255 .255 -.091 -1.000 .320 Clothing .251 .265 .086 .948 .345 Tickets .003 .265 .001 .012 .991 Others -.045 .332 -.012 -.135 .892 Male .353 .282 .121 1.251 .214 Age under24 -.069 .270 -.022 -.255 .799 Non-Europe .818 .269 .292 3.041 .003
6.5 Summary of results
To sum up, the table 6.10 shows the summary of regression results. Each potential driver has influence on different aspects of usage of mobile grocery (food and non-food) shopping.
Table 6.10 Summary table of regression analysis results
Drivers Incidence Frequency Order size Usage intention Food Non-food Food Non-food Food Non-food Grocery
Age √ Gender √ -√ √ Innovativeness √ -√ √ Time conscious -√ -√ -√ Shopping enjoyment √ √ Impulsiveness -√ -√ -√ √ Price conscious -√ Convenience √
Security & privacy √ √
√: positive association -√: negative association
Hypothesis H1 is rejected. There is no support for that middle aged people are more likely to use mobile grocery shopping than young people. From the testing results, we can only see that young people have larger order size on mobile non-food grocery shopping than older people.
Hypothesis H2 is rejected because there is no big difference between male and female on grocery shopping usage intention. However, from the regression tests, male consumers have higher usage incidence on mobile food grocery shopping and larger order size on
